A brake disc dynamic balance detection method based on multisource information fusion

This article proposes a method for detecting the dynamic balance of brake discs based on deep convolutional neural networks (DCNN) and GraphSAGE networks, aimed at addressing the issue of dynamic balance detection of automotive brake discs under real-world operating conditions. As a key component of vehicle safety, the braking system’s performance directly affects the vehicle’s braking efficiency and driving safety. During use, the brake disc may develop an imbalanced state due to factors such as uneven thickness, warping, and surface dents. However, the number of normal state data samples is often much higher than that of the imbalanced state, and traditional detection methods frequently face the challenge of low recognition accuracy in such scenarios. To overcome this challenge, this article utilizes multisource data, including vibration signals, sound signals, and brake pressure and performs feature extraction and compression through DCNN. It then combines the graph structure characteristics of the GraphSAGE network to aggregate information from adjacent nodes, significantly improving detection performance in imbalanced data scenarios. Experimental results show that the proposed method achieves an effective detection accuracy of at least 93.98% on imbalanced datasets. The average area under the curve value obtained from the ROC curve is 0.9483, indicating good classification capability. t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization further confirms the method’s effectiveness and robustness in distinguishing between different brake disc states. The study demonstrates that the proposed method solves the data imbalance issue without the need for data augmentation or significant modifications to the network structure. It offers an efficient and feasible solution for dynamic balance detection of automotive braking systems under complex operating conditions, with important engineering application value.