Next Article in Journal
Application of Rough Set Theory to Water Quality Analysis: A Case Study
Previous Article in Journal
An Evaluation of the Information Technology of Gene Expression Profiles Processing Stability for Different Levels of Noise Components
Article Menu

Export Article

Open AccessArticle

Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine

1
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.
Received: 27 September 2018 / Revised: 30 October 2018 / Accepted: 1 November 2018 / Published: 6 November 2018
  |  
PDF [6263 KB, uploaded 8 November 2018]
  |  

Abstract

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. View Full-Text
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 prognostics; integrated vehicle health management (IVHM); remaining useful life (RUL); reliability; particle filter (PF); neural network (NN); data-driven models (DDM); adoptable data-driven
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Data EISSN 2306-5729 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top