GOALI: DIAGNOSIS AND PROGNOSIS OF AUTOMOTIVE CHASSIS SYSTEMS

目标：汽车底盘系统的诊断和预测

• 批准号: 1001445
• 负责人: Krishna Pattipati
• 金额: $ 35万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1001445&HistoricalAwards=false
• 关键词: GOALI; DIAGNOSIS; PROGNOSIS; AUTOMOTIVE; CHASSIS

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.

拟议的合作项目与通用汽车公司研发中心，沃伦，密歇根州寻求开发混合模型为基础的/数据驱动的/知识为基础的预测框架，以及相关的预测和推理算法，检测和隔离耦合系统中的初期组件退化。重点将放在底盘健康状况确定（意味着转向，制动和悬架组件的健康状态，以及使用这些组件的功能，如StabiliTrak系统）到可更换组件（例如，断裂的横拉杆、磨损的刹车片或爆胎、ABS控制模块故障）。诊断和预测框架将在生产版本中实施之前在测试机队上进行验证。智能优点：汽车系统中采用的现有预测和诊断算法往往以组件为中心。由于忽略了跨子系统故障传播和不可靠的测试，他们往往不能提供正确的诊断。拟议的研究旨在克服这些局限性，通过明确建模的跨子系统的影响，通过图形模型和开发一个多层概率推理过程跨越感知数据#8594;预测功能#8594;诊断故障代码#8594;故障模式#8594;可更换的组件和子系统。耦合组件状态的时间演化模拟阶乘隐马尔可夫模型和计算效率的推理算法在多层图形模型的发展是一个新的方面所提出的努力。此外，将基于模型、数据驱动和基于知识的方法以统一的方式结合起来，解决下一代汽车中的实际诊断和预后问题，是这项工作的另一个贡献。更广泛的影响：该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行业的竞争力。PI计划通过短期课程，教程，会议演示和期刊手稿向更广泛的工业界公布这项研究的结果。