Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method

Li, Zhixiong; Yan, Xinping; Yuan, Chengqing; Peng, Zhongxiao; Li, Li

doi:10.1016/j.ymssp.2011.02.017

Cited by 126 publications

(89 citation statements)

References 21 publications

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“…The breakdowns of the transmission machinery resulted from the gearbox failures account for 80% and the malfunctions of the gearbox are mostly caused by the gear and bearing faults [1]. Therefore, since the efficient running of machinery plays an influential part in the economics of an organization, it is necessary and critical that the early detection and diagnosis of gear and bearing faults should be performed to prevent breakdown accidents and reduce the economic loss.…”

Section: Introductionmentioning

confidence: 99%

Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis

et al. 2013

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Section: Introductionmentioning

confidence: 99%

Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis

et al. 2013

Self Cite

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“…Compared with original matrix X, the reconstructed approximate matrixX comprises most of information of X and excludes redundant features, such as noise and power frequency interference [9,10].…”

Section: Basic Theories Of Pca In Signal Processingmentioning

confidence: 99%

“…4096 data points were collected with sampling frequency of 1024 Hz, and then the Hankel matrix was formed from signal ( ) with m rows and n columns. Decomposition and reconstruction of signal ( ) were proceeded by employing PCA algorithm in Section 2 [9,13]. Effect of the constructed Hankel matrix on signal processing was studied by Zhao et al [26]; they pointed out that if the number of rows was close to the number of columns, signal processing effect was better.…”

Section: Internal Law Of Effective Eigenvalues and Frequency Componentsmentioning

confidence: 99%

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Feature Frequency Extraction Based on Principal Component Analysis and Its Application in Axis Orbit

Zhao

2018

Shock and Vibration

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Vibration-based diagnosis has been employed as a powerful tool in maintaining the operating efficiency and safety for large rotating machinery. However, the extraction of malfunction features is not accurate enough by using traditional vibration signal processing techniques, owing to their intrinsic shortcomings. In this paper, the relationship between effective eigenvalues and frequency components was investigated, and a new characteristic frequency separation method based on PCA (CFSM-PCA) was proposed. Certain feature frequency could be purified by reconstructing the specified eigenvalues. Furthermore, three significant perspectives were studied via the distribution of effective eigenvalues, and theoretical derivations were subsequently illustrated. More importantly, this proposed scheme could also be used to synthesize axis orbits of larger machines. Purified curves were so explicit and the CFSM-PCA exhibited higher efficiency than harmonic wavelet and wavelet packet.

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“…In order to increase the computational efficiency of the classification procedure and enhance the accuracy of the diagnosis results, the principal component analysis (PCA) method is to select the certain features of high priority [30,31]. However, the selected features will lose the original physical representations through the feature space transformation process of PCA.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Analysis Based Ball Bearing Defect Diagnostics Using Mahalanobis Distance and Support Vector Machine

et al. 2013

Entropy

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Abstract:The objective of this research is to investigate the feasibility of utilizing the multi-scale analysis and support vector machine (SVM) classification scheme to diagnose the bearing faults in rotating machinery. For complicated signals, the characteristics of dynamic systems may not be apparently observed in a scale, particularly for the fault-related features of rotating machinery. In this research, the multi-scale analysis is employed to extract the possible fault-related features in different scales, such as the multi-scale entropy (MSE), multi-scale permutation entropy (MPE), multi-scale root-mean-square (MSRMS) and multi-band spectrum entropy (MBSE). Some of the features are then selected as the inputs of the support vector machine (SVM) classifier through the Fisher score (FS) as well as the Mahalanobis distance (MD) evaluations. The vibration signals of bearing test data at Case Western Reserve University (CWRU) are utilized as the illustrated examples. The analysis results demonstrate that an accurate bearing defect diagnosis can be achieved by using the extracted machine features in different scales. It can be also noted that the diagnostic results of bearing faults can be further enhanced through the feature selection procedures of FS and MD evaluations.

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Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method

Cited by 126 publications

References 21 publications

Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis

Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis

Feature Frequency Extraction Based on Principal Component Analysis and Its Application in Axis Orbit

Multi-Scale Analysis Based Ball Bearing Defect Diagnostics Using Mahalanobis Distance and Support Vector Machine

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