Vehicles are complex Cyber Physical Systems (CPS) that operate in a variety of environments, and the likelihood of failure of one or more subsystems, such as the engine, transmission, brakes, and fuel, can result in unscheduled downtime and incur high maintenance or repair costs. In order to prevent these issues, it is crucial to continuously monitor the health of various subsystems and identify abnormal sensor channel behavior. Data-driven Digital Twin (DT) systems are capable of such a task. Current DT technologies utilize various Deep Learning (DL) techniques that are constrained by the lack of justification or explanation for their predictions. This inability of these opaque systems can influence decision-making and raises user trust concerns. This paper presents a solution to this issue, where the TwinExplainer system, with its three-layered architectural pipeline, explains the predictions of an automotive DT. Such a system can assist automotive stakeholders in understanding the global scale of the sensor channels and how they contribute towards generic DT predictions. TwinExplainer can also visualize explanations for both normal and abnormal local predictions computed by the DT.

翻译：车辆作为运行于多样化环境中的复杂信息物理系统（CPS），其发动机、变速箱、制动器和燃油等一个或多个子系统发生故障的可能性，可能导致计划外停机并产生高昂的维护或维修成本。为预防此类问题，持续监测各子系统健康状态并识别异常传感器通道行为至关重要。基于数据驱动的数字孪生（DT）系统能够胜任此项任务。当前数字孪生技术采用多种深度学习（DL）方法，但这些方法因缺乏对预测结果的解释或理由而受到限制。这种不透明系统的局限性可能影响决策过程，并引发用户信任问题。本文提出一种解决方案：通过三层架构的TwinExplainer系统，对汽车数字孪生的预测结果进行解释。该系统能够帮助汽车领域利益相关者理解传感器通道的全局特性及其对通用数字孪生预测的贡献方式。此外，TwinExplainer还可对数字孪生计算出的正常与异常局部预测结果进行可视化解释。