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.
Predictive vs. preventive maintenance
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.
How does predictive maintenance work?
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:
Vibration analysis: Vibration sensors can be used to detect degradation in performance for equipment such pumps and motors.
Infrared: Infrared cameras are often used to identify unusually high temperature conditions.
Acoustic analysis: Acoustic analysis is performed with sonic or ultrasonic tests to find gas or liquid leaks.
Oil analysis: Oil analysis determines asset wear by measuring an asset’s number and size of particles.
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.
Predictive maintenance tools integrated into a CMMS
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.
Condition Monitoring: Within CMMS systems, condition monitoring tools help empower organizations to execute on predictive maintenance programs. Users can define boundaries of acceptable operation for assets and auto-generate work order or emails when readings fall outside of predefined boundaries.
Connected sensors & tools: These can offer real-time data streams to track events from anywhere and view AC/DC voltage, current, power and temperature data. By wirelessly syncing measurements taken using handheld tools and comparing them to condition monitoring data, organizations can gain the full picture of equipment efficiency and health.
Data integration: Data can be integrated into CMMS functionality to enable the completion of seamless workflows on a mobile device. This allows maintenance teams to respond to fault notifications while they are on the move, and then they can create, access or process work orders related to the notification in real time. Planned and unplanned maintenance is better coordinated, unscheduled downtime is reduced and response times to problems or systems failure are improved.