Deep Anomaly Detection of Intelligent Robot via Acoustic Signal Recognition

Yu  Jiang

doi:10.6180/jase.202412_27(12).0013

Deep Anomaly Detection of Intelligent Robot via Acoustic Signal Recognition

Electro-Mechanical Engineering

Yu JiangThis email address is being protected from spambots. You need JavaScript enabled to view it.

Shenyang Normal University, ShenYang, China

Received: January 1, 2024
Accepted: March 27, 2024
Publication Date: March 2, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202412_27(12).0013

With the advancement of artificial intelligence, intelligent robots, equipped with capabilities such as autonomous perception, learning adaptability, autonomous decision-making, and human-machine interaction, are widely deployed across various industries. In this paper, an acoustic signal analysis within the deep encoding-decoding architecture is devised for intelligent robot anomaly detection (DAD-IR-ASR), which comprises acoustic signal data preprocessing of intelligent robot, deep encoding-decoding architecture, accumulation-based anomaly detection. Specifically, DAD-IR-ASR designs an acoustic sensor collection device to collect acoustic signal data of robots. Subsequently, it utilizes Fourier transformation and filters to extract meaningful spectral features. Simultaneously, it designs a deep encoding-decoding architecture, employing unsupervised reconstruction training solely with normal data to adaptively learn an error threshold. Furthermore, DAD-IR-ASR conducts an accumulation-based anomaly detection strategy to determine if the intelligent robot is anomalous by comparing the cumulative sum of reconstruction errors within deep encoding-decoding architecture. Finally, the effectiveness of DAD-IR-ASR is demonstrated through comparisons with multiple existing unsupervised detection methods in intelligent robot anomaly detection task.

Keywords: Artificial intelligence; intelligent robots; deep anomaly detection

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