Technical Management Perspective Considering Ballast Water Treatment System (BWTS) Failure Statistics

Yapıcı M., Şenvar Ö.

6th GMC’26 – Global Maritime Congress, İzmir, Türkiye, 28 - 29 Eylül 2026, ss.1-18, (Tam Metin Bildiri)

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: İzmir
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.1-18
  • Galatasaray Üniversitesi Adresli: Evet

Özet

In accordance with the IMO Ballast Water Management Convention and USCG regulations, which entered into force to prevent the transfer of invasive species in global maritime trade, Ballast Water Treatment Systems (BWTS), which incorporate various failure risks in operational processes due to complex technical structures, have become mandatory on board ships. However within these systems, hybrid technologies like advanced filtration, electrolysis, and UV trigger significant operational risks when combined with the harsh conditions of the marine environment, including corrosion, high vibration, and variable water quality. This study aims to perform a reliability analysis of BWTS units used on ships from a technical management perspective and to derive statistical inferences from failure data. Within the scope of the research, Failure Mode and Effects Analysis (FMEA) was conducted and Risk Priority Numbers (RPN) were determined using data obtained from the systems and service reports. The findings reveal that sensor groups in filtration units and electrolysis power modules are the most critical failure points, and  failure distributions predominantly reveal the relationship between operational variables and maintenance-upkeep performance. Statistical results provide quantitative evidence of the burden that unexpected system downtimes impose on technical operating costs. Furthermore, the study substantiates the negative impacts of disruptions in vessel schedules and potential legal or financial sanctions from port authorities on the technical management budget. In conclusion, the study recommends to technical managers as a primary strategy the adoption of data-driven predictive maintenance models instead of reactive maintenance, the optimization of spare parts inventory based on failure frequencies, and the restructuring of personnel training processes in light of these statistical outputs.