Application of Wavelet Packet Sample Entropy in the Forecast of Rolling Element Bearing Fault Trend

Wang, Fengtao; Zhang, Yangyang; Zhang, Bin; Su, Wensheng

doi:10.1109/cmsp.2011.93

Cited by 20 publications

(14 citation statements)

References 1 publication

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“…Zhang et al set N = 2400 and successfully detected and diagnosed a rolling bearing fault [36]. Zheng et al [37] and Wang et al [38] respectively set N = 4096 and N = 2000 with a sampling frequency of 20 kHz. Li et al set N = 2400 with a sampling frequency of 12 kHz [15].…”

Section: Fault Detection Experimentsmentioning

confidence: 99%

A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method

et al. 2019

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To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. A fast sample entropy calculation method based on a kd tree is adopted to improve the real-time performance of fault detection in this paper. In view of the non-linearity and non-stationarity of the vibration signals, the vibration signal of the rolling bearing is decomposed into several sub-signals containing fault information by using a wavelet packet. Then, the energy entropy values of the sub-signals decomposed by the wavelet packet are calculated to generate the feature vectors for describing different fault types and severity levels of rolling bearings. The multiclass relevance vector machine modeled by the feature vectors of different fault types and severity levels is used to realize fault type identification and a fault severity analysis of the bearings. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments. The experimental results demonstrate that the fault detection method based on the sample entropy can effectively detect rolling bearing failure. The fault feature extraction method based on the wavelet packet energy entropy can effectively extract the fault features of vibration signals and a multiclass relevance vector machine can identify the fault type and severity by means of the fault features contained in these signals. Compared with some existing bearing rolling fault diagnosis methods, the proposed method is excellent for fault diagnosis and severity analysis and improves the fault identification rate reaching as high as 99.47%.

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Section: Fault Detection Experimentsmentioning

confidence: 99%

A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method

et al. 2019

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“…EET: Energy Entropy trend (Kim et al 2016). WPSE-EMD: Wavelet packet sample Entropy (Wang et al 2011) -Empirical mode decomposition (Lei et al 2007). Spectral-ANN: Third-order spectral + artificial neural networks (Yang et al 2002).…”

Section: Framework1 -Health-condition Monitoringmentioning

confidence: 99%

A Machine Learning Suite for Machine Components’ Health-Monitoring

Hasani¹,

Wang²,

Grosu³

2019

AAAI

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This paper studies an intelligent technique for the healthmonitoring and prognostics of common rotary machine components, with regards to bearings in particular. During a run-to-failure experiment, rich unsupervised features from vibration sensory data are extracted by a trained sparse autoencoder. Then, the correlation of the initial samples (presumably healthy), along with the successive samples, are calculated and passed through a moving-average filter. The normalized output which is referred to as the auto-encoder correlation based (AEC) rate, determines an informative attribute of the system, depicting its health status. AEC automatically identifies the degradation starting point in the machine component. We show that AEC rate well-generalizes in several run-tofailure tests. We demonstrate the superiority of the AEC over many other state-of-the-art approaches for the health monitoring of machine bearings.

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“…Feature Set. More than 50 features of time domain, frequency domain, and time-frequency domain [29,30] were extracted from the life cycle data of seven training bearings. To ensure that the first three KPCs contain as much useful information as possible, it is necessary to minimize the dimension and ensure the validity of the information of each dimension prior to using KPCA.…”

Section: Constructing High Relativementioning

confidence: 99%

Rolling Bearing Reliability Assessment via Kernel Principal Component Analysis and Weibull Proportional Hazard Model

Martínek¹,

Chen²,

Dun³

et al. 2017

Shock and Vibration

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Reliability assessment is a critical consideration in equipment engineering project. Successful reliability assessment, which is dependent on selecting features that accurately reflect performance degradation as the inputs of the assessment model, allows for the proactive maintenance of equipment. In this paper, a novel method based on kernel principal component analysis (KPCA) and Weibull proportional hazards model (WPHM) is proposed to assess the reliability of rolling bearings. A high relative feature set is constructed by selecting the effective features through extracting the time domain, frequency domain, and time-frequency domain features over the bearing’s life cycle data. The kernel principal components which can accurately reflect the performance degradation process are obtained by KPCA and then input as the covariates of WPHM to assess the reliability. An example was conducted to validate the proposed method. The differences in manufacturing, installation, and working conditions of the same type of bearings during reliability assessment are reduced after extracting relative features, which enhances the practicability and stability of the proposed method.

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Application of Wavelet Packet Sample Entropy in the Forecast of Rolling Element Bearing Fault Trend

Cited by 20 publications

References 1 publication

A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method

A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method

A Machine Learning Suite for Machine Components’ Health-Monitoring

Rolling Bearing Reliability Assessment via Kernel Principal Component Analysis and Weibull Proportional Hazard Model

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