A novel approach based on spatio-temporal attention and multi-scale modeling for mechanical failure prediction

Accurately predicting the remaining useful life (RUL) of equipment is crucial for planning production and eliminating unplanned downtime events. Specifically, the application of effective RUL prediction methods can detect potential equipment failures in advance to provide timely maintenance measures, which can help enterprises better plan and manage resources, optimize production plans, and provide strong support for subsequent maintenance decisions. The data-driven approaches have achieved great success in the field of RUL prediction by fully exploiting mechanical degradation information from historical operation data. However, these approaches have certain limitations, for instance, (1) they always fail to precisely extract spatial and temporal features in noisy environments simultaneously; (2) they often fail to effectively capture local features and global degradation trends simultaneously. To overcome the above limitations, we design an end-to-end model, termed ASATCN-TABGRU, for mechanical failure prediction, which contains an automatic shrinking attention temporal convolutional network (ASATCN) and a temporal attention bidirectional gated recurrent unit (TABGRU). In ASATCN module, to extract spatio-temporal information from historical operation data, we first perform a multi-scale modeling of historical operation data through a deliberately designed dilated causal convolution subnetwork (DCCS) to obtain local features. Then, we propose a novel soft thresholding subnetwork (STS) based on the normalization-based attention module (NAM), to capture useful temporal features through the automatic shrinking soft thresholding mechanism from the local features sequence; in addition, we design a hybrid attention subnetwork (HAS) to capture spatial features with flexible and different importance by the spatial-channel attention mechanism from historical operation data. The precise extraction of spatio-temporal features is then achieved through a connection operation. With the above encoded spatio-temporary features sequence, a TABGRU module is further proposed to capture global degradation trends by simultaneously extracting contextual information and historical influence information, thereby effectively modeling the local and global features. The experiments show that our approach has better performance and robustness, compared with other state-of-the-art approaches, particularly on the small sample dataset.