PHYSICS-INFORMED HYBRID MODELING FOR PREDICTIVE CONDITION MONITORING OF A GEAR-DRIVEN COTTON GIN MACHINE
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Modern American Journals
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Rotating machinery reliability remains a cornerstone of industrial productivity, particularly in cotton processing plants where gin machines operate under highly variable mechanical loads. Unexpected drivetrain failures may cause substantial production losses and energy inefficiencies. This study proposes a physics-informed hybrid modeling framework for predictive condition monitoring of a gearbox-driven cotton gin machine powered by a 75 kW induction motor.