VAE-Based Interpretable Latent Variable Model for Process Monitoring.

Latent variable-based process monitoring (PM) models have been generously developed by shallow learning approaches, such as multivariate statistical analysis and kernel techniques. Owing to their explicit projection objectives, the extracted latent variables are usually meaningful and easily interpretable in mathematical terms. Recently, deep learning (DL) has been introduced to PM and has exhibited excellent performance because of its powerful presentation capability. However, its complex nonlinearity prevents it from being interpreted as human-friendly. It is a mystery how to design a proper network structure to achieve satisfactory PM performance for DL-based latent variable models (LVMs). In this article, a variational autoencoder-based interpretable LVM (VAE-ILVM) is developed for PM. Based on Taylor expansions, two propositions are proposed to guide the design of appropriate activation functions for VAE-ILVM, allowing nondisappearing fault impact terms contained in the generated monitoring metrics (MMs). During threshold learning, the sequence of counting that test statistics exceed the threshold is considered a martingale, a representative of weakly dependent stochastic processes. A de la Peña inequality is then adopted to learn a suitable threshold. Finally, two chemical examples verify the effectiveness of the proposed method. The use of de la Peña inequality significantly reduces the minimum required sample size for modeling.

Imputation of Missing Values in Time Series Using an Adaptive-Learned Median-Filled Deep Autoencoder.

Missing values are ubiquitous in industrial data sets because of multisampling rates, sensor faults, and transmission failures. The incomplete data obstruct the effective use of data and degrade the performance of data-driven models. Numerous imputation algorithms have been proposed to deal with missing values, primarily based on supervised learning, that is, imputing the missing values by constructing a prediction model with the remaining complete data. They have limited performance when the amount of incomplete data is overwhelming. Moreover, many methods have not considered the autocorrelation of time-series data. Thus, an adaptive-learned median-filled deep autoencoder (AM-DAE) is proposed in this study, aiming to impute missing values of industrial time-series data in an unsupervised manner. It continuously replaces the missing values by the median of the input data and its reconstruction, which allows the imputation information to be transmitted with the training process. In addition, an adaptive learning strategy is adopted to guide the AM-DAE paying more attention to the reconstruction learning of nonmissing values or missing values in different iteration periods. Finally, two industrial examples are used to verify the superior performance of the proposed method compared with other advanced techniques.

A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network.

Deep learning networks have been recently utilized for fault detection and diagnosis (FDD) due to its effectiveness in handling industrial process data, which are often with high nonlinearities and strong correlations. However, the valuable information in the raw data may be filtered with the layer-wise feature compression in traditional deep networks. This cannot benefit for the subsequent fine-tuning phase of fault classification. To alleviate this problem, an extended deep belief network (EDBN) is proposed to fully exploit useful information in the raw data, in which raw data is combined with the hidden features as inputs to each extended restricted Boltzmann machine (ERBM) during the pre-training phase. Then, a dynamic EDBN-based fault classifier is constructed to take the dynamic characteristics of process data into consideration. Finally, to test the performance of the proposed method, it is applied to the Tennessee Eastman (TE) process for fault classification. By comparing EDBN and DBN under different network structures, the results show that EDBN has better feature extraction and fault classification performance than traditional DBN.