Deep-Learning Based Prognosis Approach for Remaining Useful Life Prediction of Turbofan Engine

Abstract

The entire life cycle of a turbofan engine is a type of asymmetrical process in which each engine part has different characteristics. Extracting and modeling the engine symmetry characteristics is significant in improving remaining useful life (RUL) predictions for aircraft components, and it is critical for an effective and reliable maintenance strategy. Such predictions can improve the maximum operating availability and reduce maintenance costs. Due to the high nonlinearity and complexity of mechanical systems, conventional methods are unable to satisfy the needs of medium- and long-term prediction problems and frequently overlook the effect of temporal information on prediction performance. To address this issue, this study presents a new attention-based deep convolutional neural network (DCNN) architecture to predict the RUL of turbofan engines. The prognosability metric was used for feature ranking and selection, whereas a time window method was employed for sample preparation to take advantage of multivariate temporal information for better feature extraction by means of an attention-based DCNN model. The validation of the proposed model was conducted using a well-known benchmark dataset and evaluation measures such as root mean square error (RMSE) and asymmetric scoring function (score) were used to validate the proposed approach. The experimental results show the superiority of the proposed approach to predict the RUL of a turbofan engine. The attention-based DCNN model achieved the best scores on the FD001 independent testing dataset, with an RMSE of 11.81 and a score of 223.

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

“…They performed a case study by estimating the RUL of turbofan engine on four subsets of C-MAPPS benchmark dataset. The main limitation of this work [40], the uncertainty inherent in their DL model was not quantified. Khawaja, Vachtsevanos, and Wu [41] developed a confidence prediction NN method with a confidence distribution node to solve the downside that the confidence limits of RUL prediction cannot generally be explicitly acquired with NN techniques.…”

“…Huang et al [39] used the classic multi-layer perceptron (MLP) approach to predict the RUL of bearings in laboratory testing and found that the prediction results outperformed reliability-based alternatives. Muneer et al [40] suggested an attention based DCNN model with time window approach to cope with the degradation and reliability of time-series forecasting problem. They performed a case study by estimating the RUL of turbofan engine on four subsets of C-MAPPS benchmark dataset.…”

Short term residential load forecasting using long short-term memory recurrent neural network

“…It is a nonlinear model where any prior knowledge of the relationship between input and output is needed [29]. Therefore, DLNN gives good results for pattern recognition [30], [31], sequence prediction [32],  ISSN: 2088-8708 Int J Elec & Comp Eng, Vol. 12, No.…”

Remaining Useful Life Estimation of Turbofan Engines with Deep Learning Using Change-Point Detection Based Labeling and Feature Engineering

“…A deep CNN approach with a time window [9] was applied to normalized raw C-MAPSS data. Muneer et al [24] combined deep CNN with an attention mechanism to extract highly abstract degradation and trend features. Temporal convolution expanded the receptive field for long sequences to improve the prediction performance [25].…”