Relevance vector machine and its applications in machine fault diagnosis

WANG Bo1,2 LIU Shu-Lin1 ZHANG Hong-Li1 JIANG Chao1

Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (5) : 145-153.

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PDF(968 KB)
Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (5) : 145-153.

Relevance vector machine and its applications in machine fault diagnosis

  • Relevance vector machine (RVM) is a new statistical machine learning algorithm based on the theory of the sparse Bayesian learning and it has been applied to many fields in recent years. The researchers both at home and abroad have paid more and more attention on RVM. However, it has not been placed a high value in the mechanical fault diagnostic research. The characteristics of RVM are stated, the advantages and disadvantages compared with Support vector machine (SVM) are discussed, and the inland and overseas research advances are reviewed, especially in the field of mechanical fault diagnostic. Moreover, some existing problems in current research are analyzed, and the research directions of RVM for the mechanical fault diagnosis in the future are prospected.
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Abstract

Relevance vector machine (RVM) is a new statistical machine learning algorithm based on the theory of the sparse Bayesian learning and it has been applied to many fields in recent years. The researchers both at home and abroad have paid more and more attention on RVM. However, it has not been placed a high value in the mechanical fault diagnostic research. The characteristics of RVM are stated, the advantages and disadvantages compared with Support vector machine (SVM) are discussed, and the inland and overseas research advances are reviewed, especially in the field of mechanical fault diagnostic. Moreover, some existing problems in current research are analyzed, and the research directions of RVM for the mechanical fault diagnosis in the future are prospected.

Key words

fault diagnosis / Relevance Vector Machine / intelligent diagnosis / research progress

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WANG Bo1,2 LIU Shu-Lin1 ZHANG Hong-Li1 JIANG Chao1. Relevance vector machine and its applications in machine fault diagnosis[J]. Journal of Vibration and Shock, 2015, 34(5): 145-153

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