A New Online Detection Approach for Rolling Bearing Incipient Fault via Self-Adaptive Deep Feature Matching

This paper presents a new online detection approach for rolling bearing's incipient fault based on self-adaptive deep feature matching (SDFM). This approach includes offline and online stages. At the offline stage, a new health state assessment algorithm is first proposed based on singular valu...

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Veröffentlicht in:IEEE transactions on instrumentation and measurement Jg. 69; H. 2; S. 443 - 456
Hauptverfasser: Mao, Wentao, Chen, Jiaxian, Liang, Xihui, Zhang, Xinming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptation models
Algorithms
Deep learning
Employee welfare
False alarms
Fault detection
Fault diagnosis
Feature extraction
health state assessment
incipient fault detection
Kurtosis
Machine learning
Matching
Noise reduction
online detection
Roller bearings
Singular value decomposition
stacked denoising autoencoder (SDAE)
support vector data description (SVDD)
Support vector machines
ISSN:0018-9456, 1557-9662
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Zusammenfassung:This paper presents a new online detection approach for rolling bearing's incipient fault based on self-adaptive deep feature matching (SDFM). This approach includes offline and online stages. At the offline stage, a new health state assessment algorithm is first proposed based on singular value decomposition (SVD) and Kurtosis criterion. Based on the assessment results, a kind of deep learning algorithm, i.e., stacked denoising autoencoder (SDAE), is introduced to extract the common deep features of normal state and early fault state. Support vector data description (SVDD) is applied to establish the offline detection model using the obtained features. At the online stage, a self-adaptive matching strategy with 1-Dimensional anchor is proposed. By utilizing the SDAE model established at offline stage, this strategy can extract more representative deep features of the target bearing via generating various proposal fragments and then determining the fault occurrence time in a self-adaptive way by feeding the online features into the SVDD model. Experiments run on the bearing data set of IEEE prognostic and health management (PHM) Challenge 2012. The results show the proposed approach has good detection performance in real time and much lower false alarm rate, with no need to acquire fault characteristic frequency in advance.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2019.2903699