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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Learning in the Model Space for Cognitive Fault Diagnosis
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Learning in the Model Space for Cognitive Fault Diagnosis

机译：在模型空间中进行认知故障诊断的学习

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摘要

The emergence of large sensor networks has facilitated the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or unformulated. In this paper, we develop an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a sliding window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using a one-class learning algorithm. The framework enables us to construct a fault library when unknown faults occur, which can be regarded as cognitive fault isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network confirm the effectiveness of the proposed framework.
机译：大型传感器网络的出现促进了大量实时数据的收集，以监视和控制复杂的工程系统。但是，在许多情况下，收集的数据可能不完整或不一致，而底层环境则可能随时间变化或不规则。在本文中，我们开发了一种创新的认知故障诊断框架来应对上述挑战。该框架在模型空间而不是信号空间中研究故障诊断。在模型空间中的学习是通过使用一系列带有滑动窗口的信号段拟合一系列模型来实现的。通过研究拟合模型空间中的学习技术，可以使用一类学习算法将故障模型与健康模型区分开。该框架使我们能够在发生未知故障时构建故障库，这可以视为认知故障隔离。本文还从理论上探讨了如何测量模型空间中两个模型之间的成对距离，并将模型距离纳入模型空间中的学习算法。巴塞罗那供水网络的三个基准应用程序和一个模拟模型的结果证实了该框架的有效性。

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