## 1 Introduction
The multidimensional feature vector can be easily composed of the multiband power spectrum with optimal bandwidth, which can be calculated from the power spectral density obtained by Fourier or Wavelet transform. Herein, we can also reduce the degree of freedom of the feature value by exchanging the narrowband frequency characteristics into wideband ones such as the one-third octave band levels. Although various anomaly-detection methods have been proposed as mentioned above, a few studies have applied them to logistic equipment such as horizontal belt conveyors. The on-line inspection method of conveyor belts by using machine vision has been investigated [23; 24]. The longitudinal tear of the conveyor belt is investigated for detection by a multispectral visual method [25]. The remaining lifetimes of steel cord conveyor belts were observed by using magnetic sensors [26]. Although there are some research cases for these belt conveyors, almost all of them are detected by the sensors other than those by vibration. Furthermore, there are few cases related to the anomaly detection of cargo lifters that vertically convey the logistic products. In any case, the techniques for the anomaly detection of logistics systems have not been yet fully studied and have been still in development. One reason for the lack of research cases in this field is that the mechanical characteristics of these operating devices can be rather complicated; such equipment is often composed of a wide variety of machine elements. Notably, however, since the structure of logistic equipment can include relatively flexible structures such as thin beams or columns, its operating state may be sensitively reflected in the vibration characteristics of the structure. A detection method involving vibration would be difficult to apply using supervised machine learning because of the following reasons. Firstly, it is difficult to recover the labels for specific malfunctions. Secondly, the vibration characteristics change dynamically as a function of the operating situation. Thirdly, a variety of unknown anomaly states in the mechanical system have the possibility to happen, and it is difficult to cover all the possible cases in advance by the detection algorithm. Although it is difficult to collect labeled data for the supervised classification, it is possible to detect an acceleration in vibrations with a sensor and to use the measured acceleration directly; differences between the equipment's normal and anomaly states can be observed in its frequency characteristics as well as in the absolute value of the raw acceleration level. As described, the accuracy of anomaly detection in a logistic operating system using the MT method can be substantially improved by considering not only the absolute vibration level that occurs during the operation of the equipment but also transitions in the frequency characteristics. Then, the MT method makes it possible to clearly and simply detect changes in state, as transitions in the vibration state can be expressed as a single scalar value. In this study, an anomaly-detection method using the MT approach and targeting the operational characteristics of a large-scale vertical transfer unit (hereinafter, a \"lifter\") is developed. Firstly, the applicability of the method for steady-state vibrations generated from a simple shaker was examined as a basic study. Then, after that, based on this finding, the properties of the lifter in normal and artificially damaged conditions were investigated through the vibration measurement results obtained by setting the vibration sensor on the surface of the lifter. From the calculation results of the Mahalanobis distances for fragment data acquired under three types of anomaly conditions, it was suggested that the operating state of the lifter can be estimated by using the differences of the Mahalanobis distances.