Abstract
AbstractA new symbiotic evolution‐based fuzzy‐neural diagnostic system (SE‐FNDS) for fault diagnosis of propeller‐shaft marine propulsion systems is proposed. The SE‐FNDS combination of fuzzy modeling, back‐propagation training, and symbiotic evolution function auto‐generates its own optimal fuzzy neural architecture, a significant advantage over previous time‐consuming manual parameter determination. Two hundred and forty samples from a test propeller‐shaft system are taken over a range of 100 to 500 rpm, during normal and experimentally‐induced faulty operation. Compared to three traditional methods, diagnostic decisions from SE‐FNDS show 93.75% agreement with real conditions and less CPU time for system construction. The presented design is useful as a core module for more advanced computer‐assisted diagnostic systems and for direct application in marine propulsion systems.
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