Fault Bearing Classification Analysis dashboard to explore, diagnose and highlight potential factors to predict the fault class based on bearing statistical manufacturing data.
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Updated
Nov 7, 2023
Fault Bearing Classification Analysis dashboard to explore, diagnose and highlight potential factors to predict the fault class based on bearing statistical manufacturing data.
This research is conducted as part of the NSBE Aerospace SIG internship program. It is focused on investigating The Feasibility of Implementing Predictive maintenance on Rotorcraft Health and Usage Monitoring Systems.
Este projeto tem por objetivo realizar Predições de defeitos em máquinas rotativas aplicando métodos de Machine Learning.
Long short-term memory based semi-supervised encoder-decoder for early prediction of failures in self-lubricating bearings
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.
Contest solution for 数境创新大赛-先进制造制造关键装置故障诊断
Researches dedicated to bearing fault diagnosis from Mandevices Laboratory
Diagnóstico de falla de rodamiento utilizando descomposición modal empírica y deep learning
Detection of defective rolling bearings with machine learning methods based on bearings acceleration data
Simulation and Modeling in Python 3
Showcase how machine learning can help plant operator monitor equipment condition through correctly analyzing measurement data collected from many sensors.
Vibration analysis tool, Signal processing tool
Cyclostationary analysis in angular domain for bearing fault identification
Improving on NASA's work with induction motor bearing fault detection using RNN-powered smart sensors.
wdcnn model for bearing fault diagnosis
Benchmark code for optimizers of bearing fault diagnosis. This code provides moduled features of data download, preprocessing, training, and logging.
Siamese network for bearing fault diagnosis
This repository contains data and code that implement common machine learning algorithms for machinery condition monitoring task.
Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).
Bearing fault diagnosis model based on MCNN-LSTM
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