Energy Systems: Gas Turbine Remote Prognostics and Sensor Validation #SiemensEnergy

 In 2009, Profs Jill Stewart, Chris Bingham and I founded the new School of Engineering at Lincoln University. As part of our growing collaboration with @Siemens we rolled out several research projects, one of which was the gas turbine remote monitoring and sensing RMS project.

In retrospect, this was an example of early Big Data. We had a dedicated data connection to Siemens’ global gas turbine fleet, and developed algorithms to deliver, prognostics, diagnostics and most-importantly, sensor validation to prevent false triggering.

  • M Gallimore, Z Yang, C M Bingham, P Stewart, N James, S Watson, A Latimer – Novelty Detection for Predictive Maintenance Scheduling for Industrial Gas Turbines. International Conference on Mechanical Engineering and Technology (ICMET), London, November, 2011
  • Z Yang, W-K Ling, M Gallimore, C M Bingham, P Stewart – Sensor Fault Detection and Measurement Reconstruction using an Analytical Optimization Approach. International Conference on Mechanical Engineering and Technology (ICMET), London, November, 2011
  • Z Yang, C M Bingham, W-K Ling, M Gallimore, P Stewart – Trend Extraction using Empirical Mode Decomposition and Non-Uniform Filter Banks, with Industry Application. International Conference on Mechanical Engineering and Technology (ICMET), London, November, 2011
  • Z Yang, C M Bingham, W-K Ling, M Gallimore, P Stewart – Intelligent Condition Monitoring via Sparse Representation and Principal Component Analysis for Industrial Gas Turbine Systems. International Conference on Mechanical Engineering and Technology (ICMET), London, November, 2011
  • Zhijing Yang; Bingham, C.; Ling, B.W.-K.; Gallimore, M.; Stewart, P.; Yu Zhang, “Trend extraction based on Hilbert-Huang transform,” Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2012 8th International Symposium on , vol., no., pp.1,5, 18-20 July 2012, doi: 10.1109/CSNDSP.2012.6292713
  • Yu Zhang; Bingham, C.; Zhijing Yang; Gallimore, M.; Stewart, P., “Applied sensor fault detection and identification using hierarchical clustering and SOMNNs, with faulted-signal reconstruction,” MECHATRONIKA, 2012 15th International Symposium , vol., no., pp.1,7, 5-7 Dec. 2012