It has been widely recognized that the practical application of deep learning to condition monitoring of industrial equipment relies heavily on having comprehensive coverage of essential features in the training dataset, i.e., the data collected should include all potential feature modes under various operating conditions. Given this limitation, this paper proposes a multi-level branch neural network (MLBNN) with self-evolution capability. In particular, the MLBNN structure can be updated online when emerging fault modes or operating conditions are encountered during equipment operation. Two case studies employing an industrial robot arm joint bearing and a motor bearing fault diagnosis dataset demonstrate the efficacy of MLBNN. We then demonstrate that MLBNN is suitable for deployment under cloud-edge computing architecture, which can further improve the speed of model update operations during self-evolution process and model inference to meet online condition monitoring requirements. Finally, the performance of MLBNN is compared to classical deep learning and transfer learning approaches under an incomplete training dataset scenario, and results show that the accuracy of MLBNN is superior to others.

中文翻译：

---

不完整训练数据集场景下工业设备状态监测的自进化多级分支神经网络


人们普遍认识到，深度学习在工业设备状态监测中的实际应用在很大程度上依赖于训练数据集中基本特征的全面覆盖，即收集的数据应包括各种运行条件下的所有潜在特征模式。鉴于这一限制，本文提出了一种具有自进化能力的多级分支神经网络（MLBNN）。特别是，当设备运行过程中遇到新出现的故障模式或运行条件时，MLBNN结构可以在线更新。采用工业机器人手臂关节轴承和电机轴承故障诊断数据集的两个案例研究证明了 MLBNN 的有效性。然后我们证明MLBNN适合部署在云边缘计算架构下，可以进一步提高自进化过程和模型推理过程中模型更新操作的速度，以满足在线状态监测需求。最后，在不完整训练数据集场景下将 MLBNN 的性能与经典深度学习和迁移学习方法进行比较，结果表明 MLBNN 的准确性优于其他方法。