Structural Vibration Data Anomaly Detection Based on Multiple Feature Information Using CNN-LSTM Model

Abstract

Structural health monitoring (SHM) system has been operating for a long time in a harsh environment, resulting in various abnormalities in the collected structural vibration monitoring data. Detecting these abnormal data not only requires user interaction but also is quite time-consuming. Inspired by the manual recognition process, a vibration data anomaly detection method based on the combined model of convolutional neural network (CNN) and long short-term memory (LSTM) network is proposed in this paper. This method simulates intelligent human decision making in two steps. First, the original data are reconstructed by two feature sequences with higher universality and smaller size. In the time domain, the residual signal is extracted from the upper and lower peak envelopes of the original data to characterize the symmetry of the data. In the frequency domain, the power spectral density sequence of the original data is extracted to characterize the interpretability of the data. Second, a CNN-LSTM model is constructed and trained which utilizes CNN to extract local high-level features of input sequence and inputs new continuous high-level feature representations into LSTM to learn global long-term dependencies of abnormal data features. For verification, the method was applied to the automatic classification of continuous monitoring data for 42 days of long-span bridge, and the average accuracy of the classification results exceeded 94% and the detection time was 78 minutes. Compared with existing methods, this method can detect abnormal data more accurately and efficiently and has a stronger generalization ability.