Dynamic Maintenance based on Fuzzy Logic

Currently, certain maritime assets face the challenge of optimizing their performance despite limited resources. They aim to minimize intervention actions on equipment while maintaining safety standards and acceptable performance levels. Ships, which are not yet autonomous, serve as maritime assets responsible for transporting personnel and systems. Keeping these ships operating at a high level of performance is crucial to ensuring the safety of both materials and personnel. This not only prevents damage to the ships but also reduces the risk of injuries to personnel and sea pollution. Organizations, the scientific community, and stakeholders have been actively developing advanced systems to monitor data from ship equipment within the scope of maintenance management. These efforts help prevent breakdowns and provide real-time information about the equipment's condition. These systems use various techniques for condition monitoring, including algorithms, statistical equations, and other methodologies applied to the collected data. In this study, Fuzzy Logic will be applied to data from selected equipment. Specifically, an air compressor from an ocean patrol vessel has been chosen for the case study. This air compressor is essential for Navy ships and has been selected by the Organization's Maintenance Management Centre to monitor working hours and operational status.