Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks

Abstract

Rolling bearings, as the main components of the large industrial rotating equipment, usually work under complex conditions and are prone to break down. It can provide a certain theoretical basis for identifying the sub-health state of the industrial equipment by the analysis from the incipient weak signals. Thus, a sub-health recognition offline algorithm based on Refined Composite Multiscale Dispersion Entropy (RCMDE) and Deep Belief Network-Extreme Learning Machine (DBN-ELM) optimized by Improved Firework Algorithm (IFWA) is proposed. First of all, in light of the drawbacks that it is easy to fall into local optima and cross the boundary for exploding fireworks in Firework Algorithm (FWA), Cauchy mutation and adaptive dynamic explosion radius factor coefficient is introduced into IFWA. Secondly, Maximum Correlation Kurtosis Deconvolution (MCKD) optimized by the improved parameters is used to process the incipient vibration signals with nonlinearity, nonstationary, and IFWA is used to adaptively adjust to the period T and the filter length L in MCKD(IFWA-MCKD). Then, each sequence of signals is further extracted the feature-RCMDE to rich sample diversity. Finally, combining the powerful unsupervised learning capability from DBN and the generalization capability from ELM, DBN-ELM can be established. What's more, in order to avoid the interference of human on the parameters, IFWA is used to optimize the number of hidden nodes in DBN-ELM, and the IFWA-DBN-ELM is established. It shows that the algorithm has the higher sub-health recognition accuracy, better robustness and generalization, which has a better industrial application prospect.