In contemporary industrial processes, factors such as raw material fluctuations and noise interference lead to significant changes in the statistical characteristics of data over time, resulting in nonstationary behavior. This nonstationary tends to obscure fault-related information in industrial systems, posing severe challenges for quality-related fault detection. This article proposes a method named reversible instance normalization temporal autoencoder (TAE) canonical correlation analysis (CCA) for quality related fault detection. First, this method quantifies the interdependencies among variables to select process variables that are highly correlated with quality indicators. Subsequently, a reversible normalization module dynamically adjusts normalization parameters to achieve local stationarization of process data. Furthermore, a TAE is utilized to extract temporal features from processed data. Then, a CCA model is established by integrating quality indicators, enabling efficient monitoring of quality indicator under nonstationary conditions. Finally, the proposed method was tested and validated in two real industrial cases.

Accurate sensor fault detection and diagnosis in industrial processes are essential for maintaining system reliability and operational safety. However, under closed-loop control conditions, the presence of inherent nonlinearities, strong multivariate coupling, and subtle fault signatures poses significant challenges to effective monitoring. To address these issues, this article proposes a novel self-supervised orthogonal decoupling autoencoder (SODAE). The core component of the model, the orthogonal decoupling autoencoder (ODAE), achieves robust feature extraction by decoupling interdependent sensor relationships through the optimization of weight matrices on the Stiefel manifold, supported by theoretical analysis. In parallel, a self-supervised learning mechanism is integrated via adversarial training to enhance sensitivity to minor sensor anomalies. The proposed SODAE is evaluated on a simulated benchmark with customized sensor fault scenarios, as well as on two complex real-world water treatment processes. Experimental results demonstrate that SODAE achieves superior performance in both sensor fault detection and diagnosis under challenging industrial conditions.

Due to the complex information transfer between devices, sensor signals exhibit complex interactions in large-scale industrial process, making fault diagnosis a challenging task. Despite the effectiveness of existing methods, models that lack the guidance of priori mechanisms are extremely data-dependent, and most methods ignore the fact that information interacts differently between strongly and weakly correlated variables. Therefore, a novel adaptive mechanism-data fusion graph convolutional network (AMDF-GCN) is proposed. The approach uses the mechanism graph structure to guide the model to aggregate strongly correlated variable features. However, due to the lack of complete mechanism knowledge of the process, a mutual information patching mechanisms graph strategy is designed to jointly construct the mechanism-data adjacency matrix. The local-global feature aggregation module is then designed to mine potential weak correlations between variables. Furthermore, to compensate for the coupling problem caused by deep mining, a node information preserving module is designed to maintain the original information of the variables. Finally, the fused features are used for category recognition. The superiority of the AMDF-GCN method is verified through a typical industrial case and an actual industrial case.

In recent years, data-driven soft sensor modeling methods have been widely used in industrial production, chemistry, and biochemical. In industrial processes, the sampling rates of quality variables are always lower than those of process variables. Meanwhile, the sampling rates among quality variables are also different. However, few multi-input multi-output (MIMO) sensors take this temporal factor into consideration. To solve this problem, a deep-learning (DL) model based on a multitemporal channels convolutional neural network (MC-CNN) is proposed. In the MC-CNN, the network consists of two parts: the shared network used to extract the temporal feature and the parallel prediction network used to predict each quality variable. The modified BP algorithm makes the blank values generated at unsampled moments not participate in the backpropagation (BP) process during training. By predicting multiple quality variables of two industrial cases, the effectiveness of the proposed method is verified.

To solve the problem of incipient fault detection, a fault targeted gated recurrent unit-canonical correlation analysis (CCA) method is proposed. First, this article proposed fault targeted gated recurrent unit (FTGRU) to establish a temporal feature extraction model. The features extracted by FTGRU are more sensitive to the incipient faults, thus increasing the accuracy of the fault detection model. Then, a fault detection model is established by CCA method. In addition, in order to ensure the universality of the detection model, a multilayer fault detection strategy is proposed. At the first layer, the basic CCA model is used. When no fault is detected at this layer, the second layer fault detection method is enabled. In the second layer, the proposed FTGRU-CCA method is used. Finally, the proposed method and detection strategy are validated by two different industrial cases.

Recently, domain adaptation (DA)-based fault diagnosis approaches have been actively studied in chemical processes to build a reliable fault diagnosis model for a new operating mode (i.e., target domain) by making use of labeled data from a historical mode (i.e., source domain). However, this raises privacy concerns, such as data leakage, since industrial data contains sensitive production information. Moreover, preprocessed source and target data used to train an effective target model will result in additional computational costs. Therefore, it is crucial to develop a novel privacy preserving DA-based fault diagnosis approach that can improve the diagnosis performance for a new mode and protect the privacy of a historical mode simultaneously. To this end, fault diagnosis is formulated as thesource-free DAproblem and proposes a temporal attention source-free adaptation (TASFA) algorithm, which only utilizes the pretrained source model and unlabeled target data to learn a diagnosis model. Specifically, for the time-series process, an attention mechanism is designed to capture and leverage the temporal correlations between source and target domains by extracting the most transferable information from the target time series. Empirical results on both the Tennessee Eastman process and the continuous stirred tank reactor demonstrate the effectiveness and efficiency of TASFA.

