Industry Internship: Fault Diagnosis of Helicopter Power Trains

本研究将开发并实验评估一种基于传感器的直升机传动系故障诊断方法。该方法是基于一种方法来处理故障特征的可变性和重叠，这是现有诊断系统的两个主要困难来源。该方法在诊断模型中采用故障特征向量；因此，它可以容忍单个签名之间的重叠，并且由于它在进程中调整其诊断模型，因此它可以应对故障签名的变化。该方法采用多值影响矩阵（MVIM）作为诊断模型，各列表示故障特征的“平均值”。MVIM方法可以利用可用的测量值评估系统的可诊断性，选择受可诊断性约束的测量值的最佳子集，执行在线诊断推理，并根据过程变化更新其诊断模型（影响矩阵）。该研究将包括：(1)详细回顾动力传动系统故障特征识别技术的当前状态，(2)研究动力传动系统故障的可用数据，以便为可行性研究量身定制MVIM方法，(3)制定可行性演示计划，以支持和证明更广泛的验证程序。(4)对可行性测试结果进行分析，进一步修正理论并制定方案，充分验证方法。//

The research will develop and experimentally evaluate a sensor-based fault diagnostic method for helicopter drive trains. This method is based on an approach designed to cope with the variability and overlap of fault signatures, two major sources of difficulty for the existing diagnostic systems. This approach employs vectors of fault signatures in its diagnostic model; therefore, it can tolerate the overlap between individual signatures and, since it adjusts its diagnostic model in-process, it can cope with fault signature variations. This approach uses a multi-valued influence matrix (MVIM) as its diagnostic model with the columns representing the "average" values of the fault signatures. The MVIM approach can assess the diagnosability of the system with it s available measurements, select an optimal subset of measurements subject to the diagnosability constraint, perform on-line diagnostic reasoning, and update it diagnostic model (influence matrix) in response to process variations. The research will include (1) a detailed review of the current state of the art techniques for fault signature identification of power train systems, (2) a study of the available data on power train failure for possible tailoring of the MVIM method for a feasibility study, (3) development of a feasibility demonstration plan to support and justify a more extensive validation program, and (4) analysis of the results of the feasibility test to further modify the theory and develop a plan to fully validate the methodology.//