Abnormal Monitoring Data Detection Based on Matrix Manipulation and the Cuckoo Search Algorithm
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-sigma method using a regression model, respectively. Results indicate that the proposed method has a better performance in abnormal detection compared with the 3-sigma method.
WOS:001219793700001
2024-05-01
12
9
1345
Enrichissement manuel à effectuer : l'auteur suivant ('3', 'Zheng, Sen') n'a pas été trouvé dans les autorités Infoscience mais dans search API -> champ 700 créé mais sans recid en 700__0
Enrichissement manuel à effectuer : aucun labo identifié dans les autorités Infoscience ni dans search API -> pas de champ 999 créé
REVIEWED
Funder | Grant Number |
University-Level Key Course of Zhejiang University of Water Resources and Electric Power | |