Intelligent ship inspection analytics: Ship deficiency data mining for port state control

Abstract

To provide safe, secure, and environment-friendly maritime transport, it is an essential point to define and take appropriate countermeasures against the substandard ships in maritime transport. Port State Control (PSC) is one of the critical inspection regimes recognized worldwide to define substandard ships. However, one of the biggest challenge faced in ship inspection is the necessity of checking many items through limited time and qualified human resources. Therefore, in this study, it is aimed to develop a decision support system to increase the effectiveness of ship inspections. In accordance with this, we proposed an intelligent ship inspection analytics (I-SIA) model based on the Knowledge Discovery in Database (KDD) process by utilizing fuzzy c-means clustering and apriori algorithms. The I-SIA model provides the determination of ship deficiency patterns based on specific ship attributes, and subsequently predicts main/sub inspection items to be focused on through former inspection records of the ship to be inspected. In the case study performed for a selected ship, I-SIA predicts critical 5 main and 23 sub-inspection items that need to be checked with high priority, among 17 main inspection items and out of more than 500 sub-inspection items. Herewith, I-SIA provides a ship-specific and dynamic ship inspection based on deficiencies recorded in previous inspections and detected during inspection. Thus, it becomes possible to enhance the effectiveness of the inspection via defined deficiency patterns.