基于多模态融合与深度强化学习的智能汽车故障诊断方法研究

Intelligent Vehicle Fault Diagnosis Method Based on Multimodal Fusion and Deep Reinforcement Learning

摘要: 智能汽车的复杂性以及其多源异构数据(如CAN总线、声音、图像等)的特性，对传统的故障诊断方法提出了严峻挑战，导致其在处理海量信息时效率和准确性均难以满足要求。针对此问题，本文提出一种结合多模态数据融合与深度强化学习的智能故障诊断新方法。该方法首先通过设计高效的深度学习特征提取与融合机制，将智能汽车中采集的CAN总线数据、声音和图像等异构多模态信息进行深度融合，形成一个统一的高维状态表示。其次，我们将智能故障诊断过程建模为一个马尔可夫决策过程(MDP)。在此框架下，我们引入一个深度强化学习(DRL)代理，使其通过与模拟环境的交互，学习并优化诊断策略。该代理能够基于全面的多模态融合状态信息，做出序列化诊断决策，以最大化累积奖励，从而有效提高诊断的准确性与效率。我们在一个基于Kaggle公开数据集构建的模拟仿真环境中对所提方法进行了验证。实验结果表明，与单一模态及传统诊断方法相比，本文方法在显著提升诊断准确率的同时，其基于深度强化学习的序贯决策能力有效减少了平均诊断步骤数，证明了该方法在提高智能汽车故障诊断的准确性与效率方面的优越性。

Abstract: The complexity of intelligent vehicles and the characteristics of their multi-source heterogeneous data, including CAN bus data, audio, and images, present formidable challenges to conventional fault diagnosis methods. This has led to difficulties in meeting the requirements for efficiency and accuracy when processing vast amounts of information. To tackle this problem, this paper presents a novel intelligent fault diagnosis approach that integrates multi-modal data fusion and deep reinforcement learning. Initially, an efficient deep learning-based feature extraction and fusion mechanism is designed to comprehensively integrate heterogeneous multi-modal information such as CAN bus data, audio, and images collected from intelligent vehicles, thereby constructing a unified high-dimensional state representation. Subsequently, the intelligent fault diagnosis process is modeled as a Markov Decision Process (MDP). Within this framework, a deep reinforcement learning (DRL) agent is introduced. This agent learns and optimizes the diagnostic strategy through interactions with a simulated environment. Leveraging the comprehensive multi-modal fusion state information, the agent can make sequential diagnostic decisions to maximize cumulative rewards, thus effectively enhancing the accuracy and efficiency of diagnosis. The proposed method was validated in a simulated environment constructed using a publicly available Kaggle dataset. The experimental results indicate that, compared to single-modal and traditional diagnostic methods, the method presented in this paper not only significantly improves diagnostic accuracy but also effectively reduces the average number of diagnostic steps through its sequential decision-making ability based on deep reinforcement learning. This demonstrates the superiority of the proposed method in improving the accuracy and efficiency of intelligent vehicle fault diagnosis.