Sparse Robust Principal Component Analysis with Applications to Fault Detection and Diagnosis

Integration of Encoding and Temporal Forecasting: Toward End-to-End NOx Prediction for Industrial Chemical Process

Forecasting NOx concentration in fluid catalytic cracking (FCC) regeneration flue gas can guide the real-time adjustment of treatment devices, and then furtherly prevent the excessive emission of pollutants. The process monitoring variables, which are usually high-dimensional time series, can provide valuable information for prediction. Although process features and cross-series correlations can be captured through feature extraction techniques, they are commonly linear transformation, and conducted or trained separately from forecasting model. This process is inefficient and might not be an optimal solution for the following forecasting modeling. Therefore, we propose a time series encoding temporal convolutional network (TSE-TCN). By parameterizing the hidden representation of the encoding-decoding structure with the temporal convolutional network (TCN), and combining the reconstruction error and the prediction error in the objective function, the encoding-decoding procedure and the temporal predicting procedure can be trained by a single optimizer. The effectiveness of the proposed method is verified through an industrial reaction and regeneration process of an FCC unit. Results demonstrate that TSE-TCN outperforms some state-of-art methods with lower root mean square error (RMSE) by 2.74% and higher R2 score by 3.77%.

Novel variation mode decomposition integrated adaptive sparse principal component analysis and it application in fault diagnosis

The sparse principal component analysis (SPCA) is widely used in the fault detection for nonlinear complex chemical processes in recent years. However, insufficient data processing, fixed models and fault type single classification cannot be used in the time-varying process. Therefore, a novel adaptive sparse principal component analysis (ASPCA) algorithm fused with improved variation mode decomposition (IVMD) (ASPCA-IVMD) is proposed for fault detection in chemical processes. The bat algorithm is innovatively integrated to optimize the parameters of the variable modulus decomposition. Then the optimized parameters are used for data preprocessing to suppress noise. In addition, based on the traditional SPCA, the threshold calculation is fused to realize the adaptive selection of principal components. After the principal components are determined, T² and Q statistics are used for fault detection. Finally, the proposed method is verified by the Tennessee Eastman process case. The results demonstrate that the proposed method can select the principal components adaptively according to the data for having the real-time property of chemical process. Meanwhile, compared with traditional methods (principal component analysis, sparse principal component analysis, deep belief network integrating dropout, adaptive unscented Kalman filter integrating radial basis function and sparse deep belief network), the detection rate of the ASPCA-IVMD method is more than 99%, which shows superiority.

Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials, fintech management, medicine, neurorobotics, and psychology, among others. Prognostics and health management (PHM) is the discipline that links the studies of failure mechanisms to system lifecycle management. There is a need, which is still absent, to produce an analytical compilation of PHM-XAI works. In this paper, we use preferred reporting items for systematic reviews and meta-analyses (PRISMA) to present a state of the art on XAI applied to PHM of industrial assets. This work provides an overview of the trend of XAI in PHM and answers the question of accuracy versus explainability, considering the extent of human involvement, explanation assessment, and uncertainty quantification in this topic. Research articles associated with the subject, since 2015 to 2021, were selected from five databases following the PRISMA methodology, several of them related to sensors. The data were extracted from selected articles and examined obtaining diverse findings that were synthesized as follows. First, while the discipline is still young, the analysis indicates a growing acceptance of XAI in PHM. Second, XAI offers dual advantages, where it is assimilated as a tool to execute PHM tasks and explain diagnostic and anomaly detection activities, implying a real need for XAI in PHM. Third, the review shows that PHM-XAI papers provide interesting results, suggesting that the PHM performance is unaffected by the XAI. Fourth, human role, evaluation metrics, and uncertainty management are areas requiring further attention by the PHM community. Adequate assessment metrics to cater to PHM needs are requested. Finally, most case studies featured in the considered articles are based on real industrial data, and some of them are related to sensors, showing that the available PHM-XAI blends solve real-world challenges, increasing the confidence in the artificial intelligence models' adoption in the industry.