Bearing fault diagnosis model based on MCNN-LSTM

Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).

This repository contains data and code that implement common machine learning algorithms for machinery condition monitoring task.

Siamese network for bearing fault diagnosis

Benchmark code for optimizers of bearing fault diagnosis. This code provides moduled features of data download, preprocessing, training, and logging.

wdcnn model for bearing fault diagnosis

Improving on NASA's work with induction motor bearing fault detection using RNN-powered smart sensors.

Bearing Fault Detection and Classification Based on Temporal Convolutions and LSTM Network in Induction Machine Systems

Simulation and Modeling in Python 3

Vibration analysis tool, Signal processing tool

Cyclostationary analysis in angular domain for bearing fault identification

Showcase how machine learning can help plant operator monitor equipment condition through correctly analyzing measurement data collected from many sensors.

Detection of defective rolling bearings with machine learning methods based on bearings acceleration data

Researches dedicated to bearing fault diagnosis from Mandevices Laboratory

Diagnóstico de falla de rodamiento utilizando descomposición modal empírica y deep learning

Long short-term memory based semi-supervised encoder-decoder for early prediction of failures in self-lubricating bearings

Bearing fault detection public datasets collection.

Contest solution for 数境创新大赛-先进制造制造关键装置故障诊断

the PLS allows both to classify the types of faults and to reduce the dimensionality of the problem by trying to maximize the covariance between X and Y, useful in supervised learning.

This project uses Explainable AI (XAI) to interpret machine learning models for diagnosing faults in industrial bearings. By applying SVM and kNN models and leveraging SHAP values, it enhances the transparency and reliability of machine learning in industrial condition monitoring.