In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module

Zhu, Junwei; Ouyang, Xupeng; Jiang, Zongkang; Xu, Yanlong; Xue, Hongtao; Yue, Huiyu; Feng, Huayuan

doi:10.3390/s25154617

This is an early access version, the complete PDF, HTML, and XML versions will be available soon.

Open AccessArticle

In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module

by

Junwei Zhu

,

Xupeng Ouyang

,

Zongkang Jiang

,

Yanlong Xu

,

Hongtao Xue

^*

,

Huiyu Yue

and

Huayuan Feng

School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China

^*

Author to whom correspondence should be addressed.

Sensors 2025, 25(15), 4617; https://doi.org/10.3390/s25154617

Submission received: 27 June 2025 / Revised: 18 July 2025 / Accepted: 24 July 2025 / Published: 25 July 2025

(This article belongs to the Special Issue Intelligent Maintenance and Fault Diagnosis of Mobility Equipment)

Download Versions Notes

Abstract

To address the challenge of fault diagnosis for in-wheel motors in four-wheel independent driving systems under variable driving conditions and harsh environments, this paper proposes a novel method based on two-stream 2DCNNs (two-dimensional convolutional neural networks) with a DCBA (depthwise convolution block attention) module. The main contributions are twofold: (1) A DCBA module is introduced to extract multi-scale features—including prominent, local, and average information—from grayscale images reconstructed from vibration signals across different domains; and (2) a two-stream network architecture is designed to learn complementary feature representations from time-domain and time–frequency-domain signals, which are fused through fully connected layers to improve diagnostic accuracy. Experimental results demonstrate that the proposed method achieves high recognition accuracy under various working speeds, loads, and road surfaces. Comparative studies with SENet, ECANet, CBAM, and single-stream 2DCNN models confirm its superior performance and robustness. The integration of DCBA with dual-domain feature learning effectively enhances fault feature extraction under complex operating conditions.

Keywords: in-wheel motor; fault diagnosis; two-stream 2DCNNs; depthwise convolution block attention

Share and Cite

MDPI and ACS Style

Zhu, J.; Ouyang, X.; Jiang, Z.; Xu, Y.; Xue, H.; Yue, H.; Feng, H. In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module. Sensors 2025, 25, 4617. https://doi.org/10.3390/s25154617

AMA Style

Zhu J, Ouyang X, Jiang Z, Xu Y, Xue H, Yue H, Feng H. In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module. Sensors. 2025; 25(15):4617. https://doi.org/10.3390/s25154617

Chicago/Turabian Style

Zhu, Junwei, Xupeng Ouyang, Zongkang Jiang, Yanlong Xu, Hongtao Xue, Huiyu Yue, and Huayuan Feng. 2025. "In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module" Sensors 25, no. 15: 4617. https://doi.org/10.3390/s25154617

APA Style

Zhu, J., Ouyang, X., Jiang, Z., Xu, Y., Xue, H., Yue, H., & Feng, H. (2025). In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module. Sensors, 25(15), 4617. https://doi.org/10.3390/s25154617

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI