The terms Preventive and Predictive maintenance are often interchanged, but drastically different. Knowing the difference can help save you time and money with your maintenance plan.
Preventive is good…but is it enough?
Preventive Maintenance (PM) is the care and servicing of assets to keep equipment and facilities in satisfactory operating condition. PM strategies include:
- Routine inspection
- System tests
- Oil changes/lubrication
- Measurements and adjustments
- Parts replacement
- Record keeping of equipment deterioration
PM keeps to a set schedule, either determined by the asset’s manufacturer or a company’s maintenance staff. Better than a “fix it when it breaks” approach, PM helps prevent equipment failure by systematically replacing deteriorating components and/or identifying and correcting issues before they lead to failure.
While a PM schedule will help reduce unplanned downtime, it does little to reduce maintenance costs for labor and spare parts. Determining the ideal PM time isn’t an exact science, because it focuses on estimates rather than equipment condition, resulting in unnecessary work.
This leads to replacement of perfectly good components, and can sometimes create new problems. Studies show that 30 percent of PM is unnecessary, and another 30 percent can be harmful if human error causes collateral damage, leading to additional downtime.
Condition monitoring for predictive maintenance
In a sense, Predictive Maintenance (PdM) encompasses preventive maintenance, but does it in a way that minimizes costs by performing maintenance only when necessary.
This approach minimizes unplanned downtime and reduces repair costs, including labor and part inventory. Employing a condition monitoring solution that tracks asset/system pressure, temperature, and humidity levels is the most effective predictive maintenance strategy, because it covers three critical bases:
- Provides real-time and historical data trends of assets and processes.
- Allows operators to detect and diagnose any issues that could snowball into problems.
- Delivers analytics and alerts to operators when needed.
That last one is especially important. Condition monitoring for predictive maintenance gives operators the power to predict and improve processes, so they can optimize systems and assets based on what’s actually happening rather than simply reacting to unexpected events…or fixing problems that may not even exist.
So, what’s the solution?
Too much of a good thing is great, but can be expensive. Applying a predictive maintenance solution to every asset in a facility just isn’t cost-efficient. Consider what’s practical before implementing a predictive maintenance solution, and give preference to assets and systems that are process-critical.
If failure of a particular asset or system will do little to no damage to the overall process, you can probably get away with a preventive, or even reactive, maintenance strategy…though we don’t recommend that last one.
A properly balanced predictive/preventive maintenance strategy will:
- Save labor costs exponentially.
- Allow for fast and precise diagnoses and identification of issues.
- Simplify the monitoring process; what once took hours will take minutes.
- Reduce troubleshooting time.
- Drastically reduce repair inventory.
- Increase process efficiency, maximizing throughput.
- Maximize asset lifespan.
The good news
When used in tandem, Parker’s SensoNODE™ Blue sensors and Voice of the Machine™ Mobile software provide an accurate and easy-to-use condition monitoring for predictive maintenance solution that uses Bluetooth technology to transmit data wirelessly.
Designed to work seamlessly together, SensoNODE Blue sensors and Voice of the Machine software allow less staff to quickly monitor more points of importance with increased accuracy, helping them cover more ground in the same amount of time, or even faster. Read more about the SensoNODE solution.
Learn more about our processing and packaging solutions here.
Contributed by Dan Davis, product sales manager for SensoNODE Sensors and SCOUT Software, Parker Hannifin.