Themes

Shop-floor Health Management

  • Critical manufacturing systems and engineering services constantly require a high availability of its engineering assets to maintain 24/7 operations.  Machine behavior changes constantly as a result of different production methods used and there is an increasing need to close the gap between production and maintenance by making machines more aware of its own health. The need to sense and make decisions quickly to proceed with the next manufacturing order is key to being competitive in manufacturing production.
  • As such, the information on shop floor availability and its effectiveness must be quickly and accurately distilled automatically in order to assess overall equipment effectiveness performance.  This key information is often lacking, based on experience of operators and the large gaps in analysing the collected data to make sense on the state of machine health still exists to date.  Data analysis tools need to be further developed to calculate a heath indicator for each machine and the overall shop floor effectiveness.  Data from machine controllers and data from PLCs and sensors need to be used in combination to ascertain the state of machine health.
Research Relevance
  • Fault detection, diagnosis and prognosis for advancing equipment health monitoring
  • Predictive maintenance to improve equipment availability
  • Assessment and monitoring of OEE for productivity improvement

 

Research Staff:

  • Dr. Chan Hian Leng, Ian
  • Mr. Chan Teck Kai
  • Mr. Chua Yong Quan
  • Dr. Geramifard Omid
  • Dr. Hong Jihoon
  • Mr. Kevin Ong Shen Hoong
  • Dr. Luo Ming
  • Ms. Wang Yu
  • Mr. Yan Weili
  • Dr. Zhou Junhong
Contact PersonChan Hian Leng Ian(hlchan@SIMTech.a-star.edu.sg)
Approach

1.    Automated Health Trending & Risk Assessment

  • Methods to detect faults and classify failures using data fusion from production data, machine controller data, discrete event/alarm data and data from sensors
  • System and methods for trending and visualising machine health and performance degradation and indicate machine OEE
  • Automated machine diagnostic tool that will indicate likely causes of faults and failure and the
  • Methods of showing the health of a fleet of manufacturing equipment and hence provide management with means to calculate the shop floor OEE automatically.


2.    Prognostic Health Management

  • Data driven prediction tools that estimate the remaining useful life of machine modules and of the machine
  • Physics-based modeling tools that captures the physical mechanisms of failure modes to predict machine health states
Research Showcases