In the era of digital transformation and big data, predictive maintenance, along with key players such as automation processes, IoT, machine learning (ML) and applications of artificial intelligence (AI), is undoubtedly a crucial factor in Industry 4.0. Leveraging predictive maintenance by incorporating it into one’s digital transformation strategy means being able to improve the resilience of one’s business.
But what exactly does it mean? What are its applications, and what are the advantages it can bring to a business? Let’s explore these questions together in this article.
Predictive Maintenance: Meaning and Applications
Predictive maintenance is a technique used by companies to monitor tools and production processes in order to prevent failures and interruptions by employing advanced technologies and data analysis.
In planning predictive maintenance processes, the following steps are followed:
- The assets to be monitored are identified;
- A database is created with the information collected;
- An information analysis model is designed;
- Constant data analysis is carried out.
Through this type of maintenance, companies are able to improve their efficiency and resilience by eliminating failures before they occur. The advantage that predictive maintenance offers, in fact, is the ability to predict failures, unlike in the cases of:
- Corrective or reactive maintenance where it is only possible to intervene to repair the failure after it has occurred;
- Preventive or scheduled maintenance where maintenance is based on time or usage intensity.
Examples of Predictive Maintenance
There are numerous examples of how predictive maintenance is used in different industrial sectors. For example, some of the sectors where these techniques are often employed are:
- Rail transport;
- Extractive industry (oil and gas): oil companies use predictive maintenance to monitor the health of their drilling facilities and prevent incidents that could cause environmental damage
- Utility services such as electricity, gas, and water
- Automotive industry
- Aviation: airlines use predictive maintenance to monitor airplane engines and identify any potential problems before they can cause issues during flight
- Manufacturing (especially the metalworking sector): manufacturing companies use predictive maintenance to monitor machine health and prevent failures that could cause production disruptions.
In particular, the manufacturing sector can benefit greatly from predictive maintenance. According to a report by the International Society of Automation, globally, there is a loss of around $650 billion per year due to machine downtime, a critical issue that represents significant disadvantages for companies, such as:
- Costs for interventions
- Costs for personnel to be reserved for interventions
- Costs in terms of time to dedicate to interventions
- Risks regarding personnel safety.
The techniques of predictive maintenance
In all the sectors mentioned so far, constant monitoring and data collection represent the basis of the techniques useful for the functioning of this type of maintenance and can take place thanks to:
- sensors and Internet of Things (IoT) applications;
- machine and deep learning technologies;
- automatic reasoning;
- big data analytics tools.
This monitoring and data collection over time translates into a constant improvement in the performance of machinery thanks to the artificial intelligence techniques employed, where the big data acquired from sensors are useful to improve the precision of machine learning algorithms and to support decision support techniques by using automatic reasoning.
The benefits of predictive maintenance for businesses
Through predictive maintenance, companies are able to obtain multiple benefits. First, predictive maintenance reduces maintenance and repair costs, as interventions are carried out before serious failures occur. Additionally, it reduces downtime and increases productivity, as machines and systems are kept in good working condition. Finally, predictive maintenance can increase workplace safety, as failures can be detected before they can cause harm to people or the surrounding environment.
In summary, the benefits that companies can obtain by leveraging the potential of predictive maintenance can be summarized in the following points:
- monitoring of equipment performance;
- production management according to a forward-looking approach that is more efficient;
- detection and resolution of anomalies before they cause failures;
- elimination of production blocks;
- elimination of waste of resources (time, costs, and personnel);
- optimization of operating status;
- planning interventions in a practical and safe manner for personnel;
- continuous improvement of machinery performance through machine learning;
- increased revenue;
- increased safety for machinery users;
- increased effectiveness of maintenance interventions.
We can affirm that the advantages of predictive maintenance are truly remarkable, especially for the manufacturing sector. To support this statement, we can also find data collected in a McKinsey research, according to which manufacturing companies that have adopted this type of maintenance:
- have obtained a reduction in maintenance costs ranging from 18 to 25%;
- have increased asset availability by a percentage ranging from 5 to 15%, effectively increasing efficiency and productivity.
Conclusions
In summary, predictive maintenance is a useful technique for companies looking to reduce maintenance costs, increase productivity, and improve workplace safety. By using sensors, data analysis, and machine learning, companies can identify potential problems before they occur and take timely action to resolve them.
In a good digital transformation strategy aimed at company growth, predictive maintenance techniques are therefore a very useful solution capable of bringing significant benefits.
Preventing failures is not only useful but also necessary in particularly sensitive sectors, considering the discipline on workplace safety, the current Legislative Decree 81/08, which specifies in Article 71 that it is the employer’s obligation to take the necessary measures to ensure that work equipment is “subject to adequate maintenance to ensure the permanence of safety requirements over time […] and is accompanied, where necessary, by appropriate instructions for use and maintenance manual”.
In addition, there is also the Machinery Directive 2006/42/EC, which is based on the same principles, stating that “for automated machinery and, where appropriate, for other machinery, a connection device must be provided which allows the fitting of a fault-finding diagnostic device”.
For constant company growth, but also for compliance with current rules, predictive maintenance techniques are an essential element. Equally essential, however, are highly specialized professional figures capable of designing a predictive maintenance system, acquiring data, and correctly processing them to extract a report with key information for the company.
In this context, Revelis represents a reliable partner for companies, able to offer the most innovative technologies and guarantee the best results in terms of performance and savings.
Contact us to discover the advantages that predictive maintenance can bring to your company. Our consultants are ready to support your business in the digital transformation journey.
