Intelligent Fault Diagnosis Method Research Of Rolling Element Bearing Based On Acoustic Emission

Modern rotating equipment develops in a way of large-scale, complication, automation and high energy consumption. Effective condition monitoring of mechanical equipment, timely detection of failure in early stage and corresponding maintenance, are the foundation of secure and reliable operation of large-scale rotating mechanical equipments. Rolling element bearing is the significant component of rotating mechanical equipment, and condition monitoring, fault diagnosis and trend prediction of which is of important practical significance.Fault diagnosis of rolling bearing integrates mechanical dynamics, modern measuring and testing technology, modern signal processing, data mining and artificial intelligent, etc. It includes pre-processing, feature extraction, pattern recognition and trend prediction four parts, and the purpose of which is to extract fault characteristic parameters that could reflect operation status of equipments, identify fault type, predict development tendency of those parameters, and make appropriate maintenance plan according to fault severity. Preprocessing methods adopted in this paper is EMD and wavelet analysis, eliminating noise element through decomposition by frequence and frequence range.’Energy to Shannon Entropy’ is proposed to select wavelet base, reducing energy leakage of frequence range in wavelet decomposition. During feature extraction aspect, RMS index and relative entropy based on RMS index are put forward, which are of simple calculation and strong robustness against noise, could effectively process with AE signal under low SNR and early fault stage. Approximate entropy and its fast algorithm are introduced, and the effect of parameters in calculation process to entropy value and computing time is discussed.Neural network based on improved particle swarm optimization is proposed in this paper for pattern recognition of rolling bearing fault. PSO is impoved based on fitness, by modifying parameters and formula itself of standard velocity updating formula and consummating search strategy through combining with other intelligent algorithms. Improved PSO extrudes global search capability or local search capability in different stage, effectively avoding the possibility of falling into local minimum in search process. Finally, regression predition model based on genetic algorithm is introduced to predict fault trend, using genetic algorithm to modify coefficients in different degree regression model. And this method is tested through an residual life prediction experiment of bearing outer-race failure under strong load and badlubrication.