Abstract
We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short-term memory (MSDCNN-LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications. First, a binary matrix is used to adjust the proportion of “0” to simulate the number of missing data in the engineering environment. Then, the GAIN model is used to impute the missing data and approximate the true sample distribution. Finally, the MSDCNN-LSTM model is used for RUL prediction. Experiments are carried out on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset to validate the proposed method. The prediction results show that the proposed method outperforms other methods when packet loss occurs, showing significant improvements in the root mean square error (RMSE) and the score function value.
Highlights
Prognosis and Health Management (PHM) aims to monitor, predict, and manage the health of the system through models and algorithms and is widely used in aviation, military equipment, industrial manufacturing, and other fields [1]
In 1987, Rubin [5] proposed that missing data mechanisms fall into three categories: missing at random (MAR), missing completely at random (MCAR), and missing not at random (MNAR)
We propose an remaining useful life (RUL) prediction framework based on data imputation to deal with missing sensor data in engineering applications
Summary
Prognosis and Health Management (PHM) aims to monitor, predict, and manage the health of the system through models and algorithms and is widely used in aviation, military equipment, industrial manufacturing, and other fields [1]. As one of the important research issues of PHM, remaining useful life prediction (RUL) can provide strategy support for establishing the best maintenance management for equipment. E data-driven method for RUL prediction is developed to analyze the equipment operation data through modeling to determine the remaining available time of equipment. Precision equipment and multisensor fusion are widely used in the industrial field, and obtaining complete monitoring data is crucial to predicting the remaining useful life (RUL) of equipment. Scholars have conducted numerous studies, and the methods can be roughly divided into three categories: ignoring data or deletion, imputation, and statistical models
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