Human Error Probability Assessment During Maintenance Activities of Marine Systems

Abstract

BackgroundMaintenance operations on-board ships are highly demanding. Maintenance operations are intensive activities requiring high man–machine interactions in challenging and evolving conditions. The evolving conditions are weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress. For example, extreme weather condition affects seafarers' performance, increasing the chances of error, and, consequently, can cause injuries or fatalities to personnel. An effective human error probability model is required to better manage maintenance on-board ships. The developed model would assist in developing and maintaining effective risk management protocols. Thus, the objective of this study is to develop a human error probability model considering various internal and external factors affecting seafarers' performance.MethodsThe human error probability model is developed using probability theory applied to Bayesian network. The model is tested using the data received through the developed questionnaire survey of >200 experienced seafarers with >5 years of experience. The model developed in this study is used to find out the reliability of human performance on particular maintenance activities.ResultsThe developed methodology is tested on the maintenance of marine engine's cooling water pump for engine department and anchor windlass for deck department. In the considered case studies, human error probabilities are estimated in various scenarios and the results are compared between the scenarios and the different seafarer categories. The results of the case studies for both departments are also compared.ConclusionThe developed model is effective in assessing human error probabilities. These probabilities would get dynamically updated as and when new information is available on changes in either internal (i.e., training, experience, and fatigue) or external (i.e., environmental and operational conditions such as weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress) factors.

Human Reliability Analysis (HRA) Using Standardized Plant Analysis Risk-Human (SPAR-H) and Bayesian Network (BN) for Pipeline Inspection Gauges (PIG) Operation: A Case Study in a Gas Transmission Plant

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Asam Posfat (H3PO4): Ionic Transformation of Phosphoric Acid in Aqueous Solution

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Critical success factors of risk‐based inspection

“…It started with the inspection and maintenance planning for fixed steel offshore structures [79][80][81][82] and later the usage of the same methodology was expanded to other areas and structures such as breakwaters [83], pipelines [84][85][86][87][88], ships and tankers [89][90][91][92][93][94], petroleum facilities [48,95,96], process plants [97,98], and bridges [99,100]. Not only has it expanded to various area of application, research on risk-based approach has also flourished in several new areas of study such as risk-based integrity modeling [67][68][69][70][71][72][101][102][103][104][105], risk-based optimization modeling [106,107], failure probability analysis [108,109], and human error reliability [110][111][112][113][114][115][116]…”