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Probability-Based Failure Evaluation for Power Measuring Equipment

Author

Listed:
  • Jie Liu

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Qiu Tang

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Wei Qiu

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
    Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA)

  • Jun Ma

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Junfeng Duan

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

Abstract

Accurate reliability and residual life analysis is paramount during the designing of reliability requirements and rotation of power measuring equipment (PME). However, the sample dataset of failure is usually sparse and contains inevitable pollution data, which has an adverse effect on the reliability analysis. To tackle this issue, this paper first applies nonlinear regression to fuse the failure rate and environmental features of PME collected from various locations. Then, a novel binary hierarchical Bayesian probability method is proposed to model the failure trend and identify outliers, in which the outlier identification structure is embedded into hierarchical Bayesian. Integrating binary hierarchical Bayesian and the bagging method, a binary hierarchical Bayesian with bagging (BHBB) framework is further introduced to improve predictive performance in a small sample dataset by resampling. Last, the influence of typical environmental features, failure rate, and reliability are obtained by the BHBB under the real sample dataset from multiple typical locations. Experiments show that our framework has superior performance and interpretability comparing with other typical data-based approaches.

Suggested Citation

  • Jie Liu & Qiu Tang & Wei Qiu & Jun Ma & Junfeng Duan, 2021. "Probability-Based Failure Evaluation for Power Measuring Equipment," Energies, MDPI, vol. 14(12), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3632-:d:577494
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    References listed on IDEAS

    as
    1. Hang Liu & Youyuan Wang & Yi Yang & Ruijin Liao & Yujie Geng & Liwei Zhou, 2017. "A Failure Probability Calculation Method for Power Equipment Based on Multi-Characteristic Parameters," Energies, MDPI, vol. 10(5), pages 1-15, May.
    2. Mishra, Madhav & Martinsson, Jesper & Rantatalo, Matti & Goebel, Kai, 2018. "Bayesian hierarchical model-based prognostics for lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 25-35.
    3. Junjie Lu & Jinquan Huang & Feng Lu, 2017. "Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle," Energies, MDPI, vol. 10(1), pages 1-15, January.
    4. Massimo Conti & Simone Orcioni, 2020. "Modeling of Failure Probability for Reliability and Component Reuse of Electric and Electronic Equipment," Energies, MDPI, vol. 13(11), pages 1-18, June.
    5. Monica Alexander & Emilio Zagheni & Magali Barbieri, 2017. "A Flexible Bayesian Model for Estimating Subnational Mortality," Demography, Springer;Population Association of America (PAA), vol. 54(6), pages 2025-2041, December.
    6. Feng Lu & Chunyu Jiang & Jinquan Huang & Yafan Wang & Chengxin You, 2016. "A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis," Energies, MDPI, vol. 9(10), pages 1-22, October.
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