ashishpatel26 / Predictive_Maintenance_using_Machine-Learning_Microsoft_Casestudy

Predictive_Maintenance_using_Machine-Learning_Microsoft_Casestudy

Problem Description

A major problem faced by businesses in asset-heavy industries such as manufacturing is the significant costs that are associated with delays in the production process due to mechanical problems. Most of these businesses are interested in predicting these problems in advance so that they can proactively prevent the problems before they occur which will reduce the costly impact caused by downtime. Please refer to the playbook for predictive maintenance for a detailed explanation of common use cases in predictive maintenance and modelling approaches.

In this notebook, we follow the ideas from the playbook referenced above and aim to provide the steps of implementing a predictive model for a scenario which is based on a synthesis of multiple real-world business problems. This example brings together common data elements observed among many predictive maintenance use cases and the data itself is created by data simulation methods.

The business problem for this example is about predicting problems caused by component failures such that the question "What is the probability that a machine will fail in the near future due to a failure of a certain component?" can be answered. The problem is formatted as a multi-class classification problem and a machine learning algorithm is used to create the predictive model that learns from historical data collected from machines. In the following sections, we go through the steps of implementing such a model which are feature engineering, label construction, training and evaluation. First, we start by explaining the data sources in the next section.

Data Sources

Common data sources for predictive maintenance problems are :

• Failure history: The failure history of a machine or component within the machine.
• Maintenance history: The repair history of a machine, e.g. error codes, previous maintenance activities or component replacements.
• Machine conditions and usage: The operating conditions of a machine e.g. data collected from sensors.
• Machine features: The features of a machine, e.g. engine size, make and model, location.
• Operator features: The features of the operator, e.g. gender, past experience The data for this example comes from 4 different sources which are real-time telemetry data collected from machines, error messages, historical maintenance records that include failures and machine information such as type and age.

import pandas as pd

telemetry = pd.read_csv('PdM_telemetry.csv')
errors = pd.read_csv('PdM_errors.csv')
maint = pd.read_csv('PdM_maint.csv')
failures = pd.read_csv('PdM_failures.csv')
machines = pd.read_csv('PdM_machines.csv')

# format datetime field which comes in as string
telemetry['datetime'] = pd.to_datetime(telemetry['datetime'], format="%Y-%m-%d %H:%M:%S")

print("Total number of telemetry records: %d" % len(telemetry.index))
print(telemetry.head())
telemetry.describe()

Total number of telemetry records: 876100
             datetime  machineID        volt      rotate    pressure  \
0 2015-01-01 06:00:00          1  176.217853  418.504078  113.077935   
1 2015-01-01 07:00:00          1  162.879223  402.747490   95.460525   
2 2015-01-01 08:00:00          1  170.989902  527.349825   75.237905   
3 2015-01-01 09:00:00          1  162.462833  346.149335  109.248561   
4 2015-01-01 10:00:00          1  157.610021  435.376873  111.886648   

   vibration  
0  45.087686  
1  43.413973  
2  34.178847  
3  41.122144  
4  25.990511  

	machineID	volt	rotate	pressure	vibration
count	876100.000000	876100.000000	876100.000000	876100.000000	876100.000000
mean	50.500000	170.777736	446.605119	100.858668	40.385007
std	28.866087	15.509114	52.673886	11.048679	5.370361
min	1.000000	97.333604	138.432075	51.237106	14.877054
25%	25.750000	160.304927	412.305714	93.498181	36.777299
50%	50.500000	170.607338	447.558150	100.425559	40.237247
75%	75.250000	181.004493	482.176600	107.555231	43.784938
max	100.000000	255.124717	695.020984	185.951998	76.791072

Telemetry

The first data source is the telemetry time-series data which consists of voltage, rotation, pressure, and vibration measurements collected from 100 machines in real time averaged over every hour collected during the year 2015. Below, we display the first 10 records in the dataset. A summary of the whole dataset is also provided.

%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns

plot_df = telemetry.loc[(telemetry['machineID'] == 1) & 
                        (telemetry['datetime'] > pd.to_datetime('2015-01-01')) & 
                        (telemetry['datetime'] <pd.to_datetime('2015-02-01')),
                        ['datetime','volt']]
sns.set_style("darkgrid")
plt.figure(figsize=(20, 8))
plt.plot(plot_df['datetime'], plot_df['volt'])
plt.ylabel('voltage')

# make x-axis ticks legible
adf = plt.gca().get_xaxis().get_major_formatter()
adf.scaled[1.0] = '%m-%d-%Y'
plt.xlabel('Date')

<matplotlib.text.Text at 0x2564dac0780>

Errors

The second major data source is the error logs. These are non-breaking errors thrown while the machine is still operational and do not constitute as failures. The error date and times are rounded to the closest hour since the telemetry data is collected at an hourly rate.

# format of datetime field which comes in as string
errors['datetime'] = pd.to_datetime(errors['datetime'],format = '%Y-%m-%d %H:%M:%S')
errors['errorID'] = errors['errorID'].astype('category')
print("Total Number of error records: %d" %len(errors.index))
errors.head()

Total Number of error records: 3919

	datetime	machineID	errorID
0	2015-01-03 07:00:00	1	error1
1	2015-01-03 20:00:00	1	error3
2	2015-01-04 06:00:00	1	error5
3	2015-01-10 15:00:00	1	error4
4	2015-01-22 10:00:00	1	error4

sns.set_style("darkgrid")
plt.figure(figsize=(20, 8))
errors['errorID'].value_counts().plot(kind='bar')
plt.ylabel('Count')
errors['errorID'].value_counts()

error1    1010
error2     988
error3     838
error4     727
error5     356
Name: errorID, dtype: int64

Maintenance

These are the scheduled and unscheduled maintenance records which correspond to both regular inspection of components as well as failures. A record is generated if a component is replaced during the scheduled inspection or replaced due to a breakdown. The records that are created due to breakdowns will be called failures which is explained in the later sections. Maintenance data has both 2014 and 2015 records.

maint['datetime'] = pd.to_datetime(maint['datetime'], format='%Y-%m-%d %H:%M:%S')
maint['comp'] = maint['comp'].astype('category')
print("Total Number of maintenance Records: %d" %len(maint.index))
maint.head()

Total Number of maintenance Records: 3286

	datetime	machineID	comp
0	2014-06-01 06:00:00	1	comp2
1	2014-07-16 06:00:00	1	comp4
2	2014-07-31 06:00:00	1	comp3
3	2014-12-13 06:00:00	1	comp1
4	2015-01-05 06:00:00	1	comp4

sns.set_style("darkgrid")
plt.figure(figsize=(10, 4))
maint['comp'].value_counts().plot(kind='bar')
plt.ylabel('Count')
maint['comp'].value_counts()

comp2    863
comp4    811
comp3    808
comp1    804
Name: comp, dtype: int64

Machines

This data set includes some information about the machines: model type and age (years in service).

machines['model'] = machines['model'].astype('category')

print("Total number of machines: %d" % len(machines.index))
machines.head()

Total number of machines: 100

	machineID	model	age
0	1	model3	18
1	2	model4	7
2	3	model3	8
3	4	model3	7
4	5	model3	2

sns.set_style("darkgrid")
plt.figure(figsize=(15, 6))
_, bins, _ = plt.hist([machines.loc[machines['model'] == 'model1', 'age'],
                       machines.loc[machines['model'] == 'model2', 'age'],
                       machines.loc[machines['model'] == 'model3', 'age'],
                       machines.loc[machines['model'] == 'model4', 'age']],
                       20, stacked=True, label=['model1', 'model2', 'model3', 'model4'])
plt.xlabel('Age (yrs)')
plt.ylabel('Count')
plt.legend()

<matplotlib.legend.Legend at 0x2564dda9898>

Failures

These are the records of component replacements due to failures. Each record has a date and time, machine ID, and failed component type.

# format datetime field which comes in as string
failures['datetime'] = pd.to_datetime(failures['datetime'], format="%Y-%m-%d %H:%M:%S")
failures['failure'] = failures['failure'].astype('category')

print("Total number of failures: %d" % len(failures.index))
failures.head()

Total number of failures: 761

	datetime	machineID	failure
0	2015-01-05 06:00:00	1	comp4
1	2015-03-06 06:00:00	1	comp1
2	2015-04-20 06:00:00	1	comp2
3	2015-06-19 06:00:00	1	comp4
4	2015-09-02 06:00:00	1	comp4

sns.set_style("darkgrid")
plt.figure(figsize=(15, 4))
failures['failure'].value_counts().plot(kind='bar')
plt.ylabel('Count')
failures['failure'].value_counts()

comp2    259
comp1    192
comp4    179
comp3    131
Name: failure, dtype: int64

Feature Engineering

The first step in predictive maintenance applications is feature engineering which requires bringing the different data sources together to create features that best describe a machines's health condition at a given point in time. In the next sections, several feature engineering methods are used to create features based on the properties of each data source.

