文献类型：期刊文献

英文题名：Bearings Degradation Assessment Based on Signal Significance Index and Deep Cumulative Features

作者：Chen, Zhihao[1];Bao, Wenjie[1];Zuo, Gangao[1];Zhao, Wenqiang[2];Hu, Yue[3];Li, Fucai[1]

机构：[1]Shanghai Jiao Tong Univ, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China;[2]State Grid Qinghai Prov Elect Power Co, Elect Power Sci Res Inst, Xining 810000, Qinghai, Peoples R China;[3]East China Univ Sci & Technol, Sch Mech & Power Engn, Key Lab Pressure Syst & Safety MOE, Shanghai 200237, Peoples R China

年份：2023

卷号：72

外文期刊名：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录：;EI(收录号：20233814765491);WOS:【SCI-EXPANDED(收录号:WOS:001136570000002)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 52175104, in part by the Marine Power Research and Development under Grant MG0709, in part by the National Basic Research Program of China under Grant 2019-JCJQ-ZD-133-00, and in part by the National Natural Science Foundation of China under Grant 52105113. The Associate Editor coordinating the review process was Dr. Huang-Chen Lee.

语种：英文

外文关键词：Deep cumulative features (DCF); degradation assessment; rolling bearings; signal significance index (SSI); succinct and fast empirical mode decomposition (SFEMD)

摘要：Rolling bearings are widely applied in rotating machinery, and its performance degradation assessment is a crucial work. Accurate degradation assessment and remaining useful life (RUL) prediction are of great significance for devising maintenance scheme and preventing sudden shutdown of machinery, with the selection of representative degradation features being the key factor. In this article, a novel performance degradation assessment model is constructed based on signal significance index (SSI) and deep cumulative features (DCF). First, succinct and fast empirical mode decomposition (SFEMD) is employed to decompose the original signal. Second, an SSI approach based on morphological pattern (MP) coding and symbolic aggregate approximation (SAX) coding is proposed to select the representative intrinsic mode function (IMF). Third, a new performance degradation feature, namely, deep cumulative degradation feature, is proposed, which has good monotonicity and formal consistency in different failure types. Finally, an integrated long short-term memory (LSTM) network is constructed for performance degradation assessment. The comparative experimental results on the public dataset show that the proposed model can provide an effective reference for the performance degradation assessment and RUL prediction.