With the rapid development of intelligent manufacturing and Industrial Internet of Things, many industrial control systems have high requirements for the security of the system itself. Failures of industrial control equipment will cause abnormal operation of industrial control equipment and waste of resources. It is very meaningful to detect and identify potential equipment abnormalities and failures in time and implement effective fault tolerance strategies. In the Industrial Internet of Things environment, the instructions and parameters of industrial control equipment often change due to changes in actual requirements. However, it is impractical to customize the learning method for each parameter value. Aiming at the problem, this paper proposes a fault diagnosis model based on ensemble learning and proposes a method of updating voting weights based on dynamic programming to assist decision-making. This method is based on Bagging strategy and combined with dynamic programming voting weight adjustment method to complete fault type prediction. Finally, this paper uses different loads as dynamic conditions; the diagnostic capability of the Bagging-based fault diagnosis integrated model in a dynamically changing industrial control system environment is verified by experiments. The fault diagnosis model of industrial control equipment based on ensemble learning effectively improves the adaptive ability of the model and makes the fault diagnosis framework truly intelligent. The voting weight adjustment method based on dynamic programming further improves the reliability of voting.

中文翻译：

---

工业物联网中自适应工控设备安全故障诊断方法

随着智能制造和工业物联网的快速发展，许多工业控制系统对系统本身的安全性提出了很高的要求。工控设备故障会导致工控设备运行异常，造成资源浪费。及时发现和识别潜在的设备异常和故障，并实施有效的容错策略非常有意义。在工业物联网环境中，工控设备的指令和参数往往会因实际需求的变化而变化。然而，为每个参数值定制学习方法是不切实际的。针对问题，本文提出了一种基于集成学习的故障诊断模型，并提出了一种基于动态规划的更新投票权重的方法来辅助决策。该方法基于Bagging策略，结合动态规划投票权重调整方法完成故障类型预测。最后，本文以不同的载荷为动力条件；通过实验验证了基于Bagging的故障诊断集成模型在动态变化的工控系统环境下的诊断能力。基于集成学习的工控设备故障诊断模型有效提高了模型的自适应能力，使故障诊断框架真正实现智能化。