Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine
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IVHM Centre, Cranfield University, Bedford MK43 0AL, UK
2
Artesis, 41480 Gebze, Kocaeli, Turkey
3
College of Engineering, Taibah University, Al-Medina Al-Munawara, Medina 42353, Saudi Arabia
*
Author to whom correspondence should be addressed.
Data 2018, 3(4), 49; https://doi.org/10.3390/data3040049
Received: 27 September 2018
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Revised: 30 October 2018
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Accepted: 1 November 2018
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Published: 6 November 2018
In the aerospace industry, every minute of downtime because of equipment failure impacts operations significantly. Therefore, efficient maintenance, repair and overhaul processes to aid maximum equipment availability are essential. However, scheduled maintenance is costly and does not track the degradation of the equipment which could result in unexpected failure of the equipment. Prognostic Health Management (PHM) provides techniques to monitor the precise degradation of the equipment along with cost-effective reliability. This article presents an adaptive data-driven prognostics reasoning approach. An engineering case study of Turbofan Jet Engine has been used to demonstrate the prognostic reasoning approach. The emphasis of this article is on an adaptive data-driven degradation model and how to improve the remaining useful life (RUL) prediction performance in condition monitoring of a Turbofan Jet Engine. The RUL prediction results show low prediction errors regardless of operating conditions, which contrasts with a conventional data-driven model (a non-parameterised Neural Network model) where prediction errors increase as operating conditions deviate from the nominal condition. In this article, the Neural Network has been used to build the Nominal model and Particle Filter has been used to track the present degradation along with degradation parameter.
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Keywords:
prognostics; integrated vehicle health management (IVHM); remaining useful life (RUL); reliability; particle filter (PF); neural network (NN); data-driven models (DDM); adoptable data-driven
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MDPI and ACS Style
Khan, F.; Eker, O.F.; Khan, A.; Orfali, W. Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine. Data 2018, 3, 49. https://doi.org/10.3390/data3040049
AMA Style
Khan F, Eker OF, Khan A, Orfali W. Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine. Data. 2018; 3(4):49. https://doi.org/10.3390/data3040049
Chicago/Turabian StyleKhan, Faisal, Omer F. Eker, Atif Khan, and Wasim Orfali. 2018. "Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine" Data 3, no. 4: 49. https://doi.org/10.3390/data3040049
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