Lag Features from Telemetry

Telemetry data almost always comes with time-stamps which makes it suitable for calculating lagging features. A common method is to pick a window size for the lag features to be created and compute rolling aggregate measures such as mean, standard deviation, minimum, maximum, etc. to represent the short term history of the telemetry over the lag window. In the following, rolling mean and standard deviation of the telemetry data over the last 3 hour lag window is calculated for every 3 hours.

# Calculate mean values for telemetry features
temp = []
fields = ['volt', 'rotate', 'pressure', 'vibration']
for col in fields:
    temp.append(pd.pivot_table(telemetry,
                               index='datetime',
                               columns='machineID',
                               values=col).resample('3H', closed='left', label='right', how='mean').unstack())
telemetry_mean_3h = pd.concat(temp, axis=1)
telemetry_mean_3h.columns = [i + 'mean_3h' for i in fields]
telemetry_mean_3h.reset_index(inplace=True)

C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\lib\site-packages\ipykernel_launcher.py:8: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).mean()

# Calculate mean values for telemetry features
temp = []
fields = ['volt', 'rotate', 'pressure', 'vibration']
for col in fields:
    temp.append(pd.pivot_table(telemetry,
                               index='datetime',
                               columns='machineID',
                               values=col).resample('3H', closed='left', label='right', how='mean').unstack())
telemetry_mean_3h = pd.concat(temp, axis=1)
telemetry_mean_3h.columns = [i + 'mean_3h' for i in fields]
telemetry_mean_3h.reset_index(inplace=True)

# repeat for standard deviation
temp = []
for col in fields:
    temp.append(pd.pivot_table(telemetry,
                               index='datetime',
                               columns='machineID',
                               values=col).resample('3H', closed='left', label='right', how='std').unstack())
telemetry_sd_3h = pd.concat(temp, axis=1)
telemetry_sd_3h.columns = [i + 'sd_3h' for i in fields]
telemetry_sd_3h.reset_index(inplace=True)

telemetry_mean_3h.head()

C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\lib\site-packages\ipykernel_launcher.py:8: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).mean()
  
C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\lib\site-packages\ipykernel_launcher.py:19: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).std()

	machineID	datetime	voltmean_3h	rotatemean_3h	pressuremean_3h	vibrationmean_3h
0	1	2015-01-01 09:00:00	170.028993	449.533798	94.592122	40.893502
1	1	2015-01-01 12:00:00	164.192565	403.949857	105.687417	34.255891
2	1	2015-01-01 15:00:00	168.134445	435.781707	107.793709	41.239405
3	1	2015-01-01 18:00:00	165.514453	430.472823	101.703289	40.373739
4	1	2015-01-01 21:00:00	168.809347	437.111120	90.911060	41.738542

For capturing a longer term effect, 24 hour lag features are also calculated as below.

temp = []
fields = ['volt', 'rotate', 'pressure', 'vibration']
for col in fields:
    temp.append(pd.rolling_mean(pd.pivot_table(telemetry,
                                               index='datetime',
                                               columns='machineID',
                                               values=col), window=24).resample('3H',
                                                                                closed='left',
                                                                                label='right',
                                                                                how='first').unstack())
telemetry_mean_24h = pd.concat(temp, axis=1)
telemetry_mean_24h.columns = [i + 'mean_24h' for i in fields]
telemetry_mean_24h.reset_index(inplace=True)
telemetry_mean_24h = telemetry_mean_24h.loc[-telemetry_mean_24h['voltmean_24h'].isnull()]

# repeat for standard deviation
temp = []
fields = ['volt', 'rotate', 'pressure', 'vibration']
for col in fields:
    temp.append(pd.rolling_std(pd.pivot_table(telemetry,
                                               index='datetime',
                                               columns='machineID',
                                               values=col), window=24).resample('3H',
                                                                                closed='left',
                                                                                label='right',
                                                                                how='first').unstack())
telemetry_sd_24h = pd.concat(temp, axis=1)
telemetry_sd_24h.columns = [i + 'sd_24h' for i in fields]
telemetry_sd_24h = telemetry_sd_24h.loc[-telemetry_sd_24h['voltsd_24h'].isnull()]
telemetry_sd_24h.reset_index(inplace=True)

# Notice that a 24h rolling average is not available at the earliest timepoints
telemetry_mean_24h.head(10)

C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\lib\site-packages\ipykernel_launcher.py:7: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with 
	DataFrame.rolling(window=24,center=False).mean()
  import sys
C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\lib\site-packages\ipykernel_launcher.py:10: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).first()
  # Remove the CWD from sys.path while we load stuff.
C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\lib\site-packages\ipykernel_launcher.py:23: FutureWarning: pd.rolling_std is deprecated for DataFrame and will be removed in a future version, replace with 
	DataFrame.rolling(window=24,center=False).std()
C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\lib\site-packages\ipykernel_launcher.py:26: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).first()

	machineID	datetime	voltmean_24h	rotatemean_24h	pressuremean_24h	vibrationmean_24h
7	1	2015-01-02 06:00:00	169.733809	445.179865	96.797113	40.385160
8	1	2015-01-02 09:00:00	170.614862	446.364859	96.849785	39.736826
9	1	2015-01-02 12:00:00	169.893965	447.009407	97.715600	39.498374
10	1	2015-01-02 15:00:00	171.243444	444.233563	96.666060	40.229370
11	1	2015-01-02 18:00:00	170.792486	448.440437	95.766838	40.055214
12	1	2015-01-02 21:00:00	170.556674	452.267095	98.065860	40.033247
13	1	2015-01-03 00:00:00	168.460525	451.031783	99.273286	38.903462
14	1	2015-01-03 03:00:00	169.772951	447.502464	99.005946	39.389725
15	1	2015-01-03 06:00:00	170.900562	453.864597	100.877342	38.696225
16	1	2015-01-03 09:00:00	169.533156	454.785072	100.050567	39.449734

Next, the columns of the feature datasets created earlier are merged to create the final feature set from telemetry.

# merge columns of feature sets created earlier
telemetry_feat = pd.concat([telemetry_mean_3h,
                            telemetry_sd_3h.ix[:, 2:6],
                            telemetry_mean_24h.ix[:, 2:6],
                            telemetry_sd_24h.ix[:, 2:6]], axis=1).dropna()
telemetry_feat.describe()

	machineID	voltmean_3h	rotatemean_3h	pressuremean_3h	vibrationmean_3h	voltsd_3h	rotatesd_3h	pressuresd_3h	vibrationsd_3h	voltmean_24h	rotatemean_24h	pressuremean_24h	vibrationmean_24h	voltsd_24h	rotatesd_24h	pressuresd_24h	vibrationsd_24h
count	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000
mean	50.380935	170.774427	446.609386	100.858340	40.383609	13.300173	44.453951	8.885780	4.440575	170.775661	446.609874	100.857574	40.383881	14.919452	49.950788	10.046380	5.002089
std	28.798424	9.498824	33.119738	7.411701	3.475512	6.966389	23.214291	4.656364	2.319989	4.720237	18.070458	4.737293	2.058059	2.261097	7.684305	1.713206	0.799599
min	1.000000	125.532506	211.811184	72.118639	26.569635	0.025509	0.078991	0.027417	0.015278	155.812721	266.010419	91.057429	35.060087	6.380619	18.385248	4.145308	2.144863
25%	25.000000	164.447794	427.564793	96.239534	38.147458	8.028675	26.906319	5.369959	2.684556	168.072275	441.542561	98.669734	39.354077	13.359069	44.669022	8.924165	4.460675
50%	50.000000	170.432407	448.380260	100.235357	40.145874	12.495542	41.793798	8.345801	4.173704	170.212704	449.206885	100.099533	40.072618	14.854186	49.617459	9.921332	4.958793
75%	75.000000	176.610017	468.443933	104.406534	42.226898	17.688520	59.092354	11.789358	5.898512	172.462228	456.366349	101.613047	40.833112	16.395372	54.826993	10.980250	5.484430
max	100.000000	241.420717	586.682904	162.309656	69.311324	58.444332	179.903039	35.659369	18.305595	220.782618	499.096975	152.310351	61.932124	27.664538	103.819404	28.654103	12.325783

telemetry_feat.head()

