Optimal Inspection Model for a Marine Compression Ignition Engine Crankshaft Using Delay Time Analysis (DTA) and Bayesian Network (BN)
Clement A. Idiapho *
Federal University of Petroleum Resources, Effurun, Delta State, Nigeria.
Ogheneovo S. Akperhe
Federal University of Petroleum Resources, Effurun, Delta State, Nigeria.
Sani. I. Awwal
Federal University of Petroleum Resources, Effurun, Delta State, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
This research emphasizes the importance of determining an optimal inspection interval tailored to the operation of marine diesel engine crankshafts on ships. The primary goal of establishing such an optimal inspection time or interval is to minimize the costs associated with crankshaft inspections and reduce downtime, thereby alleviating the financial strain on companies regarding equipment inspections. The study utilized two methodologies: delay time analysis (DTA) and Bayesian network (BN) to identify and evaluate the factors that lead to diesel engine crankshaft failures. A DTA was conducted to determine a cost-effective duration for engine crankshaft operation, while BN was employed to identify nodes illustrating the contributing factors or conditions that may lead to the failure of the Mitsubishi marine diesel engine crankshaft. Once the influencing factors and their states were defined, the likelihood of the crankshaft functioning or failing was determined. The analysis indicated that when the lubricating oil was discovered to be in good condition, there was a 70% probability of the crankshaft operating successfully and a 30% chance of failure. In contrast, the operating temperature was assessed to be 59% normal and 41% high. However, when the quality of the lubricating oil was classified as poor, the probabilities shifted to 14% for the crankshaft functioning and 86% for failure, with a high operating temperature of 79%, 67% of fatigue present, and 51% of misalignment recorded. Consequently, the findings from the BN analysis suggest that these probable factors significantly affect the performance of the crankshaft. Additionally, the results from the cost model estimation and downtime model estimation indicated that a 5-hour inspection interval is optimal, with cost and downtime estimates of $73.02 and 0.0756 hours, respectively. It can be concluded that timely inspections of the engine crankshaft can prevent premature failures.
Keywords: Delay time analysis, Bayesian network, cost model, crankshaft, failure rate