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ADVERTISEMENT FEATURE COVER STORY


non-Stop health check provides tangible added


With machine builders faced with the challenge of making the availability


and condition of their machines transparent, Lenze explains how it can help by supporting customers through the digitalization process


D


igitization is an omnipresent topic in intralogistics and offers numerous opportunities – from increased productivity


to new business models. The significance for the industry is enormous. 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, and maintaining an overview is not trivial. The majority of market participants expect a particularly high benefit in the area of maintenance. The change from time-based to condition-based maintenance increases the economic efficiency of a plant because components only have to be replaced when necessary and not before they have reached the end of their life. 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 and error prevention, which would automatically result from a better view into the plant, the process and the condition of the components. If we think one step further, application models


such as pay-per-use and the resulting business models are possible, for which the full digitalization of the condition of a system and, above all, its performance is a basic prerequisite. Only in this way is billing possible. Unfortunately, it is still often the case that


machine builders are only offered partial solutions, but the desired overall solution of digitalization usually remains unclear and is left to the customer.


approacheS for a beneficial digital tranSformation


The drive and automation specialist Lenze, on the other hand, supports its customers holistically in this transformation process. The basis is a phase model that shows all the necessary steps for digitization. The first step is about visualizing data, obtaining consolidated transparency about the installed base and system performance, and highlighting system downtimes or failures. It is not uncommon for the machine operator not to have a complete overview of this. 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 are particularly interesting. They allow significant conclusions to be drawn about the processes and operations of the networked system sections, because what is the point of increasing the performance of a single component such as a stacker crane if the goods cannot be further processed or removed because there is not yet a truck at the loading


bay? In addition, remote maintenance can significantly reduce commissioning and service costs and deploy personnel much more efficiently. 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), for example, the availability, throughput and production yield of the machine or plant can be optimized. What is special here is that the data can be compared across machines, plants 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. Based on the installed components, they also 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. It is clear that the networking of the system, a high level of transparency and a sufficiently high degree of domain knowledge are essential for this step. Once all this has been achieved, the final


step is to generate predictive models. Predictive analytics, for example, 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.


StatuS deScription or prediction?


When it comes to digitalisation in intralogistics, most people think of ‘condition monitoring’ and ‘predictive maintenance’. However, both terms are often used synonymously, although they are two different concepts. Predictive maintenance is the prediction of events or the probability of events, for example, when the probability of a gearbox defect occurring in the next 50 operating hours will rise to over 90%. With such a forecast, one could plan the replacement of the gearbox in time before the plant actually fails. Condition monitoring, on the other hand,


is a preliminary stage that enables a more in-depth description of the current condition from the interpretation of existing data. This requires a deep understanding of machines and processes in order to generate meaningful information from "bare" data. Analyses based


8 DESIGN SOLUTIONS OCTOBER 2022


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