An improved weighted recursive PCA algorithm for adaptive fault detection
TL;DR: A novel weighted adaptive recursive fault detection technique based on Principal Component Analysis (PCA) is proposed to address the issue of the increment in false alarm rate in process monitoring schemes due to the natural, slow and normal process changes (aging), which often occurs in real processes.
About: This article is published in Control Engineering Practice.The article was published on 2016-05-01. It has received 72 citations till now. The article focuses on the topics: Constant false alarm rate & False alarm.
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TL;DR: The features of different model-based and data-driven FD-HM approaches are investigated separately as well as the existing works that attempted to integrate both of them are investigated.
261 citations
TL;DR: Wang et al. as discussed by the authors proposed two representative smoothing techniques, which are based on a generic fault detection index in multivariate statistical process monitoring (MSPM), to detect incipient faults.
124 citations
TL;DR: A hierarchical statistical model structure to extract multilayer data features, including both the linear and nonlinear principal components, is designed, motivated by the deep learning strategy, to reduce the computation complexity in nonlinear feature extraction.
Abstract: In order to deeply exploit intrinsic data feature information hidden among the process data, an improved kernel principal component analysis (KPCA) method is proposed, which is referred to as deep principal component analysis (DePCA). Specifically, motivated by the deep learning strategy, we design a hierarchical statistical model structure to extract multilayer data features, including both the linear and nonlinear principal components. To reduce the computation complexity in nonlinear feature extraction, the feature-samples’ selection technique is applied to build the sparse kernel model for DePCA. To integrate the monitoring statistics at each feature layer, Bayesian inference is used to transform the monitoring statistics into fault probabilities, and then, two probability-based DePCA monitoring statistics are constructed by weighting the fault probabilities at all the feature layers. Two case studies involving a simulated nonlinear system and the benchmark Tennessee Eastman process demonstrate the superior fault detection performance of the proposed DePCA method over the traditional KPCA-based methods.
64 citations
Cites methods from "An improved weighted recursive PCA ..."
...[19] built a recursive PCA method that updates the model parameters using online process data....
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TL;DR: In this paper, a sparse dissimilarity (SDISSIM) algorithm is developed which can isolate the incipient abnormal variables that change the data distribution structure and does not need any priori fault knowledge.
58 citations
TL;DR: In this paper, the authors presented the main results of fault detection and diagnosis in a cement manufacturing plant using a new monitoring scheme based on multivariate statistical analysis and an adaptive threshold strategy.
55 citations
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TL;DR: This paper is concerned with the construction of planes of closest fit to systems of points in space and the relationships between these planes and the planes themselves.
Abstract: (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science: Vol. 2, No. 11, pp. 559-572.
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TL;DR: This final part discusses fault diagnosis methods that are based on historic process knowledge that need to be addressed for the successful design and implementation of practical intelligent supervisory control systems for the process industries.
1,902 citations
TL;DR: It is demonstrated that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring.
Abstract: This paper provides an overview and analysis of statistical process monitoring methods for fault detection, identification and reconstruction. Several fault detection indices in the literature are analyzed and unified. Fault reconstruction for both sensor and process faults is presented which extends the traditional missing value replacement method. Fault diagnosis methods that have appeared recently are reviewed. The reconstruction-based approach and the contribution-based approach are analyzed and compared with simulation and industrial examples. The complementary nature of the reconstruction- and contribution-based approaches is highlighted. An industrial example of polyester film process monitoring is given to demonstrate the power of the contribution- and reconstruction-based approaches in a hierarchical monitoring framework. Finally we demonstrate that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring. Additional topics are summarized at the end of the paper for future investigation. Copyright © 2003 John Wiley & Sons, Ltd.
1,408 citations
TL;DR: This paper uses a well-known ‘time lag shift’ method to include dynamic behavior in the PCA model and demonstrates the effectiveness of the proposed methodology on the Tennessee Eastman process simulation.
1,299 citations