	machineID	datetime	voltmean_3h	rotatemean_3h	pressuremean_3h	vibrationmean_3h	voltsd_3h	rotatesd_3h	pressuresd_3h	vibrationsd_3h	voltmean_24h	rotatemean_24h	pressuremean_24h	vibrationmean_24h	voltsd_24h	rotatesd_24h	pressuresd_24h	vibrationsd_24h
7	1	2015-01-02 06:00:00	180.133784	440.608320	94.137969	41.551544	21.322735	48.770512	2.135684	10.037208	169.733809	445.179865	96.797113	40.385160	15.726970	39.648116	11.904700	5.601191
8	1	2015-01-02 09:00:00	176.364293	439.349655	101.553209	36.105580	18.952210	51.329636	13.789279	6.737739	170.614862	446.364859	96.849785	39.736826	15.635083	41.828592	11.326412	5.583521
9	1	2015-01-02 12:00:00	160.384568	424.385316	99.598722	36.094637	13.047080	13.702496	9.988609	1.639962	169.893965	447.009407	97.715600	39.498374	13.995465	40.843882	11.036546	5.561553
10	1	2015-01-02 15:00:00	170.472461	442.933997	102.380586	40.483002	16.642354	56.290447	3.305739	8.854145	171.243444	444.233563	96.666060	40.229370	13.100364	43.409841	10.972862	6.068674
11	1	2015-01-02 18:00:00	163.263806	468.937558	102.726648	40.921802	17.424688	38.680380	9.105775	3.060781	170.792486	448.440437	95.766838	40.055214	13.808489	43.742304	10.988704	7.286129

Lag Features from Errors

Like telemetry data, errors come with timestamps. An important difference is that the error IDs are categorical values and should not be averaged over time intervals like the telemetry measurements. Instead, we count the number of errors of each type in a lagging window. We begin by reformatting the error data to have one entry per machine per time at which at least one error occurred:

errors

	datetime	machineID	errorID
0	2015-01-03 07:00:00	1	error1
1	2015-01-03 20:00:00	1	error3
2	2015-01-04 06:00:00	1	error5
3	2015-01-10 15:00:00	1	error4
4	2015-01-22 10:00:00	1	error4
5	2015-01-25 15:00:00	1	error4
6	2015-01-27 04:00:00	1	error1
7	2015-03-03 22:00:00	1	error2
8	2015-03-05 06:00:00	1	error1
9	2015-03-20 18:00:00	1	error1
10	2015-03-26 01:00:00	1	error2
11	2015-03-31 23:00:00	1	error1
12	2015-04-19 06:00:00	1	error2
13	2015-04-19 06:00:00	1	error3
14	2015-04-29 19:00:00	1	error4
15	2015-05-04 23:00:00	1	error2
16	2015-05-12 09:00:00	1	error1
17	2015-05-21 07:00:00	1	error4
18	2015-05-24 02:00:00	1	error3
19	2015-05-25 05:00:00	1	error1
20	2015-06-09 06:00:00	1	error3
21	2015-06-18 06:00:00	1	error5
22	2015-06-23 10:00:00	1	error3
23	2015-08-23 19:00:00	1	error1
24	2015-08-30 01:00:00	1	error3
25	2015-09-01 06:00:00	1	error5
26	2015-09-13 17:00:00	1	error2
27	2015-09-15 06:00:00	1	error1
28	2015-10-01 23:00:00	1	error1
29	2015-10-15 05:00:00	1	error1
...	...	...	...
3889	2015-01-16 00:00:00	100	error4
3890	2015-02-01 10:00:00	100	error1
3891	2015-02-11 06:00:00	100	error1
3892	2015-02-12 21:00:00	100	error1
3893	2015-03-08 15:00:00	100	error1
3894	2015-04-27 04:00:00	100	error4
3895	2015-04-27 22:00:00	100	error5
3896	2015-05-16 23:00:00	100	error2
3897	2015-05-17 13:00:00	100	error2
3898	2015-05-22 02:00:00	100	error3
3899	2015-07-05 16:00:00	100	error3
3900	2015-07-19 01:00:00	100	error2
3901	2015-08-14 16:00:00	100	error4
3902	2015-08-30 15:00:00	100	error4
3903	2015-09-09 06:00:00	100	error1
3904	2015-09-14 23:00:00	100	error3
3905	2015-10-03 05:00:00	100	error3
3906	2015-10-09 07:00:00	100	error1
3907	2015-10-17 02:00:00	100	error3
3908	2015-10-17 12:00:00	100	error1
3909	2015-10-24 23:00:00	100	error1
3910	2015-10-27 21:00:00	100	error2
3911	2015-11-05 02:00:00	100	error3
3912	2015-11-07 17:00:00	100	error1
3913	2015-11-12 01:00:00	100	error1
3914	2015-11-21 08:00:00	100	error2
3915	2015-12-04 02:00:00	100	error1
3916	2015-12-08 06:00:00	100	error2
3917	2015-12-08 06:00:00	100	error3
3918	2015-12-22 03:00:00	100	error3

3919 rows × 3 columns

# create a column for each error type
error_count = pd.get_dummies(errors.set_index('datetime')).reset_index()
error_count
error_count.columns = ['datetime', 'machineID', 'error1', 'error2', 'error3', 'error4', 'error5']
error_count.head(13)

	datetime	machineID	error1	error2	error3	error4	error5
0	2015-01-03 07:00:00	1	1	0	0	0	0
1	2015-01-03 20:00:00	1	0	0	1	0	0
2	2015-01-04 06:00:00	1	0	0	0	0	1
3	2015-01-10 15:00:00	1	0	0	0	1	0
4	2015-01-22 10:00:00	1	0	0	0	1	0
5	2015-01-25 15:00:00	1	0	0	0	1	0
6	2015-01-27 04:00:00	1	1	0	0	0	0
7	2015-03-03 22:00:00	1	0	1	0	0	0
8	2015-03-05 06:00:00	1	1	0	0	0	0
9	2015-03-20 18:00:00	1	1	0	0	0	0
10	2015-03-26 01:00:00	1	0	1	0	0	0
11	2015-03-31 23:00:00	1	1	0	0	0	0
12	2015-04-19 06:00:00	1	0	1	0	0	0

# combine errors for a given machine in a given hour
error_count = error_count.groupby(['machineID','datetime']).sum().reset_index()
error_count.head(13)

	machineID	datetime	error1	error2	error3	error4	error5
0	1	2015-01-03 07:00:00	1	0	0	0	0
1	1	2015-01-03 20:00:00	0	0	1	0	0
2	1	2015-01-04 06:00:00	0	0	0	0	1
3	1	2015-01-10 15:00:00	0	0	0	1	0
4	1	2015-01-22 10:00:00	0	0	0	1	0
5	1	2015-01-25 15:00:00	0	0	0	1	0
6	1	2015-01-27 04:00:00	1	0	0	0	0
7	1	2015-03-03 22:00:00	0	1	0	0	0
8	1	2015-03-05 06:00:00	1	0	0	0	0
9	1	2015-03-20 18:00:00	1	0	0	0	0
10	1	2015-03-26 01:00:00	0	1	0	0	0
11	1	2015-03-31 23:00:00	1	0	0	0	0
12	1	2015-04-19 06:00:00	0	1	1	0	0

error_count = telemetry[['datetime', 'machineID']].merge(error_count, on=['machineID', 'datetime'], how='left').fillna(0.0)
error_count.describe()

	machineID	error1	error2	error3	error4	error5
count	876100.000000	876100.000000	876100.000000	876100.000000	876100.000000	876100.000000
mean	50.500000	0.001153	0.001128	0.000957	0.000830	0.000406
std	28.866087	0.033934	0.033563	0.030913	0.028795	0.020154
min	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	25.750000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	50.500000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	75.250000	0.000000	0.000000	0.000000	0.000000	0.000000
max	100.000000	1.000000	1.000000	1.000000	1.000000	1.000000

Finally, we can compute the total number of errors of each type over the last 24 hours, for timepoints taken every three hours:

temp = []
fields = ['error%d' % i for i in range(1,6)]
for col in fields:
    temp.append(pd.rolling_sum(pd.pivot_table(error_count,
                                               index='datetime',
                                               columns='machineID',
                                               values=col), window=24).resample('3H',
                                                                             closed='left',
                                                                             label='right',
                                                                             how='first').unstack())
error_count = pd.concat(temp, axis=1)
error_count.columns = [i + 'count' for i in fields]
error_count.reset_index(inplace=True)
error_count = error_count.dropna()
error_count.describe()

C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\lib\site-packages\ipykernel_launcher.py:7: FutureWarning: pd.rolling_sum is deprecated for DataFrame and will be removed in a future version, replace with 
	DataFrame.rolling(window=24,center=False).sum()
  import sys
C:\Program Files\Microsoft\ML Server\PYTHON_SERVER\lib\site-packages\ipykernel_launcher.py:10: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...).first()
  # Remove the CWD from sys.path while we load stuff.

