Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China

Research Article

Research on Equipment Fault Prediction Method Based on Industrial Big Data

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  • @INPROCEEDINGS{10.4108/eai.17-6-2022.2322795,
        author={Zihan  Qi and Chang  Tang and Luli  He and Changjian  Jiang and Jing  Chen and Yulin  Huang},
        title={Research on Equipment Fault Prediction Method Based on Industrial Big Data},
        proceedings={Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China},
        publisher={EAI},
        proceedings_a={ICIDC},
        year={2022},
        month={10},
        keywords={industrial data; data preprocessing; main feature extraction; equipment fault prediction; bpnn},
        doi={10.4108/eai.17-6-2022.2322795}
    }
    
  • Zihan Qi
    Chang Tang
    Luli He
    Changjian Jiang
    Jing Chen
    Yulin Huang
    Year: 2022
    Research on Equipment Fault Prediction Method Based on Industrial Big Data
    ICIDC
    EAI
    DOI: 10.4108/eai.17-6-2022.2322795
Zihan Qi1, Chang Tang1, Luli He1, Changjian Jiang1, Jing Chen1,*, Yulin Huang1
  • 1: Qilu University of Technology
*Contact email: jingchen94@163.com

Abstract

Aiming at the problems of low accuracy and long prediction time of industrial equipment fault prediction, a prediction method based on main feature extraction and back propagation neural network (MFE-BPNN) was proposed. This method firstly preprocesses the missing, abnormal and high-noised industrial equipment data, then uses the method of recursive feature elimination combined with cross validation to extract the main feature variables, then designs the numbers of hidden layers and neurons, and weights of training and learning rates. This method improves the accuracy of industrial equipment fault prediction by preprocessing industrial data and establishing a prediction model based on a neural network. The prediction time is reduced by extracting the main characteristic variables. The experimental results of fan blade icing fault prediction in the field of power generation verify the effectiveness of this method.