Data Abnormal Identification and Assessment Based on Improved FCM and Covariance
At present,sensors are widely used in bridge construction monitoring,health monitoring and other fields,but they are often affected by factors such as temperature changes and other electronic equipment during the working process,which is easy to make the collected data abnormal,which brings interference to the analysis of bridge conditions.In order to identify abnormality and evaluate the overall quality of bridge sensor data,a method based on improved FCM(fuzzy C-means)and covariance matrix is introduced.This method first extracts and preprocesses the sensor data of the same type,including filling,normalizing and smoothing of missing data.Then,the improved FCM algorithm is used to cluster the differentiated dimensional data and divide them into different clusters,so as to find out the dimensions with large differences between cluster centers,and determine the identification dimension as a sequence abnormal channel.The covariance matrix is calculated to assess the positive and negative correlation between the data in each dimension.Finally,the threshold is set before anomalies in the sequence are detected and the quality of the extracted data is evaluated.Through the analysis of the half-year monitoring data of a bridge in Jintang,Chengdu,the results of the identification of the two methods verify that the proposed algorithm can accurately and quickly screen out abnormal sensors and their abnormal sequences and abnormal points,which can provide reliable support for bridge status analysis and decision-making.
万方数据
维普
