Valve Failure Prognostics in Reciprocating Compressors Utilizing Temperature Measurements, PCA-Based Data Fusion, and Probabilistic Algorithms

Journal Article (2020)
Authors

Theodoros Loutas (University of Patras)

N. Eleftheroglou (Structural Integrity & Composites)

George Georgoulas (University of Patras)

Panagiotis Loukopoulos (Cranfield University)

David Mba (De Montfort University)

Ian Bennett (Shell Global Solutions International B.V.)

Research Group
Structural Integrity & Composites
To reference this document use:
https://doi.org/10.1109/TIE.2019.2926048
More Info
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Publication Year
2020
Language
English
Research Group
Structural Integrity & Composites
Issue number
6
Volume number
67
Pages (from-to)
5022-5029
DOI:
https://doi.org/10.1109/TIE.2019.2926048

Abstract

In this paper, temperature measurements are utilized to develop health indicators based on principal component analysis toward the probabilistic estimation of the remaining useful life (RUL) of reciprocating compressors in service. Temperature degradation histories obtained from 13 actual valve failure cases constitute the training data in a data-driven prognostic approach. Two data-driven prognostic methodologies are presented and proposed based on probabilistic mathematical models, i.e., gradient boosted trees and nonhomogeneous hidden semi-Markov models. The training and testing process of all models is described in detail. RUL prognostics in unseen data are obtained for all models. Beyond the mean estimates of the RUL, the uncertainty associated with the point prediction is quantified and upper/lower confidence bounds are also estimated. Prediction estimates for 12 real-life failure cases are presented and the pros and cons of each model's performance are highlighted. Several metrics are utilized to assess the performance of the prognostic algorithms and conclusions are drawn regarding the prognostic capabilities of each of them.

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