A majority of production equipment worldwide today experiences failures in its working life cycle and must be repaired and maintained. Since production downtime incurs enormous financial implications, it is extremely crucial to find solutions for taking preventive action. Moreover, the use of sophisticated equipment—whose spare parts are expensive and sometimes unavailable due to political or technical limitations—has elevated the status of maintenance systems within companies, with it becoming a key determinant in productivity. A maintenance system not only reduces the final cost of production but also impacts the overall organizational value chain. Elements such as production speed and delivery time, product quality, production line reliability, and organizational flexibility all come under the influence of maintenance decisions. Therefore, in industries such as petrochemicals, where high-level machinery is utilized in the production systems, equipment reliability and maintenance gain greater significance. In this study, to analyze and predict equipment failures by using data mining methods, a 1304640 record dataset measured between December 20, 2021, and May 20, 2022, from 17 measurement sensors on one of the 3000 -series compressors of the Mahabad Petrochemical Complex were used. These data were examined, analyzed, and mined with Weka software. The statistical summaries and correlation relationships among the data were studied initially. Then, the reasons for equipment failure and the critical path leading to compressor shutdown were evaluated using the application of K-Means clustering, decision trees, and feature selection methods. Finally, based on the findings, recommendations were provided to focus maintenance efforts on critical sensors.