A novel multi-scale gated convolutional neural network based on informer for predicting the remaining useful life of rotating machinery

Abstract Recently, neural networks based on deep learning techniques have been employed for remaining useful life (RUL) prediction of rotating machinery. However, there are some limitations: (1) convolutional neural networks primarily use local receptive fields to extract features, which leads to a relative weakness in modeling long-term dependencies on a global scale; (2) transformers face difficulties when dealing with uncertainties such as anomalies in input sequences, missing data, or variations in sampling frequency. This paper introduces a informer-based multi-scale gated convolutional network. Initially, a multi-scale gated convolution module is constructed to effectively extract features across various levels, adeptly capturing temporal patterns and long-term dependencies within the dataset. Subsequently, the derived multi-scale degradation features are utilized in predicting the RUL through an informer network, thereby enhancing the efficiency of parallel computing. To validate the effectiveness and superiority of this method, comparative experiments were conducted using two publicly available bearing datasets and various model approaches.