Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine

Wang, Yujing; Kang, Shouqiang; Jiang, Yicheng; Yang, Guangxue; Song, Lixin; Микулович, В. И.

doi:10.1016/j.ymssp.2011.11.015

Cited by 63 publications

(28 citation statements)

References 25 publications

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“…Preliminary tests suggested that 15 nonoverlapping intervals is a threshold beyond which performance starts to degrade; hence, this value was fixed in all experiments. Wang in [37] seems to have taken the complete signal to extract the features, obtaining only eight different patterns, Yu in [19] splits …”

Section: B Signals To Patternsmentioning

confidence: 99%

Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis

Rauber

Boldt

Varejão

2015

IEEE Trans. Ind. Electron.

312

188

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Distinct feature extraction methods are simultaneously used to describe bearing faults. This approach produces a large number of heterogeneous features that augment discriminative information but, at the same time, create irrelevant and redundant information. A subsequent feature selection phase filters out the most discriminative features. The feature models are based on the complex envelope spectrum, statistical time-and frequency-domain parameters, and wavelet packet analysis. Feature selection is achieved by conventional search of the feature space by greedy methods. For the final fault diagnosis, the k-nearest neighbor classifier, feedforward net, and support vector machine are used. Performance criteria are the estimated error rate and the area under the receiver operating characteristic curve (AUC-ROC). Experimental results are shown for the Case Western Reserve University Bearing Data. The main contribution of this paper is the strategy to use several different feature models in a single pool, together with feature selection to optimize the fault diagnosis system. Moreover, robust performance estimation techniques usually not encountered in the context of engineering are employed.

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Section: B Signals To Patternsmentioning

confidence: 99%

Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis

Rauber

Boldt

Varejão

2015

IEEE Trans. Ind. Electron.

312

188

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“…To evaluate the effectiveness of the signal processing and feature extraction methods for bearings, the vibration data related to the bearing and the system investigation in this paper were provided by the Bearing Data Center of the Case Western Reserve University (CWRU), and acquired by bearing accelerometer sensors under different operating loads and bearing conditions [32]. The bearing data of CWRU has been validated in many research works and become a standard dataset for bearing studies [2,13,14,21]. …”

Section: Experiments and Analysismentioning

confidence: 99%

“…Many of the faults of rotating machinery relate to the bearings, whose running conditions directly affect the precision, reliability and life of the machine [2]. Breakdowns caused by bearing performance degradation and inappropriate operation can not only lead to huge economic losses for enterprises, but also potentially serious casualties [3].…”

Section: Introductionmentioning

confidence: 99%

Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals

Xia

Wan

et al. 2012

Sensors

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Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches.

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“…The degradation experiment requires a long time and the vibration signals during the degradation are very complex [5]. However, features extracted by traditional methods [6] are normally based on the single monitoring signal. Tran et al extracted features by the analysis of the monitoring signal in the time domain [7].…”

Section: Introductionmentioning

confidence: 99%

The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction

2018

SV-JME

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Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine

Cited by 63 publications

References 25 publications

Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis

Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis

Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals

The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction

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