	machineID	error1count	error2count	error3count	error4count	error5count
count	291400.00000	291400.000000	291400.000000	291400.000000	291400.000000	291400.000000
mean	50.50000	0.027649	0.027069	0.022907	0.019904	0.009753
std	28.86612	0.166273	0.164429	0.151453	0.140820	0.098797
min	1.00000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	25.75000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	50.50000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	75.25000	0.000000	0.000000	0.000000	0.000000	0.000000
max	100.00000	2.000000	2.000000	2.000000	2.000000	2.000000

error_count.head()

	machineID	datetime	error1count	error2count	error3count	error4count	error5count
7	1	2015-01-02 06:00:00	0.0	0.0	0.0	0.0	0.0
8	1	2015-01-02 09:00:00	0.0	0.0	0.0	0.0	0.0
9	1	2015-01-02 12:00:00	0.0	0.0	0.0	0.0	0.0
10	1	2015-01-02 15:00:00	0.0	0.0	0.0	0.0	0.0
11	1	2015-01-02 18:00:00	0.0	0.0	0.0	0.0	0.0

Days Since Last Replacement from Maintenance

A crucial data set in this example is the maintenance records which contain the information of component replacement records. Possible features from this data set can be, for example, the number of replacements of each component in the last 3 months to incorporate the frequency of replacements. However, more relevent information would be to calculate how long it has been since a component is last replaced as that would be expected to correlate better with component failures since the longer a component is used, the more degradation should be expected.

As a side note, creating lagging features from maintenance data is not as straightforward as for telemetry and errors, so the features from this data are generated in a more custom way. This type of ad-hoc feature engineering is very common in predictive maintenance since domain knowledge plays a big role in understanding the predictors of a problem. In the following, the days since last component replacement are calculated for each component type as features from the maintenance data.

import numpy as np

# create a column for each error type
comp_rep = pd.get_dummies(maint.set_index('datetime')).reset_index()
comp_rep.columns = ['datetime', 'machineID', 'comp1', 'comp2', 'comp3', 'comp4']

# combine repairs for a given machine in a given hour
comp_rep = comp_rep.groupby(['machineID', 'datetime']).sum().reset_index()

# add timepoints where no components were replaced
comp_rep = telemetry[['datetime', 'machineID']].merge(comp_rep,
                                                      on=['datetime', 'machineID'],
                                                      how='outer').fillna(0).sort_values(by=['machineID', 'datetime'])

components = ['comp1', 'comp2', 'comp3', 'comp4']
for comp in components:
    # convert indicator to most recent date of component change
    comp_rep.loc[comp_rep[comp] < 1, comp] = None
    comp_rep.loc[-comp_rep[comp].isnull(), comp] = comp_rep.loc[-comp_rep[comp].isnull(), 'datetime']
    
    # forward-fill the most-recent date of component change
    comp_rep[comp] = comp_rep[comp].fillna(method='ffill')

# remove dates in 2014 (may have NaN or future component change dates)    
comp_rep = comp_rep.loc[comp_rep['datetime'] > pd.to_datetime('2015-01-01')]

# replace dates of most recent component change with days since most recent component change
for comp in components:
    comp_rep[comp] = (comp_rep['datetime'] - comp_rep[comp]) / np.timedelta64(1, 'D')
    
comp_rep.describe()

	machineID	comp1	comp2	comp3	comp4
count	876100.000000	876100.000000	876100.000000	876100.000000	876100.000000
mean	50.500000	53.525185	51.540806	52.725962	53.834191
std	28.866087	62.491679	59.269254	58.873114	59.707978
min	1.000000	0.000000	0.000000	0.000000	0.000000
25%	25.750000	13.291667	12.125000	13.125000	13.000000
50%	50.500000	32.791667	29.666667	32.291667	32.500000
75%	75.250000	68.708333	66.541667	67.333333	70.458333
max	100.000000	491.958333	348.958333	370.958333	394.958333

comp_rep.head()

	datetime	machineID	comp1	comp2	comp3	comp4
0	2015-01-01 06:00:00	1	19.000000	214.000000	154.000000	169.000000
1	2015-01-01 07:00:00	1	19.041667	214.041667	154.041667	169.041667
2	2015-01-01 08:00:00	1	19.083333	214.083333	154.083333	169.083333
3	2015-01-01 09:00:00	1	19.125000	214.125000	154.125000	169.125000
4	2015-01-01 10:00:00	1	19.166667	214.166667	154.166667	169.166667

Machine Features

The machine features can be used without further modification. These include descriptive information about the type of each machine and its age (number of years in service). If the age information had been recorded as a "first use date" for each machine, a transformation would have been necessary to turn those into a numeric values indicating the years in service.

Lastly, we merge all the feature data sets we created earlier to get the final feature matrix.

telemetry_feat

	machineID	datetime	voltmean_3h	rotatemean_3h	pressuremean_3h	vibrationmean_3h	voltsd_3h	rotatesd_3h	pressuresd_3h	vibrationsd_3h	voltmean_24h	rotatemean_24h	pressuremean_24h	vibrationmean_24h	voltsd_24h	rotatesd_24h	pressuresd_24h	vibrationsd_24h
7	1	2015-01-02 06:00:00	180.133784	440.608320	94.137969	41.551544	21.322735	48.770512	2.135684	10.037208	169.733809	445.179865	96.797113	40.385160	15.726970	39.648116	11.904700	5.601191
8	1	2015-01-02 09:00:00	176.364293	439.349655	101.553209	36.105580	18.952210	51.329636	13.789279	6.737739	170.614862	446.364859	96.849785	39.736826	15.635083	41.828592	11.326412	5.583521
9	1	2015-01-02 12:00:00	160.384568	424.385316	99.598722	36.094637	13.047080	13.702496	9.988609	1.639962	169.893965	447.009407	97.715600	39.498374	13.995465	40.843882	11.036546	5.561553
10	1	2015-01-02 15:00:00	170.472461	442.933997	102.380586	40.483002	16.642354	56.290447	3.305739	8.854145	171.243444	444.233563	96.666060	40.229370	13.100364	43.409841	10.972862	6.068674
11	1	2015-01-02 18:00:00	163.263806	468.937558	102.726648	40.921802	17.424688	38.680380	9.105775	3.060781	170.792486	448.440437	95.766838	40.055214	13.808489	43.742304	10.988704	7.286129
12	1	2015-01-02 21:00:00	163.278466	446.493166	104.387585	38.068116	21.580492	41.380958	20.725597	6.932127	170.556674	452.267095	98.065860	40.033247	14.187985	40.676672	11.942227	8.723238
13	1	2015-01-03 00:00:00	172.191198	434.214692	93.747282	39.716482	16.369836	14.636041	18.817326	3.426997	168.460525	451.031783	99.273286	38.903462	13.707794	40.509184	10.141026	8.634082
14	1	2015-01-03 03:00:00	175.210027	504.845430	108.512153	37.763933	5.991921	16.062702	6.382608	3.449468	169.772951	447.502464	99.005946	39.389725	11.818603	44.468516	9.444955	8.332673
15	1	2015-01-03 06:00:00	181.690108	472.783187	93.395164	38.621099	11.514450	47.880443	2.177029	7.670520	170.900562	453.864597	100.877342	38.696225	12.069391	46.669661	8.609526	8.089348
16	1	2015-01-03 09:00:00	172.382935	505.141261	98.524373	49.965572	7.065150	56.849540	5.230039	2.687565	169.533156	454.785072	100.050567	39.449734	12.755234	44.016114	9.893704	7.013132
17	1	2015-01-03 12:00:00	174.303858	436.182686	94.092681	50.999589	19.017196	26.420163	7.661944	3.516734	170.866013	463.871291	99.360632	40.766639	12.848646	45.090576	9.846662	5.888262
18	1	2015-01-03 15:00:00	176.246348	451.646684	98.102389	59.198241	12.572504	31.574383	15.559351	6.562087	171.041651	463.701291	98.965877	42.396850	14.968351	37.088898	10.133452	5.702356
19	1	2015-01-03 18:00:00	158.433533	453.900213	98.878129	46.851925	5.136952	21.216569	11.400650	2.688559	171.244533	464.320613	98.853189	44.608814	17.058217	36.617908	9.867174	5.743753
20	1	2015-01-03 21:00:00	162.387954	454.140377	92.651129	54.261635	4.563331	57.747656	4.754203	5.118076	171.385039	459.937314	97.292157	45.284751	18.405763	35.819938	9.743769	5.246435
21	1	2015-01-04 00:00:00	174.243192	394.998095	99.829845	46.930738	6.268730	29.167663	10.564287	6.822855	171.880633	461.437128	96.786742	47.311018	18.249831	42.055638	10.961128	5.093464
22	1	2015-01-04 03:00:00	176.443361	459.528820	111.855296	55.296056	16.330285	20.602657	7.064583	4.651468	172.513202	456.429165	97.742700	48.416442	19.141287	37.018824	10.642956	4.618287
23	1	2015-01-04 06:00:00	186.092896	451.641253	107.989359	55.308074	13.489090	62.185045	5.118176	4.904365	172.686245	453.387589	99.304019	51.158654	18.887033	36.997459	11.042775	5.195423
24	1	2015-01-04 09:00:00	166.281848	453.787824	106.187582	51.990080	24.276228	23.621315	11.176731	3.394073	172.042428	450.418764	100.284484	52.153213	20.837993	34.051825	9.654971	5.066388
25	1	2015-01-04 12:00:00	175.412103	445.450581	100.887363	54.251534	34.918687	11.001625	10.580336	2.921501	171.219623	443.802134	102.358897	52.854420	21.298322	36.054002	9.885781	5.246894
26	1	2015-01-04 15:00:00	157.347716	451.882075	101.289380	48.602686	24.617739	28.950883	9.966729	2.356486	172.013443	444.882018	102.578580	52.789794	21.200183	38.544116	10.429692	7.192434
27	1	2015-01-04 18:00:00	176.450550	446.033068	84.521555	47.638836	8.071400	76.511343	2.636879	4.108621	170.176321	445.069594	102.359939	51.518719	18.814679	40.547527	11.133170	7.556313
28	1	2015-01-04 21:00:00	190.325814	422.692565	107.393234	49.552856	8.390777	7.176553	4.262645	7.598552	172.932248	444.618018	101.425508	52.135905	16.762469	49.373445	10.443534	8.545739
29	1	2015-01-05 00:00:00	169.985134	458.929418	91.494362	54.882021	9.451483	12.052752	3.685906	6.621183	175.121131	443.916392	102.130179	51.653294	17.435946	43.819375	10.830449	8.809530
30	1	2015-01-05 03:00:00	149.082619	412.180336	93.509785	54.386079	19.075952	30.715081	3.090266	6.530610	173.407255	446.265950	100.874614	52.529450	16.661364	47.266846	11.225440	9.068824
31	1	2015-01-05 06:00:00	185.782709	439.531288	99.413660	51.558082	14.495664	45.663743	4.289212	7.330397	170.757841	440.958228	98.716746	51.746749	17.863934	44.895080	10.675981	7.475304
32	1	2015-01-05 09:00:00	169.084809	463.433785	107.678774	41.710336	12.245544	61.759107	4.400233	9.750017	171.929104	443.448775	98.675590	51.780445	15.139300	45.766081	10.959268	6.855778
33	1	2015-01-05 12:00:00	165.518790	449.743255	110.377851	38.952082	23.170638	45.762142	14.009473	0.797364	170.908522	443.069042	98.830333	49.679550	13.985517	42.542001	11.050133	4.842842
34	1	2015-01-05 15:00:00	175.989642	419.863490	112.571146	41.514254	4.028327	20.148499	5.862629	9.702498	170.416326	443.555122	100.221328	48.481038	13.344630	39.327146	10.268539	4.884344
35	1	2015-01-05 18:00:00	188.576444	487.336742	88.967297	36.571052	8.278605	76.534023	11.892088	1.945849	173.315167	444.049581	101.633306	47.279992	15.793146	42.984028	10.006300	4.637101
36	1	2015-01-05 21:00:00	166.681364	481.685320	104.154110	38.662638	11.957697	25.052743	11.999161	4.804263	173.743459	446.505202	100.540356	45.527290	16.132288	40.754154	9.744855	4.591048
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
291370	100	2015-10-02 06:00:00	165.259415	432.364050	96.793097	38.697882	16.715588	9.197585	11.016730	9.167743	169.115085	459.202414	99.099044	39.342719	12.889019	60.409151	11.549081	5.671215
291371	100	2015-10-02 09:00:00	185.907346	465.062411	94.161434	36.156060	22.822289	64.351154	6.469484	1.656610	168.838507	455.101759	98.960206	38.277576	13.917591	56.810848	11.118412	6.061118
291372	100	2015-10-02 12:00:00	167.546991	448.203119	99.383591	39.659572	2.573507	84.299208	2.490792	2.252574	169.223690	463.630715	99.296474	38.406915	14.611939	56.534099	8.553177	5.893650
291373	100	2015-10-02 15:00:00	175.468904	441.861941	105.814802	38.788653	9.104554	48.615069	6.004070	3.244295	172.163049	458.787617	100.063674	38.458947	15.232866	49.321412	8.345687	5.292801
291374	100	2015-10-02 18:00:00	157.401371	459.332121	93.247465	42.236723	14.711827	45.268580	5.590642	2.204472	173.119397	456.196849	100.114215	39.063775	15.920072	48.742610	7.909495	5.249418
291375	100	2015-10-02 21:00:00	168.651510	430.056138	104.487324	35.735005	16.328969	45.108180	9.103806	6.093867	168.410263	448.519301	100.049480	39.083797	14.635941	50.313046	7.582133	5.108806
291376	100	2015-10-03 00:00:00	168.623762	497.504580	100.682235	40.610939	12.914771	25.775781	11.444951	3.359673	168.849711	450.330382	100.243957	38.469840	14.915283	50.148100	7.928488	5.518228
291377	100	2015-10-03 03:00:00	168.537058	441.837105	87.893111	40.076219	23.866338	36.817201	16.820180	0.482728	171.513651	452.462106	98.514174	38.626944	14.750503	44.620373	8.348839	5.182752
291378	100	2015-10-03 06:00:00	161.436752	401.579802	90.792431	35.624101	14.429283	85.801834	16.225371	1.396074	169.625319	451.508453	97.368816	38.762103	16.055332	49.218444	9.546008	5.020598
291379	100	2015-10-03 09:00:00	188.559785	491.929571	102.821662	40.034460	12.668164	41.768856	9.448383	8.454640	167.950194	453.659486	97.292065	38.635159	15.942467	59.763569	9.431896	4.476863
291380	100	2015-10-03 12:00:00	164.149847	466.463280	100.447025	40.694147	16.460088	31.589198	10.188202	5.086589	168.530246	448.103742	97.730594	39.228047	16.122100	57.394487	9.171658	4.866485
291381	100	2015-10-03 15:00:00	184.727094	487.140570	100.860907	36.430834	12.401664	57.508894	18.394116	7.153156	169.765458	458.759394	98.775532	38.935773	16.994700	55.665652	9.630112	4.896106
291382	100	2015-10-03 18:00:00	171.585447	479.021432	101.846824	49.115340	13.318878	40.471453	10.951704	0.649491	169.818254	460.880691	97.926823	39.295892	15.077105	56.097762	9.818266	4.919802
291383	100	2015-10-03 21:00:00	162.144446	404.865418	98.384468	35.389856	22.516178	36.216388	11.492089	7.269283	172.199254	459.599763	98.365107	39.964091	14.414241	59.222526	10.569736	4.693342
291384	100	2015-10-04 00:00:00	166.584930	437.980304	104.019479	43.766793	22.109109	65.256390	12.621841	1.793100	170.812061	453.681180	98.994157	40.013985	13.522301	61.197614	10.103349	4.286568
291385	100	2015-10-04 03:00:00	173.182209	452.585928	106.572235	40.534601	16.930726	38.788180	10.747137	6.510290	170.439828	455.036021	99.830401	40.129183	14.551122	65.430315	9.389132	4.484563
291386	100	2015-10-04 06:00:00	155.554082	464.175866	102.615428	36.003311	7.678204	24.248612	6.064152	5.007039	172.264492	453.800429	102.163329	40.050108	13.103400	62.190761	9.128160	4.502950
291387	100	2015-10-04 09:00:00	163.814555	433.614467	114.798438	36.454615	5.259901	40.947023	10.677648	8.252193	170.243198	455.333806	102.290708	40.197103	13.120654	57.910021	9.238228	4.803393
291388	100	2015-10-04 12:00:00	169.196188	403.488184	94.199431	39.189491	22.977467	27.176467	9.430194	13.841831	167.765844	451.355029	103.465520	40.252524	13.315211	59.466979	9.471171	4.442337
291389	100	2015-10-04 15:00:00	165.814250	446.765824	99.334107	44.464271	1.457549	58.086715	1.622380	3.173978	167.312650	439.435929	102.009695	40.435349	11.768963	60.646198	9.136786	5.149517
291390	100	2015-10-04 18:00:00	167.848340	438.393471	90.054937	40.301288	15.958412	40.168662	11.238292	3.633503	166.963446	438.276090	101.637723	40.172602	14.141910	58.372646	8.463179	5.221115
291391	100	2015-10-04 21:00:00	173.508300	439.917848	93.063793	38.750136	7.633479	44.399657	11.019912	4.952713	165.979125	435.138421	102.005617	39.497908	14.000620	59.820047	6.856021	5.392136
291392	100	2015-10-05 00:00:00	182.432617	497.264899	95.443869	40.594815	9.940475	77.558997	4.707020	3.106529	168.142646	447.915202	99.620102	39.635003	14.372707	59.563942	7.988174	5.256284
291393	100	2015-10-05 03:00:00	158.783988	438.405164	100.420803	40.153025	10.849108	72.556330	2.576581	4.504970	168.398872	448.148851	99.351099	39.518646	15.763140	57.682117	8.088214	5.301986
291394	100	2015-10-05 06:00:00	183.150826	426.209117	98.880399	34.418557	20.539063	29.605169	13.588936	7.168643	168.651040	446.075986	98.741443	39.840623	15.331755	60.839923	7.891711	5.269038
291395	100	2015-10-05 09:00:00	188.267556	407.256175	108.931184	36.553233	9.599915	40.722980	1.639521	5.724500	171.826650	441.278667	98.311919	39.196175	16.429023	62.147934	7.475540	5.448962
291396	100	2015-10-05 12:00:00	167.859576	465.992407	107.953155	42.708899	14.190347	92.277799	9.577243	0.735339	174.657123	444.147310	98.520388	38.820190	17.019808	64.730136	8.961444	5.833191
291397	100	2015-10-05 15:00:00	170.348099	434.234744	104.514343	38.607950	10.232598	49.524471	12.445345	2.596743	173.787879	448.842085	100.028549	39.375067	17.096392	64.718132	9.420879	5.738756
291398	100	2015-10-05 18:00:00	152.265370	459.557611	103.536524	40.718426	6.758667	27.051145	12.824247	2.752883	172.496791	442.086577	100.361794	38.943434	15.119775	65.929509	8.836617	6.139142
291399	100	2015-10-05 21:00:00	162.887965	481.415205	96.687092	37.162591	20.541773	55.057460	11.713728	3.539798	170.782713	448.188498	100.794970	38.980896	15.573014	61.859239	9.942610	6.191276

