State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China.
School of Food Science and Technology, Jiangnan University, Wuxi 214122, China.
Sensors (Basel). 2021 Dec 29;22(1):227. doi: 10.3390/s22010227.
This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been well investigated. In this paper, the process modeling and monitoring capabilities of several VAE variants are comprehensively studied. First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains. Afterwards, to conduct the fault diagnosis, we first define the deep contribution plot, and then a deep reconstruction-based contribution diagram is proposed for deep domains under the fault propagation mechanism. In a case study, the performance of the process monitoring capability of four deep VAE models, namely, the static VAE model, the dynamic VAE model, and the recurrent VAE models (LSTM-VAE and GRU-VAE), has been comparatively evaluated on the industrial benchmark Tennessee Eastman process. Results show that recurrent VAEs with a deep reconstruction-based diagnosis mechanism are recommended for industrial process monitoring tasks.
这项工作考虑了使用变分自动编码器 (VAE) 进行工业过程监测。变分自动编码器作为一种强大的深度生成模型，其变体已成为过程监测的热门选择。然而，其监测能力，特别是故障诊断能力，尚未得到充分研究。在本文中，综合研究了几种 VAE 变体的过程建模和监测能力。首先，以三种不同的方式定义了故障检测方案，考虑了潜在域、残差域和组合域。之后，为了进行故障诊断，我们首先定义了深度贡献图，然后在故障传播机制下提出了一种基于深度重建的深度域的贡献图。在案例研究中，对四个深度 VAE 模型（静态 VAE 模型、动态 VAE 模型和递归 VAE 模型（LSTM-VAE 和 GRU-VAE））的过程监测能力进行了比较评估，在工业基准田纳西东曼过程上。结果表明，具有基于深度重建的诊断机制的递归 VAE 推荐用于工业过程监测任务。
