Explore MATLAB source code curated for "故障诊断" with clean implementations, documentation, and examples.
Wavelet Transform in Fault Diagnosis 44118.1 Introduction 44118.2 Fundamental Principles 44218.3 Application of Wavelet Transform in Bearing Fault Diagnosis 44418.3.1 Outer Race Fault Diagnosis 44418.3.2 Rolling Element Fault Diagnosis 44818.4 Wavelet Transform in Gear Fault Diagnosis 45018.5 Wavelet Packet Analysis in Bearing Fault Diagnosis 45218.5.1 Outer Race Fault Diagnosis 45318.5.2 Rolling Element Fault Diagnosis 454
Recursive Neural Network Fault Diagnosis offers valuable insights into network behavior analysis, featuring practical code implementations for error tracing and system stability enhancement.
This example includes analytical data in txt format, utilizing MATLAB programs to perform engine waveform analysis based on wavelet analysis algorithms, ultimately enabling engine fault diagnosis.
Implementation of transformer fault prediction using Support Vector Machine, featuring data preprocessing, model training, and diagnostic classification with scikit-learn
This program implements neural networks for direct bearing fault diagnosis, taking bearing signals as input and outputting diagnostic results through advanced pattern recognition algorithms.
Artificial Neural Network Algorithm designed for fault diagnosis applications, capable of identifying and classifying various fault types through pattern recognition and data analysis.
This algorithm implements Independent Component Analysis (ICA) and Principal Component Analysis (PCA) for process monitoring and fault diagnosis, including automated data structure adaptation for enhanced stability and reliability.
Dynamic Principal Component Analysis (DPCA) effectively implements fault diagnosis through time-lagged data expansion and multivariate statistical monitoring
Self-implemented RBF neural network for classification recognition, fault diagnosis, and pattern recognition with code-level implementation details
Signal Processing, Fault Diagnosis, Energy Method, Auto-Power Spectrum, Cross-Power Spectrum, Time Domain and Frequency Domain Solutions