When industrial robots overheat – Continuous fitness check predicts potential machine faults
It keeps a constant eye on the condition of the machine, it carries out diagnostic analyses and it notifies the operator when a part needs to be replaced. The research team led by Andreas Schütze at Saarland University has developed an early warning system for industrial assembly, handling and packaging processes. Intelligent sensors continuously collect a wide array of measurement data from inside plant machinery and compare the signal patterns against those for normal operating conditions. If the system detects a difference in the patterns that indicates a potential fault, it immediately notifies the equipment operator about what remedial measures should be taken. This helps engineers to plan maintenance more effectively and protects them from unpleasant surprises and unexpected production losses.
The research team will be exhibiting their technology at the Saarland Research and Innovation Stand (Hall 2, Stand B46) at Hannover Messe, which runs from April 24th to April 28th.
Robots in production lines work tirelessly and with micrometre precision – unless of course a component fails. If, for example, the linear actuator used to precisely position a car body in front of an assembly robot is damaged, the robotic arm will no longer be able to position the car door as exactly as it normally does. The result is a door that is misaligned. Or, to take another example, the sudden failure of a machine component due to material fatigue could well result in the complete shutdown of a production line. A team of engineers led by Andreas Schütze, an expert in sensor systems from Saarland University and the Center for Mechatronics and Automation Technology, is working with a group of academic and industrial partners to prevent this sort of situation from arising.
Their system subjects machines to what is effectively a continuous medical check-up. The human equivalent would be equipping a person with an activity tracker, a continuous digital ECG and blood pressure monitor so that their state of health could be analysed at any time. ‘Our system makes it possible to continuously visualize the current condition of plant machinery and provide advance warning of potential damage. To do this we fit sensors inside the machines and these sensors are able to interact with each other and with existing process sensors. This enables us to register even the smallest of changes,’ explains Andreas Schütze. The researchers make use of the phenomenon that technical equipment will begin to make a different noise, or will vibrate or overheat long before it actually fails. The trick here is that the characteristic manner in which a machine hums or vibrates during normal operation is different to that observed when something has changed within the machine, though these differences can be very subtle and undetectable to normal senses.
This is where the system developed by the Saarbrücken mechatronics specialists comes into play. The sensors are able to detect these slight changes and can assign them automatically to specific fault profiles. ‘We have studied how signal patterns, such as the frequency of vibrations, alter during common damage or fault states,’ explains Professor Schütze. To do this the research team examined the patterns in thousands of measurement data sets and identified those associated with particular types of damage or mechanical wear. ‘We feed this information to the sensors, transforming them into smart devices that are able to detect these signal differences on their own,’ explains Nikolai Helwig from Schütze‘s team. This essentially eliminates the need for an external analyser, as the system is able to perform the analysis itself.
The goal of the research team is to develop a set of sensors and modules that will allow companies that operate plant machinery to put together a fitness check specifically tailored to the needs of their plant or equipment. ‘The customized sensors can either be integrated into the machinery when it is being made or can be retrofitted. Initially the sensors spend their time collecting baseline data, that is data that reflects the normal operating state of the machine,’ says Helwig. Once that has been done, the system is ready to continuously compare the current operational data with those typical sensor signal patterns associated with incipient equipment failure or damage. ‘Our method also opens up opportunities for other Industry 4.0 applications. For example, the system can also be used for quality control purposes by analysing whether production machinery was operating properly during a manufacturing process.
The system under development is currently entering its test phase during which the experts in sensor and measuring technology will be working with the companies Bosch Rexroth and Festo. The sensors will be used at Bosch-Rexroth to monitor the condition of machine tools, while at Festo their job will be to check the linear actuators mentioned above, specifically Festo’s spindle axes and electromechanical cylinders.
The research work is part of the collaborative project ‘Modular sensor systems for real-time process control and smart condition monitoring’ (MoSeS-Pro). The MoSeS-Pro is coordinated by Andreas Schütze and is funded by the Federal Ministry of Education and Research (BMBF). (For more information on project partners, please see the section ‘Background’ below).
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Prof. Dr. Andreas Schütze:
Tel.: +49 (0)681 302-4663, E-mail: email@example.com, http://www.lmt.uni-saarland.de
Dipl.-Ing. Nikolai Helwig: Tel.: +49 (0)681 85787-41; E-mail: firstname.lastname@example.org
The Saarland Research and Innovation Stand at Hannover Messe can be reached by calling +49 (0)681 302-68500.
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MoSeS-Pro – Modular sensor systems for real-time process control and smart condition monitoring
The goal of this collaborative project is to develop a suite of hardware and software modules that will make it easier to develop sensor systems for monitoring and controlling drives and positioning systems, paving the way for fast and precise manufacturing processes that can be monitored and adjusted in real time.
The research work has received financial support totalling €3.1 million as part of the BMBF funding programme ‘Sensor-based electronic systems for Industry 4.0 applications (SElekt I4.0)’, which is being managed by the project management agency VDI/VDE-IT. Around €540,000 in funding has been allocated to Saarland University.
The project research work at Saarland University and at the Center for Mechatronics and Automation Technology (ZeMA) is being conducted by the groups led by Professor Andreas Schütze, who is also lead project coordinator for MoSeS-Pro, and by Professor Matthias Nienhaus, a specialist in drive technology. The project is being carried with the support of the associated partners Festo AG (Rohrbach plant) and Bosch Rexroth AG (Homburg plant) and the direct project partners Sensitec GmbH, Lenord, Bauer & Co. GmbH, ESR Dipl.-Ing. Pollmeier GmbH and CANWAY Technology GmbH. In addition to ZeMA, research partners in the MoSeS-Pro project are the Fraunhofer Institute for Microelectronic Circuits and Systems (IMS) and the Department of Integrated Sensor Systems at Kaiserslautern University of Technology. http://www.moses-pro.de
Saarland University, Saarland University of Applied Sciences (htw saar) and industrial partners are working together at ZeMA – Center for Mechatronics and Automation Technology in Saarbrücken to strengthen the fields of mechatronic engineering and industrial automation in Saarland and to promote technology transfer. ZeMA is home to a large number of industry-specific development projects as well as projects aimed at transforming research findings into practical industrial applications. http://www.zema.de
The Saarland Research and Innovation Stand at Hannover Messe is organized by Saarland University’s Contact Centre for Technology Transfer (KWT). KWT is the central point of contact for companies interested in exploring opportunities for cooperation and collaboration with researchers at Saarland University. http://www.kwt-uni-saarland.de/