To monitor the plant-wide process finely, a novel distributed static magnitude-dynamic difference (DSM-DD) method is proposed in this article. First, given the high dimension of the collected data in the plant-wide process, the entire data space is divided into four orthogonal subspaces according to whether the data obey Gaussian distribution and whether it has serial correlation. Second, both the static magnitude and dynamic difference of the data in the four subspaces are used to build the monitoring model. In addition, not only the features within four subspaces are extracted but the correlation between different subspaces is also extracted to construct corresponding statistics. Third, all the statistics with physical significance are put together to form a statistic vector, and the local outlier factor method is used for constructing the synthetic index to determine whether the fault occurs. Finally, the superiority of the DSM-DD method is verified through a typical industrial case.

The dynamic time-varying characteristic has brought great challenges to the plant-wide process monitoring. In this article, a distributed adaptive principal component regression algorithm is proposed for the online indicator monitoring of large-scale dynamic process. First, the distributed data subblocks are constructed according to the process operation units. In each subblock, an adaptive resampling method based on the subblock data and plant-wide data is presented to construct the modeling sample sets, which can extract the process local and global information simultaneously. Afterwards, the indicator-related feature is extracted, and the Bayesian method is used to integrate the subblock monitoring results. Through the collaborative monitoring of the process local and global feature spaces, a refined monitoring decision can be obtained. Finally, a numerical example and Tennessee Eastman process are used to illustrate the effectiveness of the proposed method.

The incipient fault is difficult to detect because of its small amplitude and insignificant impact, however, ignoring such fault may cause irreversible damage to the system. In this article, a hierarchical latent variable extraction and multisegment probability density analysis method is proposed to detect the incipient fault. First, three data subspaces are constructed, which are named dominant, intermediate, and residual spaces, and key latent variables which contain more offline variance or online variation information will be retained. Afterward, the expanded data distribution interval and multiple data segmentsare constructed for the probability density estimation. Based on the improved symmetric divergence index, the distribution distance between the online data and offline modeling data can be evaluated, which has achieved 95.3% and 86.8% average detection rates for the faults in numerical case and Tennessee Eastman process. Finally, a real multiphase flow facility is used to demonstrate the effectiveness of the proposed method.

As industrial technology develops, industrial processes become increasingly large and complex, the traditional methods are difficult to extract features that can represent the condition of the whole process and the effect of fault on quality indicators. Therefore, a novel multiblock decouple convolutional neural network (multiblock DCN) algorithm is proposed. First, key process variables are selected, and process variables are grouped into multiple blocks for the following monitoring. Then, in each block, the proposed DCN constructs a regression model between key process variables and quality indicators, in which the regression model utilizes an improved convolutional neural network as a feature extractor and a decoupling layer as a feature regularizer. Afterward, the monitoring results of each block are integrated into a global monitoring index based on Bayesian theory. After fault detection, variable oblivion contribution plot is presented to locate faulty variables. Finally, two industrial cases are used to demonstrate the effectiveness of multiblock DCN.

Plant-wide processes often have the characteristics of large-scale and multiple operating units. Moreover, due to the closed-loop control, it is possible that the fault never affects product quality. In this article, a novel data-driven method called multisubspace orthogonal canonical correlation analysis (CCA) is proposed, which can not only tell whether the fault occurs but can also judge whether the fault affects the product quality in real time. First, to reduce process analysis complexity and to construct an accurate monitoring model, the original process variable space is divided into four subspaces. Second, the developed orthogonal CCA is conducted on process data and quality data for correlation feature extraction. Then, the quality-related and quality-unrelated features are obtained. Afterward, a total of six monitoring statistics are constructed and integrated to four statistics with physical interpretation via the Bayesian fusion strategy. Finally, the developed method is tested under an industrial case.

In this article, a novel multimode quality-related process monitoring method called multisubspace elastic network (MSEN) is proposed. To make mode partition more precisely, this article develops a novel clustering algorithm based on the neighborhood information and subtractive clustering algorithm. In each single mode, unlike conventional process monitoring models that only focus on whether the fault occurs, a novel elastic network based quality-related process monitoring model is established to judge whether the fault is quality related or not. In addition, to select the most suitable monitoring model for online data, the k-nearest neighbor rule and the voting strategy are applied. Once the fault is detected, the contribution plot method is used in both quality-related and quality-unrelated subspace for fault diagnosis. Finally, the proposed MSEN method is tested under the continuous stirred tank reactor to verify its superiority and advantage.

In this paper, we consider the privacy preserving problem in an agreement network under interception attacks. First, we introduce a consensus protocol with privacy preserving, where each node hides their initial states into a set of random sequences, and then injects the sequences into the process of consensus. Second, we assume that an attacker with limited power can intercept the data transmitted on the edges. Aiming at the case when the privacy preserving protocol fails, we propose an index to measure the degree of network privacy leakage. In the ring and small-world network, we find an optimal attacking strategy for the attacker to maximize the probability of the privacy leakage from the perspective of the attacker. Finally, we verify all the derived theoretical results by simulations.

This paper addresses the H∞filtering problem for networked control systems with quantization and multiple packet dropouts. The effects of measurement channel and control channel quantization as well as packet dropouts are considered simultaneously due to limited communication capacity and unreliable communication links. Stochastic variables satisfying the Bernoulli random binary distribution are utilized to model the multiple packet dropouts. Sufficient conditions are proposed such that the filtering error system is exponentially mean-square stable while the H∞disturbance rejection attenuation constraint is satisfied. Then, the explicit expression of the desired filter gains is described in terms of the solution to linear matrix inequalities (LMIs). Finally, a numerical example is employed to demonstrate the effectiveness of the proposed filter design approach.