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Sensors 2018, 18(2), 337; https://doi.org/10.3390/s18020337

New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network

1,2,* , 1,2
,
1,2
and
3
1
School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
2
Suzhou Key Laboratory of Precision and Efficient Machining Technology, Suzhou 215009, China
3
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
*
Author to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 12 January 2018 / Accepted: 22 January 2018 / Published: 24 January 2018
(This article belongs to the Special Issue Sensors for Fault Detection)
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Abstract

Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery. View Full-Text
Keywords: fault recognition; information entropy; probabilistic neural network; rotary machinery; feature extraction fault recognition; information entropy; probabilistic neural network; rotary machinery; feature extraction
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Jiang, Q.; Shen, Y.; Li, H.; Xu, F. New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network. Sensors 2018, 18, 337.

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