Nonlinear Model Based Information Synthesis and Health Detection with Applications to Drive-by-Wire Engines

PIS：Matthew Franchek和George Chiu，普渡大学机械工程学院建议编号：0097807标题：基于非线性模型的信息合成和健康检测及其在线控汽车引擎中的应用摘要：这项拟议的研究将从称为信息合成(IS)的信息技术(IT)的角度创建传感器软件技术。这个知识库的前提是传感器和动作信息创建了一个关键的数据集，该数据集可以产生跨越系统整个信息空间的非线性模型。IS技术将通过创建在线自适应非线性(NL)建模技术来构建，然后将其与为健康检测设计的分析功能集成在一起。IS技术将在线控内燃(IC)发动机研究中进行实验验证，以创造故障安全发动机。这项工作的有线驾驶部分将寻求工业赞助的研究支持。这项拟议研究的科学影响是创建适用于大类物理系统的IS知识库。提出的IS技术是对数据融合知识库的补充，它通过非线性动态模型来合成信息，而不是通过I/O表格映射来合成信息。因此，随着系统老化，IS在线模型调整将不再需要创建新的I/O映射。此外，这种基于模型的方法将允许将信息外推到与最初未考虑的输入信号相对应的空间。第一个贡献将是创建一种自然语言动态系统建模技术，该技术既可以应用于实验数据，也可以应用于数值模拟，或者可以从其他自然语言模型中提取，例如人工神经网络。为了在产品生命周期内保持模型的准确性，本研究的第二个贡献将开发被动在线模型自适应技术。下一个贡献将是开发一种用于健康检测的分析设计技术。具体地说，将对适应的模型系数进行分析，以提取产品健康。这些IS技术将在全电子线控发动机上进行验证。该发动机是福特V-8燃油喷射发动机，配备了电子节气门，用于燃料控制和扭矩控制管理。这里的目标是创造一种故障安全的线控驱动发动机，能够承受传感器故障，否则会导致发动机功率爆炸。

PIs: Matthew Franchek and George Chiu, School of Mechanical Engineering, Purdue UniversityProposal Number: 0097807Title: Nonlinear Model Based Information Synthesis and Health Detection with Applications to Drive-by-Wire EnginesAbstract:This proposed research will create sensor software technology from an Information Technologies (IT) point of view called information synthesis (IS). This knowledge base starts from the premise that sensor and actuation information creates a critical data set which can produce nonlinear models that span the entire information space of a system. The IS technologies will be built by creating an online adaptive nonlinear (NL) modeling technology which is then integrated with analytical functions designed for health detection. The IS technology will be experimentally validated on a drive-by-wire internal combustion (IC) engine research to create fail-safe engines. Research support for the drive-by-wire portion of this work will be sought from industrial sponsors.The scientific impact of this proposed research is the creation of an IS knowledge base applicable to a large class of physical systems. The proposed IS technology complements the data fusion knowledge base by synthesizing information via nonlinear dynamic models instead of I/O tabular maps. Therefore as the system ages, the IS online model adaptation will eliminate the need to create a new I/O mapping. Furthermore, this model based IS approach will allow information extrapolation to a space corresponding to input signals not originally considered. The first contribution will be the creation of a NL dynamic system modeling technology that can be applied to either experimental data or numerical simulations, or it can be extracted from other NL models such as an artificial neural network. To maintain model accuracy over the product life, the second contribution of this research will develop a passive online model adaptation technology. The next contribution will be the development of an analytical design technology for health detection. Specifically, the adapted model coefficients will be analyzed to extract product health. These IS technologies will be validated on a fully electronic drive-by-wire engine. The engine is a Ford V-8 fuel injected engine fitted with an electronic throttle for fueling control and torque control management. The goal here is to create a fail-safe drive-by-wire engine that can withstand sensor failures that would otherwise lead to engine power burst.