上下文规范化在飞机燃气涡轮发动机诊断中的应用
Contextual Normalization Applied to Aircraft Gas Turbine Engine Diagnosis
摘要：
航空燃气涡轮发动机故障诊断是一个复杂的问题。它涉及多项任务，包括快速准确地解释发动机传感器数据中的模式。我们已经研究了上下文规范化的软件工具的开发，以帮助发动机维修技术人员与传感器数据的解释。上下文规范化是机器学习的一种新策略。它处理由于上下文因素而导致的数据变化，而不是引擎的健康状况。它通过以上下文敏感的方式规范化数据来实现这一点。这种学习策略是在测试单元中使用飞机燃气涡轮发动机的242个观察结果开发和测试的，其中每个观察结果由在12秒间隔内收集的大约12，000个数字组成。有八类观察：七类故意植入的故障和一个健康的类。我们比较了两种实现学习策略的方法：线性回归和基于实例的学习。我们有三个主要结果。(1)对于给定的问题，基于实例的学习比线性回归更有效。(2)对于这个问题，上下文规范化比其他常见的规范化形式效果更好。(3)这里描述的算法可以作为一个有用的软件工具的基础，用于帮助技术人员解释传感器数据。
英文摘要：
Diagnosing faults in aircraft gas turbine engines is a complex problem. It involves several tasks, including rapid and accurate interpretation of patterns in engine sensor data. We have investigated contextual normalization for the development of a software tool to help engine repair technicians with interpretation of sensor data. Contextual normalization is a new strategy for employing machine learning. It handles variation in data that is due to contextual factors, rather than the health of the engine. It does this by normalizing the data in a context-sensitive manner. This learning strategy was developed and tested using 242 observations of an aircraft gas turbine engine in a test cell, where each observation consists of roughly 12,000 numbers, gathered over a 12 second interval. There were eight classes of observations: seven deliberately implanted classes of faults and a healthy class. We compared two approaches to implementing our learning strategy: linear regression and instance-based learning. We have three main results. (1) For the given problem, instance-based learning works better than linear regression. (2) For this problem, contextual normalization works better than other common forms of normalization. (3) The algorithms described here can be the basis for a useful software tool for assisting technicians with the interpretation of sensor data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
