GOALI: DIAGNOSIS AND PROGNOSIS OF AUTOMOTIVE CHASSIS SYSTEMS

与密歇根州沃伦的通用汽车研发中心提出的合作项目旨在开发基于模型/数据驱动/基于知识的混合预测框架，以及相关的预测和推理算法，以检测和隔离耦合系统中的初始部件退化。重点将是底盘健康状况的确定(即转向、制动和悬挂部件的健康状态，以及使用这些部件的功能，如StabiliTrak系统)和可更换部件(例如，断裂的拉杆、磨损的刹车片或爆胎、故障的ABS控制模块)。该诊断和预后框架将在生产版本实施之前在测试机队上进行验证。智能优点：汽车系统中使用的现有预测和诊断算法往往是以组件为中心的。由于忽略了跨子系统的故障传播和不可靠的测试，它们经常无法提供正确的诊断。拟议的研究试图克服这些限制，通过图形模型对跨子系统的影响进行显式建模，并通过开发跨越感测数据的多层概率推理过程，预测故障代码和故障模式可更换的部件和子系统。将耦合组件状态的时间演化建模为阶乘隐马尔可夫模型，并在多层图形模型中开发计算高效的推理算法是该工作的一个新方面。此外，将基于模型的方法、基于数据驱动的方法和基于知识的方法结合起来，以统一的方式解决下一代汽车的实际诊断和预测问题，是本工作的另一个贡献。更广泛的影响：拟议的研究通过降低保修成本、提高车辆可用性和客户满意度来提高美国汽车业的竞争力。PI计划通过短期课程、教程、会议报告和期刊手稿向更广泛的工业界公布这项研究的结果。

The proposed collaborative project with GM R&D center, Warren, MI seeks to develop hybrid model-based/data-driven/knowledge-based prognostic framework, and the associated prediction and inference algorithms, to detect and isolate incipient component degradations in coupled systems. The focus will be on chassis health determination (meaning the health status of steering, braking and suspension components, as well as functionalities that use these components such as the StabiliTrak system) to replaceable components (e.g., broken tie rod, worn out brake pads or blown tire, malfunctioning ABS control module). The diagnostic and prognostic framework will be validated on test fleet prior to implementing in the production versions. Intellectual Merit: Existing prognostic and diagnostic algorithms employed in automotive systems tend to be component-centric. They often fail to provide correct diagnosis due to neglect of cross-subsystem failure propagation and unreliable tests. The proposed research seeks to overcome these limitations by explicitly modeling the cross-subsystem effects via a graphical model and by developing a multi-layer probabilistic reasoning process spanning sensed data → predicted features → diagnostic trouble codes → failure modes → replaceable components and subsystems. Modeling the time evolution of coupled component states as factorial hidden Markov models and the development of computationally-efficient inference algorithms in multi-layer graphical models is a novel aspect of the proposed effort. In addition, combining model-based, data-driven and knowledge-based approaches in a unified way to solve practical diagnosis and prognosis problems in the next-generation automotive vehicles is another contribution of this work. Broader Impact: The proposed research improves the competitiveness of American automotive industry by reducing warranty costs, and enhancing vehicle availability and customer satisfaction. The PI plans to promulgate the results of this research to the broader industrial community via short courses, tutorials, conference presentations and journal manuscripts.