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Shock and Vibration
Volume 2018 (2018), Article ID 8218657, 7 pages
Research Article

Research on Fault Diagnosis Based on Singular Value Decomposition and Fuzzy Neural Network

College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China

Correspondence should be addressed to Yifan Hu; nc.ude.uebrh@g5027202102

Received 10 January 2018; Accepted 7 March 2018; Published 8 April 2018

Academic Editor: Giosuè Boscato

Copyright © 2018 Jingbo Gai and Yifan Hu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A method based on singular value decomposition (SVD) and fuzzy neural network (FNN) was proposed to extract and diagnose the fault features of diesel engine crankshaft bearings efficiently and accurately. Firstly, vibration signals of crankshaft bearings in known state under the same working condition were decomposed by EMD to obtain the modal components containing fault-feature information. Then, the singular values of modal components which include the main fault features were used as the initial vector matrix, where the eigenvectors were decomposed to form a fault characteristic matrix. At last, the fault features matrix was trained by the fuzzy neural network, in order to realize the diagnosis and identification of the crankshaft bearings in different states in the form of numerical values. The experiment showed that the numerical identification of the fuzzy neural network based on the singular value had high fault diagnosis accuracy and stability. This method can also reflect the gradual change of the crankshaft bearings’ fault to some extent, so it has the desired reliability and value.