Reliable Post-Signal Fault Diagnosis for Correlated High-Dimensional Data Streams
Reliable Post-Signal Fault Diagnosis for Correlated High-Dimensional Data Streams
复制标题
相关高维数据流的可靠信号后故障诊断
DOI：
10.1080/00401706.2021.1979100
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发表时间：
--
期刊：
影响因子：
2.5
通讯作者：
Wendong Li
中科院分区：
文献类型：
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作者：
Dongdong Xiang;Peihua Qiu;Dezhi Wang;Wendong Li
摘要传感器技术的快速发展为高维数据流的采集提供了便利。除了实时检测潜在的失控（OC）模式外，HDS的信号后故障诊断在统计过程控制领域变得越来越重要，以隔离异常数据流。现有方法在这方面的主要局限性包括：（i）它们的性能变化很大，因此无法获得可靠的诊断结果;（ii）它们经常忽略不同流之间的信息相关性。阐述了利用大规模多重测试对关联HDS进行可靠故障诊断的问题。在隐马尔可夫模型依赖的框架下，提出了一种新的诊断方法，该方法可以将漏发现概率（MDX）控制在期望的水平。大量的数值研究沿着一些理论结果表明，所提出的程序可以控制MDX正确，导致诊断具有高可靠性和效率。此外，它们的诊断性能可以显着提高，通过利用不同的数据流之间的依赖性，这是特别有吸引力的，在实践中识别集群OC流。
Abstract Rapid advance of sensor technology is facilitating the collection of high-dimensional data streams (HDS). Apart from real-time detection of potential out-of-control (OC) patterns, post-signal fault diagnosis of HDS is becoming increasingly important in the filed of statistical process control to isolate abnormal data streams. The major limitations of the existing methods on that topic include (i) they cannot achieve reliable diagnostic results in the sense that their performance is highly variable, and (ii) the informative correlation among different streams is often neglected by them. This article elaborates the problem of reliable fault diagnosis for monitoring correlated HDS using the large-scale multiple testing. Under the framework of hidden Markov model dependence, new diagnostic procedures are proposed, which can control the missed discovery exceedance (MDX) at a desired level. Extensive numerical studies along with some theoretical results show that the proposed procedures can control MDX properly, leading to diagnostics with high reliability and efficiency. Also, their diagnostic performance can be improved significantly by exploiting the dependence among different data streams, which is especially appealing in practice for identifying clustered OC streams.