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...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.展开更多
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)+1 种基金the National Basic Research Program of China(Grant No.2009CB724405)the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20110201130001)
文摘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.