290601 rows × 18 columns

final_feat = telemetry_feat.merge(error_count, on=['datetime', 'machineID'], how='left')
final_feat = final_feat.merge(comp_rep, on=['datetime', 'machineID'], how='left')
final_feat = final_feat.merge(machines, on=['machineID'], how='left')

print(final_feat.head())
final_feat.describe()

   machineID            datetime  voltmean_3h  rotatemean_3h  pressuremean_3h  \
0          1 2015-01-02 06:00:00   180.133784     440.608320        94.137969   
1          1 2015-01-02 09:00:00   176.364293     439.349655       101.553209   
2          1 2015-01-02 12:00:00   160.384568     424.385316        99.598722   
3          1 2015-01-02 15:00:00   170.472461     442.933997       102.380586   
4          1 2015-01-02 18:00:00   163.263806     468.937558       102.726648   

   vibrationmean_3h  voltsd_3h  rotatesd_3h  pressuresd_3h  vibrationsd_3h  \
0         41.551544  21.322735    48.770512       2.135684       10.037208   
1         36.105580  18.952210    51.329636      13.789279        6.737739   
2         36.094637  13.047080    13.702496       9.988609        1.639962   
3         40.483002  16.642354    56.290447       3.305739        8.854145   
4         40.921802  17.424688    38.680380       9.105775        3.060781   

  ...   error2count  error3count  error4count  error5count   comp1    comp2  \
0 ...           0.0          0.0          0.0          0.0  20.000  215.000   
1 ...           0.0          0.0          0.0          0.0  20.125  215.125   
2 ...           0.0          0.0          0.0          0.0  20.250  215.250   
3 ...           0.0          0.0          0.0          0.0  20.375  215.375   
4 ...           0.0          0.0          0.0          0.0  20.500  215.500   

     comp3    comp4   model  age  
0  155.000  170.000  model3   18  
1  155.125  170.125  model3   18  
2  155.250  170.250  model3   18  
3  155.375  170.375  model3   18  
4  155.500  170.500  model3   18  

[5 rows x 29 columns]

	machineID	voltmean_3h	rotatemean_3h	pressuremean_3h	vibrationmean_3h	voltsd_3h	rotatesd_3h	pressuresd_3h	vibrationsd_3h	voltmean_24h	...	error1count	error2count	error3count	error4count	error5count	comp1	comp2	comp3	comp4	age
count	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	...	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000	290601.000000
mean	50.380935	170.774427	446.609386	100.858340	40.383609	13.300173	44.453951	8.885780	4.440575	170.775661	...	0.027560	0.027058	0.022846	0.019955	0.009780	53.382610	51.256589	52.536687	53.679601	11.345226
std	28.798424	9.498824	33.119738	7.411701	3.475512	6.966389	23.214291	4.656364	2.319989	4.720237	...	0.166026	0.164401	0.151266	0.140998	0.098931	62.478424	59.156008	58.822946	59.658975	5.826345
min	1.000000	125.532506	211.811184	72.118639	26.569635	0.025509	0.078991	0.027417	0.015278	155.812721	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	25.000000	164.447794	427.564793	96.239534	38.147458	8.028675	26.906319	5.369959	2.684556	168.072275	...	0.000000	0.000000	0.000000	0.000000	0.000000	13.250000	12.000000	13.000000	12.875000	7.000000
50%	50.000000	170.432407	448.380260	100.235357	40.145874	12.495542	41.793798	8.345801	4.173704	170.212704	...	0.000000	0.000000	0.000000	0.000000	0.000000	32.625000	29.500000	32.125000	32.375000	12.000000
75%	75.000000	176.610017	468.443933	104.406534	42.226898	17.688520	59.092354	11.789358	5.898512	172.462228	...	0.000000	0.000000	0.000000	0.000000	0.000000	68.500000	65.875000	67.125000	70.250000	16.000000
max	100.000000	241.420717	586.682904	162.309656	69.311324	58.444332	179.903039	35.659369	18.305595	220.782618	...	2.000000	2.000000	2.000000	2.000000	2.000000	491.875000	348.875000	370.875000	394.875000	20.000000

8 rows × 27 columns

Label Construction

When using multi-class classification for predicting failure due to a problem, labelling is done by taking a time window prior to the failure of an asset and labelling the feature records that fall into that window as "about to fail due to a problem" while labelling all other records as "€œnormal." This time window should be picked according to the business case: in some situations it may be enough to predict failures hours in advance, while in others days or weeks may be needed to allow e.g. for arrival of replacement parts.

The prediction problem for this example scenerio is to estimate the probability that a machine will fail in the near future due to a failure of a certain component. More specifically, the goal is to compute the probability that a machine will fail in the next 24 hours due to a certain component failure (component 1, 2, 3, or 4). Below, a categorical failure feature is created to serve as the label. All records within a 24 hour window before a failure of component 1 have failure=comp1, and so on for components 2, 3, and 4; all records not within 24 hours of a component failure have failure=none.

labeled_features = final_feat.merge(failures, on=['datetime', 'machineID'], how='left')
labeled_features = labeled_features.fillna(method='bfill', limit=7) # fill backward up to 24h
labeled_features = labeled_features.fillna('none')
labeled_features.head()

	machineID	datetime	voltmean_3h	rotatemean_3h	pressuremean_3h	vibrationmean_3h	voltsd_3h	rotatesd_3h	pressuresd_3h	vibrationsd_3h	...	error3count	error4count	error5count	comp1	comp2	comp3	comp4	model	age	failure
0	1	2015-01-02 06:00:00	180.133784	440.608320	94.137969	41.551544	21.322735	48.770512	2.135684	10.037208	...	0.0	0.0	0.0	20.000	215.000	155.000	170.000	model3	18	none
1	1	2015-01-02 09:00:00	176.364293	439.349655	101.553209	36.105580	18.952210	51.329636	13.789279	6.737739	...	0.0	0.0	0.0	20.125	215.125	155.125	170.125	model3	18	none
2	1	2015-01-02 12:00:00	160.384568	424.385316	99.598722	36.094637	13.047080	13.702496	9.988609	1.639962	...	0.0	0.0	0.0	20.250	215.250	155.250	170.250	model3	18	none
3	1	2015-01-02 15:00:00	170.472461	442.933997	102.380586	40.483002	16.642354	56.290447	3.305739	8.854145	...	0.0	0.0	0.0	20.375	215.375	155.375	170.375	model3	18	none
4	1	2015-01-02 18:00:00	163.263806	468.937558	102.726648	40.921802	17.424688	38.680380	9.105775	3.060781	...	0.0	0.0	0.0	20.500	215.500	155.500	170.500	model3	18	none

