A condition monitoring based signal filtering approach for dynamic time dependent safety assessment of natural gas distribution process

Ahmad BahooToroody, Mohammad Mahdi Abaei, Farshad BahooToroody, Filippo De Carlo, Rouzbeh Abbassi, Saeed Khalaj

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Condition monitoring of natural gas distribution networks is a fundamental prerequisite for evaluating safety of the operation during the lifetime of the system. Due to the high level of uncertainty in the observed data, predicting the operational reliability of the networks is complicated. Moreover, there is a fluctuation in most of the monitoring data in different time scales, as most of the derived data tend to be of non-stationary nature and are complex to model or forecast. Therefore, a more realistic data driven approach for developing a reliability framework needs to be considered. This paper aims at proposing a probabilistic model to predict the complexity of the non-stationary behaviour in monitoring data. It also aims at developing a novel framework for the time dependent reliability assessment of a natural gas distribution system using condition-monitoring data. To this end a methodology by integrating Empirical Mode Decomposition (EMD) and Hierarchical Bayesian Model (HBM) is developed. The advantages of the methodology are demonstrated through a case study of a Natural Gas Regulating and Metering Station operating in Italy. Based on pressure data acquired from the case study, the model is able to predict overpressure thus directly avoiding unnecessary maintenance and safety consequences.
LanguageEnglish
Pages335-343
JournalProcess Safety and Environmental Protection
Volume123
DOIs
Publication statusAccepted/In press - Mar 2019

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Condition monitoring
natural gas
Natural gas
safety
monitoring
Monitoring
Electric power distribution
methodology
overpressure
distribution system
Decomposition
decomposition
timescale
distribution
monitoring data

Cite this

BahooToroody, Ahmad ; Abaei, Mohammad Mahdi ; BahooToroody, Farshad ; De Carlo, Filippo ; Abbassi, Rouzbeh ; Khalaj, Saeed . / A condition monitoring based signal filtering approach for dynamic time dependent safety assessment of natural gas distribution process. In: Process Safety and Environmental Protection. 2019 ; Vol. 123. pp. 335-343.
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abstract = "Condition monitoring of natural gas distribution networks is a fundamental prerequisite for evaluating safety of the operation during the lifetime of the system. Due to the high level of uncertainty in the observed data, predicting the operational reliability of the networks is complicated. Moreover, there is a fluctuation in most of the monitoring data in different time scales, as most of the derived data tend to be of non-stationary nature and are complex to model or forecast. Therefore, a more realistic data driven approach for developing a reliability framework needs to be considered. This paper aims at proposing a probabilistic model to predict the complexity of the non-stationary behaviour in monitoring data. It also aims at developing a novel framework for the time dependent reliability assessment of a natural gas distribution system using condition-monitoring data. To this end a methodology by integrating Empirical Mode Decomposition (EMD) and Hierarchical Bayesian Model (HBM) is developed. The advantages of the methodology are demonstrated through a case study of a Natural Gas Regulating and Metering Station operating in Italy. Based on pressure data acquired from the case study, the model is able to predict overpressure thus directly avoiding unnecessary maintenance and safety consequences.",
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A condition monitoring based signal filtering approach for dynamic time dependent safety assessment of natural gas distribution process. / BahooToroody, Ahmad; Abaei, Mohammad Mahdi ; BahooToroody, Farshad ; De Carlo, Filippo; Abbassi, Rouzbeh; Khalaj, Saeed .

In: Process Safety and Environmental Protection, Vol. 123, 03.2019, p. 335-343.

Research output: Contribution to journalArticleResearchpeer-review

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