Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

Yuan, Rui; Lv, Yong; Song, Gangbing

doi:10.3390/s18041210

Cited by 39 publications

(29 citation statements)

References 45 publications

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“…Where, f (t), as shown in Formula (12), is a pollution-free signal. To avoid generality loss, n(t) is assumed as mixed Gaussian noise, which is a mixture of Gaussian signals with mean and variance of (0, 1), (2,5), (4,10). α is noise weight.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…pollution-free signal. To avoid generality loss, ( ) n t is assumed as mixed Gaussian noise, which is a mixture of Gaussian signals with mean and variance of (0, 1), (2,5), (4,10 Let us take =0.01  , signal-to-noise ratio SNR1 = 13.5392 as an example. The time domain waveforms of the three signals ( ) f t , ( ) n t  , ( ) y t are shown in Figure 5, and the frequency spectrum, i.e., frequency domain waveform, is shown in Figure 6.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…In particular, corresponding impact signal will show in case of rolling bearing fault [1][2][3]. Therefore, a research hot spot is to identify the working condition of the rolling bearing by analyzing vibrational signal of the rolling bearing, and then identify the fault [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2018

Symmetry

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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....

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Section: Simulation Experimentsmentioning

confidence: 99%

Section: Simulation Experimentsmentioning

confidence: 99%

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

Wang

et al. 2018

Symmetry

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“…Energy variation of the 3× component (where X represents the frequency corresponding to the rotating speed) extracted based on Wavelet Packet Transform [43][44][45] was used to quantify the crack depth of a crack with known crack location in a rotor by Gómez et al [46], and the analytical Jeffcott rotor model and the corresponding experimental rotor with a saw-cut crack were studied to validate the method. EMD method [47][48][49] was applied to steady-state responses generated from a Jeffcott rotor to extract the 3× and 2× components in the neighborhood of 1/3 and 1/2 of the critical rotating speed by Guo et al [50], results showed that the variation of averaged amplitudes of super-harmonic components provided clear and robust signatures of early cracks in rotating rotors. However, from the literature, few researches have been carried out to identify both the location and depth of a crack in a rotor with these super-harmonic features.…”

Section: Introductionmentioning

confidence: 99%

A Super-Harmonic Feature Based Updating Method for Crack Identification in Rotors Using a Kriging Surrogate Model

Ouyang

2019

Applied Sciences

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Dynamic model updating based on finite element method (FEM) has been widely investigated for structural damage identification, especially for static structures. Despite the substantial advances in this method, the key issue still needs to be addressed to boost its efficiency in practical applications. This paper introduces the updating idea into crack identification for rotating rotors, which has been rarely addressed in the literature. To address the problem, a novel Kriging surrogate model-based FEM updating method is proposed for the breathing crack identification of rotors by using the super-harmonic nonlinear characteristics. In this method, the breathing crack induced nonlinear characteristics from two locations of the rotors are harnessed instead of the traditional linear damage features for more sensitive and accurate breathing crack identification. Moreover, a FEM of a two-disc rotor-bearing system with a response-dependent breathing crack is established, which is partly validated by experiments. In addition, the associated breathing crack induced nonlinear characteristics are investigated and used to construct the objective function of Kriging surrogate model. Finally, the feasibility and the effectiveness of the proposed method are verified by numerical experiments with Gaussian white noise contamination. Results demonstrate that the proposed method is effective, accurate, and robust for breathing crack identification in rotors and is promising for practical engineering applications.

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“…In the aspect of EMD, Abdelkader et al realized the early fault detection of rolling bearing based on the improved EMD method [8]. Yuan et al applied the EMD method to the multifault diagnosis of bearing [9]. Unfortunately, these popular methods also have unsolved problems: one is the selection of thresholds and wavelet basis in wavelet transform, and another one is the mode fixing and end effect in the EMD method [10].…”

Section: Introductionmentioning

confidence: 99%

Degradation State Identification of Cracked Ultrasonic Motor by Means of Fault Feature Extraction Method

2019

Shock and Vibration

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The cracking of piezoelectric ceramics is the main reason of failure of an ultrasonic motor. Since the fault information is too weak to reflect the condition of piezoelectric ceramics especially in the early degradation stage, a fault feature extraction method based on multiscale morphological spectrum and permutation entropy is proposed. Firstly, a signal retaining the morphological feature under different scales is reconstructed with multiscale morphological spectrum components. Then, the permutation entropy of the reconstructed signal is taken as the fault feature of piezoelectric ceramics. Furthermore, a sensitivity factor is defined to optimize the embedded dimension and delay time of permutation entropy according to double sample Z value analysis. Finally, a matrix composed of the probability distributions, obtained from permutation entropy calculation, is applied for the degradation state identification by means of probability distribution divergence. The analysis of actual test data demonstrates that this method is feasible and effective.

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Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

Cited by 39 publications

References 45 publications

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

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

A Super-Harmonic Feature Based Updating Method for Crack Identification in Rotors Using a Kriging Surrogate Model

Degradation State Identification of Cracked Ultrasonic Motor by Means of Fault Feature Extraction Method

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