0
Research Papers

Capability of the Bayesian Forecasting Method to Predict Field Time Series

[+] Author and Article Information
Nicolò Gatta, Mauro Venturini, Lucrezia Manservigi

Dipartimento di Ingegneria,
Università degli Studi di Ferrara,
Ferrara 44122, Italy

Giuseppe Fabio Ceschini, Giovanni Bechini

Siemens AG,
Nürnberg 90461, Germany

Manuscript received June 20, 2018; final manuscript received June 26, 2018; published online October 29, 2018. Editor: Jerzy T. Sawicki.

J. Eng. Gas Turbines Power 140(12), 121013 (Oct 29, 2018) (9 pages) Paper No: GTP-18-1265; doi: 10.1115/1.4040736 History: Received June 20, 2018; Revised June 26, 2018

This paper addresses the challenge of forecasting the future values of gas turbine measureable quantities. The final aim is the simulation of “virtual sensors” capable of producing statistically coherent measurements aimed at replacing anomalous observations discarded from the time series. Among the different available approaches, the Bayesian forecasting method (BFM) adopted in this paper uses the information held by a pool of observations as knowledge base to forecast the values at a future state. The BFM algorithm is applied in this paper to Siemens field data to assess its prediction capability, by considering two different approaches, i.e., single-step prediction (SSP) and multistep prediction (MSP). While SSP predicts the next observation by using true data as base of knowledge, MSP uses previously predicted data as base of knowledge to perform the prediction of future time steps. The results show that BFM single-step average prediction error can be very low, when filtered field data are analyzed. On the contrary, the average prediction error achieved in case of BFM multistep prediction is remarkably higher. To overcome this issue, the BFM single-step prediction scheme is also applied to clusters of time-wise averaged data. In this manner, an acceptable average prediction error can be achieved by considering clusters composed of 60 observations.

Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

Roumeliotis, I. , Aretakis, N. , and Alexiou, A. , 2016, “ Industrial Gas Turbine Health and Performance Assessment With Field Data,” ASME Paper No. GT2016-57722.
Simon, D. L. , and Rinehart, A. W. , 2016, “ Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics,” ASME J. Eng. Gas Turbines Power, 138(7), p. 071201. [CrossRef]
Puggina, N. , and Venturini, M. , 2012, “ Development of a Statistical Methodology for Gas Turbine Prognostics,” ASME J. Eng. Gas Turbines Power, 134(2), p. 022401. [CrossRef]
Venturini, M. , and Puggina, N. , 2012, “ Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics,” ASME J. Eng. Gas Turbines Power, 134(10), p. 101601. [CrossRef]
Venturini, M. , and Therkorn, D. , 2013, “ Application of a Statistical Methodology for Gas Turbine Degradation Prognostics to Alstom Field Data,” ASME J. Eng. Gas Turbines Power, 135(9), p. 091603. [CrossRef]
Zaidan, M. A. , Mills, A. R. , Harrison, R. F. , and Fleming, P. J. , 2016, “ Gas Turbine Engine Prognostics Using Bayesian Hierarchical Models: A Variational Approach,” Mech. Syst. Signal Process., 70–71(2016), pp. 120–140. [CrossRef]
Tahan, M. , Tsoutsanis, E. , Muhammada, M. , and Karim, Z. A. A. , 2017, “ Performance-Based Health Monitoring, Diagnostics and Prognostics for Condition-Based Maintenance of Gas Turbines: A Review,” Appl. Energy, 198(2017), pp. 122–144. [CrossRef]
Wu, D. , Amini, A. , Razban, A. , and Chen, J. , 2017, “ A Novel Approach to Forecast and Manage Daily Electrical Maximum Demand,” 30th International Conference on Efficiency, Cost, Optimisation, Simulation and Environmental Impact of Energy Systems (ECOS), San Diego, CA, July 2–6, Paper No. 372.
Igie, U. , Diez-Gonzalez, P. , Giraud, A. , and Minervino, O. , 2016, “ Evaluating Gas Turbine Performance Using Machine-Generated Data: Quantifying Degradation and Impacts of Compressor Washing,” ASME J. Eng. Gas Turbines Power, 138(12), p. 122601. [CrossRef]
Hanachi, H. , Liu, J. , Banerjee, A. , and Chen, Y. , 2016, “ Prediction of Compressor Fouling Rate Under Time Varying Operating Conditions,” ASME Paper No. GT2016-56242.
Sarkar, S. , Jin, X. , and Ray, A. , 2011, “ Data-Driven Fault Detection in Aircraft Engines With Noisy Sensor Measurements,” ASME J. Eng. Gas Turbines Power, 133(8), p. 081602. [CrossRef]
Dewallef, P. , and Borguet, S. , 2013, “ A Methodology to Improve the Robustness of Gas Turbine Engine Performance Monitoring Against Sensor Faults,” ASME J. Eng. Gas Turbines Power, 135(5), p. 051601. [CrossRef]
Van Paridon, A. , Bacic, M. , and Ireland, P. T. , 2016, “ Kalman Filter Development for Real Time Proper Orthogonal Decomposition Disc Temperature Model,” ASME Paper No. GT2016-56330.
Hurst, A. M. , Carter, S. , Firth, D. , Szary, A. , and Van De Weert, J. , 2015, “ Real-Time, Advanced Electrical Filtering for Pressure Transducer Frequency Response Correction,” ASME Paper No. GT2015-42895.
Gutierrez, L. A. , Pezzini, P. , Tucker, D. , and Banta, L. , 2014, “ Smoothing Techniques for Real-Time Turbine Speed Sensors,” ASME Paper No. GT2014-25407.
Ceschini, G. F. , Gatta, N. , Venturini, M. , Hubauer, T. , and Murarasu, A. , 2018, “ Optimization of Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series,” ASME J. Eng. Gas Turbines Power, 140(3), p. 032401. [CrossRef]
Ceschini, G. F. , Gatta, N. , Venturini, M. , Hubauer, T. , and Murarasu, A. , 2018, “ Resistant Statistical Methodologies for Anomaly Detection in Gas Turbine Dynamic Time Series: Development and Field Validation,” ASME J. Eng. Gas Turbines Power, 140(5), p. 052401. [CrossRef]
Ceschini, G. F. , Gatta, N. , Venturini, M. , Hubauer, T. , and Murarasu, A. , 2018, “ A Comprehensive Approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (DCIDS),” ASME J. Eng. Gas Turbines Power, 140(3), p. 032402. [CrossRef]
Ceschini, G. F. , Manservigi, L. , Bechini, G. , and Venturini, M. , 2018, “ Detection and Classification of Sensor Anomalies in Gas Turbine Field Data,” ASME Paper No. GT2018-75007.
Liu, L. , Kuo, S. M. , and Zhou, M. C. , 2009, “ Virtual Sensing Techniques and Their Applications,” IEEE International Conference on Networking, Sensing and Control (ICNSC), Okayama, Japan, Mar. 26–29, pp. 31–36.
Pathak, D. , and Halale, V. P. , 2016, “ An Introductory Approach to Virtual Sensors and Its Modelling Techniques,” Int. J. Sci. Eng. Res., 7(3), pp. 461–464. https://www.researchgate.net/publication/314403412_An_Introductory_Approach_to_Virtual_Sensors_and_Its_Modelling_Techniques
Braun, J. , Lu, S. , and Paniagua, G. , 2017, “ Development of High Frequency Virtual Thermocouples,” ASME Paper No. GT2017-64669.
Cavarzere, A. , and Venturini, M. , 2011, “ Application of Forecasting Methodologies to Predict Gas Turbine Behavior Over Time,” ASME J. Eng. Gas Turbines Power, 134(1), p. 012401. [CrossRef]
Lipowsky, H. , Staudacher, S. , Bauer, M. , and Schmidt, K. J. , 2009, “ Application of Bayesian Forecasting to Change Detection and Prognosis of Gas Turbine Performance,” ASME J. Eng. Gas Turbines Power, 132(3), p. 031602. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Temperature T1 dataset (raw data on the top; data filtered by DCIDS on the bottom)

