A novel data‐driven methodology for fault detection and dynamic risk assessment. (5th May 2020)

• Record Type: Journal Article
• Authors: Amin, Md. Tanjin; Khan, Faisal; Ahmed, Salim; Imtiaz, Syed
• Is Part Of: Canadian journal of chemical engineering. Volume 98:Number 11(2020)
• Page Start: 2397
• Page End: 2416
• Publication Date: 2020-05-05
• Subjects: Bayesian network -- failure prognosis -- fault assessment -- predictive safety -- risk analysis; Chemical engineering -- Periodicals; Technology -- Periodicals; 660.05
• DOI: 10.1002/cjce.23760
• Languages: English
• ISSNs: 0008-4034

Abstract

Abstract: This paper presents a novel methodology for dynamic risk analysis, integrating the multivariate data‐based process monitoring and logical dynamic failure prediction model. This concept for dynamic risk analysis is comprised of the fault assessment and dynamic failure prognosis modules. A combination of the naïve Bayes classifier, Bayesian network, and event tree analysis is utilized to manifest the concept. The naïve Bayes classifier is used for fault detection and diagnosis; it also generates a multivariate probability for a fault class in each time‐step, which is used for dynamic failure prognosis by different paths a fault can lead a process to failure. The proposed framework has been applied to two process systems: a binary distillation column and the RT 580 experimental setup in four fault scenarios, and it is found the developed technique can effectively monitor the process and predict the failure.