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Abnormal Monitoring Data Detection Based on Matrix Manipulation and the Cuckoo Search Algorithm

Author

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  • Zhenzhu Meng

    (School of Water Conservancy and Environment Engineering & Nanxun Innovation Institute, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

  • Yiren Wang

    (School of Environment and Civil Engineering, Dongguan University of Technology & Guangdong Provincial Key Laboratory of Intelligent Disaster Prevention and Emergency Technologies for Urban Lifeline Engineering, Dongguan 523808, China)

  • Sen Zheng

    (Laboratory of Environmental Hydraulics, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland)

  • Xiao Wang

    (Huai’an Hydraulic Surcey and Design Research Institute Co., Ltd., Huaian 223500, China)

  • Dan Liu

    (School of Water Conservancy and Environment Engineering & Nanxun Innovation Institute, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

  • Jinxin Zhang

    (School of Water Conservancy and Environment Engineering & Nanxun Innovation Institute, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

  • Yiting Shao

    (School of Water Conservancy and Environment Engineering & Nanxun Innovation Institute, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

Abstract

Structural health monitoring is an effective method to evaluate the safety status of dams. Measurement error is an important factor which affects the accuracy of monitoring data modeling. Processing the abnormal monitoring data before data analysis is a necessary step to ensure the reliability of the analysis. In this paper, we proposed a method to process the abnormal dam displacement monitoring data on the basis of matrix manipulation and Cuckoo Search algorithm. We first generate a scatter plot of the monitoring data and exported the matrix of the image. The scatter plot of monitoring data includes isolate outliers, clusters of outliers, and clusters of normal points. The gray scales of isolated outliers are reduced using Gaussian blur. Then, the isolated outliers are eliminated using Ostu binarization. We then use the Cuckoo Search algorithm to distinguish the clusters of outliers and clusters of normal points to identify the process line. To evaluate the performance of the proposed data processing method, we also fitted the data processed by the proposed method and by the commonly used 3- σ method using a regression model, respectively. Results indicate that the proposed method has a better performance in abnormal detection compared with the 3- σ method.

Suggested Citation

  • Zhenzhu Meng & Yiren Wang & Sen Zheng & Xiao Wang & Dan Liu & Jinxin Zhang & Yiting Shao, 2024. "Abnormal Monitoring Data Detection Based on Matrix Manipulation and the Cuckoo Search Algorithm," Mathematics, MDPI, vol. 12(9), pages 1-18, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1345-:d:1385362
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    References listed on IDEAS

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    1. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3803-3823, August.
    2. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Erratum to: Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4113-4113, September.
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