A lightweight transformer and depthwise separable convolution model for remaining useful life prediction of turbofan engines

Abstract

The degradation of turbofan engines under complex operating conditions makes it difficult to predict their remaining useful life (RUL), which affects aircraft maintenance efficiency and reliability. To maintain prediction accuracy while improving prediction speed under the limited computing power and memory resources of edge devices, a lightweight Transformer and depthwise separable convolutional neural network (DSCformer) prediction model has been proposed. In the proposed DSCformer method, a probsparse self-attention mechanism with convolutional transformation of the Value branch is developed to improve the efficiency of dot-product, and depthwise separable convolution is employed to extract local spatiotemporal features replace the decoder in Transformer. Additionally, the model’s ability to capture overall trends is improved by incorporating a scaling factor in the Bayesian optimization algorithm, which also accelerates the search for the smoothing coefficient. The evaluation on the C-MAPSS dataset shows that the proposed method achieves a root mean square error of 11.33 and 12.44, as well as scores of 634.22 and 947.35 for predicting FD002 and FD004, respectively, within a shorter training time. These results indicate that the proposed method outperforms state-of-the-art prediction methods under multiple operating conditions for aero engine RUL prediction.

ODDformer: odd–even de-stationary and decomposition techniques transformer for aircraft engine remaining useful life prediction

“…Typical deep learning approaches encompass deep belief networks (DBNs) [5], stacked autoencoders (SAEs) [6], convolutional neural networks (CNNs) [7,8], long short-term memory (LSTM) networks [9,10], and Transformer [11], among others. In the field of RUL prediction for aircraft engines, numerous studies have emphasized the application of these methods [12][13][14], with results indicating satisfactory predictive performance.…”

Predicting the Remaining Useful Life of Turbofan Engines Using Fractional Lévy Stable Motion with Long-Range Dependence

“…To ensure that the fLsm model effectively fits the LRD of stochastic sequences, we limit the range of parameters α and H to α ∈ (1, 2) and H ∈ (1/2, 1), respectively. (6) Allowing us to derive the following equation can be derived:…”

“…The primary artificial intelligence algorithms utilized are LSTM and deep learning approaches. In recent years, various improved LSTM [4,5] and deep learning optimization algorithms [6][7][8] have been proposed to improve the accuracy of RUL prediction. These advancements also aim to combine both approaches [9,10], which necessitates a large number of data samples.…”