Fault detection for distributed parameter systems (DPSs) reported so far is model based in general, and the performance heavily relies on the prior known model information. This restricts the usability of these methods in industrial applications. In this article, we make the first attempt to establish a brand-new framework that contains both online systems modeling and the fault detection of unknown high-dimensional DPSs. These two parts interact with each other in the sense that the systems modeling error is transformed into the residual signal for fault detection while the online modeling switches to offline mode depending on the fault-detection results. The high-dimensional DPSs are first decomposed into spatial features and temporal sequences. Then a receding-horizon scheme is applied for the temporal dynamics learning and the residual signal is converted by the temporal validation error. Experiments on sensor faults diagnosis for the thermal process of a 2-D battery cell are provided for method validation.

中文翻译：

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一类未知高维分布参数系统从在线系统建模到故障检测


迄今为止报道的分布式参数系统（DPS）的故障检测一般是基于模型的，并且性能很大程度上依赖于先验已知的模型信息。这限制了这些方法在工业应用中的可用性。在本文中，我们首次尝试建立一个全新的框架，其中包含在线系统建模和未知高维 DPS 的故障检测。这两部分相互作用，系统建模误差被转换为用于故障检测的残差信号，而在线建模根据故障检测结果切换到离线模式。高维 DPS 首先被分解为空间特征和时间序列。然后，将后退视野方案应用于时间动态学习，并通过时间验证误差来转换残差信号。提供了二维电池热过程传感器故障诊断实验以验证方法。