Fault Diagnosis and Identification for TE Model Using KPCA (Kernel Principal Component Analysis)
Fault diagnosis and identification for TE model based on KPCA (Kernel Principal Component Analysis) methodology
KPCA-Based Fault Diagnosis Program
A straightforward fault diagnosis program utilizing KPCA with comprehensive annotations for ease of use
Fault Diagnosis for Rolling Bearings
Application for rolling bearing fault diagnosis to identify characteristic frequencies, enabling defect analysis in rolling bearings. Includes implementation approaches for frequency domain analysis and defect pattern recognition algorithms.
Bearing Rolling Element Fault Diagnosis
Diagnosis of bearing rolling element faults, with fault type set as localized peeling of rolling element. The fault characteristic frequency is calculated as 218.98Hz based on theoretical computation of outer ring passing frequency or its harmonics.
MATLAB Implementation of KPCA with Fault Diagnosis Capabilities
Original KPCA algorithm implementation featuring T2 and SPE statistical monitoring charts for comprehensive fault diagnosis systems
Fault Simulation Demonstration for CSTR Model
Code implementation for CSTR model fault simulation demonstration - a fundamental model commonly used in fault diagnosis with practical algorithm implementation
TE (Tennessee-Eastman) System Source Code
Source code implementation of the TE (Tennessee-Eastman) system, providing a comprehensive benchmark platform for fault diagnosis simulation and control algorithm development
Code Implementation of Time-Frequency Distribution for Fault Diagnosis Applications
Complete MATLAB-based code collection for applying time-frequency distribution techniques in fault diagnosis scenarios
Particle Swarm Optimization for Enhanced BP Neural Network in Fault Diagnosis
This algorithm presents an improved particle swarm optimization (PSO) method for optimizing backpropagation (BP) neural networks, specifically designed for fault diagnosis in cascaded frequency converters. It includes both conventional BP neural network implementations and the enhanced PSO-BP neural network approach, featuring comparative analysis with example datasets to demonstrate superior diagnostic performance. Key code components involve PSO population initialization, velocity updating mechanisms, and neural network weight optimization procedures.
Source Code for Permanent Magnet Synchronous Motor Fault Diagnosis and Fault-Tolerant Control Using Kalman Filter Technology
Source code implementation for permanent magnet synchronous motor fault diagnosis and fault-tolerant control based on Kalman filter technology, originally published and indexed in the ICACMVE'07 conference proceedings. The implementation features robust state estimation algorithms and real-time fault detection mechanisms.