Predictive Maintenance


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Predictive or condition-based maintenance evaluates the condition of the machinery and recommends whether or not to intervene based on its condition, which produces great savings.

Predictive diagnosis of machinery was developed in the industry in the decade from the mid-eighties to the mid-nineties of the 20th century. Currently, predictive philosophies are applied to critical machinery in those plants that have optimized the management of their assets. Condition-based maintenance optimizes preventive maintenance so that it determines the precise moment for each technical maintenance intervention on industrial assets.

Predictive maintenance is a set of instrumented techniques for measuring and analyzing variables to characterize the operational condition of productive equipment in terms of potential failures. Its main mission is to optimize the reliability and availability of equipment at the minimum cost.

In this way, predictive analytics involves extracting an analytical model from historical data that predicts future behavior or estimates unknown outcomes.

The data that must be measured and collected depends on the techniques a company wants to use to monitor the equipment. It is possible to control vibrations, temperature, pressure, noise level or corrosion levels, among others, depending on what is most appropriate for your equipment.

Data mining for Predictive Maintenance

Accumulating data on the assets is useful because the sensors send all the information to a central system or software that allows you to analyze what is happening. Predictive maintenance is much more effective and much more accurate in systems in which the different assets are integrated.

Machine Learning for Predictive Maintenance

The most differentiating component of predictive maintenance is the construction and application of algorithms that offer a forecast. And based on result, maintenance can be planned avoiding extra labor and waste costs.

Now, with Artificial Intelligence becoming more and more sophisticated, it is possible to detect anomalies even earlier, find correlations, and receive intelligent suggestions to prevent a breakdown. This intelligent maintenance is giving rise to a new type of maintenance, prescriptive maintenance.

Predictive maintenance uses various statistical modeling, machine learning and data mining techniques to bring together all the technological information to make predictions for the future.

Data mining and analytical texts, in conjunction with statistics, allow the construction of predictive intelligence models, discovering trends and relationships, both in the set of structured and unstructured data.

Does predictive maintenance solve all problems?

Companies have assets that can suffer breakdowns and failures, but through good Maintenance Management, we can allow them to last longer, extending the useful life of the assets and making our company more efficient and profitable. Predictive Maintenance Management business is vital for the company since it allows us to anticipate possible problems of failures and breakdowns through relevant data and information.

Avoiding stoppages in companies due to machinery malfunctions and reducing breakdowns allows companies to be more profitable since by not wasting time repairing these problems, they do not lose money unnecessarily since they go ahead and invest in programs that can predict these possible breakdowns digitally.

Predictive maintenance was made to prevent breakdowns. There will always be random failures, which are impossible to predict or prevent. Furthermore, we cannot forget that predictive maintenance requires a large infrastructure. Therefore, predictive maintenance is only recommended for critical assets with predictable failure modes.

By understanding likely future outcomes, organizations can make better decisions and better anticipate outcomes by being more proactive than reactive (for example, predictive equipment maintenance).

Despite having the same purpose, predictive and preventive maintenance are different, but they form the same batch of industrial maintenance.
Preventive maintenance is intended to prevent failures, but it occurs on scheduled dates.
On the other hand, predictive maintenance has periodic monitoring through the transmission of data collected in inspections.

The main difference between the two is that the preventive measures are carried out in a predetermined period.

In prediction, the system is based on the actual state of the equipment and the determination of when maintenance should be performed. This helps minimize costs.

When should preventive maintenance be performed?

Preventive maintenance is carried out in a planned manner. After all, the goal is to prevent a failure from occurring.
However, its application does not follow the same logic as predictive because even the level of inspection is different and not as exhaustive.

Thus, preventive maintenance is not based so much on data but rather on specific and timely findings about the machine and its most critical parts. This is because it is a strategy that relies on the direction of the equipment manufacturer itself.

Thus, through manual instructions, it is possible to establish a fixed inspection program, the evaluation of which is also usually guided by the manufacturer.
While preventive maintenance is broader and more bureaucratic, predictive maintenance is more flexible and analytical.

Advantages of predictive maintenance

➢    The main advantage of predictive maintenance is to be able to act on time, which reduces downtime and increases asset availability. This improved the productivity of the machines or production line, wherever the maintenance is applied.

➢    Since maintenance is scheduled based on needs, wasted inventory and labor on unnecessary maintenance are avoided.

➢    By reducing emergency repairs and the waste mentioned above, it is possible to better control the maintenance budget.

➢    Scheduled downtime is planned, allowing to streamline maintenance and normal business activity.

➢    Optimal utilization of equipment throughout the entire life cycle.

➢    Extends the useful life of the equipment. As expected, one of the main benefits of this strategy is its ability to maximize the useful life of equipment. In this way, it is possible to optimize the use of industry investments in infrastructure.

➢    Optimize productivity. By implementing predictive maintenance, the company increases production line productivity by improving the operating conditions of the machinery. According to a PwC report, machine uptime improves by 9% in factories that apply predictive maintenance.

➢    Prevents failures. The potential to analyze potential failures at their root is what makes predictive maintenance so special. Often the symptoms are minor, but serve as clues to serious failures in the future. With preventive maintenance, your team takes action at the first signs of any problem.

➢    Reduce costs. By establishing a failure prevention policy, it is possible to considerably reduce maintenance costs. After all, work is carried out with a focus on preventing failures and interruptions in production.

➢    Ensures professional security. By implementing a predictive inspection routine, the company increases the safety of operations on the production floor.

➢    Savings with energy consumption. Some invisible faults can reduce the efficiency of your machinery, causing situations such as excessive energy consumption. An overheating machine, for example, could fall into this category. The same goes for a compressed air system that suffers leaks due to cracks in the pipe.

Processes cannot be optimized if we do not start with the detection of problems. This means that there must be a good flow of communication between departments and employees of the company so that the more and better-processed information we have, the greater our capacity to improve processes.

Many times, the absence of data and the lack of synergies between departments has to do with the lack of the right technology. The absence of an online management program, interconnected with the system and the online store, helps to optimize a large part of the processes that have to do with the storage, distribution and sale of products.

One of the key points for process improvement is knowing how to listen to employees in their day-to-day activities and monitor machine performances. What problems do they usually have? How do they think things should be done to achieve better results? This is a management task that will allow better decisions to be made, but even so, they are not positions that should be taken based only on intuition.

There is the data and the reports, which help to have a more complete overview of each area of the business and see to what extent they contribute to meeting the company’s objectives.

Lastly, planned maintenance must be implemented:

The last step in the implementation process is integrating proactive maintenance techniques into your program. This involves working with the third pillar of planned maintenance. Choose which components should be proactively maintained by looking at three factors: wearing components, failing components, and stress points. Identification of stress points is often done using infrared thermography and vibration analysis.

Implementing a total predictive maintenance program offers relatively short-term success. The trick is to maintain that long-term success. This starts with the employees. If employees understand the PM program, envision the company’s improved future, and can see how this improved future benefits them, it can create a powerful sense of cohesion. Rewarding achievement is a great way to strengthen established cohesion among employees.

Transform your supply chain with predictive maintenance solutions.

Many companies have not changed their asset maintenance strategies in decades despite having modernized other areas of their business. Changing long-standing processes is challenging and can be difficult to get buy-in from your teams. The most successful business transformation plans start with a good communication and change management strategy to help engage your teams and eliminate silos.

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