Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor

To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-08, Vol.22 (16), p.6316
Main Authors: Xue, Hongtao, Song, Ziwei, Wu, Meng, Sun, Ning, Wang, Huaqing
Format: Article
Language:English
Subjects:
Accuracy
AdaBoost algorithm
Algorithms
artificial hydrocarbon networks
Artificial intelligence
Bearings
Carbon
Classification
Clustering
Fault detection
Fault diagnosis
Faults
Hazard mitigation
Hydrocarbons
in-wheel motor
intelligent diagnosis
K-means clustering
Methods
Neural networks
Organic chemistry
Signal processing
Support vector machines
Wavelet transforms
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Summary:To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a K-means clustering and AdaBoost algorithm to solve the lower accuracy and poorer stability of traditional AHNs. Firstly, K-means clustering is used to improve the interval updating method of any adjacent AHNs molecules, and then simplify the complexity of the AHNs model. Secondly, the AdaBoost algorithm is utilized to adaptively distribute the weights for multiple weak models, then reconstitute the network structure of the AHNs. Finally, double-optimized AHNs are used to build an intelligent diagnosis system, where two cases of bearing datasets from Paderborn University and a self-made IWM test stand are processed to validate the better performance of the proposed method, especially in multiple rotating speeds and the load conditions of the IWM. The double-optimized AHNs provide a higher accuracy for identifying the mechanical faults of the IWM than the traditional AHNs, K-means-based AHNs (K-AHNs), support vector machine (SVM), and particle swarm optimization-based SVM (PSO-SVM).
ISSN:1424-8220
1424-8220
DOI:10.3390/s22166316