Causal network inference and functional decomposition for decentralized statistical process monitoring: Detection and diagnosis

Paredes, Rodrigo; Rato, Tiago J.; Reis, M. S.

doi:10.1016/j.ces.2022.118338

Cited by 7 publications

(3 citation statements)

References 82 publications

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“…In the causal inference stage, a causal network among the process variables with the direction of information propagation is determined by using the PC-NET model and the PCGC method. Subsequently, in the stage of fault detection, the occurrence of faults is detected by using a causality-based multivariate sensitivity enhancement transformation (MSET) method proposed by Reis et al 20 While in the fault isolation and identification stage, according to the inputs with a causal network, the causality-attributing RBC method is employed to locate the root cause of the fault occurrence. Finally, in the stage of fault classification, the isolated faults are classified into different grades and the corresponding measures are suggested.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Framework for Industrial Production Processes Based on Causal Network Inference

Zhang,

Rangaiah,

Dong

et al. 2024

Ind. Eng. Chem. Res.

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With the advent of Industry 4.0, the introduction of data-driven approaches into industrial processes for fault diagnosis has gained substantial attention due to their significant advantage that they mainly rely on the information on process data instead of a priori knowledge. However, the statistically based data-driven methods have difficulty eliminating the “smearing effect” between variables, which affects their effectiveness and interpretability for fault diagnosis, while the previous studies on the causal-based fault diagnosis methods are seriously insufficient. In this study, a novel data-driven fault diagnosis framework based on causal network inference was developed, in which the correlations between variables are explored by employing the partial correlation network (PC-NET) method and the causal propagation direction are determined by a newly developed partial conditional Granger causality (PCGC) method based on the transfer entropy. Subsequently, the occurrences of faults are detected by using a causal-based multivariable sensitivity enhancing transformation (MSET) approach. Finally, a causality-attributing reconstruction-based contribution (RBC) method is developed to isolate and identify the fault variables and to classify the fault grade for taking remedial measures. The effectiveness of the proposed fault diagnosis framework is verified by its application in the actuator system of the industrial sugar production process, and the results demonstrate that the proposed fault diagnosis framework can not only eliminate the smearing effects but also accurately identify fault variables and their contribution rates, exhibiting better interpretability and scalability.

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Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Framework for Industrial Production Processes Based on Causal Network Inference

Zhang,

Rangaiah,

Dong

et al. 2024

Ind. Eng. Chem. Res.

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show abstract

“…For example, the incorporation with the idea of locality preserving or neighborhood embedding has motivated different analytical algorithms for process monitoring 14,16 . Moreover, the advances achieved in machine learning continuously provide creative solutions for data‐driven process monitoring 17–19 …”

Section: Introductionmentioning

confidence: 99%

“…14,16 Moreover, the advances achieved in machine learning continuously provide creative solutions for data-driven process monitoring. [17][18][19] Given that the operating stability of an industrial process could always generate time-serial correlated samples in a successive manner, the time-serial relationship within consecutive samples is required to be given full consideration. Ku et al 20 proposed to stack successive samples together so that the time-serial correlation could be further modeled.…”

mentioning

confidence: 99%

Just‐in‐time latent autoregressive residual generation for dynamic process monitoring

Hu,

Chang

2023

Journal of Chemometrics

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With a goal of timely and adaptively exploiting the inconsistency inherited in the monitored samples of current interest, a novel dynamic process monitoring method based on just‐in‐time latent autoregressive residual generation (JITLAR2G) model is proposed. Different from the mainstream dynamic modeling and monitoring methods which usually train a signature generating mechanism and then repeatedly apply it for online monitored samples, the proposed JITLAR2G‐based approach provides a JITLAR2G model for the online monitored samples after data augmentation, so that the corresponding inconsistency within the given consecutive samples could be timely and adaptively uncovered. Instead of expressing the time‐serial relationship that generally accepted by the normal samples in the given dataset, solving the objective function designed for JITLAR2G in a just‐in‐time manner can adaptively and correspondingly seek but only one projecting vector as well as coefficient vector to generate residual, which points to the potential inconsistency inherited in the monitored samples, for the sole purpose of fault detection. As demonstrated through comparisons, the proposed JITLAR2G model can consistently guarantee its effectiveness, in terms of reducing both false alarm rate and missed alarm rate, for dynamic process monitoring, the salient performance achieved by the proposed JITLAR2G‐based method in contrast to the counterparts can be always confirmed.

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Fault detection and isolation for dynamic non-stationary processes with stationary subspace-based canonical variate analysis

Ji,

Sheng,

Liu

et al. 2024

Chemical Engineering Science

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Causal network inference and functional decomposition for decentralized statistical process monitoring: Detection and diagnosis

Cited by 7 publications

References 82 publications

A Novel Fault Diagnosis Framework for Industrial Production Processes Based on Causal Network Inference

A Novel Fault Diagnosis Framework for Industrial Production Processes Based on Causal Network Inference

Just‐in‐time latent autoregressive residual generation for dynamic process monitoring

Fault detection and isolation for dynamic non-stationary processes with stationary subspace-based canonical variate analysis

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