Intelligent sensor detects leaks

Compressed air

Andreas Mühlbauer, 11.01.2022, 06:18

Intelligent sensor detects leaks

The tedious search for leaks in compressed air systems could soon become much easier: Fraunhofer IPA is working with Sick to develop an additional leakage service for an intelligent flow sensor. Self-learning algorithms evaluate the measurement data and thus detect leaks.

The ISO 50001 standard obliges companies to save energy. They must set themselves a target for how much energy they want to save in the coming years - and then achieve it. There is great potential for savings in compressed air, for example, one of the most common and most expensive forms of energy in German industry. Around 60,000 compressed air systems are in operation in this country. Together, they consume 16.6 terawatt hours every year, which corresponds to seven percent of the total electricity consumption of German industry.

A researcher from Fraunhofer IPA is working with Sick to develop an additional leakage service for this flow sensor. © Sick

However, up to 30 percent of the energy used escapes unused through tiny leaks. However, detecting these holes, kinks or leaking connectors has so far involved a great deal of effort. "The products and methods available on the market for detecting leaks are not worthwhile for many users," says Christian Dierolf from the Industrial Energy Systems department at the Fraunhofer Institute for Manufacturing Engineering and Automation IPA. "To use them, you either have to repeatedly detect leaks with an ultrasonic device or retrofit new valves for individual monitoring of the pneumatic actuators." Many companies are therefore forced to live with waste.

Flow sensor teaches itself

In future, however, there will no longer be any reason to allow compressed air to escape unnecessarily and miss out on potential energy savings. Researcher Dierolf is working closely with Sick to develop an additional leakage service for their intelligent flow sensor. It continuously records pressure, temperature and flow rate and generates seamless curves. These curves are evaluated by a self-learning algorithm. "The key feature here is clustering: leaks are reflected in characteristic curves. The algorithm recognizes these and sounds the alarm," explains Dierolf.

Implementation should be extremely simple: The sensor, which Fraunhofer IPA and SICK are jointly developing from concept to series production, does not need to be connected to the machine control of the compressed air system or to an industrial PC. Instead, the flow sensor itself has a small display and other interfaces such as MQTT and OPC-UA, which allow automated notification of the user. The user can also access the sensor via a web interface. The algorithm will also teach itself. With this so-called unsupervised machine learning, a human only has to check at the end whether the algorithm has drawn the right conclusions from the available information.

Clustering is set to become standard

"For us, the results from the joint development project are reason enough to make clustering available to our customers as an intelligent service as standard in future generations of flow sensors," says Thomas Weber, Head of Development in the Industrial Instrumentation division at Sick.

However, it will be some time before this is achieved. This is because the additional leakage service for the intelligent flow sensor is still a prototype. A compressed air demonstrator system was recently built at the company's headquarters in Waldkirch and delivered to the Fraunhofer IPA in Stuttgart. It contains the sensor prototype, which is now being tested and further developed.

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