Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings

Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines

https://isiarticles.com/bundles/Article/pre/pdf/8078.pdf

A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm

This paper presents a novel approach to detect the milling chatter based on energy entropy. By using variational mode decomposition and wavelet packet decomposition, the cutting force signal is decomposed into two group of sub-signals respectively, and each component has limited bandwidth in spectral domain. Since milling chatter is characterized by the change of frequency and energy distribution. Therefore the energy features extracted from the two group of sub-signals are considered and the energy entropies are obtained, which can be utilized to demonstrate the condition of the milling system synthetically. Several milling tests are conducted and the results show that the proposed method can effectively detect the chatter at an early stage.

Jian Huang

Papers

An intelligent fault diagnosis method of high voltage circuit breaker based on improved EMD energy entropy and multi-class support vector machine