With each passing day, people’s view over the maintenance sector changes: it is no longer seen as a source of excessive expenses, triggered only in emergency situations, but as an essential strategic tool to cut costs and increase the productivity of industrial plants.
This shift is also accompanied by a change in the sector’s landscape itself. When we talk about maintenance, the idea is to sustain a rhythm and keep activities running every day. The ultimate goal is no longer to fix machines, but to prevent them from breaking down.
Sure, if a serious failure arises, it is up to the maintenance team to correct it as soon as possible, so that losses are avoided. However, the ideal scenario is that maintainers are not satisfied with corrective maintenance and look for tools and techniques capable of monitoring the health and condition of assets in real time, making room for proactive actions that avoid catastrophic breakdowns and unexpected downtime.
To anticipate failures and revolutionize maintenance routines, the best alternative is Predictive Maintenance. With that in mind, we have prepared a complete guide with definitions, explanations, ways to implement it, and much more. Enjoy!
Predictive Maintenance can be defined as the continuous monitoring of an equipment or system and the attempt to define its future state through the data collected over time.
By means of specific sensing such as vibration, temperature, hour meter, energy consumption, flow and pressure, it is possible to verify the tendency of that asset to fail and if there are needs for corrective interventions.
“Prediction” refers to the act or effect of stating, in advance, something that is yet to happen. This is precisely the purpose: to define the reliability of a piece of equipment based on data collection. That is, to measure how reliable this machine is with regard to its ability to continue producing for the next hours, days and weeks.
With this strategy, you can focus management on periodic monitoring to analyze variations in aspects such as vibration and temperature of the assets. This is done through measurement, direct and indirect, and interpretation of the results, which allows the manager to know if the asset is working as it should or if it needs special attention.
You must first have an understanding of the current maintenance landscape before taking the best decision for your company. Besides making the maintainers’ work more efficient, the predictive strategy keeps the industry competitive. After all, the follow-up anticipates failures, machines that do not break down keep the production running, and thus the company gains more space in the globalized market.
As seen above, because it is related to several analysis parameters, Predictive Maintenance aims to foresee and avoid serious problems in early stages. Analyzing the current scenario, it is visible that the sector is leaving the dynamics of “put out the fire” and “break and fix”, and moving towards proactive actions that avoid breakdowns.
By diving into this evolution process, the industry drastically reduces costs and, consequently, increases its profitability. Check out some of the main advantages of predictive maintenance:
It is very important to understand the differences between Predictive and Preventive Maintenance to then choose the most appropriate strategy for each operation. Although they are similar terms, in practice there are many differences, and they should not be confused.
You must already perform Preventive Maintenance in your company, right? It is extremely important when it comes to optimizing the useful life and efficiency of assets. It operates from a predetermined time interval, thinking mainly about the lifetime of the parts, performing hypothetical analyses based on triggers.
A good example of preventive maintenance would be: after 3,000 parts produced or 5,000 hours of use, change a bearing. But, not always the time of use or work quota is enough to determine if an asset needs maintenance. It is necessary to analyze its condition specifically.
Predictives arise from good Preventive Maintenance, which can also perform repairs before breakdown from a hypothetical analysis.
In the case of Predictive Maintenance, it is possible to anticipate the performance of equipment based on trend analysis and thus try to identify breakdowns and probabilities of future failure. It is designed to ensure the reliability of assets.
Let’s use the example of a car to illustrate the difference. You have already had a vehicle checked or seen someone do it, haven’t you? This is preventive work, because you take the vehicle in at a previously and hypothetically defined time to check if it is working properly.
The predictive strategy would be if the car itself had an internal monitoring system or sensors that would inform when something was out of standard, predicting a possible failure.
Random failures can occur in Preventive Maintenance: you can expose the asset to a new peak failure rate due to infant mortality and change parts in perfect condition unnecessarily. With a predictive strategy, problems are detected early and assertively, causing less waste and unnecessary expenses.
In general, Predictive Maintenance aims to boost the performance of a company’s assets, but it also seeks to achieve a number of specific goals. Check some of them out:
With these objectives in mind, keeping the company running smoothly and increasing productivity are the guarantees of Predictive Maintenance. The team’s work becomes safer and less stressful with the help of technology, it is possible to better plan the repair routine and have greater control of the equipment, besides reducing the costs of the operation as a whole.
After understanding the advantages of Predictive Maintenance and how it differs from Preventive Maintenance, it is time to learn the main pillar of the strategy: the analysis, what it monitors. Here are the 5 main techniques:
Vibration analysis is indispensable for the predictive maintenance of any rotating equipment: it is essential to detect mechanical failures and understand the cause of other defects that can stop production.
Using this technique it is possible to discover several types of changes that are harmful to production, among them: mass unbalance, shaft misalignment and warping, bearing wear, gear wear, structural problems, and backlash.
It is the representation, in graphs or images, of the infrared radiation emitted by equipment. From it, it is possible to identify, monitor, and register changes in the temperature levels of components, generating a thermal image or thermogram and avoiding overheating.
Ultrasound consists in raising the frequency of sound waves to a limit where they can be picked up by the human auditory system. In this way we can detect leaks in compressed air, steam, and other gas transportation systems, as well as electrical current leaks and mechanical defects.
