Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings

Research Article

Curve-Registration-Based Feature Extraction for Predictive Maintenance of Industrial Equipment

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  • @INPROCEEDINGS{10.1007/978-3-030-00916-8_24,
        author={Shouli Zhang and Xiaohong Li and Jianwu Wang and Shen Su},
        title={Curve-Registration-Based Feature Extraction for Predictive Maintenance of Industrial Equipment},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11--13, 2017, Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2018},
        month={10},
        keywords={Predictive maintenance Time-lagged correlation Curve registration Feature extraction},
        doi={10.1007/978-3-030-00916-8_24}
    }
    
  • Shouli Zhang
    Xiaohong Li
    Jianwu Wang
    Shen Su
    Year: 2018
    Curve-Registration-Based Feature Extraction for Predictive Maintenance of Industrial Equipment
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-00916-8_24
Shouli Zhang1,*, Xiaohong Li1, Jianwu Wang2, Shen Su3
  • 1: Tianjin University
  • 2: University of Maryland, Baltimore County
  • 3: North China University of Technology
*Contact email: zhangshoulia@163.com

Abstract

With the growing adoption of Internet of Things (IoT), predictive maintenance is gaining momentum for ensuring the reliability of industrial equipment. A common practice of predictive maintenance is to conduct feature extraction on the original sensor data, then conduct deep learning to train predictive maintenance model with the extracted data and finally, conduct prediction by model. Because of the low value density of industrial sensor data stream, feature extraction is usually based on dimensionality reduction. However, traditional methods for dimensionality reduction seldom consider time-lagged correlations which are very common among industrial sensor data streams. More importantly, time-lagged correlations are less sensitive to the traditional dimensionality reduction methods, leading to poor effect of feature extraction. In this paper, we propose a feature extraction method based on curve registration to deal with the time-lagged correlation problem. Our experimental results indicate that our method can: (1) effectively improve the accuracy of prediction; and (2) improve the performance of the prediction model.