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.

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

Vibration analysis tool, Signal processing tool

Robust Transfer Subspace Learning Based on Low-Rank and Sparse Representation for Bearing Fault Diagnosis (Measurement Science and Technology, 2024)

Official repository for the paper "Few‐shot multiscene fault diagnosis of rolling bearing under compound variable working conditions"

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

This reposotory release a bearing failure dataset, which can support intelliegnt fault diagnosis research（实验室自采轴承开源数据集，包含稳定转速和时变转速）

Researches dedicated to bearing fault diagnosis from Mandevices Laboratory

A Matlab ToolBox for Bearing Fault Diagnosis

Fault Localization of Rolling Element Bearing using Vibrational Data.

Cyclostationary analysis in angular domain for bearing fault identification

Bearing fault diagnosis using Boruta Feature selector and k-nearest neighbor algorithm

Analyzing Vibrational Data of the System using Machine Learning

1,利用经验模态分解与希尔伯特变换进行轴承数据的包络谱计算2，利用PCA对提取到的包络谱特征进行降维 3，将降维后的数据输入到堆栈降噪自动编码器进行分类器建模

Using appropriate IMFs for envelope analysis in multiple fault diagnosis of ball bearings - Paper Implementation DSP Assignment

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

This repository helps in setting up the data necessary for fault diagnosis using the bearing fault datasets of CWRU

MATLAB codes for "Sparse Bayesian learning approach for compound bearing fault diagnosis"

Bearing fault diagnosis based on biphasic current

Introduced a novel signal processing technique for improved feature extraction

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

Innovative bearing fault diagnosis using SST algorithm for time-frequency images. Accurately transform signals into efficient time-frequency representations. Leverage deep learning for precise diag…

Using Random NAS and QAT Quantizer to optimize model for Bearing Fault Diagnosis

Source codes for the paper "Few-shot bearing fault diagnosis based on meta-learning with discriminant space optimization" which published in MST

MATLAB codes for "Frequency Estimation of Vibration Signals: An Subspace Approach for Bearing Fault Diagnosis"