Fault Pattern Recognition of Rolling Bearing Based on Wavelet Packet and Support Vector Machine

Lü, Shuang; Chen, Weizeng; Li, Meng

doi:10.1109/wcica.2006.1714128

Cited by 6 publications

(7 citation statements)

References 6 publications

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“…Hence, automatically extracting failure features from machine signals without human interference is significant [17]. Up to date, intelligent methods (e.g., BPNN) have been extensively investigated and used to diagnose faults in rotating machinery [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…Up to date, intelligent methods (e.g., BPNN) have been extensively investigated and used to diagnose faults in rotating machinery [18,19]. Liang et al [17] combined BPNN with wavelet packet decomposition, where the wavelet packet decomposition coefficients were used to extract eight energy features, and the BPNN was used to do recognition with validation accuracy at 92.5%. Recently, deep-learning methods, such as the DBN-WPT combination, have been determined to overcome the obstacles in fault diagnosis in complex machines [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

2016

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Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an accurate and timely diagnosis method is necessary. With the breakthrough in deep learning algorithm, some intelligent methods, such as deep belief network (DBN) and deep convolution neural network (DCNN), have been developed with satisfactory performances to conduct machinery fault diagnosis. However, only a few of these methods consider properly dealing with noises that exist in practical situations and the denoising methods are in need of extensive professional experiences. Accordingly, rethinking the fault diagnosis method based on deep architectures is essential. Hence, this study proposes an automatic denoising and feature extraction method that inherently considers spatial and temporal correlations. In this study, an integrated deep fault recognizer model based on the stacked denoising autoencoder (SDAE) is applied to both denoise random noises in the raw signals and represent fault features in fault pattern diagnosis for both bearing rolling fault and gearbox fault, and trained in a greedy layer-wise fashion. Finally, the experimental validation demonstrates that the proposed method has better diagnosis accuracy than DBN, particularly in the existing situation of noises with superiority of approximately 7% in fault diagnosis accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

2016

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“…With the rapid development of computer information processing technology, the ability to handle fault data of large-scale electronic circuit is growing [1]. The technique can be applied to the field of fault detection of large-scale electronic circuit and collect the fault data of different properties in the designated space, thus providing a basis for the post-operation of failure data information [2].…”

Section: Introductionmentioning

confidence: 99%

Research on the Optimization Model of Fault Detection for Large-Scale Electronic Circuit

Feng¹

2014

AMR

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In current large-scale electronic circuit devices, failure data calibration capacity is not strong and it is difficult to be precise classification and intelligent judgment. It lacks of the necessary mechanisms to eliminate the error message, bringing troubles to fault detection. In order to avoid the above defect, this paper presents a fault detection method for large-scale electronic circuit based on fuzzy clustering algorithm. Firstly, the use of means clustering method, the fault information is made initial classification. Then, using the second fuzzy clustering method make fault information filtering in different categories, in order to achieve the fault data confirmation. Experimental results show that the proposed algorithm can effectively improve the accuracy of fault detection of large-scale electronic circuit.

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“…The essence of the mechanical fault diagnosis is pattern recognition [1]. According to the statistics, there are 30% of rotation machinery faults caused by bearing fault and 90% of rolling bearing fault come from the faults of inner ring and outer ring [2,3]. One common method for fault diagnosis is to use the vibration signal sending out from the running machine.…”

Section: Introductionmentioning

confidence: 99%

“…Then constitute a fault feature space using fault feature points which is extracted from the reconstruction signal. The bearing vibration signal energy distributed in different frequency band because the fault location and the bearing structure are different [2]. The optimal wavelet packet code, by which the reconstruction signal contained the most obvious fault feature could be reconstructed, needed be found in order to extract the bearing fault feature.…”

Section: Introductionmentioning

confidence: 99%

The Research of the Rolling Element Bearing Fault Signal Frequency Domain Feature Extraction Method Based on the Wavelet Packet Decomposition

Qiu

Yuan

2014

AMR

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The wavelet packet decomposition can be used to extract the frequency band containing bearing fault feature, because the fault signal can be decomposed into different frequency bands by using the wavelet packet decomposition, that is to say the optimal wavelet packet decomposition node needs to be found. A method applying the average Euclidean distance to find the optimal wavelet packet decomposition node was presented. First of all, the bearing fault signals were decomposed into three layers wavelet coefficients by which the bearing fault signals were reconstructed. The peak values extracted from the reconstructing signal spectrum constructed a feature space. Then, the minimum average Euclidean distance calculated from the feature space indicated the optimal wavelet packet node. The optimal feature space could be constructed by the feature points extracted from the signals reconstructed by the optimal wavelet packet nodes. Finally, the optimal feature space was used for the K-means clustering. The feature extraction and pattern recognition test of the four kinds of bearing conditions under four kinds of rotation speeds was detailed. The test results show this method, which can extract the bearing fault feature efficiently and make the fault feature space have the lowest within-class scatter, wons a high pattern recognition accuracy.

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Fault Pattern Recognition of Rolling Bearing Based on Wavelet Packet and Support Vector Machine

Cited by 6 publications

References 6 publications

Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

Research on the Optimization Model of Fault Detection for Large-Scale Electronic Circuit

The Research of the Rolling Element Bearing Fault Signal Frequency Domain Feature Extraction Method Based on the Wavelet Packet Decomposition

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