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Abstract
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.
Highlights
Traditional industrial control system safety fault diagnosis mainly focuses on mechanical failure of industrial control equipment
In order to be able to adaptively face the changes of the industrial control system environment, this paper proposes a safety fault diagnosis model for industrial control equipment based on integrated learning. e model is a composite model composed of multiple individual safety fault classifiers
The data set is divided into normal data, 12K drive end bearing fault data, 48K bearing drive end fault data, and 12K bearing fan end fault data according to different frequencies
Summary
Traditional industrial control system safety fault diagnosis mainly focuses on mechanical failure of industrial control equipment. With the rapid development of intelligent manufacturing and Industrial Internet of ings, the links between industrial control equipment have become closer, and the operating environment has become more complicated. In the Industrial Internet of ings environment, the instructions and parameters of industrial control equipment often change due to changes in actual needs. Since more industrial tasks need to be completed in a short time, the current, voltage, speed, load, and other parameters of industrial control equipment must be adjusted so that the operation can be successfully completed. Ese dynamic factors have brought huge challenges to equipment safety fault diagnosis in the industrial control environment. In the environment of Industrial Internet of ings, the parameters are constantly changing, so it is not practical to customize a learning method for each parameter value
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