There are 33 repositories under predictive-maintenance topic.
Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.
DataGene - Identify How Similar TS Datasets Are to One Another (by @firmai)
Transformer implementation with PyTorch for remaining useful life prediction on turbofan engine with NASA CMAPSS data set. Inspired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, 1-10.
In this project I aim to apply Various Predictive Maintenance Techniques to accurately predict the impending failure of an aircraft turbofan engine.
collection of predictive maintenance solutions for NASAs turbofan (CMAPSS) dataset
Predictive_Maintenance_using_Machine-Learning_Microsoft_Casestudy
This research project will illustrate the use of machine learning and deep learning for predictive analysis in industry 4.0.
Papers and datasets for Vibration Analysis
Datasets for Predictive Maintenance
Predictive Maintenance for Vehicle Fleets
N-CMAPSS data preparation for Machine Learning and Deep Learning models. (Python source code for new CMAPSS dataset)
Python codes “Jupyter notebooks” for the paper entitled "A Hybrid Method for Condition Monitoring and Fault Diagnosis of Rolling Bearings With Low System Delay, IEEE Trans. on Instrumentation and Measurement, Aug. 2022. Techniques used: Wavelet Packet Transform (WPT) & Fast Fourier Transform (FFT). Application: vibration-based fault diagnosis.
Predictive Maintenance System for Digital Factory Automation
This github repository contains the sample code and exercises of btp-ai-sustainability-bootcamp, which showcases how to build Intelligence and Sustainability into Your Solutions on SAP Business Technology Platform with SAP AI Core and SAP Analytics Cloud for Planning.
time-series prediction for predictive maintenance
False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics
This project is about predictive maintenance with machine learning. It's a final project of my Computer Science AP degree.
Evolutionary Neural Architecture Search on Transformers for RUL Prediction
Time-series analysis using the Matrix profile in Julia
Deep Learning applied to predictive maintenance use cases
This workshop will familiarize you with some of the key steps towards building an end-to-end predictive maintenance system leveraging Amazon SageMaker, Amazon Polly and the AWS IoT suite.
ARAKAT - Big Data Analysis and Business Intelligence Application Development Platform
CeRULEo: Comprehensive utilitiEs for Remaining Useful Life Estimation methOds
This is a repository of sample codes and implementation framework for industrial machine predictive maintenance tasks using deep learning models.
ML Approaches for RUL Prediction, Anomaly Detection, Survival Analysis and Failure Classification
Battery State Of Charge(SoC) Estimation Using Stochastic Methods & Machine Learning.
Nvidia DLI workshop on AI-based predictive maintenance techniques to identify anomalies and failures in time-series data, estimate the remaining useful life of the corresponding parts, and map anomalies to failure conditions.
Remaining Useful Life (RUL) prediction for Turbofan Engines
ProFeld: survival analysis, predictive maintenance, churn analysis, and remaining useful life prediction in Python
Foundations of Strategic Business Analytics - ESSEC Business school via Coursera.org
Illustrating a typical Predictive Maintenance use case in an Industrial IoT Scenario. By using Statistical Modelling and Data Visualization we attempt to performance Failure Analysis and Prediction of crucial industrial equipments like Boilers, Pumps, Motors etc. so that necessary actions can be taken by the management for their repair, servicing and optimal performance.
Baseline study on the development of predictive maintenance techniques using open data. Two problems are discussed: classifying a vibration signal as healthy or faulty and on the other hand, given a signal predicting time to failure based on early anomaly detection.