In actual industrial scenarios, different operating modes and workloads can lead to multiple modes of working conditions, resulting in significantly diverse feature spaces. However, the heterogeneity and complexity among these modes pose a challenge to traditional data processing methods. Therefore, this paper proposes the cross-modality manifold adaptive Network (CMAN) to facilitate cross-modal information transmission for addressing multi-modal prediction issues. Specifically, CMAN divides the prediction process into two steps. Firstly, the manifold discriminative autoencoder (MDAE) is proposed to extract both local and global manifold geometric structures. The loss function of the designed MDAE in mode recognition is formulated to minimize the ratio between within-modal and between-modal features. In this way, the autoencoder not only learns data representations but also learns to differentiate between data from different classes. This lays the foundation for determining fusion strategies between modes in subsequent steps. Secondly, in the process of multimode prediction, to assist the model in learning and understanding the mutual influences and dependencies between different modes, CMAN shares features between modes through cross connections. It can adaptively preserve task specificity while also utilizing between-task correlations. The effectiveness of the proposed method is validated in the Tennessee Eastman (TE) case and an actual power plant case. Note to Practitioners—The use of soft sensors to monitor key variables of multimode processes is essential for optimizing and controlling chemical processes. However, it is difficult for conventional methods to accurately and comprehensively utilize within- and between-modal information of multimode processes to build robust and powerful soft sensors. In addition, it is difficult to obtain mode-indicating variables in real-world processes. To address these issues, CMAN is proposed in this paper. Firstly, the historical data of each mode in a multimode industrial process are collected, and the CMAN utilizes the manifold discrimination idea to build a mode recognition model. Then, when modeling the specific modes, CMAN utilizes cross-connections to migrate knowledge between modes, which not only considers the information of the modes themselves, but also makes the features between modes cross-transferred. The gating mechanism enables adaptive optimal combination between various types of features. Finally, two sets of cases show that the proposed method has excellent prediction performance.

Effective dimensionality reduction (DR) and classification in fault diagnosis remain a significant challenge, primarily due to the increasing scale of industrial processes and the non-linear and high-dimensional features of process data. To address this challenge, we present local tangent space alignment (LTSA) integrated with a multi-distance adaptive order morphological filter (MARMF) fault diagnosis method (LTSA-MARMF). In LTSA-MARMF, LTSA that preserves the local manifold structure using tangent space is first utilized for DR to provide the required feature space data for ARMF. Next, the cosine distance and dynamic time warping distance are introduced into the distance error of ARMF, considering the spatial similarity and dynamic features to improve classification accuracy. Finally, the distance-matching result of the pattern is applied to determine the type of fault. Through simulations, it is evident that LTSA-MARMF can achieve more satisfactory fault diagnosis accuracy than other related methods on the Tennessee-Eastman process (TEP) and the actual Grid-connected PV System (GPVS).Note to Practitioners—This paper is inspired by the difficult-to-handle high-dimensional and non-linear features of process data but is also applicable to high-dimensional and non-linear data from other industrial processes. The DR and classification are important aspects of fault diagnosis. In this paper, a novel pattern-matching method is utilized for fault diagnosis, which uses LTSA and the modified multi-distance ARMF for DR and classification, respectively. In terms of mathematics, the distance error of ARMF is analyzed. The combination of multi-distance is used to enhance the accuracy of fault diagnosis. The preliminary experiments show that LTSA-MARMF is feasible, but has not been tested in the plant. We will consider testing LTSA-MARMF in an actual plant in future research.

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.

It has become a big challenge and a hot topic of research to capture the most relevant features from high-dimensional process data for enhancing fault diagnosis. To effectively extract discriminative features from high-dimensional data, a novel dimensionality reduction (DR) approach named neighborhood and locality projections with the farthest and nearest distance (FNDNLP) is first proposed for industrial process fault feature acquisition and diagnosis. By constructing intraclass weights and interclass weights, FNDNLP takes both the intraclass distance and the interclass distance into consideration in its objective function, improving the diagnostic ability of extracted features through maximizing the interclass distance, and minimizing the intraclass distance. In addition, bootstrap-based FNDNLP (BFNDNLP) is further proposed to handle the matrix decomposition problem in FNDNLP. To find the proper order through DR, the Akaike information criterion is adopted. Finally, the Naïve Bayes based classifier is utilized to achieve acceptable fault diagnosis. The simulation results from two complex industrial cases indicate that the proposed methodology can achieve higher diagnosis accuracy than other related methods. What is more, the DR features are further analyzed to show the effectiveness and benefits of the proposed BFNDNLP extraction approach.

Data-driven fault diagnosis has attracted attention with the recent trend of obtaining representative features from high-dimensional, strongly coupled, and nonlinear process data. This article presents a novel dimensionality reduction (DR) algorithm named double preserving integrated with neighborhood locality projections (DPNLP) for fault diagnosis. To further solve the singular matrix problem in DPNLP, the regularization-based DPNLP (RDPNLP) that introduces the regularization into DPNLP is finally presented. In RDPNLP, first, the double preserving weight that can both preserve neighborhood similarity and preserve local linear reconstruction is utilized to make the neighbors in the same class close to each other and the neighbors from different classes far apart. Additionally, regularization is applied to solve the singular matrix problem enhancing the ability of DR. Akaike information criterion is utilized to determine the order of DR when using RDPNLP. Through simulations on two compound multifault cases, it can demonstrate that the presented RDPNLP could achieve higher performance in fault diagnosis than other related methods.

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.

With the coming of the big data era, the data collected in the process industry shows features of high volume, high-dimensional and non-linear. Meanwhile, these process data present imbalanced feature, leading to a lack of fault information. These problems mentioned above have brought difficulties to fault diagnosis. To solve the above difficulties, a new synthetic minority over-sampling technique (SMOTE) considering the correlation of sample integrated with locality sensitive discriminant analysis (LSDA) and LightGBM fault diagnosis methodology (CSMOTE-LSDA-LightGBM) is proposed in this article. In our proposed methodology, firstly, the SMOTE fully considering correlation (CSMOTE) which uses both Euclidean and Mahalanobis distance to calculate the nearest neighbor relationship is used to resample the imbalanced samples and expand the number of small classification fault samples; secondly, the LSDA is used to dimensionality reduction (DR) to extract the fault-related critical features; finally, the LightGBM classifier is used for fault classification. The Tennessee Eastman (TE) process case is selected for simulation to verify the effectiveness of the proposed CSMOTE-LSDA-LightGBM in fault diagnosis. The simulation results of TE process case show that the proposed method has improved the accuracy of fault diagnosis compared with imbalanced data and traditional DR methods indicating the CSMOTE-LSDA-LightGBM methodology is applicable to fault diagnosis for imbalanced samples.