Monitoring data exception diagnosis method based on generative adversarial network

Abstract

The invention discloses a monitoring data exception diagnosis method based on a generative adversarial network, which is suitable for exception diagnosis of long-term measured data of a structural health monitoring system; the method includes dividing the recorded data into a plurality of sub-segments by selecting a proper basic time interval; converting the monitoring time sequence data of each sub-segment into a grayscale image by adopting a Gram angle field, training two unsupervised deep artificial neural networks including a generative adversarial network (GANs) and an auto-encoder (AE) according to the grayscale image, and verifying a training effect of the obtained network according to a prediction error of a test data set. Secondly, selecting an optimal index suitable for data anomaly diagnosis according to samples of the training set and the test set, and judging the state of the measured data in combination with a cumulative summation function. According to the method, rapidand accurate diagnosis of abnormal monitoring data can be realized, and effective data support can be provided for structural state diagnosis and abnormal early warning.

Description

technical field [0001] The invention relates to a data abnormal diagnosis method, in particular to the abnormal diagnosis of long-term monitoring data of a structural health monitoring system, and belongs to the technical field of data abnormal diagnosis. Background technique [0002] The Structural Health Monitoring System (SHMS) includes numerous sensors for monitoring structural responses such as acceleration, velocity, displacement, and strain, as well as environmental factors such as temperature, wind speed, and humidity. Long-term monitoring data can provide reference for structural damage identification and state assessment. Before carrying out damage identification and status assessment, the accuracy of monitoring data must be ensured, otherwise it will lead to wrong assessment results. However, most sensors are located in harsh environments, and it is difficult for sensors to avoid failures. Therefore, the diagnosis and elimination of abnormal data is one of the key...

AI Technical Summary

Problems solved by technology

Unsupervised learning methods can effectively avoid the disadvantages of the above-mentioned supervised learning methods, but the ability to process massive and complex information still needs to be improved

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