5 rows × 30 columns

Below is an example of records that are labeled as failure=comp4 in the failure column. Notice that the first 8 records all occur in the 24-hour window before the first recorded failure of component 4. The next 8 records are within the 24 hour window before another failure of component 4.

labeled_features.loc[labeled_features['failure'] == 'comp4'][:16]

	machineID	datetime	voltmean_3h	rotatemean_3h	pressuremean_3h	vibrationmean_3h	voltsd_3h	rotatesd_3h	pressuresd_3h	vibrationsd_3h	...	error3count	error4count	error5count	comp1	comp2	comp3	comp4	model	age	failure
17	1	2015-01-04 09:00:00	166.281848	453.787824	106.187582	51.990080	24.276228	23.621315	11.176731	3.394073	...	1.0	0.0	1.0	22.125	217.125	157.125	172.125	model3	18	comp4
18	1	2015-01-04 12:00:00	175.412103	445.450581	100.887363	54.251534	34.918687	11.001625	10.580336	2.921501	...	1.0	0.0	1.0	22.250	217.250	157.250	172.250	model3	18	comp4
19	1	2015-01-04 15:00:00	157.347716	451.882075	101.289380	48.602686	24.617739	28.950883	9.966729	2.356486	...	1.0	0.0	1.0	22.375	217.375	157.375	172.375	model3	18	comp4
20	1	2015-01-04 18:00:00	176.450550	446.033068	84.521555	47.638836	8.071400	76.511343	2.636879	4.108621	...	1.0	0.0	1.0	22.500	217.500	157.500	172.500	model3	18	comp4
21	1	2015-01-04 21:00:00	190.325814	422.692565	107.393234	49.552856	8.390777	7.176553	4.262645	7.598552	...	1.0	0.0	1.0	22.625	217.625	157.625	172.625	model3	18	comp4
22	1	2015-01-05 00:00:00	169.985134	458.929418	91.494362	54.882021	9.451483	12.052752	3.685906	6.621183	...	0.0	0.0	1.0	22.750	217.750	157.750	172.750	model3	18	comp4
23	1	2015-01-05 03:00:00	149.082619	412.180336	93.509785	54.386079	19.075952	30.715081	3.090266	6.530610	...	0.0	0.0	1.0	22.875	217.875	157.875	172.875	model3	18	comp4
24	1	2015-01-05 06:00:00	185.782709	439.531288	99.413660	51.558082	14.495664	45.663743	4.289212	7.330397	...	0.0	0.0	1.0	0.000	218.000	158.000	0.000	model3	18	comp4
1337	1	2015-06-18 09:00:00	169.324639	453.923471	101.313249	53.092274	28.155693	42.557599	7.688674	2.488851	...	0.0	0.0	1.0	89.125	29.125	14.125	134.125	model3	18	comp4
1338	1	2015-06-18 12:00:00	190.691297	441.577271	97.192512	44.025425	6.296827	47.271008	7.577957	4.648336	...	0.0	0.0	1.0	89.250	29.250	14.250	134.250	model3	18	comp4
1339	1	2015-06-18 15:00:00	163.602957	433.781185	93.173047	43.051368	18.147449	30.242516	10.870615	2.740922	...	0.0	0.0	1.0	89.375	29.375	14.375	134.375	model3	18	comp4
1340	1	2015-06-18 18:00:00	178.587550	427.300815	118.643186	50.958609	2.229649	17.168087	15.714144	5.669003	...	0.0	0.0	1.0	89.500	29.500	14.500	134.500	model3	18	comp4
1341	1	2015-06-18 21:00:00	158.851795	520.113831	101.974559	44.156671	14.554854	77.101968	4.788908	5.468742	...	0.0	0.0	1.0	89.625	29.625	14.625	134.625	model3	18	comp4
1342	1	2015-06-19 00:00:00	162.191516	453.545010	101.521779	49.136659	12.553190	33.332139	5.983913	1.893250	...	0.0	0.0	1.0	89.750	29.750	14.750	134.750	model3	18	comp4
1343	1	2015-06-19 03:00:00	166.732741	485.036994	100.284288	44.587560	11.099161	57.308864	3.052958	3.062215	...	0.0	0.0	1.0	89.875	29.875	14.875	134.875	model3	18	comp4
1344	1	2015-06-19 06:00:00	172.059069	463.242610	96.905050	53.701413	14.757880	55.874000	3.204981	2.329615	...	0.0	0.0	1.0	0.000	30.000	15.000	0.000	model3	18	comp4

16 rows × 30 columns

Modelling

After the feature engineering and labelling steps, either Azure Machine Learning Studio or this notebook can be used to create a predictive model. The recommend Azure Machine Learning Studio experiment can be found in the Cortana Intelligence Gallery: Predictive Maintenance Modelling Guide Experiment. Below, we describe the modelling process and provide an example Python model.

Training, Validation and Testing

When working with time-stamped data as in this example, record partitioning into training, validation, and test sets should be performed carefully to prevent overestimating the performance of the models. In predictive maintenance, the features are usually generated using lagging aggregates: records in the same time window will likely have identical labels and similar feature values. These correlations can give a model an "unfair advantage" when predicting on a test set record that shares its time window with a training set record. We therefore partition records into training, validation, and test sets in large chunks, to minimize the number of time intervals shared between them.

Predictive models have no advance knowledge of future chronological trends: in practice, such trends are likely to exist and to adversely impact the model's performance. To obtain an accurate assessment of a predictive model's performance, we recommend training on older records and validating/testing using newer records.

For both of these reasons, a time-dependent record splitting strategy is an excellent choice for predictive maintenace models. The split is effected by choosing a point in time based on the desired size of the training and test sets: all records before the timepoint are used for training the model, and all remaining records are used for testing. (If desired, the timeline could be further divided to create validation sets for parameter selection.) To prevent any records in the training set from sharing time windows with the records in the test set, we remove any records at the boundary -- in this case, by ignoring 24 hours' worth of data prior to the timepoint.

from sklearn.ensemble import GradientBoostingClassifier

# make test and training splits
threshold_dates = [[pd.to_datetime('2015-07-31 01:00:00'), pd.to_datetime('2015-08-01 01:00:00')],
                   [pd.to_datetime('2015-08-31 01:00:00'), pd.to_datetime('2015-09-01 01:00:00')],
                   [pd.to_datetime('2015-09-30 01:00:00'), pd.to_datetime('2015-10-01 01:00:00')]]

test_results = []
models = []
for last_train_date, first_test_date in threshold_dates:
    # split out training and test data
    train_y = labeled_features.loc[labeled_features['datetime'] < last_train_date, 'failure']
    train_X = pd.get_dummies(labeled_features.loc[labeled_features['datetime'] < last_train_date].drop(['datetime',
                                                                                                        'machineID',
                                                                                                        'failure'], 1))
    test_X = pd.get_dummies(labeled_features.loc[labeled_features['datetime'] > first_test_date].drop(['datetime',
                                                                                                       'machineID',
                                                                                                       'failure'], 1))
    # train and predict using the model, storing results for later
    my_model = GradientBoostingClassifier(random_state=42)
    my_model.fit(train_X, train_y)
    test_result = pd.DataFrame(labeled_features.loc[labeled_features['datetime'] > first_test_date])
    test_result['predicted_failure'] = my_model.predict(test_X)
    test_results.append(test_result)
    models.append(my_model)

sns.set_style("darkgrid")
plt.figure(figsize=(10, 6))
labels, importances = zip(*sorted(zip(test_X.columns, models[0].feature_importances_), reverse=True, key=lambda x: x[1]))
plt.xticks(range(len(labels)), labels)
_, labels = plt.xticks()
plt.setp(labels, rotation=90)
plt.bar(range(len(importances)), importances)
plt.ylabel('Importance')

<matplotlib.text.Text at 0x256510e55c0>

Evaluation

In predictive maintenance, machine failures are usually rare occurrences in the lifetime of the assets compared to normal operation. This causes an imbalance in the label distribution which usually causes poor performance as algorithms tend to classify majority class examples better at the expense of minority class examples as the total misclassification error is much improved when majority class is labeled correctly. This causes low recall rates although accuracy can be high and becomes a larger problem when the cost of false alarms to the business is very high. To help with this problem, sampling techniques such as oversampling of the minority examples are usually used along with more sophisticated techniques which are not covered in this notebook.

sns.set_style("darkgrid")
plt.figure(figsize=(8, 4))
labeled_features['failure'].value_counts().plot(kind='bar')
plt.xlabel('Component failing')
plt.ylabel('Count')

<matplotlib.text.Text at 0x25600910f60>

Also, due to the class imbalance problem, it is important to look at evaluation metrics other than accuracy alone and compare those metrics to the baseline metrics which are computed when random chance is used to make predictions rather than a machine learning model. The comparison will bring out the value and benefits of using a machine learning model better.

