What is the difference between preventive maintenance and predictive maintenance?

You’ve caught spring fever and now you own a beautiful little new car. However, for ecological and economic reasons (the price of gas having reached record highs!), you alternate between the bicycle, the bus and your new car to get to work. In the past three months, you have travelled approximately 2,000 km. By reading the owner’s manual, you discover that you must change the oil every three months or every 3500 km. So you conscientiously decide to make an appointment and bring your car to the garage for an oil change, which is supposed to keep your car in good condition. You have therefore carried out a preventive maintenance activity on your new car. Even if it is a good idea to check, inspect, repair equipment at regular intervals, it is important to know that the manufacturers’ guidelines are developed for average conditions of use, not for your actual conditions.

Now imagine that you own a luxury car: for example, a BMW with condition indicators for oil, air filters, etc. You drive the car for six months, drive about 5,000 km, then suddenly an alert appears on your dashboard indicating that less than 500 km remain before you need to change the oil. This is an example of a predictive interview. It prevents failures and gives you service reminders that reflect the actual use of your car with sufficient notice to fix the problem before the machine fails.

As the old saying goes, “If it’s not broken, don’t fix it.”

Preventive and predictive maintenance are two proactive methods that pursue the same objectives, but have very different approaches. Indeed, these two elements are often confused if the terminology is not well understood.

Preventive maintenance

Preventive maintenance is usually managed with the help of CMMS software (computer-aided maintenance management). It is based on the fundamental principle that the risk of failure of a piece of equipment gradually increases as it ages. The technicians therefore perform a preventive maintenance work order such as vibration measurement, lubrication, etc. to avoid a breakdown. Preventive maintenance is based on a fixed schedule and measures according to time, age, distance or usage. The advantage of this type of maintenance is that it increases the life of the equipment and provides greater peace of mind than emergency maintenance.

However, it does not take into account the actual condition of the equipment or parts). This type of maintenance sometimes leads to unnecessary or inadequate maintenance. When equipment is maintained too frequently, it is considered a waste of time and money, and if it is maintained too infrequently, breakdowns occur and we are back to an unplanned, much more costly and energy-intensive emergency maintenance.

In addition, in a context of a shortage of specialized labour and technical staff turnover, preventive maintenance often leads to new breakdowns or malfunction of the machine after restarting. It is generally accepted that, in 20% of cases, this new problem is due to human error: forgetting a stage, a part, lack of experience, lack of clear work instruction or adequate training.

Predictive Maintenance 4.0

Predictive maintenance gains in interest because doing preventive maintenance remains very costly in time and resources. In addition, the equipment sometimes still breaks down or does not function optimally. Smart factory technologies, including the Industrial Property Internet (IIOT), Artificial Intelligence (AI) and Infonuagic data have reached such an economic threshold that they offer new opportunities to be seized. Indeed, predictive maintenance consists in taking the required actions on a machine, just in time, before anything happens to it. The analysis is based on the history of the machine’s vital data and the actual condition of the equipment rather than its age or time of use.
Sensors allow critical data to be monitored when the equipment is operating at full power. No need to stop equipment for safety reasons or to empty a conveyor load. No downtime – planned or unplanned – is required to diagnose a machine. With sensors and IIOT (Industrial Internet of Things), you can collect important machine data and send alerts to your Enterprise Resource Planning (ERP) system or to your own ERP system.

Conclusion

In the past, predictive maintenance was often considered fiction because it required complex test equipment to generate data and had very high implementation costs. Data interpretation required highly qualified and rare professionals. Real-time monitoring of equipment status, 24 hours a day, 7 days a week, has been available for many years. What is really new today with the intelligent plant is the ability to analyze data and create predictive algorithms to anticipate and prevent possible failure at more affordable costs than in the past.

Today, predictive maintenance is easier to implement, easier to set up with service technicians and finally, more cost-effective for your critical machines. Powered by reliable data from IIOT sensors while machines are operating at full capacity, predictive maintenance detects potential failures and tells you what may break early enough for jobs to be well prepared and efficiently executed with parts ordered just in time.

This is what we offer to plant and maintenance managers: the predictive maintenance system SIS-X from Andromedia Technologies. To learn more about what a predictive maintenance system can do for your plant, we invite you to visit our dedicated SIS-X web page. Don’t hesitate to request a free online demo, to see the SIS-X system in action!

You can also download our free eBook on the intelligent factory 4.0.

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