A novel hidden danger prediction method in cloud-based intelligent industrial production management using timeliness managing extreme learning machine

Abstract

To prevent possible accidents, the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently. A machine learning algorithm that uses timeliness managing extreme learning machine is utilized in this article to achieve the above prediction. Compared with traditional learning algorithms, extreme learning machine (ELM) exhibits high performance because of its unique feature of a high generalization capability at a fast learning speed. Timeliness managing ELM is proposed by incorporating timeliness management scheme into ELM. When using the timeliness managing ELM scheme to predict hidden dangers, newly incremental data could be added prior to the historical data to maximize the contribution of the newly incremental training data, because the incremental data may be able to contribute reasonable weights to represent the current production situation according to practical analysis of accidents in some industrial productions. Experimental results from a coal mine show that the use of timeliness managing ELM can improve the prediction accuracy of hidden dangers with better stability compared with other similar machine learning methods.