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Sensors 2017, 17(4), 689; doi:10.3390/s17040689

Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition

1
State Key Lab of Control and Simulation of Power Systems and Generation Equipment, Department of Thermal Engineering, Tsinghua University, Beijing 100084, China
2
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 13 February 2017 / Revised: 15 March 2017 / Accepted: 23 March 2017 / Published: 27 March 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [5520 KB, uploaded 28 March 2017]   |  

Abstract

Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals. Essentially, dictionary learning is employed as an adaptive feature extraction method regardless of any prior knowledge. Second, the singular value sequence of learned dictionary matrix is served to extract feature vector. Generally, since the vector is of high dimensionality, a simple and practical principal component analysis (PCA) is applied to reduce dimensionality. Finally, the K-nearest neighbor (KNN) algorithm is adopted for identification and classification of fault patterns automatically. Two experimental case studies are investigated to corroborate the effectiveness of the proposed method in intelligent diagnosis of rotating machinery faults. The comparison analysis validates that the dictionary learning-based matrix construction approach outperforms the mode decomposition-based methods in terms of capacity and adaptability for feature extraction. View Full-Text
Keywords: rotating machinery; condition monitoring; intelligent diagnosis; dictionary learning; singular value decomposition; dimensionality reduction rotating machinery; condition monitoring; intelligent diagnosis; dictionary learning; singular value decomposition; dimensionality reduction
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Han, T.; Jiang, D.; Zhang, X.; Sun, Y. Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition. Sensors 2017, 17, 689.

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