Dynamic process fault prediction using canonical variable trend analysis
Fault prediction technology is important to avoid serious process failure. This paper is concerned with the fault prediction of dynamic industrial process with incipient faults and proposes a canonical variable trend analysis (CVTA) based fault prediction method. In the proposed method, canonical variate analysis (CVA) algorithm is firstly applied to analyze the process dynamics and extract the uncorrelated latent features, called canonical variables. Furthermore, support vector machine is adopted to model the relationship between the historical and future values of the canonical variables, which leads to the time series prediction model for the canonical variables. Based on the predicted canonical variables, an overall monitoring statistic is used to forecast the change of the process status. Simulations on a continuous stirred tank reactor (CSTR) system demonstrate that the proposed method can indicate the trend of the incipient faults effectively.
Code (1)
Tasks
PredictionTime SeriesTime Series AnalysisTime Series PredictionSimilar Papers 제목 키워드 기반
Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection
Early detection of incipient faults in industrial processes is increasingly becoming important, as these faults can slowly develop into serious abnormal events, an emergency situation, or even failure of critical equi…
Fault DetectionControl theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems
This paper is dedicated to control theoretically explainable application of autoencoders to optimal fault detection in nonlinear dynamic systems. Autoencoder-based learning is a standard machine learning method and widel…
Anomaly DetectionFault DetectionRepresentation LearningSemantic Feature Segmentation for Interpretable Predictive Maintenance in Complex Systems
Predictive maintenance in complex systems is often complicated by the heterogeneity and redundancy of monitored variables,which can obscure fault-relevant information and reduce model interpretability. This work proposes…
Semantic SegmentationMulti-Level Temporal Graph Networks with Local-Global Fusion for Industrial Fault Diagnosis
Fault detection and diagnosis are critical for the optimal and safe operation of industrial processes. The correlations among sensors often display non-Euclidean structures where graph neural networks (GNNs) are widely u…
Fault DiagnosisDynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning
Alkaline Water Electrolysis (AWE) is one of the simplest green hydrogen production method using renewable energy. AWE system typically yields process variables that are serially correlated and contaminated by measurement…
Dictionary LearningFault Detection