Aplikasi teorema bayes dalam mendukung aktivitas autonomous maintenance di pabrik gula Kedawoeng

Vivi Pathrecia Susanto, Ivan Gunawan, Lusia Permata Sari Hartanti


Autonomous Maintenance (AM) is one of the main activities in Total Productive Maintenance (TPM). This article discusses the implementation of AM at the diffuser station at the Kedawoeng Sugar Factory using Bayes' theorem. To maintain the continuity of sugar production, the Kedawoeng Sugar Factory needs to improve the maintenance system. AM improves the maintenance system without depending on the limited maintenance personnel in the factory. The AM concept demands that the production operator be involved in the maintenance process. Increasing the operator's ability to diagnose damage through damage symptoms is necessary. The Bayes theorem successfully helps operators predict machine failure to take quick and effective action to prevent a more significant impact. This study identified 11 machine malfunction symptoms that can be detected through the five senses that lead to 14 failures. One of the research findings is that if the symptoms are only abnormal sounds, the highest probability of motor-bearing gear failure is 0.762. Suppose a warning accompanies the abnormal sound on the control panel probability of motor-bearing gear failure increases to 0.987. A clear division of machine maintenance responsibilities between operators and maintenance technicians and maintenance training for operators are suggestions for the next steps in implementing TPM.


autonomous maintenance; bayes theorem; sugar mill


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