A data-driven Bayesian network learning method for process fault diagnosis

Amin, Md. Tanjin; Khan, Faisal; Ahmed, Salim; Imtiaz, Syed

doi:10.1016/j.psep.2021.04.004

Cited by 173 publications

(39 citation statements)

References 42 publications

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“…MSPCA is among the many extensions of PCA. Other extensions include dynamic PCA [ 8 ], probabilistic PCA [ 9 ], kernel PCA with kernel density estimation [ 10 ], and Bayesian network PCA [ 11 ]. Such extensions address issues arising from PCA’s underlying assumptions of linear, stationary, and multivariate normal data with Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation

Malluhi

Nounou

2022

Sensors

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Multiscale PCA (MSPCA) is a well-established fault-detection and isolation (FDI) technique. It utilizes wavelet analysis and PCA to extract important features from process data. This study demonstrates limitations in the conventional MSPCA fault detection algorithm, thereby proposing an enhanced MSPCA (EMSPCA) FDI algorithm that uses a new wavelet thresholding criterion. As such, it improves the projection of faults in the residual space and the threshold estimation of the fault detection statistic. When tested with a synthetic model, EMSPCA resulted in a 30% improvement in detection rate with equal false alarm rates. The EMSPCA algorithm also relies on the novel application of reconstruction-based fault isolation at multiple scales. The proposed algorithm reduces fault smearing and consequently improves fault isolation performance. The paper will further investigate the use of soft vs. hard wavelet thresholding, decimated vs. undecimated wavelet transforms, the choice of wavelet decomposition depth, and their implications on FDI performance.The FDI performance of the developed EMSPCA method was illustrated for sensor faults. This undertaking considered synthetic data, the simulated data of a continuously stirred reactor (CSTR), and experimental data from a packed-bed pilot plant. The results of these examples show the advantages of EMSPCA over existing techniques.

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Section: Introductionmentioning

confidence: 99%

Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation

Malluhi

Nounou

2022

Sensors

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“…The data-driven modeling method is mainly based on historical data generated in the production process and uses data mining algorithms to identify the causal relationship between variables. 14 Among them, more mainstream algorithms include algorithms that are more widely used, including system identification approach, 15 cross-correlation analysis, 16 Granger causality analysis, 17 directed transfer function/partial directed coherence analysis, 18 transfer entropy analysis, 19 Bayesian network learning, 20 , 21 and so on. Among them, Granger causality analysis has been applied to many fields and worked well.…”

Section: Introductionmentioning

confidence: 99%

Chemical Process Alarm Root Cause Diagnosis Method Based on the Combination of Data-Knowledge-Driven Method and Time Retrospective Reasoning

Song

Liu²,

Dong

et al. 2022

ACS Omega

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Due to the abrupt nature of the chemical process, a large number of alarms are often generated at the same time. As a result of the flood of alarms, it largely hinders the operator from making accurate judgments and correct actions for the root cause of the alarm. The existing diagnosis methods for the root cause of alarms are relatively single, and their ability to accurately find out complex accident chains and assist decision making is weak. This paper introduces a method that integrates the knowledge-driven method and the data-driven method to establish an alarm causal network model and then traces the source to realize the alarm root cause diagnosis, and develops the related system modules. The knowledge-driven method uses the hidden causality in the optimized hazard and operability analysis (HAZOP) report, while the data-driven method combines the autoregressive integrated moving average model (ARIMA) and Granger causality test, and the traceability mechanism uses the time-based retrospective reasoning method. In the case study, the practical application of the method is compared with the experimental application in a real petrochemical plant. The results show that this method helps to improve the accuracy of correct diagnosis of the root cause of the alarm and can assist the operators in decision making. Using this method, the root cause diagnosis of alarm can be realized quickly and scientifically, and the probability of misjudgment by operators can be reduced, which has a certain degree of scientificity.

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“…NB was adopted as a classifier for process fault diagnosis. Amin and Khan et al 15 proposed a hybrid diagnosis method of PCA and BN. This method achieved good diagnostic performance in a continuous stirred tank heater and binary distillation column because it used the correlation dimension to select the principal component and combined a vine copula and the BN theorem to capture the nonlinear dependence of high dimensional process data.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of the Dynamic Chemical Process Based on the Optimized CNN-LSTM Network

et al. 2022

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Deep learning provides new ideas for chemical process fault diagnosis, reducing potential risks and ensuring safe process operation in recent years. To address the problem that existing methods have difficulty extracting the dynamic fault features of a chemical process, a fusion model (CS-IMLSTM) based on a convolutional neural network (CNN), squeeze-and-excitation (SE) attention mechanism, and improved long short-term memory network (IMLSTM) is developed for chemical process fault diagnosis in this paper. First, an extended sliding window is utilized to transform data into augmented dynamic data to enhance the dynamic features. Second, the SE is utilized to optimize the key fault features of augmented dynamic data extracted by CNN. Then, IMLSTM is used to balance fault information and further mine the dynamic features of time series data. Finally, the feasibility of the proposed method is verified in the Tennessee-Eastman process (TEP). The average accuracies of this method in two subdata sets of TEP are 98.29% and 97.74%, respectively. Compared with the traditional CNN-LSTM model, the proposed method improves the average accuracies by 5.18% and 2.10%, respectively. Experimental results confirm that the method developed in this paper is suitable for chemical process fault diagnosis.

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A data-driven Bayesian network learning method for process fault diagnosis

Cited by 173 publications

References 42 publications

Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation

Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation

Chemical Process Alarm Root Cause Diagnosis Method Based on the Combination of Data-Knowledge-Driven Method and Time Retrospective Reasoning

Fault Diagnosis of the Dynamic Chemical Process Based on the Optimized CNN-LSTM Network

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