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Web diagnosis of production plants

A newly developed diagnostics tool ensures worldwide remote monitoring of complex production plants in real time.



Industry 4.0, Java, Spring, AngularJS, web services / REST, LabView


To develop an easy-to-understand diagnostics tool for production plants.

Starting situation

LCA Automation AG builds production plants for the automobile industry. Smooth operation is important ‒ interruptions cost the Küssnacht-based company's customers dear. In order to prevent this, diagnoses are carried out in the plant on a regular basis. The problem: any interruptions can only be analysed after the fact, when it is already too late. In addition, the weekly diagnostic reports are usually too complex for the responsible experts to interpret sufficiently.


Together with the experts at LCA, researchers at the FHNW School of Engineering have developed a new diagnostics tool. At its core are numerous sensors, webcams and meters for measuring vibration, temperature and electricity. The measurements are continuously analysed and sent, pre-processed, to a central database at regular intervals. Intelligent software interprets the data and warns automatically if the values measured are unusual. The diagnostics tool can also process video data: for example, a conveyor belt is filmed with a simple webcam. If any signs of wear are detected on its surface, the software raises a timely alarm. The monitoring tool is web-based, so the engineers and managers responsible can monitor the state of the plants at any time. If a problem arises, they can retrieve an image of the situation immediately with the click of a mouse. With feedback from their customers, LCA Automation can optimise the plants on an ongoing basis.


Project information

Client LCA Automation
Execution FHNW Institute of Automation, FHNW Institute of Mobile and Distributed Systems
Funding Kommission für Technologie und Innovation KTI
Team Prof. Dr. David Zogg, Prof. Dr. Jürg Luthiger, Thekla Müller, Matthias Krebs, Emanuel Hediger, Mark Zeman