MAHMAMT – Monitoring und Analyse von Schwermaschinen mittels berührungsloser Messverfahren und -technologien

Short Description

Heavy machinery, such as rig equipment, cranes, heavy-duty vehicles and cut-off and grinding machines are in use around the world in diverse industries. Therefore operational as well as maintenance costs play substantial roles. Hence the maximization of operational performance and efficiency are inevitable. Heavy machinery of any type is typically equipped with a large number of sensors for surveillance, machinery control, or sub-component control purposes. However, there is a lack of sensors for monitoring machine efficiency and performance.

Moreover, an integration of additional sensors and measurement technologies is difficult or even impossible in most cases due to technical, economical, or even legal issues. In order to avoid these problems, TDE GmbH, a company in the field of rig monitoring, is - in collaboration with JOANNEUM Research – developing a non-invasive measurement system, consisting of optical sensors, acoustic sensors, thermal sensors, and current/voltage sensors, as well as appropriate analysis and evaluation algorithms for determining relevant machinery "Key Performance Indicators" (KPIs).

The non-invasive character as well as the compact and autonomous design of the measurement system thereby allows for the acquisition of time synchronized data streams of heavy machinery, as well as for the direct efficiency and performance analysis.
In terms of machine state detection, novel and innovative machine learning and pattern recognition methods and algorithms are developed and applied, allowing for an automatic determination of the relevant machinery KPIs.

The core innovations of the "MAHMAMT" project are given by the fusion of different measurement modalities. Especially the fusion and combination of modality specific properties and features, e.g. audio frequencies, image based features, or power consumption tolerances, will be utilized for detection of different machine states.

The tracked and monitored KPIs are finally presented to a corresponding operator through a customized graphical user interface. In this way essential information on the current status of the machine, as well as assistance for decision making in terms of machine efficiency and production process control are provided.

Project Partners

  • TDE Thonhauser Data Engineering GmbH
  • JOANNEUM Reserach Forschungsgesellschaft mbH

Contact Address

Dr. Gerhard Thonhauser