Rolling Bearing Fault Diagnosis Based on EWT Sub-Modal Hypothesis Test and Ambiguity Correlation Classification

Abstract

Because of the cyclic symmetric structure of rolling bearings, its vibration signals are regular when the rolling bearing is working in a normal state. But when the rolling bearing fails, whether the outer race fault or the inner race fault, the symmetry of the rolling bearing is broken and the fault destroys the rolling bearing's stable working state. Whenever the bearing passes through the fault point, it will send out vibration signals representing the fault characteristics. These signals are often non-linear, non-stationary, and full of Gaussian noise which are quite different from normal signals. According to this, the sub-modal obtained by empirical wavelet transform (EWT), secondary decomposition is tested by the Gaussian distribution hypothesis test. It is regarded that sub-modal following Gaussian distribution is Gaussian noise which is filtered during signal reconstruction. Then by taking advantage of the ambiguity function superiority in non-stationary signal processing and combining correlation coefficient, an ambiguity correlation classifier is constructed. After training, the classifier can recognize vibration signals of rolling bearings under different working conditions, so that the purpose of identifying rolling bearing faults can be achieved. Finally, the method effect was verified by experiments.using wavelet transform. Reference [14] mainly studies the fault diagnosis of local defects of rolling bearings based on wavelet feature extraction. In Reference [15], the feature of rolling bearing signals is extracted by using the time bound integral of continuous wavelet transform coefficient. But wavelet decomposition is constrained by wavelet basis functions [14][15][16], and the number of decomposition layers and thresholds of wavelets will affect the noise filtering effect during noise filtering [13,16]. Empirical mode decomposition (EMD) is a very good method for analyzing non-stationary signals, which enjoys many applications in fault diagnosis of rolling bearings [17,18]. However, EMD has problems such as modal mixture and endpoint effects [19,20]. Combining EMD's adaptability and theoretical framework of wavelet analysis, Gilles proposed a new signal processing method, namely empirical wavelet transform (EMT) [21]. The method adaptively divides the signal frequency spectrum into several frequency bands by extracting maximum value point of the frequency domain, and constructs a suitable orthogonal wavelet filter bank to separate each frequency band. Each separated frequency band corresponds to a signal in the time domain. This signal is referred to as a modal of the original signal. This method avoids the problems of EMD modal in aliasing and endpoint effects, while inheriting the strength of EMD and wavelet analysis methods, thus it is a very good method for processing non-stationary signals.According to the above analysis, we propose a novel rolling bearing fault diagnosis method based on an empirical wavelet transform (EWT) sub-modal hypothesis test and ambiguity correlation classification....

Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform

“…Ma et al [29] proposed a method to extract the features of bearing faults based on the complete ensemble EMD (CEEMD) by enhancing the mode characteristic and via the introduction of adaptive noise to diagnose the bearing faults of rotatory machines. Ge et al [30] proposed a fault diagnosis method based on an empirical wavelet transform sub modal hypothesis test and ambiguity correlation classification to diagnose the rolling bearing faults using vibration signals. However, the authors concentrated only on rolling bearing faults.…”

“…However, they failed to build a full adaptive wavelet transform. Thus, the proposed method uses a new approach called empirical wavelet transform (EWT) to build a family of wavelets adapted to the processed signal [24,30]. The empirical wavelet transform is defined in a step-by-step manner rather than in a single mathematical formulation as is the case of the classic wavelet transform.…”

Induction Motor Fault Detection and Classification using RCNN and SURF Based Machine Learning Algorithms and Infrared Thermography

“…The accuracy, sensitivity, and specificity of proposed fault classifier and existing methods have been estimated using the following equations (10)(11)(12): Figure 15 shows the proposed and existing methods' performance analysis. From figure 15, the proposed fault classifier has taken 95.8% accuracy; 94% sensitivity, and 93.9% specificity during the motor fault detection in the specificity of the thermal image than existing methods ANN, CNN, and SVM.…”

“…In this study, 3D-DWT is applied for effective image decomposition. Ge et.al [12] utilized a fault discovering scheme according to an empirical wavelet transform sub-model hypothesis examination and uncertainty correlation categorization to analyse the rolling bearing failures by applying the vibration signals. Conversely, the authors determined only on rolling bearing failures.…”

An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

“…With the continuous development of signal processing technology, modern time-frequency analysis methods have made great progress in their ability to extract fault features from non-stationary, non-linear signals [5][6][7].…”