Predictive Maintenance

Predictive maintenance is an area within maintenance in the industry based on CBM (Condition Based Monitoring), whose objective is to extend the useful life of assets and thereby reduce costs, reducing unscheduled stops and downtime.

Why is predictive maintenance important?

Predictive maintenance identifies possible failure modes in equipment, which can prevent breakdown. This type of maintenance, whose perspective is more preventive, saves money because it avoids costly breakdowns and reduces downtime.

The benefits of predictive maintenance include reduced costs, increased productivity, improved quality, and fewer unscheduled interventions.

By implementing predictive maintenance techniques, companies can significantly reduce these repair costs and be able to carry out scheduled interventions taking into account reduction in downtime, material collection with estimated time as well as labor hours.

How to implement a predictive maintenance program

There are several ways to implement a predictive maintenance program in your business.

The best and most effective option is to hire a professional company to do the work for you. In this case, ROYSE is the solution to your problem.

Predictive maintenance programs help companies identify potential problems before they become costly problems. By doing so, companies can avoid downtime and reduce non-escalated repairs. The key to implementing a successful predictive maintenance program is creating a system that identifies potential problems before they occur.

The benefits of predictive maintenance

Predictive maintenance has been around since the 1950s, but it wasn’t until the early 2000s that it really started to gain traction. In fact, according to the International Society of Automation, there were fewer than 100 predictive maintenance systems installed worldwide.

A predictive maintenance program helps prevent breakdowns before they occur. This allows companies to spend less on parts and labor when there are no major problems.

Predictive maintenance is not only used to reduce production stops but also to extend the useful life of assets, and perform proactive tasks or reduce preventive tasks such as alignment verification, bearing lubrication, imbalances and/or wear on internal elements. of the assets, being able to carry out maintenance using predictive techniques.

In addition, predictive techniques can also be applied to equipment after commissioning or start-up as verification of correct installation and delivery of assets in facilities.

Increased availability of machinery
Improved overall reliability
Less raw material losses due to unplanned stops
Reduction in the rate of interventions and spending on spare parts
Increased security thanks to monitoring
Reduction in general and catastrophic failures thanks to root cause analysis

5 steps to perform predictive maintenance

Machine operation monitoring

It is the first stage and consists of having the characterization of the equipment through the data extracted from the applied techniques to know the state of the machinery. Data collection can be carried out offline and online depending on the criticality of the asset and the client’s request.

Each process will have its own relevant parameters, as well as different reading frequencies.

Process modeling and targeted maintenance

Once there is a history and characterization of the machine’s signature, the stage of generating a model based on historical data begins where we will see what behavioral trajectories have been produced.

At this point, algorithms are designed that relate the parameters to each other, in order to detect patterns that are repeated or that always occur under the same conditions of use and environment. From this moment, it will be possible to design a model of the normal behavior of the equipment, that is, how the set should respond under normal conditions.

Modeling of limit/alarm scenarios

Scenarios are understood to limit those operating environments under conditions in which the machinery will be more likely to fail. Thanks to this, it is possible to close the circle and delimit the operating scenarios, since both the “normal curve” and its limits are available.

CBM (Condition Based Monitoring) Maintenance

At this stage and having followed the previous steps, it will be possible to prioritize the truly necessary or critical actions. This allows the company to improve coordination between departments and facilitate planning.

Continuous CBM Monitoring

The last step consists of assuming its cyclical nature and carrying out monitoring to improve data collection systems and prediction of behavioral models.

Thanks to this last step, predictive maintenance systems manage to learn on the fly and be more precise when identifying patterns.