Smart plastics make maintenance intelligent

9th October 2019
Posted By : Anna Flockett
Smart plastics make maintenance intelligent

‘Industry 4.0’, ‘Internet of Things’ and ‘Smart Factory’ - these concepts 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 and the associated costs 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. Sensors are also used to detect breakages and monitor the push/pull forces being applied. By networking with the new igus communication module (, 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 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 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. This information can be viewed on the screen of the machine control system and if an online connection is chosen an SMS or e-mail can provide the maintenance team the relevant details if unexpected operating states occur or maintenance is required. At an early stage, users are informed if there is a need to obtain replacement parts. A wide range of other scenarios, including automatic initiation of maintenance work or the ordering of replacement parts can be implemented.

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