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Southern Manufacturing & Electronics

Farnborough, Hants(GU14 6XL)

11/02/2020 - 13/02/2020

Southern Manufacturing and Electronics is the most comprehensive annual industrial exhibition in the (more)

Intelligent maintenance with Igus plastics

Intelligent maintenance with Igus plastics

The concepts of Industry 4.0, the Internet of Things and Smart Factory are no longer a vague promise of what is to come. Instead of planning maintenance at fixed intervals or reacting to failures or faults, continually monitoring the health of a machine enables predictive maintenance. Repair or replacement is then only carried out whenever it is actually necessary. At the same time, unscheduled shutdowns can be eliminated.

Igus’ smart plastics monitor the health of energy chains, cables and bearings. For energy chains, integrating sensors at the pin/bore connection between the links measures abrasion and wear status. By networking with the new Igus communication module (icom.plus), the sensors can be integrated into the customer’s IT infrastructure, production management systems such as SCADA and MES, or online into cloud solutions.

The icom.plus is programmed with initial service life algorithms based on Igus configuration tools and at the customer’s request can be operated offline. The user can therefore decide how the module is connected and how the data is managed, while establishing a balance between runtime maximisation and IT security. If online connection of the icom.plus is chosen, the service life information is continuously compared with that on the igus cloud, which enables maximum machine run time with a minimum risk of failure.

Igus performs 10 billion annual test cycles of energy chains and cables and the results are incorporated into its free service life calculator. In this way, it is possible to predict how long an e-chain will work reliably in the respective machine application. Thanks to the isense components, the service life is continually updated, giving the customer additional reassurance. This is because each update takes into account the current ambient conditions of the application and, thanks to machine learning, precise information on its lifetime can be obtained.

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