# An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference
###### Abstract  
Structural health monitoring systems continuously monitor the operational state of structures, generating a large amount of monitoring data during the process. The structural responses of extreme events, such as earthquakes, ship collisions, or typhoons, could be captured and further analyzed. However, it is challenging to identify these extreme events due to the interference of faulty data. Real-world monitoring systems suffer from frequent misidentification and false alarms. Unfortunately, it is difficult to improve the system's built-in algorithms, especially the deep neural networks, partly because the current neural networks only output results and do not provide an interpretable decision-making basis. In this study, a deep learning-based method with visual interpretability is proposed to identify seismic data under sensor faults interference. The transfer learning technique is employed to learn the features of seismic data and faulty data with efficiency. A post hoc interpretation algorithm, termed Gradient-weighted Class Activation Mapping (Grad-CAM), is embedded into the neural networks to uncover the interest regions that support the output decision. The in situ seismic responses of a cable-stayed long-span bridge are used for method verification. The results show that the proposed method can effectively identify seismic data mixed with various types of faulty data while providing good interpretability. structural health monitoring; extreme event detection; long-span bridge; transfer learning; interpretable deep learning +
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proposed a distributed sensor-fault detection and diagnosis system, which transmits sensed data periodically to a distant node and obtains a quick decision about the presence of a fault. [PERSON] et al. [11] obtained cable tension data by utilizing source separation during the pre-processing stage to eliminate the effects of sensor faults. [PERSON] et al. [12] combined the generative adversarial networks with an unsupervised learning method to achieve sensor anomaly detection through a computer vision approach, which also addresses the issue of class imbalance in anomalous data. It is worth noting that the aforementioned methods have achieved good accuracy in anomaly detection, but they mainly focus on detecting sensor faults and ignore that extreme events can produce similar anomalous data, which should be given special attention. In practical engineering, the extreme events data detected by the SHM system is often mixed with anomaly data caused by sensor faults. It is a challenge to distinguish the two in massive data. Researchers have been working to enable SHM systems to identify various types of extreme events in recent years. [PERSON] et al. [13] analyzed the impact of a ship collision on the bridge using SHM data, GPS measurements were utilized to determine modal parameters and assess the bridge's status. [PERSON] et al. [14] used Kernel Density Estimation (KDE) method to identify bridge waterways, then applied Velocity Obstacle (VO) method to detect potential ship collisions. [PERSON] et al. [15] proposed a typhoon detection method based on long short-term memory (LSTM) network, which can predict the wind speed of the bridge using the time-shift data correction (TSDC) method. In seismic monitoring, [PERSON] et al. [16] used an artificial neural network approach to detect seismic s-wave and p-wave onset times, which has more robustness compared to traditional manual identification methods. [PERSON] et al. [17] developed ConvNetQuake, a CNN for seismic detection and location from a single waveform, achieving faster detection speed than established methods. [PERSON] et al. [18] used the vast manually labeled data archives of the Southern California Seismic Network to train a CNN seismic wave phases detection and achieved sensitive and robust performance. [PERSON] et al. [19] utilized unsupervised learning techniques in conjunction with support vector data description for anomaly detection.