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. 2018 Jan 24;18(2):337.
doi: 10.3390/s18020337.

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

Quansheng Jiang  1   2 , Yehu Shen  3   4 , Hua Li  5   6 , Fengyu Xu  7
Affiliations

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

Quansheng Jiang et al. Sensors (Basel). .

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.

Keywords: fault recognition; feature extraction; information entropy; probabilistic neural network; rotary machinery.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Structure diagram of the PNN.
Figure 2
Figure 2
The structure of the feature fusion model based on information entropy and the PNN.
Figure 3
Figure 3
The working principle diagram of the rotor test rig.
Figure 4
Figure 4
Singular spectrum entropy of a single-span rotor under the unbalance fault.
Figure 5
Figure 5
Singular spectrum entropy of a single-span rotor under the rubbing fault.
Figure 6
Figure 6
Power spectrum entropy of a single-span rotor under the unbalance fault.
Figure 7
Figure 7
Power spectrum entropy of a single-span rotor under the rubbing fault.
Figure 8
Figure 8
Approximate entropy of a single-span rotor under the unbalance fault.
Figure 9
Figure 9
Approximate entropy of a single-span rotor under the rubbing fault.
See this image and copyright information in PMC

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