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Virtual sensors enable self-maintaining machines

Posted: 04 Nov 2014 ?? ?Print Version ?Bookmark and Share

Keywords:iMAIN? virtual sensors? industrial machines?

A cloud site accessible to internal users via a wide range of interfacesincluding smartphones, tablets and laptopsserves as storage of information on the stress history of different manufacturing plants.

"The more data we collect, the easier it is to know the right time to implement preventive measures," explained Wabner. "We develop algorithms that enable machines to learn from experience and decide on the right time to replace components or determine when they have reached their optimum stress loading. The real data are compared with a simulated model that can be used to calculate the breaking point of the material."

The EU-sponsored iMAIN project was launched in September 2012 and brings together manufacturers, industrial users, computer scientists and engineers in a concerted effort to develop new, advanced technologies for the maintenance of industrial machines.

"The virtual sensors have long since passed the proof-of-concept stage and are already being successfully used in real-life applications. And the private cloud solution for data sharing has reached the test stage," reported Wabner.

A prototype version of the system is being used by a project partner in Sloveniathe Gorenje Group, which manufactures home appliancesfor the condition monitoring of a universal press supplied by Litostroj Ravne, another project partner. This factory forms metal panels used in the construction of washing machines, refrigerators and other appliances.

"Since this system was introduced, Gorenje has better information enabling it to predict possible outages in advance, and also facilitate and optimise press operations by monitoring the stress and strain on the machines. We regularly compare the recorded data with the results of tests conducted at our facility at Fraunhofer IWU," said Wabner.

The ultimate aim is to be able to produce a system capable of predicting the stress-related failure of components in practice by the time the project ends next summer.


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