Predictive maintenance is based on the continuous analysis of the condition of components during operation directly on the machine. Predictive maintenance software uses networked technology such as sensors to collect various data and intelligently detect potential failures of individual parts at an early stage. In this way, maintenance is always tailored to the actual condition of the machine and not carried out on the basis of routine or time-based intervals. Production systems organize themselves and signal independently when maintenance is required. This allows people to concentrate on solving problems instead of wasting time on detailed analysis. Predictive maintenance ensures optimized machine operation.

Experts estimate that the consistent use of IoT (Internet of Things) and big data, including predictive maintenance, can prevent up to 70% of all unplanned machine and system failures. Optimized scheduling of maintenance work and the deployment of technicians can also save around a third of current time and effort. In the long term, the improved planning and optimization of maintenance intervals also leads to an extension of the service life of the machines and systems. The potential and added value are therefore extremely significant.

A generic solution that is suitable for all types of production lines does not and will not exist. The degree to which sensors, artificial intelligence and big data technologies are used varies greatly depending on the specific requirements of each electronics company. The earning potential of predictive maintenance is not profitable in all cases and for all components.

There is no question that maximizing availability and adapting maintenance to the requirements of production is of great importance in an increasingly flexible production and ever more closely linked supply chain. Openness is therefore crucial when selecting a system. Modular and incrementally expandable solutions such as the ASM Factory Equipment Center allow users to follow their own path to an integrated smart factory and predictive maintenance at their own pace and according to their needs.

The predictive maintenance approach makes it possible to carry out maintenance only when it is actually necessary, while at the same time minimizing the impact on ongoing production as well as personnel and material costs. This involves analyzing trends from the machines’ sensor data using condition monitoring. This method allows potential faults to be predicted in advance, enabling maintenance to be carried out in line with requirements.

The maintenance intervals specified by the manufacturers generally do not take into account the actual load and use of the machines by the customer. As a result, maintenance is often carried out unnecessarily too early. It is ideal to carry out maintenance based on the load, performance and condition of the machines. Condition monitoring makes this possible and not only leads to optimized maintenance scheduling and increased machine availability, but also contributes significantly to improved quality and higher yields.