An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression

Guo, Junchao; Zhang, Hao; Zhen, Dong; Shi, Zhanqun; Gu, Fengshou; Ball, Andrew

doi:10.1016/j.measurement.2019.107240

Cited by 29 publications

(19 citation statements)

References 39 publications

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“…The autoregressive (AR) model is a statistical method to deal with time series. It uses the values of the same variable in different periods to predict the present values and assumes that they are linear [ 13 ]. Therefore, it is called autoregressive.…”

Section: A Brief Description Of the Methods For Vibration Datamentioning

confidence: 99%

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A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation

Zheng

Song

et al. 2020

Sensors

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The axle box bearing of bogie is one of the key components of the rail transit train, which can ensure the rotary motion of wheelsets and make the wheelsets adapt to the conditions of uneven railways. At the same time, the axle box bearing also exposes most of the load of the car body. Long-time high-speed rotation and heavy load make the axle box bearing prone to failure. If the bearing failure occurs, it will greatly affect the safety of the train. Therefore, it is extremely important to monitor the health status of the axle box bearing. At present, the health status of the axle box bearing is mainly monitored by vibration information and temperature information. Compared with the temperature data, the vibration data can more easily detect the early fault of the bearing, and early warning of the bearing state can avoid the occurrence of serious fault in time. Therefore, this paper is based on the vibration data of the axle box bearing to carry out adaptive fault diagnosis of bearing. First, the AR model predictive filter is used to denoise the vibration signal of the bearing, and then the signal is whitened in the frequency domain. Finally, the characteristic value of vibration data is extracted by energy operator demodulation, and the fault type is determined by comparing with the theoretical value. Through the analysis of the constructed simulation signal data, the characteristic parameters of the data can be effectively extracted. The experimental data collected from the bearing testbed of high-speed train are analyzed and verified, which further proves the effectiveness of the feature extraction method proposed in this paper. Compared with other axle box bearing fault diagnosis methods, the innovation of the proposed method is that the signal is denoised twice by using AR filter and spectrum whitening, and the adaptive extraction of fault features is realized by using energy operator. At the same time, the steps of setting parameters in the process of feature extraction are avoided in other feature extraction methods, which improves the diagnostic efficiency and is conducive to use in online monitoring system.

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Section: A Brief Description Of the Methods For Vibration Datamentioning

confidence: 99%

“…The Yule–Walker equation is an equation describing the relationship between the parameters of autoregressive sequence and its covariance function. The common equation set form is shown in Equation (2) [ 13 , 14 ]:

…”

Section: A Brief Description Of the Methods For Vibration Datamentioning

confidence: 99%

A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation

Zheng

Song

et al. 2020

Sensors

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“…where 𝑠(𝑡) indicates the periodic impulse component caused by rolling element bearing defect, and its model is formulated as [40]:…”

Section: Simulation Modelmentioning

confidence: 99%

Transient impulses enhancement based on adaptive multi-scale improved differential filter and its application in rotating machines fault diagnosis

Guo

Shi

et al. 2022

ISA Transactions

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“…(28) Among them, the BSD has outstanding fault identification performance. Previous studies revealed that the BSD method can eliminate Gaussian noise and extract feature parameters more effectively by combining it with autoregression (29) and singular value decomposition. (30) For these reasons, we propose a novel fault diagnosis method based on the BSD to extract the vibration as well as system-invariant characteristics of rolling-element bearings under variable operation conditions.…”

Section: Introductionmentioning

confidence: 99%

Novel Fault Diagnosis Approach for Rolling-element Bearings Based on Bispectral Analysis

Zhang¹,

Wang²,

Wu³

et al. 2022

Sensors and Materials

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Under variable operation conditions, the fault diagnosis of rolling-element bearings encounters the problems of ambiguous characteristic frequencies and inconsistent disturbance features. Conventional diagnosis methods have adopted extra hardware or signal preprocessing to overcome these problems, which usually resulted in difficulties in implementation and incorrect diagnosis. Here, we propose a novel approach based on the strategy of extracting features that are insensitive to changes in operation conditions. This may have the advantages of preventing interference from signal resampling and preprocessing. First, we derive the bispectral expressions of vibration signals for rolling-element bearings in use. Next, we use simulated and measured experimental data detected by numerous fluid and vibration sensors to identify the proposed model. Finally, experiments on the fault diagnosis of bearings are performed to validate our proposed approach. Results show that the proposed bispectral distribution method has the advantage of insensitivity to the operation conditions. In experiments involving three levels of fault severity, our proposed diagnosis model always correctly identified the fault type of rolling-element bearings under different operating conditions. With the advantages of directly extracting the insensitive bispectral features without the requirement of additional hardware and signal preprocessing, our proposed approach is simple and easy to implement, giving it good application prospects in engineering practice.

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An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression

Cited by 29 publications

References 39 publications

A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation

A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation

Transient impulses enhancement based on adaptive multi-scale improved differential filter and its application in rotating machines fault diagnosis

Novel Fault Diagnosis Approach for Rolling-element Bearings Based on Bispectral Analysis

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