It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents.Due to diversity and complexity,the compound faults detection of rotating machinery under ...It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents.Due to diversity and complexity,the compound faults detection of rotating machinery under non-stationary operation turns to be a challenging task.Multiwavelet with two or more base functions may match two or more features of compound faults,which may supply a possible solution to compound faults detection.However,the fixed basis functions of multiwavelet transform,which are not related with the vibration signal,may reduce the accuracy of compound faults detection.Moreover,the decomposition results of multiwavelet transform not being own time-invariant is harmful to extract the features of periodical impulses.Furthermore,multiwavelet transform only focuses on the multi-resolution analysis in the low frequency band,and may leave out the useful features of compound faults.To overcome these shortcomings,a novel method called adaptive redundant multiwavelet packet(ARMP) is proposed based on the two-scale similarity transforms.Besides,the relative energy ratio at the characteristic frequency of the concerned component is computed to select the sensitive frequency bands of multiwavelet packet coefficients.The proposed method was used to analyze the compound faults of rolling element bearing.The results showed that the proposed method could enhance the ability of compound faults detection of rotating machinery.展开更多
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 Nos. 50975220 and 51035007)the National Basic Research Program of China ("973" Program) (Grant No. 2009CB724405)the Important National Science and Technology Specific Projects (Grant No.2010ZX04014-016)
文摘It is significant to detect the fault type and assess the fault level as early as possible for avoiding catastrophic accidents.Due to diversity and complexity,the compound faults detection of rotating machinery under non-stationary operation turns to be a challenging task.Multiwavelet with two or more base functions may match two or more features of compound faults,which may supply a possible solution to compound faults detection.However,the fixed basis functions of multiwavelet transform,which are not related with the vibration signal,may reduce the accuracy of compound faults detection.Moreover,the decomposition results of multiwavelet transform not being own time-invariant is harmful to extract the features of periodical impulses.Furthermore,multiwavelet transform only focuses on the multi-resolution analysis in the low frequency band,and may leave out the useful features of compound faults.To overcome these shortcomings,a novel method called adaptive redundant multiwavelet packet(ARMP) is proposed based on the two-scale similarity transforms.Besides,the relative energy ratio at the characteristic frequency of the concerned component is computed to select the sensitive frequency bands of multiwavelet packet coefficients.The proposed method was used to analyze the compound faults of rolling element bearing.The results showed that the proposed method could enhance the ability of compound faults detection of rotating machinery.
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.
