Remaining useful life prediction based on an integrated neural network

Unexpected failures and unscheduled maintenance activities of mechanical systems might incur considerable waste of resources and high investment costs. Thus, in recent years, prognostics and health management (PHM) has received a lot of attention because of its importance in maintenance cost reduction and machine fault prognostics. The remaining useful life (RUL) of machinery is defined as the length from the current time to the end of its useful life, which is the core technology of PHM. During the operation of machines and equipment, a large amount of data generated by different sensors in the system is collected using various methods. These data often characterize the health status of machinery to a certain extent. By applying the systematic approach to these data, valuable information for strategic decision-making can be obtained. However, traditional machine learning algorithms are usually not efficient enough to handle the complex and nonlinear characteristics of the system and deal with big data. With the rapid development of modern computational hardware and theory, deep learning algorithms show unique advantages in characterizing the system complexity and processing big data. Because of the low-accuracy prediction of the RUL of machines or equipment, a neural network integrating the one-dimensional convolutional neural network (1D CNN) and the bidirectional long short-term memory (BD-LSTM) was proposed. To extract the features of the time series and generate more training samples, the sliding window algorithm was used to process the data and the Kalman filter was applied to denoise the data. Then, the dataset was standardized and the RUL labels were set. Instead of artificial feature extraction, this study used 1D CNN to extract features from the data and discarded the pooling layer of CNN. The extracted high-dimensional features were inputted into the BD-LSTM for regression prediction, and the neural network was integrated by bagging to predict the RUL. Finally, the effectiveness and superiority of the model compared with the machine or deep learning model were verified using the National Aeronautics and Space Administration dataset. Results showed that the proposed model can more accurately predict the RUL than the machine or deep learning model.