In the following, we use an evaluation function that computes many important evaluation metrics along with baseline metrics for classification problems. For a detailed explanation of the metrics, please refer to the scikit-learn documentation and a companion blog post (with examples in R, not Python),

from sklearn.metrics import confusion_matrix, recall_score, accuracy_score, precision_score

def Evaluate(predicted, actual, labels):
    output_labels = []
    output = []
    
    # Calculate and display confusion matrix
    cm = confusion_matrix(actual, predicted, labels=labels)
    print('Confusion matrix\n- x-axis is true labels (none, comp1, etc.)\n- y-axis is predicted labels')
    print(cm)
    
    # Calculate precision, recall, and F1 score
    accuracy = np.array([float(np.trace(cm)) / np.sum(cm)] * len(labels))
    precision = precision_score(actual, predicted, average=None, labels=labels)
    recall = recall_score(actual, predicted, average=None, labels=labels)
    f1 = 2 * precision * recall / (precision + recall)
    output.extend([accuracy.tolist(), precision.tolist(), recall.tolist(), f1.tolist()])
    output_labels.extend(['accuracy', 'precision', 'recall', 'F1'])
    
    # Calculate the macro versions of these metrics
    output.extend([[np.mean(precision)] * len(labels),
                   [np.mean(recall)] * len(labels),
                   [np.mean(f1)] * len(labels)])
    output_labels.extend(['macro precision', 'macro recall', 'macro F1'])
    
    # Find the one-vs.-all confusion matrix
    cm_row_sums = cm.sum(axis = 1)
    cm_col_sums = cm.sum(axis = 0)
    s = np.zeros((2, 2))
    for i in range(len(labels)):
        v = np.array([[cm[i, i],
                       cm_row_sums[i] - cm[i, i]],
                      [cm_col_sums[i] - cm[i, i],
                       np.sum(cm) + cm[i, i] - (cm_row_sums[i] + cm_col_sums[i])]])
        s += v
    s_row_sums = s.sum(axis = 1)
    
    # Add average accuracy and micro-averaged  precision/recall/F1
    avg_accuracy = [np.trace(s) / np.sum(s)] * len(labels)
    micro_prf = [float(s[0,0]) / s_row_sums[0]] * len(labels)
    output.extend([avg_accuracy, micro_prf])
    output_labels.extend(['average accuracy',
                          'micro-averaged precision/recall/F1'])
    
    # Compute metrics for the majority classifier
    mc_index = np.where(cm_row_sums == np.max(cm_row_sums))[0][0]
    cm_row_dist = cm_row_sums / float(np.sum(cm))
    mc_accuracy = 0 * cm_row_dist; mc_accuracy[mc_index] = cm_row_dist[mc_index]
    mc_recall = 0 * cm_row_dist; mc_recall[mc_index] = 1
    mc_precision = 0 * cm_row_dist
    mc_precision[mc_index] = cm_row_dist[mc_index]
    mc_F1 = 0 * cm_row_dist;
    mc_F1[mc_index] = 2 * mc_precision[mc_index] / (mc_precision[mc_index] + 1)
    output.extend([mc_accuracy.tolist(), mc_recall.tolist(),
                   mc_precision.tolist(), mc_F1.tolist()])
    output_labels.extend(['majority class accuracy', 'majority class recall',
                          'majority class precision', 'majority class F1'])
        
    # Random accuracy and kappa
    cm_col_dist = cm_col_sums / float(np.sum(cm))
    exp_accuracy = np.array([np.sum(cm_row_dist * cm_col_dist)] * len(labels))
    kappa = (accuracy - exp_accuracy) / (1 - exp_accuracy)
    output.extend([exp_accuracy.tolist(), kappa.tolist()])
    output_labels.extend(['expected accuracy', 'kappa'])
    

    # Random guess
    rg_accuracy = np.ones(len(labels)) / float(len(labels))
    rg_precision = cm_row_dist
    rg_recall = np.ones(len(labels)) / float(len(labels))
    rg_F1 = 2 * cm_row_dist / (len(labels) * cm_row_dist + 1)
    output.extend([rg_accuracy.tolist(), rg_precision.tolist(),
                   rg_recall.tolist(), rg_F1.tolist()])
    output_labels.extend(['random guess accuracy', 'random guess precision',
                          'random guess recall', 'random guess F1'])
    
    # Random weighted guess
    rwg_accuracy = np.ones(len(labels)) * sum(cm_row_dist**2)
    rwg_precision = cm_row_dist
    rwg_recall = cm_row_dist
    rwg_F1 = cm_row_dist
    output.extend([rwg_accuracy.tolist(), rwg_precision.tolist(),
                   rwg_recall.tolist(), rwg_F1.tolist()])
    output_labels.extend(['random weighted guess accuracy',
                          'random weighted guess precision',
                          'random weighted guess recall',
                          'random weighted guess F1'])

    output_df = pd.DataFrame(output, columns=labels)
    output_df.index = output_labels
                  
    return output_df

evaluation_results = []
for i, test_result in enumerate(test_results):
    print('\nSplit %d:' % (i+1))
    evaluation_result = Evaluate(actual = test_result['failure'],
                                 predicted = test_result['predicted_failure'],
                                 labels = ['none', 'comp1', 'comp2', 'comp3', 'comp4'])
    evaluation_results.append(evaluation_result)
evaluation_results[0]  # show full results for first split only

Split 1:
Confusion matrix
- x-axis is true labels (none, comp1, etc.)
- y-axis is predicted labels
[[119594     21      0      4      3]
 [    18    515      3      5      1]
 [     0      0    860      0      1]
 [    13      0      2    372      1]
 [     2      2      6      0    497]]

Split 2:
Confusion matrix
- x-axis is true labels (none, comp1, etc.)
- y-axis is predicted labels
[[95266    13     0     4     3]
 [   19   399     2     1     1]
 [    0     0   700     0     0]
 [   12     0     2   291     1]
 [    2     2     4     0   392]]

Split 3:
Confusion matrix
- x-axis is true labels (none, comp1, etc.)
- y-axis is predicted labels
[[71724     7     0     4     3]
 [   17   300     1     1     1]
 [    0     1   547     0     0]
 [   11     0     0   212     1]
 [    2     1     3     0   274]]

	none	comp1	comp2	comp3	comp4
accuracy	0.999327	0.999327	0.999327	0.999327	0.999327
precision	0.999724	0.957249	0.987371	0.976378	0.988072
recall	0.999766	0.950185	0.998839	0.958763	0.980276
F1	0.999745	0.953704	0.993072	0.967490	0.984158
macro precision	0.981759	0.981759	0.981759	0.981759	0.981759
macro recall	0.977566	0.977566	0.977566	0.977566	0.977566
macro F1	0.979634	0.979634	0.979634	0.979634	0.979634
average accuracy	0.999731	0.999731	0.999731	0.999731	0.999731
micro-averaged precision/recall/F1	0.999327	0.999327	0.999327	0.999327	0.999327
majority class accuracy	0.981152	0.000000	0.000000	0.000000	0.000000
majority class recall	1.000000	0.000000	0.000000	0.000000	0.000000
majority class precision	0.981152	0.000000	0.000000	0.000000	0.000000
majority class F1	0.990486	0.000000	0.000000	0.000000	0.000000
expected accuracy	0.962796	0.962796	0.962796	0.962796	0.962796
kappa	0.981922	0.981922	0.981922	0.981922	0.981922
random guess accuracy	0.200000	0.200000	0.200000	0.200000	0.200000
random guess precision	0.981152	0.004446	0.007062	0.003182	0.004158
random guess recall	0.200000	0.200000	0.200000	0.200000	0.200000
random guess F1	0.332269	0.008698	0.013642	0.006265	0.008148
random weighted guess accuracy	0.962755	0.962755	0.962755	0.962755	0.962755
random weighted guess precision	0.981152	0.004446	0.007062	0.003182	0.004158
random weighted guess recall	0.981152	0.004446	0.007062	0.003182	0.004158
random weighted guess F1	0.981152	0.004446	0.007062	0.003182	0.004158