Multiscale global and local self-attention-based network for remaining useful life prediction

Abstract

Remaining useful life (RUL) prediction plays an important role in Prognostics Health Management (PHM) and can significantly improve equipment safety and reliability. Recently, while deep learning based methods have swept the RUL prediction field, most existing methods still have difficulties in simultaneously extracting multi-scale global and local degradation feature information from raw multi-sensors monitoring data. To address these issues, we propose a novel Multi-scale Global and Local Self-attention based Network (MGLSN) for RUL prediction. MGLSN consists of the global and local feature extraction sub-networks based on self-attention, which work in parallel to simultaneously extract the global and local degradation features of equipment and can adaptively focus on more important parts. While the global network captures long-term dependencies between time steps, the local network focuses on modeling local temporal dynamics. The design of parallel feature extraction can avoid mutual influence of information from global and local aspects. Moreover, MGLSN adopts the multi-scale feature extraction design (multi-scale self-attention and convolution) to capture the global and local degradation patterns at different scales respectively, which can be combined to better reflect the degradation trend. Experiments on widely used CMAPSS, N-CMAPSS and PHM08 challenge datasets provided by National Aeronautics and Space Administration (NASA) show that MGLSN significantly outperforms the state-of-the-art RUL prediction methods.

Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

“…The first prediction time (FPT) is an important index to evaluate the rolling bearing degrading from the normal state to a degenerate state, which is important for RUL prediction. To better illustrate the degradation trend of rolling bearings, a segmented function is used to characterize the degradation process of bearings, which is shown in Equation (3).…”

“…The DBOVMD algorithm is used to denoise the raw vibration signals based on the XJTU-SY dataset. The initialization settings of modal component k are [3,10], and the penalty factor α is [100, 2500]. The fitness curve in the noise reduction process of the algorithm is shown in Figure 5.…”

“…However, due to the harsh operating environment of the equipment and the highly variable workload, rolling bearing faces inevitable risks of wear and failure [2]. The prediction of equipment's remaining useful life is one of the key technologies in fault prediction and health management (PHM) [3]. If the rolling bearing degrades to the required threshold, the machine will fail.…”

DRLSTM-DA: a novel aeroengine remaining useful life prediction method based on deep optimization of multiple sensors information

“…Data collected by different sensors needs to be normalized before being input into DRLSTM-DA. Therefore, minmax normalization is employed in this study restricting sensor parameters and operating parameters within the range [0,1], and the calculation formula is as follows [31]:…”