Predictive maintenance is maintenance that directly monitors the condition and performance of equipment during normal operation to reduce the likelihood of failures. It attempts to keep costs low by reducing the frequency of maintenance tasks, reducing unplanned breakdowns and eliminating unnecessary preventive maintenance.
With predictive maintenance, organizations consistently monitor and test conditions such as lubrication and corrosion. Methods for accomplishing predictive maintenance include infrared testing, acoustic (partial discharge and airborne ultrasonic), vibration analysis, sound level measurements and oil analysis. Computerized maintenance management systems (CMMS), condition monitoring, data integration, and integrated tools and sensors can also facilitate success with condition monitoring.
For example, CMMS empowers companies to define boundaries for acceptable equipment operation, import readings, graph results and automatically trigger an email or generate a work order when boundaries are exceeded.
Though the best maintenance programs include a balance of both, preventive maintenance and predictive maintenance are different strategies. Preventive maintenance is determined using the average or expected life cycle of an asset, whereas predictive maintenance is identified based on the condition of equipment.
While predictive maintenance is more complex to establish than a preventive maintenance schedule based on manufacturer recommendations, it can be more effective for a business to save time and money. For example, taking vibration measurements on an electric engine at recommended intervals more accurately detects bearing wear and allows organizations to take action such as replacing a bearing before total failure occurs.
Predictive maintenance evaluates the condition of equipment by performing periodic or continuous (online) equipment condition monitoring. Most predictive maintenance is performed while equipment is operating normally to minimize disruption of everyday operations. This maintenance strategy leverages the principles of statistical process control to determine when maintenance tasks will be needed in the future.
For example, rather than changing a vehicle’s oil because drives hit 3,000 miles, predictive maintenance empowers organizations to collect oil sample data and change the oil based on the results of asset wear. For predictive maintenance to be effective, it requires both hardware to monitor the equipment and software to generate the corrective work order when a potential problem is detected. Specific types of predictive maintenance include:
Additionally, tools such as CMMS, condition monitoring, connected tools and sensors, and data integration can help companies act on the analytics collected by these devices and sensors.
Whether you need to track assets through oil viscosity, temperature or vibration, the tools within CMMS systems can help develop accurate predictions when a piece of equipment will require maintenance or replacement.
Studies have shown that organizations spend approximately 80% of their time reacting to issues rather than proactively preventing them. Predictive maintenance puts predictive maintenance ahead of the game. It helps predict failures and actively monitor performance. As a result, it saves time and money. Organizations that commit to a predictive maintenance program can expect to see significant improvements in asset reliability and a boost in cost efficiency, such as:
The best predictive maintenance programs take time to develop, implement and perfect. The timeline to achieve gains such as these varies, but some clients see positive returns in as little as a year.
Predictive maintenance requires more time and effort to develop then a preventive maintenance schedule. To be truly effective, employees must be trained on how to use the equipment and interpret the analytics they pull. However, once the commitment is made, predictive maintenance can revitalize not only a maintenance team, but an organization as a whole. There are condition monitoring contractors who can perform the labor required and analyze the results for your organization.
Sometimes predictive maintenance is not the answer to maintenance woes. It might not be the most cost-effective method to manage all assets with predictive maintenance. For example, changing light bulbs on the plant floor. Rather than running diagnostics on the bulb, leveraging a run-to-failure strategy (waiting until the light bulb goes out to change it) makes more sense. There are a few factors to consider when identifying which assets should be considered for predictive maintenance:
There are many applications of predictive maintenance in a wide variety of industries such as:
Implementing a predictive maintenance program should be a methodical process from start to finish. The key is to have a long-term view of what to do in order to put all of the foundational components into place.
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