Output-only Structural Damage Detection via Enhanced Random Vibration Analysis using LSTM/GRU model

Structural health monitoring provides significant and obvious potential in enhancing our life safety and extending the service life of systems. In practice, structures are often exposed to random excitations without knowing the exact characteristics of its source. This paper proposes a novel method of the implementation of LSTM and GRU models for characterizing anomalies in output-only random vibration signals. In the proposed approach, the time response of a 3D printed PLA beam is measured when subjected to a random excitation and used to train LSTM and GRU models. Healthy time response and four additional cases that contain a small mass at varied locations along the beam are used as model inputs. These inputs represent normal and abnormal signals which are then classified to diagnose the health state of the structure. In this study, the random vibration time responses with large amplitude (so-called signal caricature) were selectively employed for creating the output-only models. The results illustrate the signal caricature data set leading to both more accurate and efficient characterization of the structural health state, compared to utilizing the entire time response as an input to the models. Modal properties obtained by traditional vibration analysis support the effectiveness of utilizing the signal caricature with LSTM/GRU models, demonstrating great potential in identifying defects in practical applications.