Diagnosis of abnormal sensor data based on interpretable deep learning
Aiming at the problem of poor interpretability and low efficiency in multisource sensor data anomaly diagnosis for bridge health monitoring,an interpretable convolutional neural network(CNN)data anomaly detection method based on feature visualization is proposed. Fully considering the completeness of anomaly patterns and multisource sensor types,a library of multisource sensor monitoring data anomaly patterns is constructed by integrating data augmentation methods. Concurrently,anomaly features are extracted using a CNN and feature and class activation map(CAM)visualization method is utilized to deeply analyze the features of anomaly types,thereby achieving an interpretive analysis of the deep learning network. Experimental results indicate that considering multisource sensor types can fully exploit the effective information in the data,the overall accuracy of the model reaches 99.37％,and both the recall and precision rates of all anomaly patterns exceed 96％. This method can also capture the feature learning process of time-series deep learning classification models,providing prerequisites for the analysis and early warning of continuous monitoring data of bridge structures.
deep learningCNNfeature visualizationabnormal diagnosishealth monitoring
万方数据
维普
