This brief proposes an independent component analysis-principal component analysis (ICA-PCA) integrating with relevance vector machine (RVM) for multivariate process monitoring. Given the fact that the distribution of industrial process variables is mostly non-Gaussian and PCA cannot well deal with the non-Gaussian part. A hybrid ICA-PCA method is proposed to simultaneously extract the non-Gaussian and Gaussian information of multivariate processes. ICA is first used to monitor the non-Gaussian part of the process and then the Gaussian part of the residual process can be extracted using PCA. After feature extraction, a Bayesian-based classifier named RVM is established to make fault detection for the sake of both preventing the chosen of threshold as in traditional method and compensating for the single statistic. The performance of the proposed approach is validated using the Tennessee Eastman process. Simulation results verified the effectiveness of the proposed method.

Variational auto-encoders (VAEs) have been widely used in soft sensing due to their ability to provide a probabilistic description of the hidden space. However, VAEs are static models that do not consider process dynamics, which can limit the ability of VAEs to accurately model complex industrial processes. To tackle this problem, this paper proposes a model called adaptive multi-head self-attention based supervised VAE (AMSA-SVAE). In AMSA-SVAE, an adaptive multi-head self-attention mechanism (AMSA) is proposed based on the multi-head self-attention mechanism (MSA). AMSA can dynamically extract different attention information depending on specific tasks. By adjusting the attention weights based on the input sequence, AMSA allows for more accurate and efficient modeling of complex industrial processes. Then, AMSA is used as the encoder and decoder of SVAE for soft sensing. Furthermore, with the data generation capabilities of VAE, an adaptive multi-head self-attention based VAE (AMSA-VAE) framework is proposed to address the issue of missing data. The AMSA-VAE is used to dynamically fill in missing data, thereby extending the capabilities of AMSA-SVAE. Finally, the performance of AMSA-SVAE is verified by a set of real industrial data, and the ability of AMSA-VAE framework is demonstrated by simulating different degrees of data missing rates. By combining the dynamic modeling capabilities of AMSA-SVAE with the data generation capabilities of AMSA-VAE, the proposed approach provides a robust solution to the challenges of incomplete data in soft sensing. Note to Practitioners — Soft sensors are widely used to measure key parameters in industrial processes, but missing values in the data are common due to sensor failures or transmission signal interference. This poses a significant challenge for traditional soft sensors, which require complete data to accurately model. Meanwhile, the dynamic nature of industrial process data further complicates the modeling process. To solve these challenges, this paper proposes an AMSA-SVAE model for soft sensing and an AMSA-VAE framework for filling in the missing values in the data, thereby extending the capabilities of AMSA-SVAE to handle missing data. When facing a dataset with missing values, AMSA-VAE framework is first used to fill in the missing values before the filled complete data is fed into AMSA-SVAE for modeling. Finally, the proposed approaches are evaluated through two sets of experiments using a real industrial dataset, showing the excellent performance of AMSA-SVAE and AMSA-VAE framework in modeling dynamic industrial process data and addressing the missing data problem.

Data-driven fault diagnosis techniques have been widely used in industrial processes. However, facing a large amount of high-dimensional, nonlinear, and strongly coupled process data, traditional data-driven methods achieve low diagnostic accuracy due to ignoring the structural features inside data. To overcome this problem, this article proposes an improved locality preserving projections based on the heat-kernel and cosine weight matrix named Heat-Kernel and Cosine Weights Locality Preserving Projections (HC-LPP). In HC-LPP, a novel weight matrix construction strategy is employed, where a heat-kernel function is combined with a cosine function to optimize the weight matrix between data in terms of shortening distance and angle correlation, respectively. With the new weight matrix, the proposed HC-LPP considers both the distance and the correlation of samples (the shorter the distance is, the closer the neighbors are; the smaller the angle is, the more similar the neighbors are). The dimensionality reduction process of HC-LPP can well maintain the spatial geometric structure of data. Finally, the proposed HC-LPP integrated with the AdaBoost. M2 classifier is applied to the Tennessee Eastman process and the PROcess NeTwork Optimization process for fault diagnosis performance verification. Simulation results show, the proposed HC-LPP achieves better performance in diagnostic accuracy compared with other related methods.

Machine learning techniques have been successfully utilized as effective soft sensing for industrial processes. Unfortunately, as modern industrial processes become increasingly complex in the terms of scale and integration, the collected process data become more complicated in terms of dimensionality. Thus, it becomes a great challenge to establish an accurate soft sensor model for dynamic processes. In this article, a novel stacked input-enhanced supervised autoencoder (SISAE) integrated with gated recurrent unit (GRU) is proposed. In SISAE-GRU, the presented SISAE model is used to extract high-level and useful features from collected high-dimensionality process data. Different from the standard stacked autoencoder (SAE) model, the original input variables are embedded into each autoencoder (AE) unit of the presented SISAE model during the pretraining process by supervised learning, enhancing the feature extraction performance of SAE. Considering the dynamic nature between extracted features and output variables, GRU is utilized to learn the dynamic relationships and a dynamic model of soft sensing is finally constructed using a fully connected (FC) layer. To illustrate the effectiveness and superiority of the proposed SISAE-GRU model, two industrial datasets from the debutanizer column and the purified terephthalic acid (PTA) process are utilized. The experimental results show that the proposed SISAE-GRU can reduce the root-mean-squared error (RMSE) by 53.6% and 46.1% on average compared with some other state-of-the-art methods for the debutanizer column and PTA, respectively.