Fault Diagnosis With Process Uncertainty

本课题研究机械系统的故障诊断问题。意外的机械故障可能导致停产、昂贵的维修工作和安全问题。这两种传统的维护方法要么让机器运转到出现故障，要么按预定的时间间隔安排维修。另一种方法是监控运行中的机器状况，并仅在必要时进行维修。研究的重点是这种故障诊断方法，并试图处理故障特征的可变性，这是现有诊断系统的主要困难来源。在该方法中，多值影响矩阵(MVIM)在线监测诊断信号，并在检测到异常时进行标记。该方法提供了一种适合于在线应用的诊断推理方法，并建立了选择和优化诊断传感器的框架。两个诊断问题，即机械加工中的刀具破损和直升机动力总成的故障诊断，将被用来检验MVIM方法。

This project addresses the topic of fault diagnosis of mechanical systems. Unexpected machinery breakdown can lead to lost production, expensive repair work, and safety problems. The two traditional methods of maintainance involve either letting machines run to breakdown or scheduling repairs at predetermined intervals. An alternative is to monitor machine condition in process, and implement repairs only when necessary. The research focuses on this fault diagnosis approach and attempts to cope with fault signature variability, the main source of difficulty for existing diagnostic systems. In the proposed method, multi valued influence matrix (MVIM), diagnostic signals are monitored on line, and flagged upon the detection of an abnormality. The method offers a methodical approach to diagnostic reasoning which is suited to on line application and also establishes a framework to select and optimize diagnostic sensors. Two diagnostic problems, tool breakage in machining and fault diagnosis of helicopter power trains will be used to test the MVIM method.