By Anke Henrich
As if by magic, the robot elegantly positions and moves car body parts, cartons, plastic parts and even fragile glass panels or sensitive electronic components around a hall. The robot gripper from Schmalz nestles up against the workpiece contour and grips it with the required level of sensitivity. The strong mechanism bears the load, while employees program the path. Such lifting and crane systems must be reliable without exception. If not, replenishment is not provided or the entire production process could come to a standstill.
This is one reason why predictive maintenance is so important for Albert Winter, Head of Business Development at J. Schmalz GmbH. "Predictive maintenance provides our customers with added value in the long term for many reasons," explains Winter. The company sees itself as a market leader in automation with vacuums.
Digital early warning system
Maintenance based on the collection and communication of real-time data is one of the greatest challenges for mechanical and plant engineering companies. During this process, inductive coil technology enables sensors to continuously measure the metallic abrasion in hydraulic and lubrication systems and inform the operator in good time in the event of deviations. Algorithms provide the information on the different sizes of the small ferrous and nonferrous particles that is needed for this.
"SYSTEMS CRY FOR HELP WHEN SOMETHING IS WRONG"
Larger applications are possible too. For instance, Deutsche Bahn plans to use the Acoustic Infrastructure Monitoring (AIM) tool for its approximately 1,500 escalators. Here, the system monitors the noises made by the escalators and triggers an alarm at an early stage in the event of abnormalities.
These digital early warning systems also offer enormous possibilities for enhancing customer loyalty. After all, companies that can offer the early detection of problems and maintenance at production-friendly times save their customers a lot of work and trouble - while also allowing them to save money. This "smart service" of the manufacturer on the basis of process and machine data increases the availability of systems and lowers maintenance costs. Moreover, digital assistance systems can minimize the number of faults caused by human behavior, which is still the most frequent cause of errors. All this makes conventional maintenance systems look rather long in the tooth, as these systems offer less for more money.
Predictive maintenance as a purchasing argument
J. Schmalz, the vacuum specialist from Glatten near Stuttgart, is therefore continuously refining its predictive maintenance offering. Winter remembers: "It was an arduous path. At first, we only realized individual projects, for instance for the automotive industry. But now we offer a much broader range. In many sectors, for example in the growing markets of packaging, electronics or glass, predictive maintenance is becoming a crucial purchasing argument because its benefits are undisputed."
The competition for the dominant position in the field of predictive maintenance is also well underway on the international stage. Companies are utilizing vastly different resources and political influence in their bid for supremacy.
Cooperations between design engineers and IT specialists
"German mechanical engineering companies are in a leading position with their industry partnerships," says Peter-Michael Synek, Deputy Managing Director of the VDMA Fluid Power association. He adds dryly: "At least they are right now. After all, their competitors from China, India and North America have recognized the opportunity presented by predictive maintenance with the expansion of software-driven functionalities and have made this a key topic on their agenda."
Globally, there will therefore be an increasing number of cooperations between design and information technology experts on equal terms, he forecasts. Together, they will turn old business models upside down. Indeed, Volkswagen boss Herbert Diess recently announced that it is no longer the engine that plays the leading role in its production halls, but the software. The CEO explained that Volkswagen models are no longer just vehicles, but rather a hub in a networked world of cars.
The automotive and mechanical engineering sectors are facing the same international challenges, for example in terms of the competition from China. Here, the government has declared the digital transformation of its industries as a state objective. It is subsidizing companies in a targeted manner and building a dense network of knowledge from universities and factories. This will continuously make Chinese machines better, cleverer and more competitive internationally.
Artificial intelligence plays a decisive role
In the end, however, it will not be five-year plans that decide the market share of predictive maintenance systems, but rather the successful deployment of artificial intelligence (AI). The disruptive power of these new neural networks is enormous.
While Chinese mechanical engineering companies enjoy the advantage of massive state funding, when it comes to the application of artificial intelligence, the limelight is taken by the Americans and their disruptive digital giants - and not just from Silicon Valley. Because in the future it won’t be necessary to build machines in order to maintain them. And the more data is provided for artificial intelligence, the more precisely it can specify tailor-made offers for customers.
This is the concern of long-established mechanical engineering companies all over the world: Will predictive maintenance bring new competitors to the fore? Will virtual mechanical engineers develop platforms through which they can offer highly attractive services for their customers? It is conceivable that they will approach their customers and tell them: "Give us your fault and maintenance logs and we’ll look after all your machinery with our predictive systems. We don’t need much – only your data."
Protection against cybercrime
The mechanical engineering companies do have one asset, however: the trust of their customers. The key aspect of predictive maintenance is the discretion of the service provider. After all, thanks to the continuous exchange of data, this company is informed of genuine business secrets such as the production goals of its customer.
Therefore, it is not only the good reputation of a mechanical engineering company that is a vital prerequisite for predictive maintenance, but also the quality of its data protection measures. Studies back this up. For more than half of German mechanical engineering companies, the greatest hurdle for predictive maintenance is IT security, as ultimately, they are handling the sensitive data of third parties.
This is why Christian Decker, CEO of Desma Schuhmaschinen GmbH from Achim, sends out a specific warning to other companies during an interview with the insurance broker VSMA. "Cybercrime will become much more perfidious in the future. Criminals will take advantage of every opportunity that artificial intelligence offers them. Their algorithms will also learn from failed attacks. Some mechanical engineering companies do not understand how easy it is to use Google to find special search engines, which show the IP addresses of machines - including information such as how they are controlled."
The interdisciplinary path to success
The art of engineering and software expertise merge together for predictive maintenance. Interdisciplinary knowledge is becoming a key requirement all over the world. For this reason, many German companies and universities are cooperating more closely than ever before. One example of this is in Jena, where the German Federal Ministry of Education and Research is supporting small and medium-sized enterprises with the "Innovations for the production, services and work of tomorrow" network program. This focuses on smart services: the pooling of physical and internet-based services to provide a basis for digital business models. Another express goal is to promote joint projects between researchers and factories for developing integrated smart service platforms.
The Swabian vacuum specialist Schmalz has long since realized that it can only succeed in the field of machine data by taking an interdisciplinary approach. Therefore, the family company not only links vocational training with university knowledge, but also with technical and IT expertise at all levels.
Schmalz recently founded "Campus Schwarzwald" together with other companies and the University of Stuttgart. In Freudenstadt, students taking the master’s course in mechanical engineering and technology management focusing on leadership and sustainability will also learn about another key aspect of the future of mechanical engineering: utilizing their technical expertise and digital knowledge to recognize customers’ wishes before they have realized them themselves - thinking predictively, in other words.