Maximal-overlap adaptive multiwavelet for detecting transient vibration responses from dynastic signal of rotating machineries  

作　　者：HE ShuiLong ZI YanYang ZHAO ChenLu CHEN BinQiang WANG XiaoDong HE ZhengJia 

机构地区：[1]School of Mechanical Engineering, Xi’an Jiaotong University [2]State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University [3]School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta 30332, GA, USA [4]Technology Center, CNPC Logging Co.

出　　处：《Science China(Technological Sciences)》2014年第1期136-150,共15页中国科学（技术科学英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.51275384);the Key Project of National Natural Science Foundation of China(Grant No.51035007);the National Basic Research Program of China(Grant No.2009CB724405);the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20110201130001)

摘　　要：Vibration signal is an important prerequisite for mechanical fault detection. However, early stage defect of rotating machiner- ies is difficult to identify because their incipient energy is interfered with background noises. Multiwavelet is a powerful tool used to conduct non-stationary fault feature extraction. However, the existing predetermined multiwavelet bases are independ- ent of the dynamic response signals. In this paper, a constructing technique of vibration data-driven maximal-overlap adaptive multiwavelet （MOAMW） is proposed for enhancing the extracting performance of fault symptom. It is able to derive an opti- mal multiwavelet basis that best matches the critical non-stationary and transient fault signatures via genetic algorithm. In this technique, two-scale similarity transform （TST） and symmetric lifting （SymLift） scheme are combined to gain high designing freedom for matching the critical faulty vibration contents in vibration signals based on the maximal fitness objective. TST and SymLift can add modifications to the initial multiwavelet by changing the approximation order and vanishing moment of mul- tiwavelet, respectively. Moreover, the beneficial feature of the MOAWM lies in that the maximal-overlap filterbank structure can enhance the periodic and transient characteristics of the sensor signals and preserve the time and frequency analyzing res- olution during the decomposition process. The effectiveness of the proposed technique is validated via a numerical simulation as well as a rolling element beating with an outer race scrape and a gearbox with rub fault.

关 键 词：fault diagnosis maximal-overlap adaptive multiwavelet two-scale similarity transform symmetric lifting rotating machineries 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH825