Predictive maintenance refers to the maintenance strategy employed to determine the condition of in-service equipment, in order to predict when an equipment/device failure might occur. The strategy is driven by predictive analytics and it helps businesses or organizations to plan maintenance before the occurrence of the actual failure. This, in turn, helps organizations to reduce costs and maximize device or equipment uptime, as the equipment is shut down and sent for maintenance right before its imminent failure.
Predictive maintenance is considered a condition-based maintenance. It allows scheduling of corrective maintenance and prevent unexpected machine/equipment failures by performing the maintenance at the right time.
Unlike preventive maintenance, predictive maintenance is carried out on the basis of the actual condition of the machine or equipment to prevent equipment failures. In preventive maintenance, maintenance is carried out at regular intervals even when the equipment is in working condition, in order to prevent its sudden breakdown. The main advantage of predictive maintenance is that it reduces costs by preventing unplanned reactive maintenance and also by reducing the costs associated with preventive maintenance at regular intervals.
Further, by performing periodic or continuous monitoring of equipment, the maintenance strategy ensures that a machine or equipment is shut down only when its failure is imminent. This not only allows businesses to take up the maintenance activity when it is most cost-effective to do so but also minimizes the production hours lost due to sudden failures and the time period for which the equipment is being maintained.
In this maintenance system, the condition of an equipment or device is carried out with the help of testing technologies like infrared, acoustic (partial discharge and airborne ultrasonic), corona detection, vibration analysis, oil analysis, sound level measurements, and some other specific online tests. IoT plays a key role in predictive maintenance by connecting different systems and by sharing and analyzing the collected data.
The costs of some monitoring techniques and condition monitoring equipment used for predictive maintenance are quite high. Moreover, high levels of skills and expertise are required to interpret and analyze the condition monitoring data. So, the upfront cost associated with this maintenance system is higher than preventive maintenance. To deal with this, many companies avail the services provided by condition monitoring contractors.
Therefore, it is advisable to consult the manufacturer of the particular equipment, along with condition monitoring experts to find out if predictive maintenance is suitable for your assets/equipment. In general, this maintenance system is ideal for equipment or devices that perform critical functions, or have failure modes that can be predicted efficiently with periodic monitoring.
The increasing dependence on big data and IoT is boosting the market for predictive maintenance. The market is projected to worth $11 billion globally by the year 2022. So, in the coming days, we can expect rising demand for and adoption of this maintenance system across
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