Title: A scalable algorithm for identifying multiple sensor faults using disentangled RNNs Show Chinese title

Title: 一种利用解缠RNN识别多个传感器故障的可扩展算法

Abstract: 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. Show Chinese abstract

Abstract: 检测和识别传感器故障对于现代系统的高效、安全、合规和可持续运行至关重要。这些系统的日益复杂性为传感器故障检测与隔离（SFD-SFI）任务带来了新的挑战。在现代复杂的传感器系统中，任何SFD-SFI方法的关键推动因素之一就是所谓的分析冗余，即构建传感器观测值的分析模型（无论是从基本原理推导而来还是以数据驱动的方式从历史数据中识别得出）。简而言之，SFD的目标是通过比较传感器测量值与模型预测值来生成并监控残差，从而识别出有故障的传感器（即故障传感器会产生与模型表示的预期行为不一致的大残差）。本文介绍了一种解耦的循环神经网络（RNN），旨在应对\textit{抹去}效应，即传感器故障传播到非故障传感器导致产生误导性的大残差的问题。此外，引入残差生成的概率模型使我们能够开发一种用于识别故障传感器的新程序。所提出的算法计算复杂度相对于传感器数量呈线性增长，而传统SFI问题具有组合性质。最后，我们在石化厂收集的真实数据集上实证验证了所提出的SFD-SFI架构的性能。