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research-article

Capability of the Bayesian Forecasting Method to Predict Field Time Series

[+] Author and Article Information
Nicolo' Gatta

Dipartimento di Ingegneria, Università degli Studi di Ferrara, Ferrara, Italy
ncl.gatta@gmail.com

Mauro Venturini

Dipartimento di Ingegneria, Università degli Studi di Ferrara, Ferrara, Italy
mauro.venturini@unife.it

Lucrezia Manservigi

Dipartimento di Ingegneria, Università degli Studi di Ferrara, Ferrara, Italy
lucrezia.manservigi@unife.it

Giuseppe Fabio Ceschini

Siemens AG, Nürnberg, Germany
giuseppe.ceschini@siemens.com

Giovanni Bechini

Siemens AG, Nürnberg, Germany
giovanni.bechini@siemens.com

1Corresponding author.

ASME doi:10.1115/1.4040736 History: Received June 20, 2018; Revised June 26, 2018

Abstract

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 multi-step 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 multi-step 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 (c) 2018 by ASME
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