The 7 biggest advantages of Predictive Maintenance

Predictive maintenance detects problems before they occur

With classic reactive machine maintenance, measures are only taken when a problem already exists. This can lead to high downtime costs in production. Predictive maintenance, on the other hand, takes a new approach to machine maintenance. A machine that uses predictive maintenance technology informs specialists before a malfunction occurs that a certain component is worn and will soon need to be replaced. The advantages are manifold: spare parts can be ordered in good time, repairs can be planned, and machine downtime can be avoided. The high time, personnel and material costs of preventive maintenance can also be reduced. Or, if necessary, they can be eliminated altogether in the future when machines are equipped with predictive maintenance across the board.

As one of the most important technologies of Industry 4.0,predictive maintenance is already a reality in some industries. These include in particular the mobility industries, the energy sector and production. In the automotive industry, engines and chassis can already report to the workshop or manufacturer when a repair is imminent. In this way, the relevant part is replaced before a failure occurs. In electromobility, battery performance in particular is measured to detect possible deterioration at an early stage. It also allows manufacturers to collect data for future improvement of the components.

With the help of predictive maintenance, failures in wind turbines can be largely avoided by sensors performing vibration analyses on components that are susceptible to wear. There are also many potential applications for the manufacturing industry. In precision injection molding, for example, highly sensitive production robots can be monitored. If the first anomalies are detected, the machine can be adjusted preventively before expensive faulty batches occur.

How predictive maintenance works

In practice, predictive maintenance consists of three phases. In the first step, known as condition monitoring, process and machine data, such as vibration, pressure and temperature of the equipment, are continuously read out by sensors attached to the system.

The data obtained is then analyzed in real time using machine learning algorithms. Based on historical defects of this and comparable equipment, the algorithm can identify deviations from the normal state.

In the third phase, the evaluated data is forwarded via the network to the service center or directly to the manufacturer. If anomalies are present, those responsible can initiate maintenance even before the problem escalates.

Machine learning and edge computing

In order to make predictions about the operational capability of machines, huge amounts of data have to be collected, which can be in very different formats. These include, for example, equipment parameters such as vibration speed or degree of heating, but also environmental characteristics such as humidity.

All of this data must be stored, processed and analyzed – and in real time. Technically, in-memory databases are used to enable lower access times. Machine learning methods such as process mining algorithms are used to comb through the flood of data and examine it for recurring patterns. Using Big Data analytics, the data is then further processed.

Another important technical pillar is edge computing. Processing the masses of data in real time would hardly be possible if the information always had to be sent to a central data center first. That’s why many machines have their own microcontrollers, which take over a large part of the computing work. In this way, data processing already takes place at the edge of the network, i.e. directly where the data is collected. This makes predictive maintenance one of the most important use cases of the Internet of Things (IoT). By making physical machines Internet-capable, they can communicate with each other and exchange data independently.

When does the use of predictive maintenance make sense?

The use of predictive maintenance makes particular sense for machine manufacturers, for example, who want to open up a new service model with regard to their customers. However, there are scenarios in which predictive maintenance is actually not worthwhile. Therefore, the relationship between the cost of a production stoppage and the cost of implementing predictive maintenance must always be considered.

Companies considering the use of predictive maintenance should therefore first and foremost quantify the concrete added value that the technology is to provide for them. It is also important to focus on one project and complete it before tackling others. Since the introduction of predictive maintenance requires an enormous amount of technological expertise that can only be fully covered by a few companies, it is also important to work with external experts at an early stage in order to complete projects successfully and in a time-efficient manner.