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.

In-wheel motors (IWMs) have become a promising solution for electric vehicles due to their compact design, high integration, and flexible torque control. However, their exposure to harsh operating conditions increases the risk of mechanical, electrical, and magnetic faults, making reliable fault diagnosis essential for ensuring driving safety and system reliability. Although considerable progress has been made in fault diagnosis techniques related to IWMs, a systematic review in this area is still lacking. To address this gap, this paper provides a comprehensive review of fault diagnosis techniques for IWMs. First, typical faults in IWMs are analyzed with a focus on their unique structural and failure characteristics. Then, the applications and recent research progress of three major categories of fault diagnosis approaches—model-based, signal-based, and knowledge-based methods—in the context of IWMs are critically reviewed. Finally, key challenges and pain points in IWM diagnosis are discussed, along with promising future research directions.