The term predictive maintenance comes from Industry 4.0 and has become an integral part of today’s smart production. Predictive maintenance is basically about using measurement data from machines and systems to determine maintenance intervals of the individual components and machines on the basis of this data.
The goal of predictive maintenance is always to maintain the machines and systems proactively and with foresight, so that malfunction times are minimised and the maintenance effort can also be reduced to a minimum. Ideally, predictive maintenance can be used to accurately predict malfunctions and problems so that a company can act before real failures occur and problems persist.
As a result of predictive maintenance, production times and the service life of the machines used can be extended, since maintenance is always carried out “on point”. In addition, excessive costs can be effectively prevented by unnecessary maintenance intervals. Predictive maintenance is an Industry 4.0 tool that strengthens productivity and effectiveness in production and provides companies with targeted information on the entire machine and plant park.
Predictive maintenance versus conventional maintenance
Traditional maintenance was usually understood in a reactive way. Although reactive maintenance is easy to implement, it is a significant risk to organisations. This is because only when errors and malfunctions occur is the system reacted to and the problem analysed and the necessary troubleshooting carried out. In contrast to predictive maintenance, reactive maintenance can therefore neither prevent nor predict malfunctions, often resulting in considerable downtime.
In the worst case, the urgently needed spare parts for the repair of systems and machines are not available and thus the downtime increases considerably. If such a case affects an entire production line or a machine that is important for the production process, this can in the worst case lead to economic problems for the company. Predictive maintenance is thus the safe and above all effective variant of maintenance, which should now be standard in Industry 4.0.
The advantages of predictive maintenance
Imagine if you received an alert from a mobile app ahead of any fault occurring. Instead of having to guesstimate when the part will be obsolete based on past observations or hope to catch it through regular monitoring; predictive analytics tell you when to replace the part, reducing planned downtime and keeping the product running for an optimum amount of time. Predictive maintenance also eliminates unnecessary repair costs, a large unknown for both manufacturers and end-users. When an electronic component in a product fails, identifying the problem may take 5 minutes – or 5 hours. The same holds true for replacing broken or worn-down parts.
Major breakdowns are expensive, both because of lost operating time as well as secondary financial losses. Worse, the larger or more complex the machinery, the greater the impact maintenance has on production and runtime costs. Even a small flaw in the system, if not caught early, can lead to unexpected and costly downtime. With preventative maintenance, replacing parts too early also carries an unnecessary financial burden on the business. That’s where predictive maintenance & Industry 4.0 steps in:
Three important steps for the use of predictive maintenance
Those who want to rely on the principle of predictive maintenance in their own company must bear in mind that three important steps lead to success in the long term:
The first step is to create a database and implement the corresponding sensors on and in the machines. Since many manufacturers now operate in the area of Industry 4.0 and its standards, such sensors are already integrated into many machines and systems. In the course of this, however, all relevant data must also be collected by the experts. Those who only measure the bare data of the machine, but leave out values such as room temperature and air humidity, for example, can often not bring the data into a meaningful context. Predictive maintenance must therefore always collect as much data as possible.
In the second step, the data must be combined in a database and placed in relation to each other. Here, many providers on the market offer practical solutions that can be operated either in the provider’s data center or directly in the company itself. This is where the quality of the data and its possible applications is decided. Because only through targeted and structured storage and fast access to the immense data sets can these be analysed by the intelligent algorithms.
The last step is essential for predictive maintenance. In this step, failure probabilities are calculated for all relevant components on the basis of the collected data. The better the data, the more accurately the probabilities can be calculated. Predictive maintenance is based on these probabilities and predictions. Before an event X occurs, component Y can be replaced in order to prevent event X.
Predictive analytics tells you when to replace the part, reducing planned downtime and keeping the product running for an optimum amount of time.
Preventive versus predictive
Even if the terms preventive maintenance and predictive maintenance initially sound similar and are also used in a similar way, they still differ enormously. Similar to predictive maintenance, preventive maintenance also tries to avoid downtimes or keep them as short as possible. Preventive maintenance, however, does not collect any data but determines the maintenance intervals according to a fixed pattern or experience. In the worst case, for example, wear parts that still function smoothly are replaced. In the long run, this causes considerable costs, as the material costs for the company increase without any concrete cause.
On the other hand, excessive wear cannot be detected either. If a component wears out particularly quickly and, above all, faster than the maintenance plan provides for, an unexpected failure occurs, which means further costs for the company. In the end, preventive maintenance is all about guessing as well as possible or estimating based on experience when replacement and maintenance would make sense. It is of crucial importance for the cost-benefit factor to arrange maintenance and repairs as early as necessary and as late as possible. These points become obsolete with predictive maintenance. This is because the condition of all relevant components can be checked on the basis of the data collected. This means that neither unworn components are replaced nor signs of wear are missed. Although the initial effort involved in this form of maintenance may seem higher, the data and data records collected can be used specifically to improve the performance of the systems.
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