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IoT-based acoustic data collection systems have been proposed for detecting anomalies in mechanical equipment. However, traditional systems using audible sound do not work well due to interference from various sound sources. To solve this problem, we have previously proposed an ultrasonic data collection system, but the clear and high-quality sound required for anomaly detection results in an increase in data volume. This paper proposes a method for extracting only the necessary frequency band based on the periodicity of the target signal, aiming to reduce the amount of data. Our experiments demonstrate that the proposed method efficiently determines the appropriate frequency band in which the characteristics of the target equipment appear, thereby enabling the amount of data to be reduced by more than 45%.
Shigyo et al. (Sun,) studied this question.