Open rotating mechanical fault datasets (开源旋转机械故障数据集整理)
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.
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.
Deep Learning applied to predictive maintenance use cases
remaining useful life, residual useful life, remaining life estimation, survival analysis, degradation models, run-to-failure models, condition-based maintenance, CBM, predictive maintenance, PdM, prognostics health management, PHM
The FP-SNS-DATALOG2 function pack represents an evolution of FP-SNS-DATALOG1 and provides a comprehensive solution for saving data from any combination of sensors and microphones configured up to the maximum sampling rate. Please check st.com where a more recent version of the software might be available.
An implementation of Dissolved Gas Analysis (DGA) assessment algorithms and guidelines in the form of Excel Add-ins.
Tidy multi-material machine tool wear dataset for prognostics and health monitoring.
Vibration analysis tool, Signal processing tool
ViMag: A Visual Vibration Toolbox
Code basis for the paper "Monitoring and Adapting the Physical State of a Camera for Autonomous Vehicles" (2023)
Showcase how machine learning can help plant operator monitor equipment condition through correctly analyzing measurement data collected from many sensors.
The FP-SNS-DATALOG1 function pack provides a comprehensive solution to save data from any combination of sensors and microphones configured up to the maximum sampling rate available on STWIN and SensorTile.box
Bearing fault detection public datasets collection.
A simple tool for Condition Monitoring.
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.
Course project on Internet of Things and Signal Analysis for Condition Monitoring
Implementation of a CM system for a wind turbine and wind farm
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