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Imagine a production machine exhibiting an unusual increase in vibration while in operation.
Through sensors and data analysis, maintenance teams can detect these symptoms before machine failure occurs, allowing component replacements to be scheduled without abruptly halting production. This approach is known as predictive maintenance, a maintenance strategy that utilizes actual equipment condition data to predict when a component is likely to fail. This information is obtained through various condition monitoring techniques such as vibration analysis, infrared thermography, lubricating oil analysis, and Internet of Things (IoT)-based sensors. By performing maintenance only when there are indications of deterioration, predictive maintenance can improve asset reliability while optimizing maintenance costs (Jardine, Lin, and Banjevic, 2006).
In contrast to these approaches, corrective maintenance is a maintenance strategy implemented after a component or system experiences damage. In other words, repairs are only undertaken when the equipment is no longer capable of performing its intended function. This strategy is often referred to as run-to-failure maintenance because the equipment is operated until it fails before replacement or repair is required (Mobley, 2002). Corrective maintenance is generally applied to components that are not critical, have low replacement costs, or do not pose significant consequences if damaged. While seemingly simple because it does not require a complex monitoring system, this approach has the potential to cause unplanned downtime when applied to equipment that plays a vital role in the production process.
The main differences between predictive maintenance and corrective maintenance lie in implementation time, cost, and operational risk levels. Predictive maintenance is proactive because maintenance actions are taken before failure occurs based on an analysis of the asset’s condition. This approach requires a larger initial investment in sensors, analytical software, and skilled labor, but can reduce the cost of major breakdowns, reduce downtime, and extend equipment lifespan. Conversely, corrective maintenance has a relatively low initial implementation cost because it does not require a dedicated monitoring system. However, if a critical piece of equipment suddenly fails, the costs of emergency repairs, lost production time, and potential disruptions to workplace safety can be significantly higher than the costs of planned maintenance (Dhillon, 2006).
Each strategy has advantages and limitations depending on the characteristics of the assets being managed. Predictive maintenance excels in improving reliability, reducing the risk of sudden failure, extending component life, and optimizing maintenance schedules based on actual conditions. However, its implementation requires investment in technology, data collection systems, and adequate analytical capabilities. On the other hand, corrective maintenance has the advantage of simple implementation and low initial costs, making it suitable for components that are inexpensive or do not significantly impact operational continuity. However, this strategy has disadvantages such as a high risk of sudden failure, emergency repair costs, potential production losses, and the possibility of further damage to other components if failures are not promptly addressed (Wireman, 2014).
Thus, no single maintenance strategy can be universally applied to all types of industrial equipment. The choice between predictive and corrective maintenance must consider the criticality of the asset, the cost of failure, safety risks, and the company’s operational objectives. In practice, many organizations combine both approaches within an asset management framework to achieve a balance between cost efficiency and system reliability. Through the right maintenance strategy, companies can not only minimize downtime and operational costs but also increase productivity, safety, and competitiveness amidst industrial developments that increasingly prioritize reliability and data-driven decision-making.
Writer: Brian Arga Prasidio Putra
Editor: Brian Arga Prasidio Putra
Reference
Dhillon, B.S. (2006). Maintainability, Maintenance, and Reliability for Engineers. Boca Raton, FL: CRC Press.
Jardine, A.K.S., Lin, D. dan Banjevic, D. (2006). ‘A review on machinery diagnostics and prognostics implementing condition-based maintenance’, Mechanical Systems and Signal Processing, 20(7), hlm. 1483–1510.
Mobley, R.K. (2002). An Introduction to Predictive Maintenance. Edisi ke-2. Burlington, MA: Butterworth-Heinemann.
Wireman, T. (2014). Total Productive Maintenance. New York: Industrial Press.
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