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首页> 外文会议>International Conference on Control, Automation, Robotics Vision >Sparse Causal Residual Neural Network for Linear and Nonlinear Concurrent Causal Inference and Root Cause Diagnosis
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Sparse Causal Residual Neural Network for Linear and Nonlinear Concurrent Causal Inference and Root Cause Diagnosis

机译：稀疏因果剩余神经网络用于线性和非线性并发因果因因果推理和根本原因诊断

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摘要

Reliable and effective fault diagnosis methods are necessary for complex industrial processes that consists of various units. After a process fault is detected, it remains a challenging task to locate the root cause unit and determine the propagation path of the fault. In this paper, a novel method, termed Sparse Causal Residual Neural Network (SCRNN), is proposed and applied for modern industrial root cause diagnosis. The advantage of SCRNN lies in that it can not only recognize linear and nonlinear causal relationships in parallel, but also automatically determine the causality lags and deduce the time delay of causal transmission. Besides, due to the specially designed sparse constraint and optimization algorithm, the SCRNN model can realize the function of key dependent variable selection, avoiding the high computational complexity and complicated procedure brought by pairwise comparison. The feasibility of the proposed method is illustrated through the benchmark TE process.
机译：可靠且有效的故障诊断方法是由各种单位组成的复杂工业过程所必需的。在检测到流程故障后，定位根原因单元并确定故障的传播路径仍然是一个具有挑战性的任务。本文提出了一种新的方法，称为稀疏因果剩余神经网络（SCRNN），并应用现代工业根本原因诊断。 SCRNN的优点在于它不仅可以并行识别线性和非线性因果关系，而且还可以自动确定因果关系滞后并推断出因子传输的时间延迟。此外，由于专门设计的稀疏约束和优化算法，SCRNN模型可以实现关键相关变量选择的功能，避免了通过成对比较带来的高计算复杂性和复杂的过程。通过基准TE过程说明了所提出的方法的可行性。

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