Qingchao Jiang

Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference

Multivariate statistical process monitoring involves dimension reduction and latent feature extraction in large-scale processes and typically incorporates all measured variables. However, involving variables without beneficial information may degrade monitoring performance. This study analyzes the effect of variable selection on principal component analysis (PCA) monitoring performance. Then, it proposes a fault-relevant variable selection and Bayesian inference-based distributed method for efficient fault detection and isolation. First, the optimal subset of variables is identified for each fault using an optimization algorithm. Second, a sub-PCA model is established in each subset. Finally, the monitoring results of all of the subsets are combined through Bayesian inference. The proposed method reduces redundancy and complexity, explores numerous local behaviors, and provides accurate description of faults, thus improving monitoring performance significantly. Case studies on a numerical example, the Tennessee Eastman benchmark process, and an industrial-scale plant demonstrate the efficiency.

Bayesian Fault Diagnosis With Asynchronous Measurements and Its Application in Networked Distributed Monitoring

Conventional Bayesian fault diagnosis assumes that all measurements are available synchronously; however, this condition does not always hold in practical industry because a process can be characterized by multiple sampling or transmitting rates. This paper introduces a Bayesian fault diagnosis system incorporating both historical and online information to address the asynchronous measurement problem. First, the Expectation Maximization approach is utilized to deal with the historical asynchronous measurements; second, the online incomplete measurements are handled through a Bayesian marginalization method within a moving horizon. Then, a Bayesian diagnosis system revealing both the underlying fault status of the whole plant and the unavailable statuses of the corresponding local units is established, which is more robust for practical application. The proposed scheme is tested on a numerical example, the distributed monitoring problem of Tennessee Eastman benchmark process, and the distributed monitoring problem of an industrial tail gas treatment plant. Monitoring results demonstrate the feasibility and efficiency of the proposed approach.

Data-Driven Distributed Local Fault Detection for Large-Scale Processes Based on the GA-Regularized Canonical Correlation Analysis

Large-scale processes have become common, and fault detection for such processes is imperative. This work studies the data-driven distributed local fault detection problem for large-scale processes with interconnected subsystems and develops a genetic algorithm (GA)-regularized canonical correlation analysis (CCA)-based distributed local fault detection scheme. For each subsystem, the GA-regularized CCA is first performed with its all coupled systems, which aims to preserve the maximum correlation with the minimal communication cost. A CCA-based residual is then generated, and corresponding statistic is constructed to achieve optimal fault detection for the subsystem. The distributed fault detector performs local fault detection for each subsystem using its own measurements and the information provided by its coupled subsystems and therefore exhibits a superior monitoring performance. The regularized CCA-based distributed fault detection approach is tested on a numerical example and the Tennessee Eastman benchmark process. Monitoring results indicate the efficiency and feasibility of the proposed approach.

Multiobjective Two-Dimensional CCA-Based Monitoring for Successive Batch Processes with Industrial Injection Molding Application

Successive batch processes generally involve within-batch and batch-to-batch correlations, and monitoring of such batch processes is imperative. This paper proposes a multiobjective two-dimensional canonical correlation analysis (M2D-CCA)-based fault detection scheme to achieve efficient monitoring of successive batch processes. First, three-way historical batch process data are unfolded into two-way time-slice data. Second, for each time-slice measurement, CCA is performed between the current measurement and previous measurements from both time and batch directions, which takes the within-batch and batch-to-batch correlations into account. To determine the involved measurements and eliminate the influence of unrelated variables, multiobjective evolutionary optimization is performed, which tries to maximize the preserved canonical correlation coefficients and minimize the number of involved variables. Finally, based on the established M2D-CCA model, an optimal fault detection residual is generated for each time-slice measurement. The M2D-CCA fault detection scheme performs fault detection using the current measurement and the information provided by its previous samples and batches, and therefore exhibits a superior monitoring performance. The M2D-CCA fault detection approach is tested on a numerical example and an industrial injection molding process. Monitoring results verify the feasibility and superiority of the proposed monitoring scheme.

Data-Driven Optimized Distributed Dynamic PCA for Efficient Monitoring of Large-Scale Dynamic Processes

Dynamic principal component analysis (DPCA) is generally employed in monitoring dynamic processes and typically incorporates all measured variables. However, for a large-scale process, the inclusion of variables without fault-relevant information may cause redundancy and degrade monitoring performance. In this paper, the influence of variable and time-lagged variable selection on the DPCA monitoring performance is analyzed. Then, a fault-relevant performance-driven distributed monitoring scheme is proposed to achieve efficient fault detection and diagnosis. First, performance-driven process decomposition is performed, and the optimal subset of variables and time-lagged variables for each fault are selected through a stochastic optimization algorithm. Second, local DPCA models are established to characterize the process dynamics and generate fault signature evidence. Finally, a Bayesian diagnosis system with the most efficient evidence sources is established to identify the process status. Case studies on a numerical example and the Tennessee Eastman benchmark process demonstrate the efficiency of the proposed monitoring scheme.