Effectiveness of Industrial Machine Maintenance with Condition-Based Monitoring Method

Abstract

Effective industrial machine maintenance is crucial for ensuring the long-term reliability, safety, and operational efficiency of production systems. Traditional methods such as Time-Based Maintenance (TBM) often lead to excessive downtime and unnecessary costs due to their inability to address real-time machine conditions. To overcome this limitation, Condition-Based Monitoring (CBM) has been developed as a more efficient approach. CBM allows real-time monitoring of machine conditions, performing maintenance only when necessary, based on the actual condition of the machinery. This research aims to evaluate the effectiveness of the CBM method in industrial machine maintenance, focusing on cost efficiency, reliability, and performance improvements. The study uses a literature review approach, involving the collection and analysis of relevant academic articles on the implementation of CBM across various industrial sectors. The results show that CBM can reduce maintenance costs by up to 25% and extend machine life by reducing unforeseen failures. Moreover, the implementation of CBM also increases machine reliability and reduces unplanned downtime by providing more accurate predictions of machine failure. However, challenges such as high initial costs and the need for adequate training remain barriers. Therefore, CBM proves effective in improving industrial machine maintenance efficiency and reliability, although challenges in its implementation persist.