This article presents a risk-based methodology to estimate shutdown inspection and maintenance interval considering system availability. Most inspection and maintenance activities are performed when the plant/unit is in the operational state. However, some inspection and maintenance activities require the plant to be in a nonoperational or shutdown state. In most cases, operating companies adopt a shutdown schedule based on the original equipment manufacturer's (OEM) suggested recommended periods. However, this may not be the best strategy as OEM recommended duration is general and may not reflect the current state of operation. The proposed methodology is unique in the sense that it identifies a shutdown interval by identifying the critical equipment in terms of risk considering availability and safety of the operating unit. It optimizes process plant shutdown interval to minimize the risk (in dollar terms). The Markov process is used to establish the state diagram to calculate system availability. The proposed methodology is comprised of three steps namely, risk-based equipment selection, shutdown availability modeling of a complex system using the Markov process, and risk-based shutdown inspection and maintenance interval modeling. It can be applied to process plants such as those for liquefied natural gas processing, petrochemicals, and refineries. The key elements for the success of the proposed methodology are the plant-specific data and identification of critical equipment.

Item Type:	Refereed Article
Keywords:	operational safety, plant availability, risk-based inspection, risk-based maintenance, shutdown interval estimation, equipment, gas plants, inspection, maintenance, Markov processes, plant shutdowns, risk perception, safety engineering, availability
Research Division:	Engineering
Research Group:	Engineering practice and education
Research Field:	Risk engineering
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in engineering
UTAS Author:	Khan, F (Professor Faisal Khan)
ID Code:	120623
Year Published:	2015
Web of Science® Times Cited:	13
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2017-08-29
Last Modified:	2017-12-05
Downloads:	0