一种基于解纠缠RNN的多传感器故障可扩展识别算法
A scalable algorithm for identifying multiple sensor faults using disentangled RNNs
摘要：
检测和识别传感器故障的问题对于现代系统的高效、安全、合规和可持续运行至关重要。其日益增加的复杂性为传感器故障检测和隔离（SFD-SFI）任务带来了新的挑战。现代复杂传感器系统中采用的任何SFD-SFI方法的关键使能因素之一是所谓的分析冗余，这只不过是建立传感器观测的分析模型（从第一原理导出或以数据驱动的方式从历史数据中识别）。简而言之，SFD相当于通过将传感器测量值与模型预测值进行比较来生成和监控残差，并认为故障传感器将导致较大的残差（即，有缺陷的传感器生成的测量值与模型表示的预期行为不一致）。在本文中，我们介绍了一个解纠缠的递归神经网络（RNN）的目标，以应付的\textit{smearing-out}效果，即传播的传感器故障的非故障传感器，导致大误导残差。此外，引入的概率模型的残留生成允许我们开发一种新的程序，用于识别故障传感器。所提出的算法的计算复杂度是线性的传感器的数量，而不是组合性质的SFI问题。最后，我们经验验证了所提出的SFD-SFI架构的性能，使用一个真实的数据集收集在石化厂。
英文摘要：
The problem of detecting and identifying sensor faults is critical for efficient, safe, regulatory-compliant and sustainable operations of modern systems. Their increasing complexity brings new challenges for the Sensor Fault Detection and Isolation (SFD-SFI) tasks. One of the key enablers for any SFD-SFI methods employed in modern complex sensor systems, is the so-called analytical redundancy, which is nothing but building an analytical model of the sensors observations (either derived from first principles or identified from historical data in a data-driven fashion). In a nutshell, SFD amounts to generate and to monitor residuals by comparing the sensor measurements with the model predictions with the idea that the faulty sensors will result in large residuals (i.e. the defective sensors generate measurement that are inconsistent with their expected behavior represented by the model). In this paper we introduce a disentangled Recurrent Neural Network (RNN) with the objective to cope with the \textit{smearing-out} effect, i.e. the propagation of a sensor fault to the non-faulty sensors resulting in large misleading residuals. Moreover, the introduction of a probabilistic model for the residual generation allows us to develop a novel procedure for the identification of the faulty sensors. The computational complexity of the proposed algorithm is linear in the number of sensors as opposed to the combinatorial nature of the SFI problem. Finally, we empirically verify the performances of the proposed SFD-SFI architecture using a real data set collected at a petrochemical plant.
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