Graph dynamic autoencoder for fault detection

doi:10.1016/j.ces.2022.117637

Return

Graph dynamic autoencoder for fault detection

2022-06-01

PRE

Lu Liu

Haitao Zhao *

Zhengwei Hu

DOI：10.1016/j.ces.2022.117637

Original

Original request for help

Save

Abstract

En 中文

Dynamic information is a non-negligible part of time-correlated process data, and its full utilization can improve the performance of fault detection. Traditional dynamic methods concatenate the current process data with a certain number of previous process data into an extended vector before performing feature extraction. However, this simple way of using dynamic information inevitably increases the input dimensionality and it is inappropriate to treat previous process data as equally important. To address these problems, this paper proposes a novel nonlinear dynamic method, called graph dynamic autoencoder (GDAE), for fault detection. GDAE utilizes a graph structure to model the dynamic information between different data points. GDAE firstly embeds the current data point and previous data points as the features of the central node and its neighbors, respectively, then convolves the feature of the central node with the features of its neighbors to derive the updated feature for the central node, and finally, an encoder-decoder structure is adopted to extract the key low-dimensional feature. Due to the utilization of the graph structure, the extended high-dimensional vectors utilized by traditional dynamic fault detection methods are avoided in GDAE. Furthermore, with the dynamically constructed graph, GDAE is able to adaptively assign different weights to its neighbors by updating the adjacency matrix of the graph. Experimental results obtained from a numerical simulation and the Tennessee Eastman process illustrate the superiority of GDAE in terms of missed detection rate (MDR) and false alarm rate (FAR). The source code of GDAE can be found in https://github.com/luliu-fighting/Graph-Dynamic-Autoencoder. (c) 2022 Elsevier Ltd. All rights reserved.

Keywords:

Process monitoring

Dynamic fault detection

Graph convolution

Autoencoder