###### Abstract  
Structural health monitoring (SHM) systems are widely used for civil infrastructure monitoring. Data acquired from the SHM systems play an important role in assessing structural integrity and determining further maintenance activities. Considering that sensors in the SHM systems are installed in a harsh environment for long-term measurements, some sensors can malfunction and produce faulty data. As a large amount of measured data is often desired to be automatically processed and can adversely affect structural assessments, identifying such abnormal data is important. This paper provides critical investigations of the automated detection of data anomalies using existing deep-learning-based classification in conjunction with a simple rule-based approach. The issues investigated in this study include (1) the presence of ambiguous data that cannot be categorized as an anomaly class in the literature, (2) information loss during the conversion of time-series data into images for the deep-learning-based approach, and (3) additional issues, such as misclassification by trained models and requirements of the threshold section in the rule-based approach. The results of these key investigations can be utilized to develop an effective anomaly detection process. structural health monitoring; anomaly detection; rule-based approach; deep learning +
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achieve reasonably high performance, they are intrinsically limited to data with a single type of data anomaly. As a result, deep learning was introduced to resolve problems associated with statistics-based identification. Recently, deep learning has been widely used for the automatic analysis of time-series data, such as prediction [21], classification [22; 23], and anomaly detection [24; 25; 26; 27; 28; 29; 30]. One of the earliest approaches employing deep learning was the development of a deep neural network in which the inputs were images converted from measured time-history data [25]. For better classification performance, an approach using a convolutional neural network (CNN) was proposed with enhanced input information, combining images of time- and frequency-domain data [26]. Furthermore, multichannel images consisting of the time-domain response, spectrogram, and probability density function information were proposed to further increase the information in the input images for accurate anomaly detection [27]. Although data enhancement in the images used for training the neural networks was shown to improve the classification performance, the methods employing converted images have critical issues, such as ambiguous labeling of training images, imbalanced data for each class, and limited performance due to low-resolution converted images. Therefore, investigating effective and practical anomaly detection using deep learning is necessary. Most importantly, the measured time-history data often have an intrinsic ambiguity that cannot classify a dataset into either normal or abnormal cases. For example, the \"_Trend_\" category [26] indicates a data anomaly with an overall increasing or decreasing trend; knowing that no dataset has exactly a zero mean, data labeling implies ambiguity that makes the deep learning approaches difficult. Furthermore, the converted images used as input to the CNN typically have a low resolution compared to the original time histories, which can lead to similar effects on the quantization errors. These issues should be appropriately considered before applying image-classification-based deep-learning approaches for detecting data anomalies. This paper presents a comparative study of two automated data anomaly detection schemes of CNN- and rule-based classifications to provide in-depth discussions regarding the important issues described previously.