Complete guide to Predictive Maintenance

Predictive maintenance, according to the Brazilian law NBR5462, is “maintenance that ensures the monitoring of assets, based on the systematic application of analysis techniques. Using centralized supervision or sampling methods, it aims to minimize preventive maintenance to a minimum and reduce corrective maintenance”. It is a strategy applied mainly to predict and avoid failures in machinery and equipment. In other words, it is a maintenance model based on continuous monitoring and data analysis focused entirely on working in advance and identifying problems beforehand, avoiding potential unplanned shutdowns. Therefore, when we use predictive maintenance techniques, we monitor the assets so that, if necessary, we can schedule an intervention at the right time without it having any impact on personal safety, operational safety, and productivity in the process.

Predictive inspection uses available techniques and tools to monitor the condition of assets. This way, condition-based maintenance is carried out, which provides great benefits for efficient and assertive maintenance management.

Do you want to know more about predictive maintenance, its applications and how it has become stronger in the context of Industry 4.0? Keep reading!

Predictive Maintenance in the Context of Industry 4.0

What is Industry 4.0?

Industry 4.0, also known as the 4th industrial revolution, is a concept that incorporates technologies into the industrial sector, making it an ally of Maintenance Management. As an example, we can mention predictive maintenance techniques, which incorporate technologies that allow for more reliable inspection or condition analysis.

The digitalization of industrial activities can involve innovations such as artificial intelligence, IoT or Internet of Things, cloud data, big data, among others. The incorporation of these technologies provides optimization for all processes within an industry, guaranteeing an increase in productivity.

Types of technologies implemented in Industry 4.0  

IoT or Internet of Things: interconnected objects capable of communicating and interacting with each other and with the user, and that can be configured and controlled remotely. In this context, the use of wireless sensors for machine monitoring comes into play.

Cloud Data: it is the storage of information in databases, servers, networks, and software. This feature allows industries to access information and services from remote devices, as well as being a secure way of storing sensitive data relating to the company and its clients. Rely on certified partners for this process, guaranteeing data protection with ISOs 27001, 27018 and 27701.

Artificial Intelligence: it is the use of machine learning in the interpretation and analysis of events and trends to support actions and decisions that relate to the industrial context. It is important that the chosen company knows how to apply machine learning to asset behavior, adding field learning. This technology allows automated failure diagnosis services to assist and increase the reliability and assertiveness of technical analysis.

Big Data:  with the ever-increasing volume and complexity of data available, processing this information by traditional means is no longer viable. This is why statistical techniques and machine learning are used to select the most relevant data for the business, in a way that would be impossible to obtain it from human analysis.

Why use technology in the industry?

There are many reasons for using new technologies in the industry: reducing production costs, reducing maintenance costs (OPEX), reducing failures, accidents and downtime in the production flow, extending the useful life of equipment, greater efficiency, profitability and predictability, among others.

Industry-oriented technological solutions are increasingly seeking to offer creative and innovative solutions to prevent these problems from having a profound and irreversible impact on production processes.

In the event of failures and unforeseen events in the operation of equipment, the time used to carry out maintenance and the loss of production caused by the abrupt shutdown may not be recovered quickly.

Industry 4.0 is, therefore, largely responsible for the development of solutions capable of constantly and safely monitoring assets. This means that potential failures can be detected in advance and any downtime for repairs, if necessary, can be planned and executed without any negative consequences for the production flow.

Furthermore, it is thanks to Industry 4.0 that occupational safety rates for industry professionals have increased. Better working conditions have a direct impact on the quality of deliveries. Today, technology plays a fundamental role within industries, providing support for activities to be carried out more efficiently and, above all, safely.

But the benefits brought about by Industry 4.0 are not restricted solely to the production aspect. New technologies applied to the industrial context also bring positive changes to the environment. Sustainability has received an important focus when it comes to industrial technological development.

Industry 4.0 not only guarantees efficiency and automation for industry activities, but also helps to adapt production activities to sustainability agendas and environmental concerns. Advances in Industry 4.0 allow companies to save natural resources, reduce the use of raw materials and the production of waste. There are also solutions for waste control and recycling in this context.

Predictive maintenance in Industry 4.0 

Predictive maintenance 4.0 is already a reality in the routine of many industries. In the past, predictive maintenance actions, such as monitoring the health of industrial equipment, depended completely on the availability of manpower. Data collection was limited by the number of professionals available, and the time taken to complete this task could be very long, depending on the number of machines in the plant.

Due to the need to be very close to the equipment to carry out these activities, the safety of maintenance professionals was jeopardized. We can take the example of the new NR12 regulations, which stipulate that some rotating assets cannot have data collected while in operation.

In addition, the cost of this process used to be high, since delays in detecting failures led to equipment problems and, consequently, interruptions in the production flow, generating financial losses.

With the advent of Industry 4.0 and technological innovations for predictive maintenance, it is now possible to monitor extremely complex equipment remotely and constantly. This allows failures to be detected very early on, when the equipment shows subtle signs of malfunction, which would not be noticeable without the help of sensors and extremely advanced analysis tools.

The implementation of solutions such as wireless vibration and temperature sensors for monitoring industrial machinery has led to a significant increase in safety, resource savings and profitability for companies in various industries. Innovations such as wireless sensors offer agility in the application, collection, and reading of data from equipment. In addition, they allow for greater cadence in monitoring, generating more information which, with the help of software designed to assist maintenance teams in interpreting the data, anticipates failures quickly and assertively.

In short, Predictive Maintenance is an essential maintenance strategy for those looking for an efficient maintenance system, which is gaining ground within companies, especially in the industrial sector. New technologies and IOT solutions, combined with Maintenance 4.0, have made it possible to put this monitoring model into practice in the process and the teams’ daily activities.

Predictive maintenance has numerous benefits that are directly linked to operational efficiency on the shop floor, cost reduction in maintenance, and safety. As this is an extremely important subject for industries, we have created a complete and up-to-date guide on Predictive Maintenance, where you will find relevant information on the subject to keep you up to date.

Predictive Maintenance X Preventive Maintenance

Predictive maintenance and preventive maintenance, despite having a very similar end goal – preventing failures and extending the useful life of assets – have different approaches to maintenance management. While predictive maintenance, as stated above, is a maintenance model based on monitoring the condition of an asset, that is, based on the continuous supervision of the main quality indicators of each machine, preventive maintenance is a model that works with the regular inspection of assets, performed at predetermined intervals, or according to prescribed criteria, aimed at reducing the probability of failure or the degradation of the functioning of an item.

In this case, the standard practice is to carry out maintenance with part replacements based on a predetermined schedule, created based on manufacturer recommendations and, above all, the operating history of each machine.

Among the main disadvantages of working preventively is excessive maintenance, where parts that are still in good condition can be replaced prematurely, because it is a maintenance model that does not consider the actual condition of the equipment as a relevant factor, generating a possible waste of resources and labor. Preventive maintenance is not considered an efficient method for detecting incipient problems.

Predictive maintenance: application and benefits

Predictive maintenance can be applied to the monitoring of various industrial machines or equipment, especially those assets that are considered critical, that is, those that can cause more risks or negative effects on a company’s productivity.

Finally, the main objective of this maintenance model is to continuously monitor the condition of assets (such as industrial machinery and components, electrical equipment, among others) using sensors and/or other data collection tools. Using this data, it will then be possible to analyze and detect patterns, trends or signs of wear.

The main benefits of predictive maintenance

• Continuous monitoring: monitoring the condition of assets based on collected parameters, such as vibration and temperature.
• Data analysis for quick and assertive action: by means of intelligent dashboards it is possible to see, clearly and quickly, the assets that present alerts and make decisions based on this.
• Reduced production costs: without unplanned shutdowns and reduced downtime, significant financial losses are avoided.
• Increased operational availability of assets and their parts: when the asset can operate during predetermined time intervals.
• Improved safety, even contributing to the implementation of NR12: it establishes measures that contribute to safer and more efficient operation of industrial equipment.
• Extended useful life: correcting problems before they become critical, helping to avoid irreparable damage to equipment.
• Reduced maintenance costs (Opex) and more predictability for investment costs in new projects (Capex).

Main predictive maintenance techniques

Predictive maintenance works on the basis of a combination of techniques, which together are able to carry out continuous monitoring of assets. Due to the wide variety of machines and equipment, the techniques are adapted to the specific needs of each system or piece of equipment, thus helping to create an efficient maintenance program. Learn about some of the techniques:

Vibration analysis

Vibration analysis is a technique used to monitor the vibrations of machinery and equipment to detect abnormal patterns. Vibration should not be seen as something undesirable, since machines have oscillatory movements that are part of their normal operation. The problem arises when this vibration oscillates beyond normal levels, which may indicate natural wear of some component, or problems such as unbalance, misalignment, looseness, deteriorated bearings, among others, which may require immediate maintenance action.

This analysis can be carried out using vibration sensors installed in assets and through the data extracted, it is possible to analyze and identify trends or vibration peaks that may indicate imminent failures. The analysis takes place within the range of the rate of change of dynamic forces that are generated continuously. These same forces affect the characteristic vibrational level of each component (electronic or mechanical).

Vibration monitoring using wireless sensors can be useful in a wide variety of industrial machines, such as motors, compressors, fans, pumps, turbines, vibrating screens, among others, and in the most diverse industrial sectors.

These machines usually have more than one spot to monitor, and vibration sensors can be installed in bearing housings, gearboxes, and shaft ends, for example. The number of sensors will depend on the complexity and size of each machine, as well as the level of monitoring desired. Still in terms of benefits, we can mention the ease with which sensors can carry out triaxial collections in an automated way, as well as 24-hour monitoring. Another point in favor of wireless vibration sensors to be considered is the issue of hard-to-reach points to be monitored, which could represent a considerable maintenance hazard.

