Remaining Useful Life Prediction of Turbo Engine Based on CNN-BiLSTM Model With Feature Enhancement Approaches

Abstract

This article proposes a novel method for predicting the remaining useful life (RUL) of turbine engines by integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) frameworks with chaotic mapping (CM) and fractional-order (FO) filters (called CBCF). The method follows a sequence of CM, FO filter, CNN, and BiLSTM. CM is used for preprocessing to extract meaningful features from engine data, while the FO filter enhances image details produced by CM, contributing to more accurate RUL predictions. The enhanced images are then used as inputs to the CNN-BiLSTM model. CNN is employed to automatically extract and learn hierarchical features from the engine data through convolutional layers, capturing spatial patterns and local dependencies for more effective feature representation. BiLSTM is then used to process the time-sequenced data, integrating forward and backward network information to capture both past and future dependencies, thereby improving prediction accuracy. The commercial modular aero-propulsion system simulation (C-MAPSS) dataset, which is a benchmark dataset in this field, is utilized to evaluate the feasibility and effectiveness of the proposed method. The C-MAPSS dataset provides comprehensive data on engine degradation and has been widely used for validating RUL prediction models. Experimental results show that the integration of CM and FO filters with the CNN-BiLSTM model enhances performance, significantly reducing the root-mean-square error (RMSE) and demonstrating the potential of this approach for accurate RUL predictions.