Date:
14. June 2021
Reading time: minutes

Condition monitoring: a success factor for digitalization in mechanical engineering

Condition monitoring, the system-supported monitoring of machines, is an important component of the digital transformation in industry. So far, however, most machine manufacturers offer only basic options for condition monitoring. In contrast, end-to-end solutions that meet more complex requirements, such as condition monitoring across entire machine parks, are still very scarce.

What can machine manufacturers do to help their customers make progress with machine condition monitoring – and what benefits can they themselves derive?

Condition monitoring in mechanical engineering

Condition monitoring system – for improved reliability in production

Condition monitoring makes it possible to continuously monitor the technical condition of machines. To this end, machine data is collected with the aid of sensors, transmitted in real time and evaluated. This data includes, for example, technical measured values such as speeds, temperatures, fill levels and vibrations. As soon as these parameters move outside a defined normal range, this can indicate progressive wear or an imminent defect. Based on these findings, responsible parties can immediately order maintenance measures and repairs.

These are the benefits of a condition monitoring system

A condition monitoring system helps to increase plant efficiency and also safety. Specifically, permanent machine monitoring has the following benefits for machine operators:

Simplified troubleshooting

In the event of failures or malfunctions, troubleshooting often takes a lot of time. A condition monitoring system reveals exactly where an error has occurred. This means that qualified technicians can be specifically assigned to rectify the fault.

Preventive intervention

Without machine condition monitoring, faults in machine components are often only noticed when the functionality of a plant is already impaired. With the help of condition monitoring, however, critical conditions can be identified much earlier. Thus, the approach allows preventive measures to be initiated even before major failures occur.

Optimized maintenance

If maintenance intervals follow defined time intervals, inspections are carried out that may not be necessary at certain points in time or should have been carried out much earlier. Long-term process monitoring provides valuable information that can be used to align maintenance intervals more closely to actual requirements. In the long run, plant effectiveness and reliability improve significantly and inspection and maintenance costs can be saved.

Minimizing downtime

Machine downtime is not just annoying for operating companies. If the operating sequence is disrupted, downtime can even cause enormous financial damage. Minimizing downtime is therefore a high priority in industry. Those who use Condition Monitoring to identify errors, intervene preventively and optimize their maintenance strategy can significantly minimize downtime.

The goal: End-to-end condition monitoring solutions

Many mechanical engineers today equip their products with basic condition monitoring options. For instance, operating companies have the option of recording and documenting specific sensor values of an individual plant. However, the full potential of condition monitoring is not yet exhausted. With the help of end-to-end approaches, it would be possible, for example, to compare the conditions of several machines in order to derive improvement measures from the findings. Similarly, modern maintenance approaches such as predictive maintenance can only be implemented with comprehensive solutions. But what are the technological prerequisites for this?

Cloud solutions are necessary if machines from different manufacturers are to be monitored centrally at different sites. First, different data sources have to be networked with each other. The aim should be to combine, process and evaluate all relevant operating parameters. In machine parks which have evolved over many years and are thus usually very heterogeneous, however, this represents a tremendous challenge. Medium-sized industrial companies in particular have neither the financial resources nor the IT expertise to implement central machine monitoring themselves. Consequently, they need compatible solutions that are already prefabricated, easy to integrate, and cost-efficient.

What roles do AI algorithms play in condition monitoring?

Compared to manual process monitoring, system-based condition monitoring already offers considerable added value. However, even this approach can be further optimized. For instance, the challenge of a classic condition monitoring system is that the recorded conditions must be permanently monitored by qualified personnel. However, specialists of this kind are hard to find on the labor market. Employing them 24/7 is also difficult to realize. This is where AI algorithms come into play. Not only can they take over machine condition monitoring and sound the alarm in the event of deviations from the norm. They are often also capable of learning, so that over time they can provide increasingly precise forecasts regarding critical incidents. On this basis, inspections and maintenance can then be planned in a targeted and forward-looking manner. In other words: Thanks to AI, classic machine monitoring becomes predictive maintenance​​​. This maintenance approach is in turn an important component of the smart factory, in which not only production but also maintenance largely organizes itself.

Important step towards digital production

Condition monitoring and predictive maintenance are two fundamental elements of Industry 4.0. Until now, corresponding apps and systems were usually limited to a specific machine manufacturer. It was only possible to implement interoperable solutions with a great deal of effort. With bundled expertise and the neutral technology platform, ADAMOS is eliminating this obstacle. The alliance is thus making an important contribution to the digital transformation of German industrial companies.

ADAMOS enables networking and continuity

Machine operators need end-to-end, cross-vendor solutions to implement central machine monitoring and other future initiatives around Industry 4.0. ADAMOS, a strategic alliance of more than 30 German mechanical engineering companies, is well aware of these requirements. Together, the alliance is working towards developing solutions that enable consistency and extensive networking in production. The ADAMOS ecosystem thus contributes significantly to the harmonization of digital solutions. It offers impressively easy access for solution providers and customers from the manufacturing industry. Two relevant components of the ADAMOS ecosystem are ADAMOS HUB and ADAMOS STORE.

  1. ADAMOS HUB: The ADAMOS HUB is a cloud-based IIoT platform created for cross-vendor networking and data exchange between machines and applications. For this purpose, it provides interfaces and integration points with which a technology-independent exchange of machine data is possible. This provides machine operators with a central system for integrating data from a wide range of production systems. A manufacturer-independent condition monitoring solution can also be optimally implemented on this basis.
     
  2. ADAMOS STORE: The ADAMOS STORE is a vendor-neutral marketplace where machine operators can find a wide variety of industrial apps for controlling, managing and analyzing their machinery. Ready-made solutions for machine condition monitoring are also available here. The same applies to apps from the areas of remote maintenance, predictive maintenance, remote control and the smart factory. Since all applications are “HUB-integrated,” even complex requirements such as a global status comparison across all machines can be met.

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