Multiclass anomaly detection in imbalanced structural health monitoring data using convolutional neural network

Structural health monitoring (SHM) system aims to monitor the in-service condition of civil infrastructures, incorporate proactive maintenance, and avoid potential safety risks. An SHM system involves the collection of large amounts of data and data transmission. However, due to the normal aging of sensors, exposure to outdoor weather conditions, accidental incidences, and various operational factors, sensors installed on civil infrastructures can get malfunctioned. A malfunctioned sensor induces significant multiclass anomalies in measured SHM data, requiring robust anomaly detection techniques as an essential data cleaning process. Moreover, civil infrastructure often has imbalanced anomaly data where most of the SHM data remain biased to a certain type of anomalies. This imbalanced time-series data causes significant challenges to the existing anomaly detection methods. Without proper data cleaning processes, the SHM technology does not provide useful insights even if advanced damage diagnostic techniques are applied. This paper proposes a hyperparameter-tuned convolutional neural network (CNN) for multiclass imbalanced anomaly detection (CNN-MIAD) modelling. The hyperparameters of the proposed model are tuned through a random search algorithm to optimize the performance. The effect of balancing the database is considered by augmenting the dataset. The proposed CNN-MIAD model is demonstrated with a multiclass time-series of anomaly data obtained from a real-life cable-stayed bridge under various cases of data imbalances. The study concludes that balancing the database with a time shift window to increase the database has generated the optimum results, with an overall accuracy of 97.74%.