华东交通大学学报2017，Vol.34Issue(4)：110-116,7.
基于隐马尔科夫模型的滚动轴承性能退化评估
Rolling Bearing Performance Degradation Assessment Based on Hidden Markov Model
摘要
Abstract
The running condition of rolling beating is closely related to the degree of its degradation.If the degradation degree of rolling beatings can be assessed online quantitatively,the equipment maintenance strategy will be pertinent.This paper makes the node energy values decomposed of non-faulty samples by wavelet packet,which are taken together with time-domain features as the original characteristics of the signals.The original features are classified into training data and test data after the nonlinear flow-based dimensionality reduction.The HMM model is trained by the non-faulty samples.After the model is stabilized and the model is maintained unchanged,samples to be tested are input into the trained HMM through iterations.Then,the maximum output likelihood is obtained as performance degradation index,which is adopted to evaluate the performance of rolling bearings.The proposed method is verified by fatigue life test of the bearing and the envelope demodulation.Results of performance degradation method are in agreement with those obtained from the accelerated fatigue tests of bearings.关键词
隐马尔科夫/滚动轴承/小波包分解/时域特征/性能退化评估/包络解调Key words
Hidden Markov/rolling bearing/wavelet packet decomposition/time domain feature/performance degradation assessment/envelope demodulation分类
信息技术与安全科学引用本文复制引用
周建民,郭慧娟,张龙..基于隐马尔科夫模型的滚动轴承性能退化评估[J].华东交通大学学报,2017,34(4):110-116,7.基金项目
国家自然科学基金项目(51205130,51665013) （51205130,51665013）
