Abstract
Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great significance for improving the reliability and safety of the engine system. Due to the high dimension and complex features of sensor data in RUL prediction, this paper proposes four data-driven prognostic models based on deep neural networks (DNNs) with an attention mechanism. To improve DNN feature extraction, data are prepared using a sliding time window technique. The raw data collected after normalizing is simply fed into the suggested network, requiring no prior knowledge of prognostics or signal processing and simplifying the proposed method’s applicability. In order to verify the RUL prediction ability of the proposed DNN techniques, the C-MAPSS benchmark dataset of the turbofan engine system is validated. The experimental results showed that the developed long short-term memory (LSTM) model with attention mechanism achieved accurate RUL prediction in both scenarios with a high degree of robustness and generalization ability. Furthermore, the proposed model performance outperforms several state-of-the-art prognosis methods, where the LSTM-based model with attention mechanism achieved an RMSE of 12.87 and 11.23 for FD002 and FD003 subset of data, respectively.
Highlights
Prognostics is a discipline of engineering that focuses on predicting a system’s future state or behavior by using synthesis observations, calibrated mathematical models, and simulation [1]
We have proposed a new data-driven approach that aims to accurately predict remaining useful life (RUL) and overcome the uncertainty inherent in deep neural networks (DNNs) predictions in the literature by incorporating the sliding time window technique for sample preparation and long short-term memory (LSTM) network with an attention mechanism to map the relationship between features and the RUL effectively
Electronics 2021, 10, 2453 information included in the input signal and for which its performance in RUL prediction outperforms the traditional deep learning (DL) approaches in the literature. (iii) We have proposed a cost-effective approach to predict the remaining useful life of a turbofan engine where the parameters and computational cost of the training process are considerably decreased using dimensionality reduction processing. (iv) We have conducted a comparative analysis of four different deep neural network architectures in order to evaluate which technique relative to DNNs has excellent features extraction and generalization abilities
Summary
Prognostics is a discipline of engineering that focuses on predicting a system’s future state or behavior by using synthesis observations, calibrated mathematical models, and simulation [1]. Prognostics is an attempt to estimate the remaining useful life (RUL) of a component in an engineering system. In many industries, rotating machinery is a critical component and is vulnerable to failure because of harsh working conditions and long operating hours [2]. To avoid critical damage and abrupt stopping of machine operation, rotating machinery failures should be detected as early as possible [7]. Industrial internet advancements have enabled sensor data available from multiple machines across different domains and industries [17]. These sensor readings can determine the health of the equipment. As a result, developing these models can help achieve goals such as predicting the machine’s RUL based on sensor data. RUL may be calculated by using historical trajectory data, which is useful for optimizing maintenance schedules in order to minimize engineering problems and reduce costs
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