Local-Global Modeling and Distributed Computing Framework for Nonlinear Plant-Wide Process Monitoring With Industrial Big Data

Decentralized Kernel Ridge Regression Based on Data-Dependent Random Feature

Random feature (RF) has been widely used for node consistency in decentralized kernel ridge regression (KRR). Currently, the consistency is guaranteed by imposing constraints on coefficients of features, necessitating that the RFs on different nodes are identical. However, in many applications, data on different nodes vary significantly on the number or distribution, which calls for adaptive and data-dependent methods that generate different RFs. To tackle the essential difficulty, we propose a new decentralized KRR algorithm that pursues consensus on decision functions, which allows great flexibility and well adapts data on nodes. The convergence is rigorously given, and the effectiveness is numerically verified: by capturing the characteristics of the data on each node, while maintaining the same communication costs as other methods, we achieved an average regression accuracy improvement of 25.5% across six real-world datasets.

Partial Cross Mapping Based on Sparse Variable Selection for Direct Fault Root Cause Diagnosis for Industrial Processes

Root cause diagnosis of process industry is of significance to ensure safe production and improve production efficiency. Conventional contribution plot methods have challenges in root cause diagnosis due to the smearing effect. Other traditional root cause diagnosis methods, such as Granger causality (GC) and transfer entropy, have unsatisfactory performance in root cause diagnosis for complex industrial processes due to the existence of indirect causality. In this work, a regularization and partial cross mapping (PCM)-based root cause diagnosis framework is proposed for efficient direct causality inference and fault propagation path tracing. First, generalized Lasso-based variable selection is performed. The Hotelling T2 statistic is formulated and the Lasso-based fault reconstruction is applied to select candidate root cause variables. Second, the root cause is diagnosed through the PCM and the propagation path is drawn out according to the diagnosis result. The proposed framework is studied in four cases to verify its rationality and effectiveness, including a numerical example, the Tennessee Eastman benchmark process, the wastewater treatment process (WWTP), and the decarburization process of high-speed wire rod spring steel.

Multi-node knowledge graph assisted distributed fault detection for large-scale industrial processes based on graph attention network and bidirectional LSTMs

Modern industrial processes are characterized by extensive, multiple operation units, and strong coupled correlation of subsystems. Fault detection of large-scale processes is still a challenging problem, especially for tandem plant-wide processes in multiple fields such as water treatment process. In this paper, a novel distributed graph attention network-bidirectional long short-term memory (D-GATBLSTM) fault detection model is proposed for large-scale industrial processes. Firstly, a multi-node knowledge graph (MNKG) is constructed using a joint data and knowledge driven strategy. Secondly, for large-scale industrial process, a global feature extractor of graph attention networks (GATs) is constructed, on the basis of which, sub-blocks are decomposed based on MNKG. Then, local feature extractors of bidirectional long short-term memory (Bi-LSTM) for each sub-block are constructed, in which correlations among multiple sub-blocks are considered. Finally, a multi-subblock fusion collaborative prediction model is constructed and the comprehensive fault detection results are given by the grid search method. The effectiveness of our D-GATBLSTM is exemplified in a secure water treatment process case, where it outperforms baseline models compared, with 27% improvement in precision, 15% increase in recall, and overall F-score enhancement of 0.22.

Explicit Representation and Customized Fault Isolation Framework for Learning Temporal and Spatial Dependencies in Industrial Processes

Typically, industrial processes possess both temporal and spatial dependencies due to intravariable dynamics and intervariable couplings. The two dependencies have different manifestations, indicating diverse process characteristics. However, the existing methods fail to separate temporal and spatial information well, leading to inappropriate representation and inaccurate fault detection and isolation results. This study proposes an explicit representation and customized fault isolation framework to tackle temporal and spatial characteristics, so as to identify and locate anomalies affecting different dependencies. First, we design a double-level separation method for temporal and spatial information. In the first level, we construct two independent auto-encoding modules to extract temporal correlation and spatial graph structure in parallel. In the second level, we propose an information aliasing loss function to guild the two modules to distinguish between temporal and spatial characteristics, further facilitating information separation. By monitoring the explicit temporal and spatial statistics obtained by the two modules, spatiotemporal dependencies of anomalies can be determined for subsequent isolation. Furthermore, we propose a customized isolation strategy for anomalies in temporal and spatial characteristics. By quantifying changes in intravariable temporal dynamics and intervariable spatial graph structure individually, temporal impact and spatial propagation of faults can be finely characterized and isolated. Three examples are adopted to verify the performance of the proposed framework, including a numerical example, a real condensing system of the thermal power plant process, and the Tennessee Eastman benchmark process.

Industrial Process Monitoring Based on Dynamic Overcomplete Broad Learning Network

Most industrial processes feature high nonlinearity, non-Gaussianity, and time correlation. Models based on overcomplete broad learning system (OBLS) have been successfully applied in the fault monitoring realm, which may relatively deal with the nonlinear and non-Gaussian characteristics. However, these models barely take time correlation into full consideration, hindering the further improvement of the monitoring accuracy of the network. Therefore, an effective dynamic overcomplete broad learning system (DOBLS) based on matrix extension is proposed, which extends the raw data in the batch process with the idea of "time lag" in this article. Subsequently, the OBLS monitoring network is employed to continue the analysis of the extended dynamic input data. Finally, a monitoring model is established to tackle the coexistence of nonlinearity, non-Gaussianity, and time correlation in process data. To illustrate the superiority and feasibility, the proposed model is conducted on the penicillin fermentation simulation platform, the experimental result of which illustrates that the model can extract the feature of process data more comprehensively and be self-updated more efficiently. With shorter training time and higher monitoring accuracy, the proposed model can witness an improvement of average monitoring accuracy by 3.69% and 1.26% in 26 process fault types compared to the state-of-the-art fault monitoring methods BLS and OBLS, respectively.

