自编码
计算机科学
故障检测与隔离
人工智能
恒虚警率
断层(地质)
潜变量
采样(信号处理)
警报
机器学习
数据挖掘
生产(经济)
基线(sea)
深度学习
模式识别(心理学)
工程类
地震学
执行机构
地质学
海洋学
滤波器(信号处理)
航空航天工程
经济
计算机视觉
宏观经济学
作者
Ahmed Maged,Chun Fai Lui,Salah Haridy,Min Xie
标识
DOI:10.1080/00207543.2023.2175591
摘要
In modern large-scale industrial processes, data are often high dimensional time-dependent due to the frequent sampling, dynamic nature and large number of variables. Appropriate monitoring of such processes allows for efficient decision-making that can improve the baseline of manufacturing companies either through decreasing production costs or enhancing production efficiency. Various latent variable-based control charts have been proposed for addressing high dimensional data; however, many of these methods assume that the data are independent and normally distributed. The violation of these assumptions results in an increased false alarm rate, in addition to the deterioration in the performance of such methods. In this study, we propose a Variational Autoencoder-Long Short Term Memory (VAE-LSTM) deep learning based T2 chart that integrates the unique features of both VAE and LSTM for intelligent fault detection of time-dependent high dimensional processes. The effectiveness and applicability of the proposed model are demonstrated through extensive simulations, an open-source online dataset, and a real case study.
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