RUL Estimation Enhancement using Hybrid Deep Learning Methods

Ikram Remadna,Labib Sadek Terrissa,Nourddine Zerhouni,Soheyb Ayad

doi:10.36001/ijphm.2021.v12i1.2378

Abstract

The turbofan engine is one of the most critical aircraft components. Its failure may introduce unwanted downtime, expensive repair, and affect safety performance. Therefore, It is essential to accurately detect upcoming failures by predicting the future behavior health state of turbofan engines as well as its Remaining Useful Life. The use of deep learning techniques to estimate Remaining Useful Life has seen a growing interest over the last decade. However, hybrid deep learning methods have not been sufficiently explored yet by researchers.In this paper, we proposed two-hybrid methods combining Convolutional Auto-encoder (CAE), Bi-directional Gated Recurrent Unit (BDGRU), Bi-directional Long-Short Term Memory (BDLSTM), and Convolutional Neural Network (CNN) to enhance the RUL estimation. The results indicate that the hybrid methods exhibit the most reliable RUL prediction accuracy and significantly outperform the most robust predictions in the literature.

Highlights

As an essential part of the aircraft, the turbofan engine is a complex and sophisticated system; its safety and reliability are indispensable
The results showed the superiority and effectiveness of the Convolutional Neural Network (CNN) model over other machine learning models such as the Multilayer Perceptron (MLP), the Support Vector Machine (SVM), and the Relevance Vector Machine (RVM)
The results demonstrate that the combination of AE and Bidirectional Long-Short Term Memory (BDLSTM) outperformed the other methods, such as MLP, CNN, Long-Short Term Memory (LSTM), BDLSTM and Autoencoder-LSTM

Summary

Introduction

As an essential part of the aircraft, the turbofan engine is a complex and sophisticated system; its safety and reliability are indispensable. Traditional maintenance is either reactive way (fixation or replacement of engine component after the detection of its breakdown) or proactive way (controlling the scheduling maintenance tasks based on the assumption of a certain level of performance degradation whether maintenance is essential or not). Both ways are inefficient and unable to eradicate faults or to conduct them (Ding & Kamaruddin, 2015). An intelligent maintenance strategy, referred to as predictive maintenance, coordinates the scheduling maintenance tasks based on the fault diagnosis, fault prognosis, and RUL estimation

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Conclusion