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An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique

Author

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  • Ruijun Guo

    (Inner Mongolia Power Research Institute, Hohhot 010020, China)

  • Guobin Zhang

    (Inner Mongolia Power Research Institute, Hohhot 010020, China)

  • Qian Zhang

    (Inner Mongolia Power Research Institute, Hohhot 010020, China)

  • Lei Zhou

    (Inner Mongolia Power Research Institute, Hohhot 010020, China)

  • Haicun Yu

    (Inner Mongolia Power Research Institute, Hohhot 010020, China)

  • Meng Lei

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • You Lv

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
    Key Laboratory of Power Station Energy Transfer Conversion and System, North China Electric Power University, Beijing 102206, China)

Abstract

The induced draft (ID) fan is an important piece of auxiliary equipment in coal-fired power plants. Early fault detection of the ID fan can provide predictive maintenance and reduce unscheduled shutdowns, thus improving the reliability of the power generation. In this study, an adaptive model was developed to achieve the early fault detection of ID fans. First, a non-parametric monitoring model was constructed to describe the normal operating characteristics with the multivariate state estimation technique (MSET). A similarity index representing operation status was defined according to the prediction deviations to produce warnings of early faults. To deal with the model accuracy degradation because of variant condition operation of the ID fan, an adaptive strategy was proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the MSET model, thereby improving the fault detection results. The proposed method was applied to a 300 MW coal-fired power plant to achieve the early fault detection of an ID fan. In addition, fault detection by using the model without an update was also compared. Results show that the update strategy can greatly improve the MSET model accuracy when predicting normal operations of the ID fan; accordingly, the fault can be detected more than 4 h earlier by using the strategy with the adaptive update when compared to the model without an update.

Suggested Citation

  • Ruijun Guo & Guobin Zhang & Qian Zhang & Lei Zhou & Haicun Yu & Meng Lei & You Lv, 2021. "An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique," Energies, MDPI, vol. 14(16), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4787-:d:609768
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    1. Jose Aguilar & Douglas Ardila & Andrés Avendaño & Felipe Macias & Camila White & José Gomez-Pulido & José Gutierrez de Mesa & Alberto Garces-Jimenez, 2020. "An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building," Energies, MDPI, vol. 13(12), pages 1-24, June.
    2. Yolanda Vidal & Francesc Pozo & Christian Tutivén, 2018. "Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data," Energies, MDPI, vol. 11(11), pages 1-18, November.
    3. Jinfu Liu & Mingliang Bai & Zhenhua Long & Jiao Liu & Yujia Ma & Daren Yu, 2020. "Early Fault Detection of Gas Turbine Hot Components Based on Exhaust Gas Temperature Profile Continuous Distribution Estimation," Energies, MDPI, vol. 13(22), pages 1-13, November.
    4. Xinwei Cong & Caiping Zhang & Jiuchun Jiang & Weige Zhang & Yan Jiang & Linjing Zhang, 2021. "A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 14(5), pages 1-21, February.
    5. Hong Wang & Hongbin Wang & Guoqian Jiang & Jimeng Li & Yueling Wang, 2019. "Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling," Energies, MDPI, vol. 12(6), pages 1-22, March.
    6. Yichen Zhou & Xiaohui Yang & Lingyu Tao & Li Yang, 2021. "Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network," Energies, MDPI, vol. 14(11), pages 1-21, May.
    7. Icksung Kim & Woohyun Kim, 2021. "Development and Validation of a Data-Driven Fault Detection and Diagnosis System for Chillers Using Machine Learning Algorithms," Energies, MDPI, vol. 14(7), pages 1-24, April.
    8. Milu Zhang & Tianzhen Wang & Tianhao Tang & Zhuo Liu & Christophe Claramunt, 2019. "A Synchronous Sampling Based Harmonic Analysis Strategy for Marine Current Turbine Monitoring System under Strong Interference Conditions," Energies, MDPI, vol. 12(11), pages 1-13, June.
    9. Jun-Hyun Shin & Jin-O Kim, 2020. "On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant," Energies, MDPI, vol. 13(17), pages 1-12, September.
    10. Peng Guo & Nan Bai, 2011. "Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods," Energies, MDPI, vol. 4(11), pages 1-17, November.
    11. Yong Xiao & Weiguo Pan & Xiaomin Guo & Sheng Bi & Ding Feng & Sheng Lin, 2020. "Fault Diagnosis of Traction Transformer Based on Bayesian Network," Energies, MDPI, vol. 13(18), pages 1-16, September.
    12. Lv, You & Lv, Xuguang & Fang, Fang & Yang, Tingting & Romero, Carlos E., 2020. "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants," Energy, Elsevier, vol. 192(C).
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