Joint learning strategy of multi-scale multi-task convolutional neural network for aero-engine prognosis

Remaining useful life (RUL) prediction and health status (HS) assessment are two key tasks in aero-engine prognostics and health management (PHM) system. However, existing deep learning-based prognostic models perform RUL prediction and HS assessment tasks separately, without considering the correlation between these two tasks. Secondly, traditional deep learning can only extract single-scale features, which limits the ability to extract complex degradation features from high-dimensional condition monitoring data. Therefore, this work proposes a multi-scale and multi-task convolutional neural network for joint learning of aero-engine RUL prediction and HS assessment. Firstly, multi-sensor data with multiple cycles are converted into image samples to integrate more condition monitoring information that is beneficial to prognosis. Then, the multi-scale feature fusion block is designed as the shared network for multi-task, utilizing convolutional layers with filters of different sizes to enhance the ability to extract complex degradation features from high-dimensional condition monitoring data. And a multi-layer concatenation block is constructed to integrate multi-scale features at different levels to fully utilize the important information at different levels. On this basis, a multi-task joint learning block is constructed and a joint loss function is developed for joint learning of RUL prediction and HS assessment. Finally, experiments on two engine degradation datasets, CMAPSS and N-CMAPSS, demonstrate that the proposed network has excellent RUL prediction and HS assessment performance, and outperforms other state-of-the-art methods.