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

Jiang, Qingchao; Yan, Xuefeng; Huang, Biao

doi:10.1109/tie.2015.2466557

Cited by 321 publications

(159 citation statements)

References 35 publications

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“…Recently, the theoretical analysis and experimental verification conducted by Ghosh et al have fully demonstrated the monitoring performance improvement, which is mainly due to the utilizing of historical fault information to support the variable selection . On the basis of this pioneering study, Jiang et al developed the performance‐driven distributed monitoring model, and extended it to the monitoring of the large‐scale nonlinear process . The experiment results of their researches also verified the necessity of appropriate variable selection.…”

Section: Introductionmentioning

confidence: 96%

Large‐scale dynamic process monitoring based on performance‐driven distributed canonical variate analysis

Liu

Song

Zhao

et al. 2020

Journal of Chemometrics

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As a typical process monitoring method for the large‐scale industrial process, the distributed principal components analysis (DPCA) needs to be improved because of its rough selection for the variables in each subblock. Moreover, for DPCA, the process dynamic property is ignored and invalid fault diagnosis may occur. Therefore, a performance‐driven distributed canonical variate analysis (DCVA) is proposed. Firstly, with historical fault information, the genetic algorithm is utilized to select appropriate variables for each subblock; secondly, canonical variate analysis is introduced to capture the dynamic information for performance improvement; finally, a novel fault diagnosis method is developed for the DCVA model. Case studies on a numerical example and the Tennessee Eastman benchmark process demonstrate the effectiveness of the proposed model. Highlights A novel fault diagnosis approach based on the distributed CVA model is presented. The dynamic property of process data for each sub‐block is firstly captured by CVA. With the genetic algorithm, the historical fault information is utilized to select appropriate variables in each sub‐block. The superiority of the developed method is validated on the numerical example and the TE process.

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Section: Introductionmentioning

confidence: 96%

Large‐scale dynamic process monitoring based on performance‐driven distributed canonical variate analysis

Liu

Song

Zhao

et al. 2020

Journal of Chemometrics

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“…Multivariate statistical process monitoring (MSPM) is a research branch in the field of fault diagnosis. Since not relying on accurate mathematical models, MSPM has been used widely in the field of fault diagnosis …”

Section: Introductionmentioning

confidence: 99%

A new incipient fault monitoring method based on modified principal component analysis

Yang

Wang

Liu

2019

Journal of Chemometrics

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A novel multivariate statistical process monitoring (MSPM) method based on modified principal component analysis (PCA) is proposed to solve the low detection rate problem of incipient fault. In this modified PCA, on the basis of normal PCA model, the columns of loading matrix are reordered by mutual information between different statistic component matrices and training data. Then, instead of cumulative percent variance criterion, the principal component subspace is selected according to the largest mutual information. The selected PC subspace can maximally reflect fault characteristics into a new statistical index. Besides, a detection index based on the sliding average control chart statistic is proposed, which eliminates the effect of noise by proper averaging of the most recent samples, greatly improving the ability and accuracy of fault detection. The case study of a numerical simulation and Tennessee Eastman process shows that the proposed method can effectively detect incipient fault.

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“…Therefore, the fault detection of mechanical equipment is of great significance to ensure the safety of the industrial production process and the economic benefits. In recent years, the multivariate statistical process monitoring (MSPM) technique has been developed and used to detect the faults in industrial production process, such as principal component analysis (PCA) [1], partial least squares (PLS) [2], and independent component analysis (ICA) [3]. These classical monitoring methods perform dimension reduction on the process data and extract few components to construct monitoring statistics which can reflect the characteristics of the original data, at this point, the performance of dimension reduction will affect the monitoring effect.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding

She

Tian

Liang

et al. 2018

Shock and Vibration

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The dimension reduction methods have been proved powerful and practical to extract latent features in the signal for process monitoring. A linear dimension reduction method called nonlocal orthogonal preserving embedding (NLOPE) and its nonlinear form named nonlocal kernel orthogonal preserving embedding (NLKOPE) are proposed and applied for condition monitoring and fault detection. Different from kernel orthogonal neighborhood preserving embedding (KONPE) and kernel principal component analysis (KPCA), the NLOPE and NLKOPE models aim at preserving global and local data structures simultaneously by constructing a dual-objective optimization function. In order to adjust the trade-off between global and local data structures, a weighted parameter is introduced to balance the objective function. Compared with KONPE and KPCA, NLKOPE combines both the advantages of KONPE and KPCA, and NLKOPE is also more powerful in extracting potential useful features in nonlinear data set than NLOPE. For the purpose of condition monitoring and fault detection, monitoring statistics are constructed in feature space. Finally, three case studies on the gearbox and bearing test rig are carried out to demonstrate the effectiveness of the proposed nonlinear fault detection method.

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

Cited by 321 publications

References 35 publications

Large‐scale dynamic process monitoring based on performance‐driven distributed canonical variate analysis

Large‐scale dynamic process monitoring based on performance‐driven distributed canonical variate analysis

A new incipient fault monitoring method based on modified principal component analysis

Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding

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