Structural health monitoring data exception identification method based on space-time diagram convolutional network

Abstract

The invention relates to a structural health monitoring data exception identification method based on a space-time diagram convolutional network, and solves the problem that an existing exception identification method based on building structure health monitoring data is difficult to distinguish sensor faults and structural variation. The identification method comprises: carrying out space-time correlation modeling on structure monitoring data by utilizing a space-time diagram convolutional network capable of learning an adjacent matrix, hierarchically applying information of adjacent nodes ofeach order to data regression, and designing a corresponding network structure and an objective function penalty term; and a monitoring system is used to establish initial measured data as a trainingset, a network is trained and an adjacent matrix is acquired, subsequent measured data is input into the network and then a model residual error and a diagnosis index are calculated, and the diagnosis index and a key adjacent edge are combined to determine whether data abnormity originates from a sensor fault or structural variation. Data modes of sensor abnormity and structure abnormity can be effectively distinguished, the fault sensor can be accurately identified, and the method is suitable for management and maintenance of various structure health monitoring systems.

Description

technical field [0001] The invention relates to the field of building structure health monitoring, in particular to a method for identifying anomalies in structural health monitoring data based on spatio-temporal graph convolutional networks. Background technique [0002] The building structure health monitoring system timely identifies damage, evaluates the structural state and service performance through real-time monitoring, and provides scientific basis for the safe operation and maintenance of the structure. The bridge health monitoring system records a large amount of data such as environmental conditions, structural temperature, deformation, stress, and acceleration in real time, laying a foundation for the study of bridge long-term mechanical behavior and its evolution law. [0003] In recent years, with the rapid development of the field of building structural health monitoring, the methods of structural anomaly diagnosis and structural state assessment based on mon...

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Problems solved by technology

[0004] Based on the above deficiencies, the purpose of the present invention is to provide a structural health monitoring data anomaly identification method based on spatio-temporal graph convolutional network, which solves the shortcomings of existing structural anomaly diagnosis methods that are difficult to distinguish between sensor failure and structural variation, and realizes the fault sensor Localization and identification of structural variants

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