Predictive maintenance (PdM) is a strategy that directly monitors the condition and performance of equipment during normal operation to reduce the likelihood of failures. Through condition monitoring, predictive maintenance attempts to keep costs low by reducing the frequency of maintenance tasks, reducing unplanned breakdowns, and eliminating unnecessary preventive maintenance.
Organizations monitor and test conditions such as lubrication and corrosion with predictive maintenance. 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, companies using a CMMS can define boundaries for acceptable equipment operation. Users can import readings or graph results. And the software automatically triggers an email or generates a work order when limits are exceeded.
Predictive Maintenance vs. Preventive Maintenance
Though the best strategies include both preventive maintenance and predictive maintenance, they are very different strategies. So, what’s the difference between predictive maintenance vs. preventive maintenance? Teams plan preventive maintenance based on an asset’s expected life cycle, while predictive maintenance is identified based on equipment conditions.
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 a Predictive Maintenance Program Work?
Predictive maintenance evaluates equipment condition by performing periodic or continuous (online) equipment condition monitoring. Teams perform most predictive maintenance while equipment operates normally, which minimizes the disruption of everyday operations. If you strategize your maintenance and leverage the principles of statistical process control, you can determine when maintenance tasks are needed.
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 a predictive maintenance program to be effective, teams need to implement predictive maintenance technologies, like condition monitoring sensors that let teams monitor equipment. Connected software generates corrective work orders when a potential problem is detected. Specific types of predictive maintenance techniques include:
- Vibration analysis detects degradation in performance for equipment such as pumps and motors.
- Infrared cameras identify unusually high-temperature conditions.
- Acoustic analysis finds gas or liquid leaks.
- Oil analysis determines asset wear by measuring an asset’s number and size of particles.
Additionally, predictive maintenance 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
A CMMS software can incorporate data from condition monitoring sensors to help determine when equipment needs maintaining or replacing.
- Condition monitoring: Within CMMS systems, condition monitoring tools help empower organizations to execute on PdM 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 into a CMMS completes the seamless workflow from PC to 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. Teams that coordinate planned and unplanned maintenance, reduce unscheduled downtime, and improve response times to problems or failures.
How Much Can You Benefit From a Predictive Maintenance Program?
Studies have shown that organizations spend approximately 80% of their time reacting to issues rather than proactively preventing them. It helps predict failures and actively monitor performance. As a result, it saves time and money. Organizations that commit to a PdM program can expect to see significant improvements in asset reliability and a boost in cost efficiency, such as:
- 10x Return on Investment (ROI)
- 25-30% reduction in maintenance costs
- 70-75% elimination of breakdowns
- 35-45% reduction in downtime
- 20-25% increase in production
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 Pros & Cons
Predictive maintenance requires more time and effort to develop than a preventive maintenance schedule. Train your employees on using the equipment and interpreting the analytics they pull. PdM revitalizes a maintenance team and an organization as a whole. Fluke Reliability offers condition monitoring contracting to perform required labor and analysis for your organization.
When PdM Does Not Make Sense
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. It makes more sense rather than running diagnostics on the bulb, leveraging a run-to-failure strategy (waiting until the light bulb goes out to change it). There are a few factors to consider when identifying which assets should receive predictive maintenance:
- What is the impact on production if the asset failures unexpectedly?
- Can cost-effective tasks be performed proactively to prevent, or to diminish to a satisfactory degree, the consequences of the failure?
- What is the average cost of repairing this asset?
Predictive Maintenance Applications
There are many applications of predictive maintenance in a wide variety of industries, such as:
- Finding three-phase power imbalances from harmonic distortion, overloads, or degradation or failure of one or more phases
- Identifying motor amperage spikes or overheating from bad bearings or insulation breakdowns
- Locating potential overloads in electrical panels
- Measuring supply side and demand side power at a common coupling point to monitor power consumption
- Capturing increased temperatures within electrical panels to prevent component failures
- Detecting a drop-in temperature in a steam pipeline that could indicate a pressure leak.
How to Implement a Predictive Maintenance Strategy
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 to put all of the foundational components into place.
Design your Program
Get positive buy-in from management and be prepared to discuss and quantify the benefits and goals. Frequently failing equipment provides the most potential for cost reductions and reliability improvements. Compare the cost of implementing a PdM to the average cost of equipment failures. As stated above, sometimes it does not make sense. Depending on the asset, a corrective method of maintenance could be cheaper.
Select your Technology
Choose which of the above technologies would be most effective to monitor the condition of your equipment. Is your organization more interested in vibration analysis, infrared thermography, ultrasonic inspection, or oil analysis? Select the tools that will provide that information.
Allocate Proper Resources
Develop and train an implementation team to perform activities. Carve out time in the schedule for tasks, such as data collection, analysis, reporting and tracking, and allocate funding for PdM technology investments or a contractor to assist.
Perform System Integrations
Leverage the tools within and integrate them into a CMMS to help turn condition monitoring data into action. For example, a company offering equipment monitoring services, lubrication engineering, and reliability engineering can record negative diagnostic reports and automatically generate corrective work orders.
Coordinate PM & Predictive Maintenance Programs
Leveraging both preventive and PdM makes for the best maintenance programs. Use each method where applicable and decide which strategy to apply based on disruption due to equipment downtime, cost of parts and labor time, and equipment history.
Utilize CMMS Reports & Dashboards
With reporting and dashboard tools, organizations can consistently document work order history, failures, costs, and trends. This helps to track progress for key stakeholders.
Fluke Reliability Makes Your PdM easier
At Fluke Reliability, we believe in an interconnected industrial workspace. Our technology talks to each other, from eMaint CMMS to our sensors, tools, and other software. We look to the future of IIoT and its impact on customer work-life.
To learn more about predictive maintenance programs and practices, speak to one of our specialists.