This study addresses the increasingly complex challenge of multiple-fault diagnosis in modern intelligent automotive electronic systems by proposing an innovative deep learning-based solution. The research integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and the Transformer architecture to construct a multi-modal fault diagnosis model. By collecting real-world operational data from vehicle electronic systems, including fault samples from key modules such as the Engine Control Unit (ECU), Body Control Module (BCM), and safety systems, a comprehensive dataset comprising 12 major fault types was established. Experimental results demonstrate that the proposed hybrid deep learning model achieves a multiple-fault identification accuracy of 96.8%, representing a 23% performance improvement over traditional diagnostic methods. The integration of Explainable AI (XAI) techniques provides the diagnostic results with visual interpretability, aiding maintenance technicians in understanding the model’s diagnostic logic. The findings of this research can be applied in smart factories, automotive service centers, and on-board diagnostic (OBD) systems, offering significant practical value in enhancing vehicle safety and reducing maintenance costs.

This paper investigates the feedback control for networked discrete-time finite-distributed delays with quantization and packet dropout, and systems induce theH∞control problem. The compensation scheme occurs in a random way. The quantization of system state or output signal is in front of being communicated. It is shown that the design of both a state feedback controller and an observer-based output feedback controller can be achieved, which ensure the asymptotical stability as well as a prescribedH∞performance of the resulting closed-loop system satisfying dependence on the size of the discrete and distributed delays. Numerical examples are given to illustrate the effectiveness and applicability of the design method in this paper.

This paper investigates the problem of robust stabilization for genetic regulatory networks with interval time-varying delays, which are subject to norm-bounded time-varying parameter uncertainties. The time delays including lower and upper bounds of delay are assumed to appear in both the mRNA and protein. The regulatory functions are assumed to be globally Lipschitz continuous. The resulting delay-range-dependent robust controller with interval range is designed in terms of improved bounding technique. A sufficient condition for the solvability of the problem is obtained via a linear matrix inequality (LMI). When this LMI is feasible, an explicit expression of a desired state feedback controller is also given. The theory developed in this paper is demonstrated by two numerical examples.