## 1 Introduction  
Recent improvements in technologies related to IoT (Internet of Things) have made real-time monitoring of various types of structures possible. In the field of structural health monitoring, sensor-embedding and wireless-transfer technologies have been developed to monitor the structural integrity of buildings and civil engineering facilities. In the logistics industry, as product delivery has become more efficient and less costly, the working time required for inspection and repair necessitated by unexpected problems can be a major issue, especially for an automatic logistic system operating in a twenty-four-hour active environment. In a conventional management system involving mechanical devices, the majority of maintenance work is done by maintenance workers based on their experience and intuition related to anomaly detection, the timing of which is typically driven by a stoppage of the operation due to some mechanical problem that has already occurred. This can lead to non-negligible economic losses. Thus, it is important to detect a deterioration or change in the mechanical state of a logistic operating system early and accurately in order to predict the time of potential failure and to avoid stoppages of the system to the extent possible. To this end, various damage-detection algorithms [1] and their application to mechanical structures have been studied. A number of studies have been conducted on damage-detection methods for rotating equipment such as bearings [2] and gearboxes [3], the failure of which can cause an entire system to be shut down. When detecting damage of machinery, the use of physical data such as the mechanical ones including vibration and the sound, electrical, and thermal ones can be effective as a basic measure [4]. Using acoustic emissions [5] from damaged parts can also provide an essential clue in damage detection. In addition to such basic measures, a variety of methods for detecting damage features by using machine learning such as neural networks [6; 7] has been proposed in recent years. [PERSON] et al. validated the applicability of SIDL (shift-invariant dictionary learning), which is an extension of regular dictionary learning and makes each atom replicated and shifted at an arbitrary position within the signal [8]. [PERSON] et al. have suggested a useful method for nonnegative matrix factorization (NMF) to detect damage to a gearbox [9]. This investigation described a feature extraction and feature selection scheme for hybrid fault diagnosis of a gearbox based on S transform, nonnegative matrix factorization, and mutual information. [PERSON] et al. also proposed a method for detecting abrasion in gearboxes [10], in which a model-based technique for detecting wear in a multistage planetary gearbox used by lifting cranes was proposed, and established a vibration signal model which dealt with cyclostationary and autoregressive models. In each of these cases, estimating changes in mechanical conditions based on a large amount of accumulated physical data is necessary for effective anomaly detection. [PERSON] et al. investigated previously proposed anomaly-detection methods based on data mining [11], grouping the techniques into three categories: classification, clustering, and a hybrid method combining the two. According to this scheme, classification corresponds to neural networks, support vector machines, or genetic algorithms used to classify an unknown condition as normal or anomalous by using a classifier learned with a large amount of physical data collected under normal and anomalous conditions. Presenting research examples of classification problems addressed by a machine-learning-based model, [PERSON] et al. have proposed a noise-prediction method using random forest regression [12] and a noise-detection method [13] for HVAC (Heating, Ventilation, and Air Conditioning) noises using recursive partitioning. However, such a method is difficult to apply in the absence of sufficient anomaly spectral density with a single value.