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Digitization, Networking And Communication Of Maintenance

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Digitization, Networking And Communication Of Maintenance
Digitization, Networking And Communication Of Maintenance

Video: Digitization, Networking And Communication Of Maintenance

Отличия серверных жестких дисков от десктопных
Video: Industrial Communication Networks - Basis of Digitalization 2023, February

Predictive maintenance is a current topic and is part of the upcoming Motion, Drives & Automation, or MDA for short, which will take place at the Hanover Fair. Machine and vehicle users do not want to wait for a component to fail, but they also do not want to replace it on suspicion. They don't have to either, because drive and fluid technology companies have developed systems with which, for example, rolling bearings, gears, electric motors, hydraulic pumps and compressed air fluids can be continuously monitored.

Intelligently evaluate the results of condition monitoring

“In networked production and in the Industry 4.0 environment, predictive maintenance is an important component. Intelligent components can record the data in real time using sensors and transfer them to an evaluation system,”says Peter Synek, Deputy Managing Director of the Fluid Power Association at VDMA.

This possibility is nothing new at first: “Condition Monitoring” was a central topic of past MDA fairs. What is new is the knowledge that can be drawn from condition monitoring. Intelligently evaluated, the data obtained in real time allow the service or replacement of components at exactly the right time - without predefined maintenance intervals and before they fail.

Monitor and evaluate data continuously

How this should work can be demonstrated on rolling bearings. The reference variables here are vibration, temperature and speed. They are continuously recorded and evaluated so that irregularities are detected and their impact on the bearing life is calculated. At the same time, "hot runner" can be determined by temperature monitoring. Schaeffler offers this service, for example, for rolling bearings in wind turbines and rail vehicles. The data is evaluated and made available in the company's own cloud. The user does not need to have any more knowledge about the evaluation of sensor data.

These micro services for data based maintenance are currently in high demand. With hydraulic drives, for example, particle counters can be integrated into predictive maintenance systems. Parker developed such a system. Stefan Nilgen from Parker Hannifin explains: “With our 'Total System Health Management' we collect the data of the system including the periphery, analyze it and evaluate it in order to be able to take appropriate measures. So we have an overview of the overall productivity and the costs of complex systems and can take preventive measures of all kinds. Corrective measures can thus become superfluous or can be planned in such a way that failures are avoided.”

“Odin” is supposed to recognize 99% of all mistakes

At Bosch Rexroth, the predictive maintenance tool is called “Odin” and will also be shown at the Hanover Fair. The abbreviation stands for “Online Diagnostic Network” and, in addition to sensor technology and cloud-based applications, also includes the methodology of machine learning in order to carry out preventive maintenance measures. Tapio Torikka, who is responsible for the development of the system, explains: “An expert who monitors a system using traditional means detects an error with a 43% probability. Our system has an error detection rate of 99%.”

Predictive maintenance is gaining momentum thanks to Big Data and other Industry 4.0 topics: It will be easier to collect and process relevant data. This trend will be supported by cooperations: Aventics and the sensor specialist IFM will work together in the future to monitor and analyze operating data, for example of pneumatic cylinders, independently of the machine control online. Schaeffler and IBM have entered into a strategic partnership with the aim of continuously monitoring drives in critical applications such as wind turbines and trains and optimizing the knowledge gained through machine learning.

Such systems are discussed under the generic term “predictive analytics”. Visitors to the MDA who are interested in this development can also visit the Industrial Automation and Digital Factory trade fairs taking place in parallel. There companies in the IT industry will present their ideas of Industry 4.0 and the Industrial Internet of Things and include preventive maintenance. (kj)

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