Interaction-Aware Graph Neural Networks for Fault Diagnosis of Complex Industrial Processes

Fault diagnosis of complex industrial processes becomes a challenging task due to various fault patterns in sensor signals and complex interactions between different units. However, how to explore the interactions and integrate with sensor signals remains an open question. Considering that the sensor signals and their interactions in an industrial process with the form of nodes and edges can be represented as a graph, this article proposes a novel interaction-aware and data fusion method for fault diagnosis of complex industrial processes, named interaction-aware graph neural networks (IAGNNs). First, to describe the complex interactions in an industrial process, the sensor signals are transformed into a heterogeneous graph with multiple edge types, and the edge weights are learned by the attention mechanism, adaptively. Then, multiple independent graph neural network (GNN) blocks are employed to extract the fault feature for each subgraph with one edge type. Finally, each subgraph feature is concatenated or fused by a weighted summation function to generate the final graph embedding. Therefore, the proposed method can learn multiple interactions between sensor signals and extract the fault feature from each subgraph by message passing operation of GNNs. The final fault feature contains the information from raw data and implicit interactions between sensor signals. The experimental results on the three-phase flow facility and power system (PS) demonstrate the reliable and superior performance of the proposed method for fault diagnosis of complex industrial processes.

Decentralized adaptively weighted stacked autoencoder-based incipient fault detection for nonlinear industrial processes

Modern industrial processes often exhibit large-scale and nonlinear characteristics. Incipient fault detection for industrial processes is a big challenge because of the faint fault signature. To improve the performance of incipient fault detection for large-scale nonlinear industrial processes, a decentralized adaptively weighted stacked autoencoder (DAWSAE) -based fault detection method is proposed. First, the industrial process is divided into several sub-blocks and local adaptively weighted stacked autoencoder (AWSAE) is established for each sub-block to mine local information and obtain local adaptively weighted feature vectors and residual vectors. Second, the global AWSAE is established for the whole process to mine global information and obtain global adaptively weighted feature vectors and residual vectors. Finally, local statistics and global statistics are constructed based on local and global adaptively weighted feature vectors and residual vectors to detect the sub-blocks and the whole process, respectively. The advantages of proposed method are verified by a numerical example and Tennessee Eastman process (TEP).

Dynamic Batch Process Monitoring Based on Time-Slice Latent Variable Correlation Analysis

Batch processes are generally characterized by complex dynamics and remarkable data collinearity, thereby rendering the monitoring of such processes necessary but challenging. This paper proposes a data-driven time-slice latent variable correlation analysis-based model predictive fault detection framework to ensure accurate fault detection in dynamic batch processes. The three-way batch process data are first unfolded into the two-way time slice. For each single time slice, process data are mapped to both major latent variables and residual subspaces to deal with the variable-wise data collinearity and extract dominant data information. A measurement status is then determined with a canonical correlation analysis of the major latent variables and correlated variables, using both the time and batch perspectives. Prediction-based residuals are generated, which provide the basis for identifying the property of faults detected, namely, static or dynamic. Based on experiments using a simulated penicillin production and an industrial inject molding process, the proposed monitoring scheme has been proven feasible and effective.

Metric Learning-Based Fault Diagnosis and Anomaly Detection for Industrial Data With Intraclass Variance

Industrial system monitoring includes fault diagnosis and anomaly detection, which have received extensive attention, since they can recognize the fault types and detect unknown anomalies. However, a separate fault diagnosis method or anomaly detection method cannot identify unknown faults and distinguish between different fault types simultaneously; thus, it is difficult to meet the increasing demand for safety and reliability of industrial systems. Besides, the actual system often operates in varying working conditions and is disturbed by the noise, which results in the intraclass variance of the raw data and degrades the performance of industrial system monitoring. To solve these problems, a metric learning-based fault diagnosis and anomaly detection method is proposed. Fault diagnosis and anomaly detection are adaptively fused in the proposed end-to-end model, where anomaly detection can prevent the model from misjudging the unknown anomaly as the known type, while fault diagnosis can identify the specific type of system fault. In addition, a novel multicenter loss is introduced to restrain the intraclass variance. Compared with manual feature extraction that can only extract suboptimal features, it can learn discriminant features automatically for both fault diagnosis and anomaly detection tasks. Experiments on three-phase flow (TPF) facility and Case Western Reserve University (CWRU) bearing have demonstrated that the proposed method can avoid the interference of intraclass variances and learn features that are effective for identifying tasks. Moreover, it achieves the best performance in both fault diagnosis and anomaly detection.

A Novel Distributed Fault Detection Approach Based on the Variational Autoencoder Model

In large-scale industrial fault detection, a distributed model is typically established on the basis of blocked units. However, blocked distributed methods consider units as independent of one another and disregard the relationship between units, thus leading to incomplete information on local units. In fact, the operation status of a unit is affected by a local unit and its surrounding neighboring units. In addition, the fault detection performance of a system is seriously reduced once data are missing from the data source. Variational autoencoder (VAE) is not only a popular deep generative model but also has a powerful nonlinear feature extraction capability. In this study, VAE is extended to the distributed case. In this study, a distributed fault detection method DVAE based on VAE is proposed. This method can not only describe local and neighboring information, but it can also reconstruct missing data. First, system variables are divided into local and neighboring units in accordance with the system mechanism. Second, for each local unit, a DVAE model is established to map the multivariable data onto the latent variable space. The obtained latent variable contains the information on a local unit and can reflect the complex relationship with its neighboring units. Lastly, Euclidean distance is used to detect system faults. When applied on the Tennessee Eastman process for verification, the proposed method shows good performance in fault detection and missing data reconstruction.