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

Abstract

Accurate remaining useful life (RUL) prediction is one of the most challenging problems in the prognostics of turbofan engines. Recently, RUL prediction methods for turbofan engines mainly involve data-driven models. Preprocessing the sensor data is essential for the performance of the prognostic models. Most studies on turbofan engines use piecewise linear (PwL) labeling, which starts with a constant initial RUL value in normal/healthy operating time. In this study, we designed a prognostic procedure that includes difference-based feature construction, change-point-detection-based PwL labeling, and a 1D-CNN-LSTM (one-dimensional–convolutional neural network–long short-term memory) hybrid neural network model for RUL prediction. The procedure was evaluated on the subset FD001 of the C-MAPSS dataset. The proposed procedure was compared with machine learning and deep learning models with and without the new difference feature. Also, the results were compared with the studies that used similar labeling approaches. Our analysis of the numerical results underscores the clear superiority of the proposed 1D-CNN-LSTM model with the difference feature in RUL prediction, with a score of 437.2 and an RMSE value of 16.1. This result illustrates the superior predictive capability of the 1D-CNN-LSTM model, which outperformed traditional machine learning methods and one of the earliest deep learning methods. These findings emphasize the superior predictive capability of the 1D-CNN-LSTM model and underline the potential of the feature engineering process for more accurate and robust RUL prediction in the context of turbofan engine prognostics.

A Two-Stage Attention-Based Hierarchical Transformer for Turbofan Engine Remaining Useful Life Prediction

“…Table 5 shows the comparison results. [56], GCT [36], DCNN [57], ELSTMNN [58], DATCN [59], AGCNN [60], BiLSTM attention model [61], DAST [46], DLformer [37], 1D-CNN-LSTM [62], CNN-LSTM-SAM [63], and BiLSTM-DAE-Transformer [42]. As presented in Table 5, the proposed STAR framework consistently outperforms existing RUL prediction models across all the datasets, showcasing its superior predictive capabilities.…”

“…To benchmark its effectiveness, we compare the proposed model against a suite of existing methods widely recognized in the field. These methods include BiLSTM [57], the gated convolutional Transformer (GCT) [36], DCNN [58], ELSTMNN [59], DATCN [60], AGCNN [61], BiLSTM attention model [62], DAST [46], DLformer [37], 1D-CNN-LSTM [63], CNN-LSTM-SAM [64], and BiLSTM-DAE-Transformer [42]. Table 5 shows the comparison results.…”

Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory

“…Ensarioglu et al [ 20 ] offered a novel strategy for the prediction of RUL involving the creation of features based on differences, labelling through change-point detection and piecewise linear techniques and the utilisation of a hybrid 1D-CNN-LSTM neural network. Li et al [ 21 ] presented a data-driven approach employing a CNN with a time window strategy for sample preparation, aiming to improve feature extraction.…”

“…Ensarioglu et al [20] offered a novel strategy for the prediction of RUL involving the creation of features based on differences, labelling through change-point detection and piecewise linear techniques and the utilisation of a hybrid 1D-CNN-LSTM neural network.…”

A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation

“…In [23], a fusion model named B-LSTM combines a comprehensive learning system for feature extraction with Long Short-Term Memory (LSTM) to handle temporal information in time-series data. Ensario glu et al [24] introduced an innovative approach that incorporated difference-based feature construction, change-point-detection-based piecewise linear labeling, and a hybrid 1D-CNN-LSTM neural network to predict RUL. In [25][26][27], LSTM networks are employed and trained in a supervised manner to directly estimate RUL.…”