Predictive maintenance is an advanced form of planned maintenance that monitors asset conditions in real time. It is a proactive strategy based on sensors that alert maintenance teams when preventive maintenance is needed to maintain optimal performance levels. The primary objective of predictive maintenance is to reduce unexpected breakdowns and maintenance downtime. This type of maintenance seeks to define the best time to work on an asset, so that the frequency of maintenance is as low as possible and the reliability is as high as possible without incurring unnecessary costs. Predictive maintenance is one of the most revolutionary advances, offering significant advantages compared to traditional maintenance techniques.
It is several times more effective than traditional reactive and planned maintenance techniques, since it leads to shorter downtime and a significant reduction in repair costs. Predictive maintenance ensures that tasks are carried out at the right time, and compared to preventive maintenance, it ensures that equipment that requires maintenance only shuts down just before an imminent failure occurs. The three main types of predictive maintenance are vibration analysis, oil analysis, and motor circuit analysis. Vibration analysis monitors changes in a machine's vibrations, setting normal vibration levels and alerting managers only if a machine deviates from usual levels. Oil analysis involves extracting oil from a machine and analyzing it for wear particles, the presence of water, and viscosity.
Motor circuit analysis monitors the stator and rotor of a motor for ground faults or contamination. The Internet of Things (IoT) is essential for implementing a successful predictive maintenance program, as are sensors and predictive maintenance techniques such as vibration analysis, oil analysis, thermal imaging, and equipment observation. Reliability-centered maintenance provides a systematic method for determining whether predictive maintenance is a good option as an asset maintenance strategy for the specific asset of interest. Although predictive maintenance has some drawbacks (high start-up costs, the need for specialized knowledge, and the limitations of some equipment), it allows maintenance to be carried out only when necessary, helping installations to reduce costs, save time and maximize resources. In general terms, the maintenance manager and the maintenance team use predictive maintenance tools and asset management systems to monitor impending equipment failures and maintenance tasks.
