A New Blind-Source-Separation Method and its Application to Fault Diagnosis of Rolling Bearing

Shen, Yongjun; Yang, Shaopu

doi:10.1515/ijnsns.2006.7.3.245

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…The fractional Fourier transform can not only analyze the signal in the time domain and frequency domain, but also obtain the direction of the maximum projection of the signal in the fractional domain by rotating the signal; a multilevel fault diagnosis method is constructed with different transformation orders, but due to the precision selection of the transformation order and the need for a lot of calculations when transforming, there are still some problems in the application of this method in real-time fault detection. In addition, the existing methods cannot achieve a good effect when processing signals containing both integer-order faults and fractional-order faults [ 36 , 37 , 38 ]. In multilevel fault diagnosis methods, new methods using adaptive processing of signal samples have been proposed one after another, which can detect and locate multiple faults more efficiently and reliably than traditional fault diagnosis methods [ 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform

Wen

2022

Sensors

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Motors are the main driving power for equipment operation, and they are also a major factor to promote the development of the motor and the load it drives and its motor control system toward a low-carbon future, reduce carbon emissions, and improve the industrial economy and social economic efficiency. Due to high-speed, long-period, and heavy-load operation, various faults occur; since the existing integer-order Fourier transform methods have not enough able to detect fractional-order faults and lack robustness, it is difficult to realize the fine diagnosis of motor faults, which reduces the safety and reliability of the motor control system. For this reason, on the basis of the powerful extraction ability of the fractional Fourier transform (FRFT) for micro fault features, especially the extraction ability to fit fractional frequency domain faults, this paper intends to establish a multilevel fine fault diagnosis method for fractional-order or integer-order faults. Firstly, this is accomplished by performing the fractional Fourier transform on the acquired data with faults and feature extraction in the multilevel fractional frequency domain and then optimizing the feature extraction model. Secondly, one further step search method is established to determine the projection direction with the largest fault feature. Thirdly, taking the extracted multilevel fault features as input, a multilevel fine fault diagnosis method based on the SVM model is established. Finally, three typical digital simulation examples and actual operating data collected by the ZHS-2 multifunctional motor test bench with a flexible rotor are employed to verify the effectiveness, robustness, and accuracy of this new method. The main contribution and innovation of this paper are that the fractional Fourier transform method based on time domain and frequency domains is introduced. This method can extract the small fault features in the maximum projection direction of the signal in the fractional domain, but detection with other time–frequency methods is difficult; the extracted multilevel fault features are used as input, and the corresponding fault diagnosis model is established, which can improve the accuracy of fault detection and ensure the safe and reliable operation of industrial equipment.

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Section: Introductionmentioning

confidence: 99%

Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform

Wen

2022

Sensors

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“…Blind source separation is a technique for separating the independent components from the mixed signals observed by the sensors under the condition that the source signals and transmission channels are unknown. Now BSS has been successfully applied to the source separation of mechanical faults [1][2][3][4][5]. However the traditional blind separation method of mechanical fault sources are restricted to non-gauss, stationary and mutually independent source signals, which may cause many problems in practical applications, because the mechanical fault signal, which always exhibits non-stationarity, non-gauss and non-independence, generally cannot meet these conditions.…”

Section: Introductionmentioning

confidence: 99%

Application of the Blind Source Separation Based on Time-Frequency Analysis in Mechanical Fault Diagnosis

Zhang

et al. 2014

AMR

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Blind source separation provides a new method for the separation of mechanical sources under high level background noise, as well as the diagnosis of the compound fault. At present, the blind source separation has been successfully applied to the mecanical fault diagnosis. But the traditional mechanical source separation methods are restricted to non-gauss, stationary and mutually independent source signals. However, the mechanical fault signals do not suffice to these conditions, and generally exhibit non-stationarity and non-independence. For the non-stationary signal, its spectral feature is time-varying. Thus only the time-domain or frequency-domain analysis is not sufficient to describe the characteristics of non-stationary signal. The time-frequency analysis, which can provide the information about that the spectrum of the signal varies with the time, is a useful tool for non-stationary signal analysis. In this paper, combined time-frequency analysis with blind source separation, a blind source separation method for the non-stationary signal of the mechanical equipment based on time-frequency analysis is proposed and studied. The simulation and experimental results show that the proposed approach is feasible and effective.

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“…Accordingly, a lot of time-frequency methods suitable for non-stationary signal, such as wavelet transform, Wigner distribution, Gabor transform, etc., have been applied in fault diagnosis [3][4][5][6][7][8][9][10][11]. For example, Peng and Chu [3] presented a summary about the application of the wavelet transform in machine fault diagnosis, and gave some useful prospects of the wavelet transform combined with other tools in condition monitoring and fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…[10] presented a non-linear BSS approach and applied it to failure detection of gear tooth. Shen and Yang [11] presented a novel BSS method based on Fractional Fourier Transform, which could be used to separate the mixed non-stationary signals successfully and was applied for fault diagnosis of rolling bearing in freight train successfully.…”

Section: Introductionmentioning

confidence: 99%

Two De-Noising Methods Based on Gabor Transform

Shen

Zhang

Yang

et al. 2011

AEF

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Abstract. Two de-noising methods, named as the averaging method in Gabor transform domain (AMGTD) and the adaptive filtering method in Gabor transform domain (AFMGTD), are presented in this paper. These two methods are established based on the correlativity of the source signals and the background noise in time domain and Gabor transform domain, that is to say, the uncorrelated source signals and background noise in time domain would still be uncorrelated in Gabor transform domain. The construction and computation scheme of these two methods are investigated. The de-noising performances are illustrated by some simulation signals, and the wavelet transform is used to compare with these two new de-noising methods. The results show that these two methods have better de-noising performance than the wavelet transform, and could reduce the background noise in the vibration signal more effectively.

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A New Blind-Source-Separation Method and its Application to Fault Diagnosis of Rolling Bearing

Cited by 7 publications

References 13 publications

Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform

Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform

Application of the Blind Source Separation Based on Time-Frequency Analysis in Mechanical Fault Diagnosis

Two De-Noising Methods Based on Gabor Transform

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