In various industrial processes, soft sensors have become important tools for predicting key quality variables. However, traditional soft sensor models only use data from the same sampling moments as the key quality variables, thus wasting information from other sampling moments. In light of this, a novel soft sensor model named triple-gated bidirectional variational pyramid network (G3-BiVPN) is proposed. Within G3-BiVPN, the multirate dataset is first segmented into multiple datasets each with a single sampling rate. For each dataset, a corresponding bidirectional variational autoencoder (BiVAE) is utilized for feature extraction. BiVAEs are used as backbone models to form a bidirectional pyramid structure. A triple gating mechanism consisting of attention gate (AG), temporal gate (TG), and spatial gate (SG) is integrated into BiVAEs to regulate information flow. Information can be flowed bidirectionally through different levels, with each level establishing a regression relationship with the key quality variables and selecting the optimal level as the final output. The core advantage of G3-BiVPN lies in its utilization of the multirate nature of the data. Finally, the efficiency of G3-BiVPN has been validated through two sets of real-world industrial process data with multiple sampling rates.Note to Practitioners—Due to sensor specification differences or practical needs, industrial process variables often exhibit a multiplicity in sampling rates. The core concept of G3-BiVPN is the bidirectional transmission and fusion of features at different rates. Initially, multirate features are extracted using BiVAEs and filtered through AGs to retain the relevant feature information for the output. Subsequently, these filtered features are stacked, with higher sampling rate features forming the bottom layers and lower sampling rate features occupying the top layers. In this architecture, feature information from different sampling rates can freely flow bidirectionally between the layers. During the downward transmission of information, upsampling is performed. To mitigate issues of information redundancy, further filtering is applied through TG after the upsampling. And, during the upward transmission, downsampling is employed. To prevent loss of detailed information during downsampling, SG is utilized before downsampling. This bidirectional propagation enhances the effective fusion of information across different layers, ensuring comprehensive understanding of the data at each level. Through this intricate feature interaction, G3-BiVPN can adapt to the complexity of multirate data, effectively utilizing available information from different sampling rates to accurately predict key quality variables.

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.

Soft sensing using the neural network technique has been increasingly applied to industrial processes. Recently, the security and robustness of neural network-based soft sensors have become primary concerns. In addition, current studies indicated that neural networks are vulnerable to adversarial attacks. In other words, small perturbations imposed on the input can lead to significant deviations in the output. If a soft sensor for key process variables is attacked, considerable damage may be brought to industrial processes. This article focuses on the attack methods for neural network-based industrial soft sensors. Considering the characteristics of industrial soft sensors, this article proposes two new adversarial attack methods. The first method, called the mirror output attack (MOA), is a subtle attack method that flips the output curve to change the direction of outputs. The second method, called the translation MOA (TMOA), is easy to make operators misoperate. TMOA translates the output curve while flipping the output curve to achieve the purpose of changing the output conditions. The effectiveness of MOA and TMOA is demonstrated in an industrial case study of the sulfur recovery unit process. Simulation results show that the neural network-based industrial soft sensors can be attacked by both the proposed adversarial attack methods. The study of adversarial attack methods can provide a basis for defending against attacks, thereby enhancing the security and robustness of soft sensors.

Traditional soft sensors typically rely only on labeled data to predict key variables, despite the significant amount of unlabeled data that could provide valuable information. To solve this problem, a quality regularization-based semisupervised adversarial transfer model (QR-SATM) is proposed. The idea of transfer learning is used in QR-SATM. QR-SATM comprises a pretraining model and a regression model. The pretraining model is an unsupervised model. And the regression model is a supervised model with a similar structure to the pretraining model, allowing for easy transfer between the two models. First, the pretraining model is trained with unlabeled data to extract features. Then, the trained parameters of pretraining model are transferred to the regression model, and the regression model is fine-tuned with labeled data. During fine-tuning the regression model, an improved quality regularization is introduced in order to select useful features and prevent overfitting. QR-SATM is validated by a real industrial dataset of purified terephthalic acid. The experimental results show the effectiveness of the proposed QR-SATM in accurately predicting key variables.

As the production demand and external environment change, the same production process may have multiple stable working conditions, i.e., multimode process. The traditional process monitoring methods cannot be directly applied to industrial data with multipeak distribution. In order to address the multimode process monitoring problem, a cross-modality shared-specific network (CMSS-Net) is proposed in this article. First, to address the problem of unavailability of mode indicator variable, CMSS-Net adds a loss term based on discriminative idea to the loss function, which improves the mode recognition ability by maximizing the interclass distance and minimizing the intraclass distance. The multimode process is then monitored. Since different modes originate from the same production process, there exists some common information among modes. CMSS-Net extracts the shared information by minimizing the difference in the distribution of features among modes. At the same time, the gating mechanism is used to fuse the shared information between modes and the unique features of the modes into a multivariate feature fusion. This improves the performance of the model due to the information being enriched across the modes, while increasing the transparency of the model's decision-making process. Finally, the proposed method is developed as soft sensors for Tennessee Eastman process and power plant gas turbine emission process. It is compared with some popular methods. The experimental results demonstrate the effectiveness and superiority of CMSS-Net when applied to the multimode process.