论文标题
数据驱动的故障诊断分析和时间序列数据的开放式分类
Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data
论文作者
论文摘要
动态系统的故障诊断是通过检测时间序列数据的变化（例如由系统降解和故障组件引起的残差）来完成的。通用多级分类方法用于故障诊断的使用使训练数据不平衡和未知的故障类别变得复杂。另一个复杂的因素是，不同的故障类别可能导致相似的残留输出，尤其是对于小故障，这会导致分类歧义。在这项工作中，开发了用于使用Kullback-Leibler Divergence进行故障诊断应用的数据驱动分析框架。提出了一个数据驱动的故障分类算法，该算法可以处理不平衡的数据集，类重叠和未知故障。此外，提出了一种算法来估计训练数据包含来自已知故障实现的信息时的故障大小。提出的框架的一个优点是，它也可以用于定量分析故障诊断性能，例如，分析对不同幅度的故障进行分类的容易性。为了评估所提出方法的实用性，已经从内部燃烧发动机测试工作台收集了来自不同故障场景的多个数据集，以说明数据驱动的诊断系统的设计过程，包括定量错误诊断分析和开发的开放设置故障分类算法的评估。
Fault diagnosis of dynamic systems is done by detecting changes in time-series data, for example residuals, caused by system degradation and faulty components. The use of general-purpose multi-class classification methods for fault diagnosis is complicated by imbalanced training data and unknown fault classes. Another complicating factor is that different fault classes can result in similar residual outputs, especially for small faults, which causes classification ambiguities. In this work, a framework for data-driven analysis and open-set classification is developed for fault diagnosis applications using the Kullback-Leibler divergence. A data-driven fault classification algorithm is proposed which can handle imbalanced datasets, class overlapping, and unknown faults. In addition, an algorithm is proposed to estimate the size of the fault when training data contains information from known fault realizations. An advantage of the proposed framework is that it can also be used for quantitative analysis of fault diagnosis performance, for example, to analyze how easy it is to classify faults of different magnitudes. To evaluate the usefulness of the proposed methods, multiple datasets from different fault scenarios have been collected from an internal combustion engine test bench to illustrate the design process of a data-driven diagnosis system, including quantitative fault diagnosis analysis and evaluation of the developed open set fault classification algorithm.
