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FEATURE MACHINE BUILDING, FRAMEWORKS & SAFETY
inTralogisTics: a non- sTop healTh check
Lenze examines how digitalization brings tangible added value to intralogistics
digiTizaTion in
sponsored by In the next step, Lenze supports its customers
with digital services and cloud services for all aspects of the machine. By reporting the OEE (Overall Equipment Efficiency), the availability, throughput and production yield of the machine or plant can be optimised. Data can be compared across machines and, above all, plants, which provides information about the ‘real’ performance. Based on this data and with the existing domain knowledge, initial models are derived that reduce downtimes via condition monitoring and allow a precise statement about the general condition of the machine. If a fault occurs frequently in a system that does not occur in another identical networked system, the cause of the fault can be eliminated after the analysis. The final step is to generate predictive
models. Predictive analytics independently point out abnormalities that would lead to a possible plant shutdown. As is so often the case, the automotive industry is at the forefront of these innovations. The first predictive maintenance projects with Lenze are already being implemented in Europe and Asia.
The fuTure has begun - firsT projecTs in The auTomoTive indusTry
The projects already being implemented use data from around 1,000 drive packages each, which are distributed across several plants. The data is stored and evaluated locally within the company’s own network. In this way, initial experience can be gained with the handling, amount of data, processing and analysis of the information. The system has an open design and distributes
D
igitization is an omnipresent topic in intralogistics and offers numerous opportunities. To understand the scope, it is
worth taking a look at the figures: Worldwide, the estimated costs caused by unplanned downtime alone amount to up to 56 billion dollars per year. The savings potential for OEMs and end customers is correspondingly large. Machine builders are therefore faced with the challenge of making the availability and condition of their machines transparent in order to reduce maintenance work and material stockpiling and increase plant efficiency and availability. There are many opinions and approaches
to digitalization. The majority of market participants expect a particularly high benefit in the area of maintenance. We often hear ‘condition monitoring’ and
‘predictive maintenance’ in this context – and with it the expectation of immediately reducing the costs of unplanned shutdowns through better visibility into the condition of the components and preventing possible plant faults in good time. This is accompanied by the idea of efficiency gains
1 DESIGN SOLUTIONS MARCH 2023 8
and error prevention, which would automatically result from a better view into the plant, the process and the condition of the components.
approaches for a beneficial digiTal TransformaTion
As a drive and automation specialist, Lenze supports its customers holistically in this transformation process. The first step is about visualizing data, obtaining consolidated transparency about the installed base and system performance, and highlighting system downtimes or failures. The focus is on the machine or the entire system, which distinguishes this concept from earlier models where only individual components or machine sections could be assessed. The visualization of system performance and
balancing of system utilization allows significant conclusions to be drawn about the processes and operations of the networked system sections. In addition, remote maintenance can significantly reduce commissioning and service costs and deploy personnel much more efficiently.
the load to several edge controllers, which communicate with the higher-level data servers or data lake. This enables external third-party components to be connected to the system and is still scalable upwards via the number of edge controllers. This makes the knowledge gained interesting for large installations such as baggage conveyor systems in airports or fully automated warehouses, where it is not uncommon to find more than 10,000 drive packages and components from a wide range of suppliers in use. The second step is then about predictive
maintenance in practice and the application of the algorithms already verified in the laboratory environment for the detection of system anomalies as well as the verification of the required data for the domain. An immediate transfer of theoretical and laboratory findings in the practical environment cannot always be guaranteed. For example, error patterns can have a negative effect on the algorithms due to the components installed around the sensors and drive packages and their own behaviour, and the adaptation and robustness of the data-based model to such influences is an interesting finding from this investigation. So are we already in the ‘day after
tomorrow’ and are the developed processes already sufficient to ensure efficiency gains and avoid unplanned plant shutdowns? The future remains exciting...
Lenze
www.Lenze.com
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