Industry Internship: Fault Diagnosis of Helicopter Power Trains

该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。 这种方法是基于一种旨在科普 变化和重叠的故障签名，两个主要来源 现有诊断系统的困难。 这种方法 在其诊断模型中采用故障特征向量; 因此，它可以容忍个人之间的重叠， 签名，并且由于它在过程中调整其诊断模型， 它可以科普故障特征变化。 这种方法使用 多值影响矩阵（MVIM）作为其诊断模型， 表示故障的“平均”值的列 签名. MVIM方法可以评估 系统及其可用测量，选择最佳子集 测量值受可诊断性约束，执行 在线诊断推理，并更新诊断模型 （影响矩阵）响应于过程变化。 该研究将包括（1）详细审查目前的 故障特征识别的现有技术 动力传动系统，（2）对动力传动系统可用数据的研究 列车故障可能裁剪的MVIM方法， 可行性研究，（3）可行性论证的发展 计划支持和证明更广泛的验证计划， 以及（4）分析可行性测试的结果，以进一步 修改理论，并制定一个计划，以充分验证 方法论。

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.//