The intelligent fault diagnosis of video data has become a demanding task in industrial applications. However, existing models require expensive computational cost and memory demand, which makes this technology applied in factories impossible. To address this problem, a temporal–spatial attention-based action recognition method (TARM) integrating TAB (temporal-attention-based frame splitting model), SAB (spatial-attention-based agent focusing mode) and LSB (long-short term feature learning mode) is proposed. TAB first extracts important frames from raw videos. Then, SAB refines video data by reinforcing their essential features and weakening unnecessary features. Furthermore, LSB monitors action type of video data by establishing recurrent convolutional architectures. Finally, the performance of TARM in terms of training time and fault diagnosis accuracy are validated by comparing with six state-of-the-art video diagnosis methods.

视频数据的智能故障诊断已成为工业应用中的一项艰巨任务。然而，现有模型需要昂贵的计算成本和内存需求，这使得该技术无法应用于工厂。为了解决这个问题，一种基于时空注意力的动作识别方法（TARM）集成了 TAB（基于时空注意力的帧分割模型）、SAB（基于空间注意力的代理聚焦模式）和 LSB（长短期特征学习模式）提出。TAB 首先从原始视频中提取重要帧。然后，SAB 通过增强视频数据的基本特征和削弱不必要的特征来细化视频数据。此外，LSB 通过建立循环卷积架构来监控视频数据的动作类型。最后，