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Comparison of Different Classification Algorithms for Fault Detection and Fault Isolation in Complex Systems

机译：复杂系统故障检测的不同分类算法的比较

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Due to the lack of sufficient results seen in literature, feature extraction and classification methods of hydraulic systems appears to be somewhat challenging. This paper compares the performance of three classifiers (namely linear support vector machine (SVM), distance-weighted k-nearest neighbor (WKNN), and decision tree (DT) using data from optimized and non-optimized sensor set solutions. The algorithms are trained with known data and then tested with unknown data for different scenarios characterizing faults with different degrees of severity. This investigation is based solely on a data-driven approach and relies on data sets that are taken from experiments on the fuel system. The system that is used throughout this study is a typical fuel delivery system consisting of standard components such as a filter, pump, valve, nozzle, pipes, and two tanks. Running representative tests on a fuel system are problematic because of the time, cost, and reproduction constraints involved in capturing any significant degradation. Simulating significant degradation requires running over a considerable period; this cannot be reproduced quickly and is costly.
机译：由于文献中看到的缺乏足够的结果，液压系统的特征提取和分类方法似乎有所具有挑战性。本文比较了三分类器的性能（即线性支持向量机（SVM），距离加权k最近邻（WKNN）和决策树（DT）的性能，使用来自优化和非优化的传感器集解决方案的数据。算法是具有已知数据的培训，然后用未知数据进行测试，以进行不同的情况，表征具有不同程度的严重程度的故障。该研究仅基于数据驱动方法并依赖于从燃料系统的实验中取出的数据集。系统在本研究中使用是一种典型的燃料输送系统，包括标准组件，如过滤器，泵，阀门，喷嘴，管道和两个罐。由于时间，成本和繁殖，燃料系统上的代表性测试是有问题的捕获任何显着的降级所涉及的约束。模拟显着的降级需要在相当时的时间内运行;这不能快速复制并且是cos 。。

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