Thermography:

Thermography is a predictive maintenance technique used to identify high temperatures using equipment such as pyrometers or more robust cameras. It plays an extremely important role in the preventive and predictive maintenance of industrial assets, helping to identify problems before they cause failures.

When it comes to temperature, the use of sensors also stands out for this monitoring, being very effective when it comes to contact temperatures, for example, we can identify temperature increases and take action before the temperature rises any further. There are success cases in which this type of monitoring ( via sensors) has helped to reduce fires in assets such as harvesters and conveyor belts.

Current and voltage monitoring:

The electric current method is a technique widely used to monitor and follow up the flow of current and voltage in an attempt to identify abnormal patterns in motors and other electrical equipment. It is used to detect variations that occur in the electric current, which indicate failures or operational anomalies such as bearing wear, phase unbalance, starting problems or overloading. The main function of maintenance carried out on electric motors is to increase reliability and avoid wasting energy.

Monitoring the health of electrical equipment can be done using sensors capable of monitoring operating conditions in real time. Data is collected and sent to cloud systems for analysis, with the aim of diagnosing equipment performance indices.

Predictive maintenance P-F curve  

The P-F curve is a concept widely used in predictive maintenance to graph the relationship between time and the health condition of an asset. The P-F Curve on the graph refers to the “Potential Failure” (P) and “Functional Failure” (F) points. These points are very important for understanding the behavior of an asset over time and making informed decisions about the necessary maintenance.

P is the point in time when it is possible to detect early signs that an asset is deteriorating or approaching failure. At this point, there are no visible effects on the asset’s functionality yet, but the first symptoms can be detected using monitoring and analysis techniques. Early detection at this point is crucial, as it allows measures to be taken to prevent the failure from progressing.

F is the point at which the functional failure of the asset occurs. This is when the asset ceases to operate as expected and is no longer capable of fulfilling its designated function. This failure can result in unplanned shutdowns, productivity loss, high repair costs and, possibly, safety and environmental impacts.

When using the P-F curve in analyses, the aim is to monitor and identify Point P, so that maintenance teams can intervene before the asset reaches Point F. This allows maintenance to be scheduled based on actual wear and failure conditions, rather than at fixed time intervals (as in preventive maintenance). This saves resources by avoiding unnecessary maintenance and reduces the chances of catastrophic failures.

Successful implementation of the P-F Curve can lead to a significant reduction in maintenance costs, increased asset reliability and production optimization. However, it is important to note that the application of the P-F Curve requires investing in monitoring technology, data analysis and adequate training for maintenance staff.

The importance of ISO 9001 for predictive maintenance

ISO 9001 is an international standard for quality management systems (QMS) that aims to guarantee and certify the quality of processes within companies. Being ISO 9001 certified proves that the business is concerned with keeping its processes, products and services constantly evolving and improving.

Predictive maintenance is listed as a technique suggested by the ISO 9001 standard to maintain good quality levels in the production process. For predictive maintenance companies, having this certification can be considered a major plus. It suggests that the company has undergone rigorous inspections to ensure the efficiency and quality of its products and services and that, as a result, it will most likely deliver a more than satisfactory result for its clients on every occasion.

In addition, for industries in general, implementing predictive maintenance will also help the business obtain the ISO 9001 certification.

How to implement the predictive maintenance process

It is clear that Industry 4.0 has transformed the way preventive maintenance is performed on industrial equipment. Now you are convinced of the advantages of implementing new technologies to help your company’s team of maintenance professionals carry out their tasks and want to know how to take the next step?

Follow the list of solutions below and learn more about what is known as maintenance 4.0:

1- Wireless asset monitoring sensors: from the data collected by these sensors, it is possible to monitor the operation of equipment and anticipate possible failures. This improves the reliability rate of your industrial plant and saves time and resources. Dynamox offers sensors that perform triaxial collection and are certified for explosive atmospheres, as well as IP66, IP68 and IP69 degrees of protection.

2- Data analysis platforms: once the data has been collected by sensors such as those mentioned above, it needs to be analyzed and evaluated so that preventive or corrective actions can be taken. Rely on an intuitive platform that triggers alarms according to the criticality of the failures and that has all the necessary tools for assertive decision-making.

3- Digitalized routes and integrated dashboards for maintenance management: Control and management dashboards for maintenance activities help to synchronize and visualize what needs to be done, according to the priority listed by the team, or the business risk that a failure may represent. Having a digitalized route with a checklist of what needs to be analyzed in each piece of equipment has proven to be an important tool. The possibility of having an integrated view of inspection routes and sensor monitoring on the same dashboard provides even more assertiveness. Learn more about DynaSens, for route management, and DynaNeo, for visual maintenance management, important allies in this process.

4- Training for maintenance professionals: for all these solutions to be effective, professionals need to be trained to use these resources and explore all the possibilities they can offer. It is essential to allocate part of the maintenance investment to constantly updating the team, for more insights to be produced.

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