Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Smart machine maintenance: New AI system also detects unknown faults

25.05.2020

A new maintenance system is helping to make sensors smart. A research team led by Professor Andreas Schütze of Saarland University is combining artificial intelligence with sensors that gather status data on industrial machinery. The system is able to detect damage, wear and error states, and, uniquely, is also able to recognize when previously unknown machine states arise, learning from them and assigning them to their underlying root causes. This approach offers small and medium-sized companies a means of automating their machine maintenance and servicing operations, allowing them to plan more precisely and avoid unpleasant surprises.

Vast numbers of sensors are constantly collecting data from today’s industrial machinery. And there’s a lot that can be learned from these huge data sets. When a machine is operating normally, the way that it vibrates, shakes, hums or heats up is unique to that device.


Steffen Klein (l.) and Christopher Schnur, research assistants in the group led by Andreas Schütze, are currently conducting research into the new system.

Oliver Dietze

But when machine components start to wear out, these characteristic features undergo subtle changes. Minute temperature fluctuations, slight changes in vibrational behaviour, minor shifts in measurement data can all act as early warning signals that can indicate when a component is beginning to show signs of wear.

It is therefore crucial to be able to detect these subtle variations within the sea of data being produced. ‘A single sensor can generate a terabyte of raw data in just a few days,’ explains Professor Andreas Schütze, an expert in measurement and sensor technology at Saarland University. But in addition to detecting these changes, it is equally important to know how to interpret them.

Schütze and his team have been working with partners in industry and academia to develop a system that is able to extract the useful signal data from the vast quantities of data being generated.

‘By independently assigning signal patterns to specific damage, wear or error states, the system is able to make the machine’s status permanently visible,’ says Andreas Schütze.

The program continuously compares real-time sensor data with the data associated with normal machine operation and with the typical signal patterns that indicate an incipient malfunction or emerging wear defects. If the system detects a difference between these signal patterns, it will notify the equipment operator and indicate how to respond.

The researchers working at Saarland University and at the Center for Mechatronics and Automation Technology (ZeMA) in Saarbrücken have developed a whole suite of hardware and software modules that can be combined to produce a tailor-made monitoring system for a wide variety of industrial machinery and equipment.

The system is even able to detect unknown faults, to learn from them and then to assign these faults to their corresponding root cause. This is something wholly new. Up until now, AI-based monitoring systems were not able to evaluate previously unknown events. ‘Artificial intelligence works by pattern recognition.

If something completely new happens and the system doesn’t recognize this novel pattern, it will have effectively reached the limits of its capabilities. We’ve developed our system to a level where it can recognize states that is has not previously encountered and can notify the operator accordingly,’ explains Andreas Schütze.

The technical term is ‘novelty detection’. If a novel event begins to appear more frequently and more data on it becomes available, the program is able to assign its cause and the consequences that follow from it.

Over the course of multiple research projects, Schütze’s group filtered out from the vast quantities of measurement data those signal patterns that were associated either with changes in a machine’s behaviour or with machine damage. They then created mathematical models, which included simulations of sensor faults, and used these models to teach their system.

The program exploits machine learning techniques to automatically acquire new knowledge and to detect deviations from normal behaviour. ‘The algorithms also incorporate recently acquired data in their analyses. It is therefore possible for the system to detect and to interpret anomalies,’ explains Tizian Schneider, a doctoral student who is currently conducting research into the new system.

The knowledge generated by the system can be linked to other AI functions, such as the automated ordering of spare parts. This makes it easier to plan maintenance operations on large or difficult-to-access plant machinery. The system is also able to transfer information to human maintenance operatives in a clearly understandable form.

To ensure that maintenance personnel are able to interpret the numerical data correctly, Schütze’s team has also examined ways of translating the data into useful information for the user. ‘The system breaks down the information into a form that is both relevant and easily understood by the maintenance workers,’ explains Tizian Schneider.

Schütze and his team now want to help small and medium-sized companies become acquainted with the new technology. The researchers run training courses at the ‘Mittelstand 4.0 Competence Centre’, which is located at the ZeMA site in Saarbrücken and which is funded by the Federal Ministry of Economics and Energy. They are currently developing an AI-based assistance system specifically for small and medium-sized companies. ‘The system is particularly attractive for these small and medium-sized enterprises that want to use digitalization to boost their competitiveness.’ explains Andreas Schütze.

