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Mechanical Fault Diagnosis of a DC Motor Utilizing United Variational Mode Decomposition, SampEn, and Random Forest-SPRINT Algorithm Classifiers

1
HLJ Province Key Lab of Senior-Education for Electronic Engineering, Heilongjiang University, Harbin 150080, China
2
Key Laboratory of Information Fusion Estimation and Detection, Heilongjiang Province, Harbin 150080, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(5), 470; https://doi.org/10.3390/e21050470
Received: 9 April 2019 / Revised: 29 April 2019 / Accepted: 30 April 2019 / Published: 6 May 2019
(This article belongs to the Section Signal and Data Analysis)
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Abstract

Traditional fault diagnosis methods of DC (direct current) motors require establishing accurate mathematical models, effective state and parameter estimations, and appropriate statistical decision-making methods. However, these preconditions considerably limit traditional motor fault diagnosis methods. To address this issue, a new mechanical fault diagnosis method was proposed. Firstly, the vibration signals of motors were collected by the designed acquisition system. Subsequently, variational mode decomposition (VMD) was adopted to decompose the signal into a series of intrinsic mode functions and extract the characteristics of the vibration signals based on sample entropy. Finally, a united random forest improvement based on a SPRINT algorithm was employed to identify vibration signals of rotating machinery, and each branch tree was trained by applying different bootstrap sample sets. As the results reveal, the proposed fault diagnosis method is featured with good generalization performance, as the recognition rate of samples is more than 90%. Compared with the traditional neural network, data-heavy parameter optimization processes are avoided in this method. Therefore, the VMD-SampEn-RF-based method proposed in this paper performs well in fault diagnosis of DC motors, providing new ideas for future fault diagnoses of rotating machinery. View Full-Text
Keywords: variational mode decomposition; sample entropy; random forest; SPRINT algorithm variational mode decomposition; sample entropy; random forest; SPRINT algorithm
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Guo, Z.; Liu, M.; Qin, H.; Li, B. Mechanical Fault Diagnosis of a DC Motor Utilizing United Variational Mode Decomposition, SampEn, and Random Forest-SPRINT Algorithm Classifiers. Entropy 2019, 21, 470.

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