Classification of failures and failure time estimation within the scope of maintenance planning in production systems

This study evaluates and categorizes problems in a manufacturing plant to identify the major problems affecting productivity, with a particular focus on machine failures. First, historical failure data is quantitatively analyzed using Pareto analysis, the failures are ranked according to frequency and total downtime, and it is revealed that the oil management system is the most important cause of failures. Given the high competition in today's manufacturing industry, machine failures, especially those involving critical systems such as oil management, have significant impacts on business operations by affecting production planning and maintenance. To address this, the study also aimed to develop a prediction model for failures of the oil management system. Using 150 past failure records, the suitability of the Weibull distribution for modeling downtime was evaluated using the Least Squares Method (LSM) and Maximum Likelihood Estimation Method (MLE) in Minitab software. MLE was determined to be the most accurate method, providing reliable downtime predictions necessary for efficient maintenance planning and effective failure management, ultimately aiming to minimize failure-related costs and maximize production uptime.