Motor Fault Diagnosis M-File
This MATLAB M-file enables motor fault diagnosis by analyzing operational data to identify common failure types including rotor bar defects, stator winding shorts, and bearing faults through signal processing algorithms.
Probabilistic Neural Network Classification for Transformer Fault Diagnosis Based on PNN
Classification and Prediction Using Probabilistic Neural Networks for Transformer Fault Diagnosis Based on PNN
EEG Signal Processing using FFT Analysis (Part 2)
FFT spectral analysis enables extraction of EEG signals from different frequency bands. These extracted signals can be used for diagnosing brain disorders and analyzing electrical activity patterns in brain tissue and functional states. The workflow includes: 1. Converting experimental EEG data files to text format (after 50Hz notch filtering) to obtain Matlab-compatible data (0661.txt). 2. Importing data into Matlab, extracting Fp1 channel signals, applying FFT to isolate α, β, θ, and δ bands, then performing inverse FFT for time-domain reconstruction. 3. Calculating power spectra for each frequency band.
BP Neural Network Optimization Algorithm Based on Genetic Algorithm
BP networks are a type of multi-layer feedforward neural network, named after the error backpropagation learning algorithm used to adjust network weights during training. Proposed by Rumelhart et al. in 1986, BP neural networks feature simple architecture, numerous adjustable parameters, diverse training algorithms, and strong operability, leading to widespread adoption. Approximately 80%–90% of neural network models utilize BP networks or their variants. While BP networks form the core of forward networks and represent the most refined part of neural networks, they suffer from limitations such as slow learning convergence.
EEG Signal Processing Using FFT (Part 3)
EEG signal extraction can be performed using FFT spectrum analysis. The extracted EEG signals from different frequency bands enable diagnosis of neurological disorders and analysis of brain electrical activity and functional states. Key implementation steps include: 1. Converting experimental EEG data (pre-filtered with 50Hz notch) to text format for Matlab compatibility (0661.txt). 2. Importing data into Matlab, extracting Fp1 channel signals, applying FFT to isolate α, β, θ, and δ bands, and performing inverse FFT for time-domain reconstruction. 3. Computing power spectral density for each frequency band.
Wavelet Neural Network Diagnostic
Source code for wavelet neural network diagnostics implementing MATLAB simulation of wavelet neural networks with enhanced algorithm implementation details
Enhanced PCA for Fault Detection and Diagnosis
Enhanced PCA applied to the TE chemical process for fault detection and diagnosis, with simulation results demonstrating superior performance compared to traditional PCA methods, including implementation details for anomaly detection algorithms.
PLS_Toolbox for Fault Detection and Diagnosis
PLS_Toolbox is a MATLAB toolbox specialized in fault detection and diagnosis, implementing various algorithms including PCA and PLS, along with post-processing methods such as Q-statistics and T2-statistics.
LVQ Neural Network Classification Example for Breast Tumor Diagnosis
LVQ Neural Network Classification Implementation for Breast Tumor Diagnosis with Code Integration
Breast Tumor Diagnosis Using LVQ Neural Network Classification in MATLAB
Implementing LVQ Neural Network Classification for Breast Tumor Diagnosis in MATLAB with Code Implementation Details