Background
The AI system was developed by Professor Andreas Schütze and his research team as part of the collaborative project ‘Modular Sensor Systems for Real-Time Process Control and Smart State Monitoring’ (MoSeS-Pro) and the projects ‘MessMo – Measurement-Assisted Assembly’ (European Regional Development Fund, ERDF) and ‘EaSy-ML’(supported by the Saarland government’s ERDF-technology funding programme ZTS). Two new collaborative projects aimed at developing the AI system further are also now starting and will involve a total of twenty project partners from academia and industry, including the companies HYDAC, Festo and Schaeffler.

Press photographs are available at https://www.uni-saarland.de/universitaet/aktuell/artikel/nr/21907.html
and can be used at no charge. Please read and comply with the conditions of use.

Contact:
Prof. Dr. Andreas Schütze,
Tel.: +49 (0)681 302-4663; Email: schuetze@lmt.uni-saarland.de
Tizian Schneider: Tel.: +49 (0)681 85787-48; Email: t.schneider@zema.de
http://www.lmt.uni-saarland.de

ZeMA – Center for Mechatronics and Automation Technology in Saarbrücken is a research hub for collaborative projects involving researchers from Saarland University, Saarland University of Applied Sciences (htw saar) and industrial partners. ZeMA conducts industrially relevant development work aimed at boosting industrial performance and transferring ideas and technology from the academic world to the industrial sector. http://www.zema.de

Wissenschaftliche Ansprechpartner:

Prof. Dr. Andreas Schütze,
Tel.: +49 (0)681 302-4663; Email: schuetze@lmt.uni-saarland.de
Tizian Schneider: Tel.: +49 (0)681 85787-48; Email: t.schneider@zema.de

Claudia Ehrlich | Universität des Saarlandes

More articles from Information Technology:

nachricht Spintronics: Faster data processing through ultrashort electric pulses
02.07.2020 | Martin-Luther-Universität Halle-Wittenberg

nachricht Multi-sensor system for the precise and efficient inspection of roads, railways and similar assets
01.07.2020 | Fraunhofer IPM

All articles from Information Technology >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: The lightest electromagnetic shielding material in the world

Empa researchers have succeeded in applying aerogels to microelectronics: Aerogels based on cellulose nanofibers can effectively shield electromagnetic radiation over a wide frequency range – and they are unrivalled in terms of weight.

Electric motors and electronic devices generate electromagnetic fields that sometimes have to be shielded in order not to affect neighboring electronic...

Im Focus: Gentle wall contact – the right scenario for a fusion power plant

Quasi-continuous power exhaust developed as a wall-friendly method on ASDEX Upgrade

A promising operating mode for the plasma of a future power plant has been developed at the ASDEX Upgrade fusion device at Max Planck Institute for Plasma...

Im Focus: ILA Goes Digital – Automation & Production Technology for Adaptable Aircraft Production

Live event – July 1, 2020 - 11:00 to 11:45 (CET)
"Automation in Aerospace Industry @ Fraunhofer IFAM"

The Fraunhofer Institute for Manufacturing Technology and Advanced Materials IFAM l Stade is presenting its forward-looking R&D portfolio for the first time at...

Im Focus: AI monitoring of laser welding processes - X-ray vision and eavesdropping ensure quality

With an X-ray experiment at the European Synchrotron ESRF in Grenoble (France), Empa researchers were able to demonstrate how well their real-time acoustic monitoring of laser weld seams works. With almost 90 percent reliability, they detected the formation of unwanted pores that impair the quality of weld seams. Thanks to a special evaluation method based on artificial intelligence (AI), the detection process is completed in just 70 milliseconds.

Laser welding is a process suitable for joining metals and thermoplastics. It has become particularly well established in highly automated production, for...

Im Focus: A structural light switch for magnetism

A research team from the Max Planck Institute for the Structure of Dynamics (MPSD) and the University of Oxford has managed to drive a prototypical antiferromagnet into a new magnetic state using terahertz frequency light. Their groundbreaking method produced an effect orders of magnitude larger than previously achieved, and on ultrafast time scales. The team’s work has just been published in Nature Physics.

Magnetic materials have been a mainstay in computing technology due to their ability to permanently store information in their magnetic state. Current...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

VideoLinks
Industry & Economy
Event News

International conference QuApps shows status quo of quantum technology

02.07.2020 | Event News

Dresden Nexus Conference 2020: Same Time, Virtual Format, Registration Opened

19.05.2020 | Event News

Aachen Machine Tool Colloquium AWK'21 will take place on June 10 and 11, 2021

07.04.2020 | Event News

 
Latest News

The lightest electromagnetic shielding material in the world

02.07.2020 | Materials Sciences

Spintronics: Faster data processing through ultrashort electric pulses

02.07.2020 | Information Technology

International conference QuApps shows status quo of quantum technology

02.07.2020 | Event News

VideoLinks
Science & Research
Overview of more VideoLinks >>>