Robust Residual Generation for Model-Based Fault Diagnostic Systems

9523087 Danai这项研究项目致力于开发用于汽车发动机等机器的稳健故障诊断的创新方法。该方法包括反馈训练和模式分类。模式分类从残差变换和故障特征估计两个方面进行。故障特征通过多值影响矩阵(MVIM)方法进行估计，该方法还提供了故障特征的唯一性和重复性的度量。在残差变换中，对一批测量故障数据迭代训练时滞连接神经网络。该学习算法调整时滞网络的参数，使下一组变换后的残差在训练集范围内具有更好的检测和诊断性能。俄亥俄州立大学汽车研究中心对残留物生成方法进行了实验评估。这些实验包括传感器和执行器故障，并在广泛的发动机运行条件下进行。

9523087 Danai This research project deals with the development of innovative approaches for robust fault diagnosis in machines such as automotive engines. The approach involves feedback training and pattern classification. Pattern classification is adopted from both residual transformation and fault signature estimation. Fault signatures are estimated via the multi-valued influence matrix (MVIM) method which also provides measures of uniqueness and repeatability of fault signatures. In residual transformation, a time-delay connectionist net is trained iteratively on a batch of measurement - fault data. The learning algorithm adjusts the parameters of the time-delay net such that the next set of transformed residuals will have better detection and diagnostic performance within the range of training set. The residual generation method is experimentally evaluated at the center for Automotive Research of the Ohio State University. The experiments include sensor and actuator faults and are carried out over a broad range of engine operating conditions.***