Fault Diagnosis of Rolling Bearing Based on S Transform and Image of Invariant Moments

Guo, Jun; Wang, Bao Zeng; Peng, Wei; Wei, Xing

doi:10.4028/www.scientific.net/amr.706-708.798

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…A CNN classifier can be used to identify spectral images transformed from timedomain vibration signals to diagnose rolling bearing faults (Youcef Khodja et al, 2020) [121]. Another fault diagnosis method is based on signal conversion into a moment-invariant S-transform and image Hu processing; that is, using the bearing signal and time-frequency spectrum to create two-dimensional images (Guo, JF et al, 2013) [122]. Deep CNN can use transfer learning to train with bispectrum images of fault signals for fault diagnosis (Chhaya Grover et al, 2022) [123].…”

Section: Other Types Of Vibration Image Methodsmentioning

confidence: 99%

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Yan

Yang

et al. 2022

Sensors

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As a precision mechanical component to reduce friction between components, the rolling bearing is widely used in many fields because of its slight friction loss, strong bearing capacity, high precision, low power consumption, and high mechanical efficiency. This paper reviews several excellent kinds of study and their relevance to the fault detection of rolling bearings. We summarize the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings. The fault signal types are divided into one-dimensional and two-dimensional images, which account for 40.14% and 31.69%, respectively, and their classification is clarified and discussed. We counted the proportions of various methods in the references cited in this paper. Among them, the method of one-dimensional signal detection with external sensors accounted for 3.52%, the method of one-dimensional signal detection with internal sensors accounted for 36.62%, and the method of two-dimensional signal detection with external sensors accounted for 19.72%. The method of two-dimensional signal detection with internal sensors accounted for 11.97%. Among these methods, the highest detection rate is 100%, and the lowest detection rate is more than 70%. The similarities between the different methods are compared. The research results summarized in this paper show that with the progress of the times, a variety of new and better research methods have emerged, which have sped up the detection and diagnosis of rolling bearing faults. For example, the technology using artificial intelligence is still developing rapidly, such as artificial neural networks, convolutional neural networks, and machine learning. Although there are still defects, such methods can quickly discover a fault and its cause, enrich the database, and accumulate experience. More and more advanced techniques are applied in this field, and the detection method has better robustness and superiority.

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Section: Other Types Of Vibration Image Methodsmentioning

confidence: 99%

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Yan

Yang

et al. 2022

Sensors

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show abstract

“…With wider window, it provides better frequency resolution for lower frequency. Due to its superior properties, S transform has already been successfully applied in the fields of fault diagnosis and condition monitoring [19][20][21][22]. So, S transform is adopted in this paper to conduct the time-frequency analysis of vibration signals.…”

Section: Fault Diagnosis Of Rolling Element Bearing Based On S Transf...mentioning

confidence: 99%

Fault diagnosis of rolling element bearing based on S transform and gray level co-occurrence matrix

Zhao

Tang

Tan

2015

Meas. Sci. Technol.

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Time-frequency analysis is an effective tool to extract machinery health information contained in non-stationary vibration signals. Various time-frequency analysis methods have been proposed and successfully applied to machinery fault diagnosis. However, little research has been done on bearing fault diagnosis using texture features extracted from time-frequency representations (TFRs), although they may contain plenty of sensitive information highly related to fault pattern. Therefore, to make full use of the textural information contained in the TFRs, this paper proposes a novel fault diagnosis method based on S transform, gray level co-occurrence matrix (GLCM) and multi-class support vector machine (Multi-SVM). Firstly, S transform is chosen to generate the TFRs due to its advantages of providing frequency-dependent resolution while keeping a direct relationship with the Fourier spectrum. Secondly, the famous GLCM-based texture features are extracted for capturing fault pattern information. Finally, as a classifier which has good discrimination and generalization abilities, Multi-SVM is used for the classification. Experimental results indicate that the GLCM-based texture features extracted from TFRs can identify bearing fault patterns accurately, and provide higher accuracies than the traditional time-domain and frequency-domain features, wavelet packet node energy or two-direction 2D linear discriminant analysis based features of the same TFRs in most cases.

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Fault Diagnosis of Rolling Bearing Based on S Transform and Image of Invariant Moments

Cited by 2 publications

References 2 publications

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Fault diagnosis of rolling element bearing based on S transform and gray level co-occurrence matrix

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