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At CLAAS, the 2018 generation of machinery is the first to automatically send data for the purposes of improving service – only with the customer’s consent, of course. This marks a step toward predictive maintenance. It offers benefits for manufacturers, dealers, and customers.
Text: Jörg Huthmann
What will machine maintenance look like in the future? Thanks to loads of data, it will be meticulously synchronized, that much is quite certain. Anyone who wants to know in greater detail would do well to ask Axel Holtkotte. The data analysis specialist at CLAAS Service and Parts GmbH uses a CLAAS combine harvester in Hungary to describe how the principle of predictive maintenance works: The harvester is in use. A sensor installed on a shaft under heavy strain records some unusual measurements and sends a data message about this abnormality to CLAAS. At CLAAS, a computer capable of learning with the help of artificial intelligence analyzes the message. It collates the message with several terabytes of other data and arrives at the conclusion that a breakdown warning needs to be triggered. The probability of a malfunction will rise dramatically in the days ahead.
The spare part that may be needed is located in the central warehouse in Hamm and could be in Hungary 48 hours after being requested. The CLAAS service specialist tracking the matter on his monitor doesn’t want to take any risks. He decides to send the spare part on its way to Hungary with the next regularly scheduled transport. If it is needed after all, then it will be available immediately and the combine harvester will only be briefly out of commission. If it isn’t needed, then it will return after the harvest.
CLAAS knows exactly what kind of stress its machines face. Load calculations reveal which parts are subject to particularly heavy wear and what to expect concerning their longevity. Workshop reports and sales figures of spare parts provide some statistical insight. A high level of reliability is an important quality factor and selling point, especially in terms of harvesters, which have to deliver maximum power and performance in increasingly shorter harvest periods. The problem is that every combine harvester, forage harvester, and tractor is used differently in reality, which means they face entirely different loads and stress.
Readings of oil pressure gauges and tachometers, temperature sensors, level indicators, and many other sensors provide clues to the condition of individual machines. Transmitting such data via telemetry is part of the current standards and makes it possible to check the condition of machinery across continents. This method, known as condition monitoring, pertains to individual machines, however.
Predictive maintenance goes one crucial step further and uses key building blocks from the Industry 4.0 kit. It starts with cutting-edge sensors that can not only store the readings over longer periods, but also transmit them. The Internet of Things (IoT) refers precisely to this interplay of machinery with smart sensors and the connecting of this data. Large populations or fleets in a market can generate tremendous amounts of data. This trove of big data forms the basis of predictive maintenance. Analyses performed by algorithms capable of learning will then be able to make increasingly accurate predictions, including about the likelihood that certain wear parts will break
Predictive maintenance will, in fact, create a win-win-win situation. CLAAS will integrate dealers and workshops and use the insights gained in doing so to improve products and cut production and warranty costs. In turn, this will open up new opportunities for dealers and workshops to support customers even more intensively with innovative service deals. CLAAS customers stand to benefit the most from predictive maintenance, because reliability and service will constantly improve.
working against downtime
If a passenger car breaks down, it is inconvenient at the very least. If a train breaks down on an important line, it can trigger a chain reaction. And outages at power plants or a chemical factory can reach catastrophic proportions, which is why the industrial sector relies on testing and repair procedures to reduce breakdowns – and has been doing so for a very long time.
first-gen sensors made out of glass
Highly visible equalization tanks for certain liquids were an early type of sensor that indicate a developing problem in good time.
the age of oil isn’t over yet
Even in the era of maintenance-free encapsulated ball bearings and tremendous advances in materials science, the oil can and grease gun aren’t quite ready to become artifacts in a museum. Every farmer knows where his machines need lubrication, and checking oil levels is not only part of the refueling process for drivers of passenger cars.
industry 4.0 and the land of the clueless
Industry 4.0 needs fast data networks – ideally the 5G standard. Its design enables 100 billion mobile devices or other wireless systems to be connected at the same time. But 5G is only going to be rolled out in 2020. And the expansion of digital infrastructure in Germany is progressing slowly at the moment, especially in rural regions.
3d printing is the future
Dealers and workshops might be able to produce smaller spare parts by themselves in future with 3D printers. It also works with metallic materials through a sintering process. So instead of requesting parts from a warehouse, the workshop printer will simply access a centralized database.
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