The internal gear pump is simple in structure, small in size and light in weight. It is an important basic component that supports the development of hydraulic system with low noise. However, its working environment is harsh and complex, and there are hidden risks related to reliability and exposure of acoustic characteristics over the long term. In order to meet the requirements of reliability and low noise, it is very necessary to make models with strong theoretical value and practical significant to accurately monitor health and predict the remaininglife of the internal gear pump. This paper proposed a multi-channel internal gear pump health status management model based on Robust-ResNet. Robust-ResNet is an optimized ResNet model based on a step factor $h$ in the Eulerian approach to enhance the robustness of the ResNet model. This model was a two-stage deep learning model that classified the current health status of internal gear pumps, and also predicted the remaining useful life (RUL) of internal gear pumps. The model was tested in an internal gear pump dataset collected by the authors. The model was also proven to be useful in the rolling bearing data from Case Western Reserve University (CWRU). The accuracy results of health status classification model were 99.96% and 99.94% in the two datasets. The accuracy of RUL prediction stage in the self-collected dataset was 99.53%. The results demonstrated that the proposed model achieved the best performance compared to other deep learning models and previous studies. The proposed method was also proven to have high inference speed; it could also achieve real-time monitoring of gear health management. This paper provides an extremely effective deep learning model for internal gear pump health management with great application value.

In recent years, joint entity–relation extraction (ERE) models have become a hot research topic in natural language processing (NLP). Several studies have proposed a span-based ERE framework, which utilizes simple span embeddings for entity and relation classification. This framework addresses the issues of overlap and error propagation that were present in previous entity–relation extraction models. However, span-based models overlook the influence of lexical information on the semantic representation of the span and fail to consider relations with a strong intrinsic connection between span pairs. To tackle these aforementioned issues, we present a new ERE model called ER-LAC (Span-based Joint Entity and Relation Extraction Model with Multi-level Lexical and Attention on Context Features). This model is designed with multi-granularity lexical features to enhance the semantic representation of spans, and a transformer classifier is employed to capture the internal connections between span pairs, thereby improving the performance of relational classification. To demonstrate the effectiveness of the proposed model, ablation experiments were conducted on the CoNLL04 dataset. The proposed model was also compared with other models on three datasets, showcasing its computational efficiency. The results indicate that the introduced lexical features and classifier enhance the F1 score for entity extraction by 0.84% to 2.04% and improve the F1 score for relationship classification by 0.96% to 2.26% when compared to the previous state-of-the-art (SOTA) model and the baseline SpERT model, respectively.

Spiking Neural Network (SNN) is one of the mainstream frameworks for brain-like computing and neuromorphic computing, which has the potential to overcome current AI challenges, for example, low-power learning dynamic processes. However, there is still a huge gap in performance between SNN and artificial neural networks (ANN) in traditional supervised learning. One solution for this problem is to propose a better spiking neuron model to improve its memory ability for temporal data. This paper proposed a leaky integrate-and-fire (LIF) neuron model with dynamic postsynaptic potential and a layer-span connecting method for SNN trained using backpropagation. The dynamic postsynaptic potential LIF model allows the neurons dynamically release neurotransmitters in an SNN model, which mimics the activity of biological neurons. The layer-span connecting method enhances the long-distance memory ability of SNN. We also first introduced a cosh-based surrogate gradient for the backpropagation training of SNNs. We compared the SNN with cosh-based surrogate gradient (CSNN), CSNN with dynamic postsynaptic potential (Dyn-CSNN), layer-span connecting CSNN (Las-CSNN), and SNN model with all the proposed methods (DlaCSNN-BP) in three image classification and one text classification datasets. The experimental results exhibited that proposed SNN methods could outperform most of the previously proposed SNNs and ANNs in the same network structure. Among them, the proposed DlaCSNN-BP got the best classification performance. This result indicates that our proposed method can effectively improve the effect of SNN in supervised learning and reduce the gap with deep learning. This work also provides more possibilities for putting SNN into practical application.