Grahic Jump Location
Fig. 2

Temperature T2 dataset (raw data on the top; data filtered by DCIDS on the bottom)

Grahic Jump Location
Fig. 3

Scenario with two stationary trends and step magnitude at 20%, by using raw (top) and DCIDS processed (bottom) data

Grahic Jump Location
Fig. 4

Average prediction error versus step change magnitude

Grahic Jump Location
Fig. 5

Single-step prediction of temperature T1 (steady-state SS1)

Grahic Jump Location
Fig. 6

Single-step prediction of temperature T1 (steady-state SS2)

Grahic Jump Location
Fig. 7

Single-step prediction of temperature T1 (steady-state SS3)

Grahic Jump Location
Fig. 8

Single-step prediction of temperature T2 (steady-state SS1)

Grahic Jump Location
Fig. 9

Single-step prediction of temperature T2 (steady-state SS2)

Grahic Jump Location
Fig. 10

Multistep prediction of temperature T1 (steady-state SS1)

Grahic Jump Location
Fig. 11

Multistep prediction of temperature T1 (steady-state SS2)

Grahic Jump Location
Fig. 12

Multistep prediction of temperature T1 (steady-state SS3)

Grahic Jump Location
Fig. 13

Multistep prediction of temperature T2 (steady-state SS1)

Grahic Jump Location
Fig. 14

Multistep prediction of temperature T2 (steady-state SS2)

Grahic Jump Location
Fig. 15

Single-step prediction of averaged data of temperature T1 (steady-state SS1)

Grahic Jump Location
Fig. 16

Single-step prediction of averaged data of temperature T1 (steady-state SS2)

Grahic Jump Location
Fig. 17

Single-step prediction of averaged data of temperature T1 (steady-state SS3)

Grahic Jump Location
Fig. 18

Single-step prediction of averaged data of temperature T2 (steady-state SS1)

Grahic Jump Location
Fig. 19

Single-step prediction of averaged data of temperature T2 (steady-state SS2)

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In