Jet dyeing machines, essential for producing high-quality and environmentally friendly textiles, face persistent issues with defects that lead to production stoppages, compromised cloth quality, and significant financial losses for companies. These challenges hinder operational efficiency and undermine the competitive edge of textile manufacturers in a rapidly evolving market. Jet Dyeing machines continue to innovate to produce high quality and environmentally friendly textiles, with the discovery of defects causing cloth production to stop, cloth quality to decline, and company losses. The Fuzzy-FMECA approach enhances accuracy and adaptability in identifying failure risks, improving maintenance for complex jet dyeing systems. This study aims to identify the root causes of jet dyeing machine damage for preventive maintenance design. Studies using robust fuzzy-FMECA can identify critical components of jet dyeing machines with a high degree of accuracy. This can improve machine reliability and reduce fabric quality failures. The dominant machine failures identified in jet dyeing components are leakage, short circuits, and installation errors. The Pareto analysis shows that leaks, tears, and short circuits are responsible for over 70% of total failures. The most critical components include the main pump and electric socket, both with an RPN score of 7.42, representing a significant 30% of overall risk. Other high-risk components such as the steam pipe packing and heat exchanger steam pipe also have an RPN of 7.25. These findings indicate that over 60% of the failures arise from just a few key components. These findings have succeeded in identifying the critical components of the jet dyeing machine (main pump and socket) which have the highest potential risk of failure. The proposed preventive maintenance design can reduce these risks, but needs to be refined with consistent, competent and monitored inspections. The preventive maintenance design significantly mitigates risks, requiring ongoing refinement through regular, skilled, and supervised inspections to ensure optimal effectiveness.