Performed through magnetic particle testing, it is a technique used in industries to identify surface and subsurface discontinuities. From it, it is possible to detect defects such as cracks, cold joints, inclusions, cold drops, double lamination, lack of penetration, folds, segregations, among others.
Finally, this technique is intended to provide useful and accurate information about the condition of the lubricant and the condition of the machine. In addition to monitoring oil contamination, it is also possible to analyze the wear of metals and additives in oils to define a better changeover time.
Oil analysis makes it possible to find some problems such as contamination, gear failures, oxidation, misalignment, additive depreciation, and wear of mechanical components.
There are many affordable solutions for asset sensing and monitoring, an excellent investment for any company. Ideally, you should start with the techniques that make the most sense for the types of equipment in your organization, but some methods can make this process easier.
We have put together a simple step-by-step procedure so that you can quickly see the results of monitoring:
Critical equipment are ones whose failure can result in major costs and problems for industries. The higher the criticality, the more fundamental the asset is to production and, therefore, the higher the priority when it comes to implementing maintenance.
A popular tool for studying criticality is ABC analysis, which can be used to rank the risk priority of assets. It is generally based on three criteria: frequency of failure, level of difficulty in detecting the failure, and impact caused by the failure on overall operations.
As we can see from the table, criticality is divided into three classes:
Class A: high criticality;
Class B: medium criticality;
Class C: low criticality.
The ideal is to start the predictive maintenance program on equipment with high and medium criticality (A and B). This will considerably reduce the downtime of the machines, ensuring excellent results.
Once you have listed the assets, you need to think about the failures and where they are coming from. Identify which variables are most important for monitoring the assets, such as vibration, temperature, hour meter, power consumption, among others. It is in this step that you will start applying the techniques mentioned above.
Then we move on to talk about sensing and monitoring. You need to find tools, sensors, and software that provide the variables and run the techniques accurately to get the best diagnosis for anticipating failures.
Such devices are of increasing quality every day, which makes it more advantageous to use them as a great extra pair of eyes for your industry’s installations. Some of them collect data automatically, without requiring a technician to get close to the equipment.
Within Predictive Maintenance, there are two methods of monitoring assets: offline and online. It is important to understand both alternatives to choose the most adequate one for each plant, because the decision impacts the maintenance costs and the profitability of companies.
In the first case, sensors collect data manually and depend directly on the maintainers, who perform the collections. Online monitoring, on the other hand, uses Artificial Intelligence and, by means of IoT sensors installed in the machines, automatically collects data all the time, without requiring the presence or assistance of the maintenance professional.
With information about the most critical assets and data about the failures, all that remains is to take action and put it into practice. Here comes the change in mentality that must be instilled in everyone on the team: now you no longer have to wait for equipment to break down. Instead, you can tackle the failure at the right time, right when the first signs and anomalous variations in the asset’s condition appear.
Some excellent allies for this step are softwares that use artificial intelligence to analyze equipment data. This is because many platforms deliver the information and diagnostics ready and in real time, not requiring the manager to waste time interpreting the data. To show how this works in practice, we will use the case of one of TRACTIAN‘s customers.
The example illustrated by the image below shows the role of the platform in an Embraer asset. The aircraft manufacturer, which previously implemented mostly corrective and preventive maintenance, changed its routine after installing IoT sensors and TRACTIAN’s maintenance management software.
With a more assertive maintenance management, the company started to avoid problems and was able, for example, to stop potential unbalance and saturation deviations in a motor-pump filter.
TRACTIAN’s platform generated an insight – as shown in the image above -, the Embraer maintenance team soon checked the asset and in most cases was able to find the defect before it caused serious consequences for production.
With this strategy of anticipating failures, which can be as serious as total stop or equipment breakdown, the industry has seen a considerable increase in the availability and reliability of the assets that received the sensors.
There are several positive points after the adoption of predictive maintenance: automation of maintenance demands, online monitoring, failure reports in its initial condition, higher productivity, cost and waste reduction, better use of time, among others.
When adopting other maintenance strategies – such as preventive or corrective maintenance, for example – it is common for many managers to use spreadsheets to record and analyze data, and paper Work Orders to document a team’s work.
In Predictive Maintenance, with the help of technology, all this is left behind. Manual records take a lot of time and work, and the routine of the maintainers can be made more efficient with IoT sensors and artificial intelligence present in software.
Thus, data collection becomes fast and efficient, as they can analyze thousands of pieces of information in a matter of seconds and with much higher accuracy. The need for manual and intermittent collections disappears, as do difficult interpretations and extensive accounting. The technology itself provides the insights needed for action.
We usually think of these technologies as something very far away from our reality. In fact, the application of improvement processes related to predictive maintenance has become increasingly easier and more feasible, and can be much closer to your budget than you might think.
TRACTIAN is a good example of an alternative for companies that want to transform their maintenance routines. The predictive system is composed of IoT sensors, which sense the equipment, and an integrated and intelligent platform that performs the asset management and monitoring of the entire industrial plant in a remote and intuitive way for managers.
With state-of-the-art technology and resources developed with maintainers in mind, the goal of the TRACTIAN platform is to make maintenance professionals’ work less stressful through hardware and software tools that make them the best in their category.
About the author:
Santa Catarina State University graduated engineer, with more than 10 years of experience in Industry 4.0, metalworking, machine manufacturing and hydraulics. Head of Customer Success at TRACTIAN.
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