Fault detection of multimode non-Gaussian dynamic process using dynamic Bayesian independent component analysis

Xu, Ying; Deng, Xiaogang

doi:10.1016/j.neucom.2016.03.015

Cited by 53 publications

(20 citation statements)

References 40 publications

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“…And the detailed Matlab Simulink modules are available in http://depts.washington.edu/control/LARRY/TE/download.html. In the present studies, this process has been a benchmark case widely used to evaluate different monitoring strategies . The TE process is composed of 5 major operation units, including the product condenser, the reactor, the compressor, the separator, and the stripper, as shown in Figure .…”

Section: Case Studymentioning

confidence: 99%

“…In the present studies, this process has been a benchmark case widely used to evaluate different monitoring strategies. [38][39][40][41][42] The TE process is composed of 5 major operation units, including the product condenser, the reactor, the compressor, the separator, and the stripper, as shown in Figure 9. According to the G/H mass ratios, 6 operating modes can be generated.…”

Section: Te Processmentioning

confidence: 99%

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Multimode complex process monitoring using double‐level local information based local outlier factor method

Wang

Deng

Cao

2018

Journal of Chemometrics

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Industrial processes typically have multiple operating modes with complex data distribution and locality faults, which challenges the traditional multivariate statistical process monitoring methods. To address this problem, a double‐level local information‐based local outlier factor (LOF) method is proposed in this work for multimode complex process monitoring. First, to handle the multimodality, the local neighborhood standardization strategy is adopted to utilize the statistical information of local data structure. Second, the variable LOF method is proposed to determine reasonable boundary for complex data distribution and simultaneously reflect local variable behaviors. For better online monitoring, a weighting strategy is applied to emphasize the local variable information, and the Bayesian inference is employed to integrate the LOF value of each variable. To isolate the fault variables, a contribution plot is designed. Finally, a numerical example and the benchmark Tennessee Eastman process are used to demonstrate the effectiveness and superiority of the proposed method.

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Section: Case Studymentioning

confidence: 99%

Section: Te Processmentioning

confidence: 99%

Multimode complex process monitoring using double‐level local information based local outlier factor method

Wang

Deng

Cao

2018

Journal of Chemometrics

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“…The benchmark is set with 19 different faults [30]. These faults can be classified into four classes: control valve faults ( 1-7), pneumatic servomotor faults (8)(9)(10)(11), positioner faults (12)(13)(14), and general faults/external faults (15)(16)(17)(18)(19).…”

Section: Simple Examplementioning

confidence: 99%

“…Aiming at this point, a Noisy-ICA method [18] is proposed to fix the classical ICA model. Dynamic Bayesian Independent Component Analysis [19] is proposed to detect faults of multimode process. Probability is introduced to improve performance of ICA [20].…”

Section: Introductionmentioning

confidence: 99%

Statistical Process Monitoring with Biogeography-Based Optimization Independent Component Analysis

Wei

Cheng

et al. 2018

Mathematical Problems in Engineering

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Independent Component Analysis (ICA), a type of typical data-driven fault detection techniques, has been widely applied for monitoring industrial processes. FastICA is a classical algorithm of ICA, which extracts independent components by using the Newton iteration method. However, the choice of the initial iterative point of Newton iteration method is difficult; sometimes, selection of different initial iterative points tends to show completely different effects for fault detection. So far, there is still no good strategy to get an ideal initial iterative point for ICA. To solve this problem, a modified ICA algorithm based on biogeography-based optimization (BBO) called BBO-ICA is proposed for the purpose of multivariate statistical process monitoring. The Newton iteration method is replaced with BBO here for extracting independent components. BBO is a novel and effective optimization method to search extremes or maximums. Comparing with the traditional intelligent optimization algorithm of particle swarm optimization (PSO) and so on, BBO behaves with stronger capability and accuracy of searching for solution space. Moreover, numerical simulations are finished with the platform of DAMADICS. Results demonstrate the practicability and effectiveness of BBO-ICA. The proposed BBO-ICA shows better performance of process monitoring than FastICA and PSO-ICA for DAMADICS.

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“…This motivates the study of the data-driven fault diagnosis methods including principal component analysis (PCA), independent component analysis (ICA), canonical correlation analysis (CCA), and partial least squares (PLS). [5][6][7][8] Among these methods, PCA is the most popular, which extracts the lowdimensional data feature information from the high-dimensional process data by orthogonal linear transformation. Researchers have performed in-depth studies on PCA and its extension methods.…”

Section: Introductionmentioning

confidence: 99%

Multi‐block principal component analysis based on variable weight information and its application to multivariate process monitoring

Wang

Deng

2017

Can J Chem Eng

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The traditional principal component analysis (PCA)‐based process monitoring method builds a global statistical model and omits the mining of the local variable behaviours, which may degrade the fault detection performance. Considering this problem, this paper proposes a variable weight information‐based multi‐block PCA (VWI‐MBPCA) method. Firstly, a sequence hierarchical clustering algorithm is proposed to divide the full PCA component space into several sub‐blocks, where the components sharing similar variable weight information are gathered together and then the sub‐block T2 statistic is constructed for monitoring sub‐block components. Further, the variables with small weight information on each component sub‐block are extracted to build an additional sub‐PCA model, where the trueTˆ2 statistic is developed to compensate the sub‐block T2 statistic. In order to integrate the monitoring results of each sub‐block, Bayesian inference is applied to construct an overall T2 statistic. To identify the faulty variables, a multi‐block PCA contribution plot is designed by choosing some specific blocks to highlight fault information. Finally, simulations on a numerical example and the benchmark Tennessee Eastman (TE) process are used to demonstrate the strengths of the proposed method.

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Fault detection of multimode non-Gaussian dynamic process using dynamic Bayesian independent component analysis

Cited by 53 publications

References 40 publications

Multimode complex process monitoring using double‐level local information based local outlier factor method

Multimode complex process monitoring using double‐level local information based local outlier factor method

Statistical Process Monitoring with Biogeography-Based Optimization Independent Component Analysis

Multi‐block principal component analysis based on variable weight information and its application to multivariate process monitoring

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