Feature extraction of rolling bearing multiple faults based on correlation coefficient and Hjorth parameter

Yu, Mingyue; Fang, Minghe

doi:10.1016/j.isatra.2022.02.015

Cited by 25 publications

(9 citation statements)

References 17 publications

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“…The failure types of bearings can be discerned by calculating their characteristic frequencies. 28 Table 2 presents the calculation formulas for the characteristic frequencies of bearing failures. In this paper, fr represents the rotation frequency; fc represents the feature frequency of the cage failure; fi represents the feature frequency of the inner ring failure; fb represents the feature frequency of the ball element failure; fo represents the feature frequency of the outer ring failure; fn denotes the rotation speed; d stands for bearing diameter; D refers to the pitch diameter of the bearing; N represents the number of ball elements in the bearing.…”

Section: Methods Proving and Analysismentioning

confidence: 99%

“…In this paper, fr represents the rotation frequency; fc represents the feature frequency of the cage failure; fi represents the feature frequency of the inner ring failure; fb represents the feature frequency of the ball element failure; fo represents the feature frequency of the outer ring failure; fn denotes the rotation speed; d stands for bearing diameter; D refers to the pitch diameter of the bearing; N represents the number of ball elements in the bearing. The formulas in Table 2 were obtained from reference 28.…”

Section: Methods Proving and Analysismentioning

confidence: 99%

Section: Characteristic Frequencies Of Ball Bearing Failuresmentioning

confidence: 99%

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A failure diagnosis method of ball bearing based on optimized multiscale variational modal extraction with synchro squeezing transformation

Li,

Yu,

et al. 2024

Noise & Vibration Worldwide

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When a compound failure occurs in a bearing, the failure information included in vibration signals is featured by being weak, complex and combined. This fact makes it difficult to identify a compound failure precisely. Variational mode extraction (VME) solves the problem of variational mode decomposition (VMD) in being difficult to determine the number of decomposition layers and has certain applications in the identification of bearing failures. However, the initial expected central frequency of VME and option of penalty factor is crucial to the extraction of expected mode. To precisely determine the central frequency and penalty factor of VME and fulfill the correct extraction of feature information of compound failures in bearing, the paper has proposed a method based on synchro squeezing transform (SST) and information entropy to adaptively determine the parameters in VME (SST-VME). Firstly, SST is used to make time-frequency analysis of original vibration signals and characteristic frequency bands with larger energy are chosen from time-frequency spectrum to adaptively determine the expected central frequency of VME. Secondly, as information entropy has representation capacity for information included in the signal, penalty factor of VME was adaptively determined according to information entropy. Thirdly, original signals were subjected to VME according to expected central frequency and penalty factor adaptively determined; the expectation mode obtained was denoised by singular value decomposition algorithm. Finally, the type of compound failures of bearing is determined according to the frequency spectrum of denoised signals. To verify the effectiveness of proposed method, a comparison with VMD algorithm is conducted. As indicated by the result, the proposed method is more precise and comprehensive than VMD algorithm to extract the feature information corresponding to compound faults of bearing, and thereby correctly determines the type of compound failures.

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Section: Methods Proving and Analysismentioning

confidence: 99%

Section: Methods Proving and Analysismentioning

confidence: 99%

Section: Characteristic Frequencies Of Ball Bearing Failuresmentioning

confidence: 99%

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A failure diagnosis method of ball bearing based on optimized multiscale variational modal extraction with synchro squeezing transformation

Li,

Yu,

et al. 2024

Noise & Vibration Worldwide

Self Cite

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“…Correlated kurtosis (CK) [22,23], which considers the periodicity of bearing fault signals and effectively isolates non-periodic components, represents a potential solution to this problem. Additionally, the correlation coefficient offers insights into signal similarities [24]. Leveraging the strengths of correlated kurtosis and the correlation coefficient, weighted correlated kurtosis (WCK) has been devised as a comprehensive objective function.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Parameter-Optimized Variational Mode Extraction and an Improved One-Dimensional Convolutional Neural Network

Zhang,

Tao

2024

Applied Sciences

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When faults occur in rolling bearings, vibration signals exhibit sensitivity to periodic impact components, susceptibility to complex background noise, and non-stationary and nonlinear characteristics. Consequently, using traditional signal processing methods to effectively identify bearing faults presents significant challenges. To facilitate the accurate fault diagnosis of bearings in noisy conditions, we propose an intelligent fault diagnosis method using the Archimedes optimization algorithm (AOA), coupled with a one-dimensional multi-scale residual convolutional neural network (1D-MRCNN), to optimize the variational mode extraction (VME) parameters. First, we introduce a weighted correlated kurtosis (WCK) indicator, formulated using the correlation coefficient and correlated kurtosis as the objective function, to optimize the VME’s center frequency ω and penalty factor α, enabling targeted signal extraction. Second, deep learning techniques are employed to construct the 1D-MRCNN. The neural network then processes the extracted signal for feature extraction and automated fault-type identification. Our simulation results show that the WCK objective function effectively isolates impact components under fault conditions, and our experimental validation confirms that the proposed method accurately identifies diverse fault types across multiple noise levels.

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“…In Hjorth parameters, complexity parameter indicates the similarity among signals and sinusoidal. Meanwhile, complexity parameter is insensitive to noise components (Yu and Fang, 2022). With greater complexity parameter, the signal presents more similarity to sinusoidal signal; the simpler the signal is, the more helpful it will be to extract the fault characteristics from signals (Grover and Turk, 2020).…”

Section: Introductionmentioning

confidence: 99%

Identification of rotor–stator rub-impact faults based on dual complexity parameters

Guo

Yunhong

2022

Journal of Vibration and Control

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For effective identification of rub-impact faults between rotor and stator of equipment, the paper has contributed the method integrating dual complexity parameters and variational mode decomposition (VMD). Firstly, to effectively separate the fault characteristics involved in signals, VMD algorithm was applied to decompose vibration signals and the component signals were obtained; secondly, taking account of the large difference of fault feature information involved in different component signals and in order to make options of sensitive fault component signals and reduce the loss of fault features, multi-scale Lempel–Ziv complexity and complexity parameter in Hjorth parameters were brought. Starting from 2 different perspectives of complexity evaluation, choose from the sensitive component signals containing more fault characteristics with these 2 complexity parameters; thirdly, signals were reconstructed based on selected sensitive component signals, and meanwhile, singular value difference spectrum algorithm was used to denoise reconstructed signals to further lessen the influence of noises; finally, the rub-impact fault between rotor and stator was identified by square demodulation spectrum (SDS) of denoised signal. The effectiveness of the proposed method has been proved by comparative analysis with other approaches as well as validation analysis of rub-impact fault signals in multiple situations.

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Feature extraction of rolling bearing multiple faults based on correlation coefficient and Hjorth parameter

Cited by 25 publications

References 17 publications

A failure diagnosis method of ball bearing based on optimized multiscale variational modal extraction with synchro squeezing transformation

A failure diagnosis method of ball bearing based on optimized multiscale variational modal extraction with synchro squeezing transformation

Bearing Fault Diagnosis Based on Parameter-Optimized Variational Mode Extraction and an Improved One-Dimensional Convolutional Neural Network

Identification of rotor–stator rub-impact faults based on dual complexity parameters

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