Human errors are significant contributors to the overall risk in the industry. In fact, 80% of marine failures and shipping accidents are caused by human errors. Human errors, additionally, are often a root or significant cause of a system failure which could lead to tremendous undesirable consequences such as fatalities and financial losses. Currently, most industries use the latest technologies in order to maximise the availability and reliability of their equipment and minimise human interventions. However, the human role is still vital in different phases of a plant life cycle, especially during maintenance. Traditional Reliability, Availability and Maintainability (RAM) methodology, a well-known methodology for optimising maintenance strategies in an organisation, does not incorporate human reliability analysis. Therefore, this research aims to develop a novel framework for improved maintenance engineering decision-making, which can be achieved by fusing probabilistic human and hardware availability assessment techniques. The proposed framework has a systematic process that helps analysts, engineers, and decision-makers consider and manage risk effectively in a complex system. A publicly available case study in an offshore oil and gas platform is selected as an example for demonstration purposes. The values of human error probabilities (HEPs) for maintenance activities are calculated by using the Human Error Assessment and Reduction Technique (HEART). These maintenance activities' reliability values are improved by using a novel Human-based Decision-Making Grid (H-DMG). This framework, including H-DMG, can improve a system's availability, reduce downtime cost, and reduce human errors.
|Date of Award||31 Aug 2021|
- The University of Manchester
|Supervisor||Moray Kidd (Supervisor) & Richard Kirkham (Supervisor)|
- Human reliability assessment
- Reliability, Availability, and Maintainability
- Decision-making Grid