Acoustic-Based Fault Detection for Robotic Arms

Abstract

With the rise of Industry 4.0 and smart manufacturing, the integration of the Industrial Internet of Things and autonomous systems has become crucial for improving productivity and operational efficiency. For robotic arms, the core of automation, fault diagnosis is essential for ensuring production line reliability and sustainable operations. Traditional diagnostic methods, particularly those based on visual sensing, face challenges such as high deployment costs, insufficient contactlessness, and enormous data volumes. Acoustic-based fault detection has become a promising technique for monitoring the condition and ensuring the reliability of robotic arms. It is especially important in industrial automation, where noninvasive and real-time diagnostics are essential. This article systematically reviews recent state-of-the-art studies on acoustic signal acquisition, preprocessing, feature extraction, and classification for robotic arm fault diagnosis. Combining advanced signal processing techniques, such as time–frequency analysis, wavelet transforms, and empirical mode decomposition, with machine learning and deep learning models, has significantly improved fault detection in terms of accuracy and robustness. This review provides a detailed discussion of current research achievements and future directions, highlighting hybrid models. By integrating acoustic-sensing-based fault diagnosis with large-scale artificial intelligence models deployed in edge computing environments, users or companies can not only leverage neural network architectures to identify abnormal sound patterns amid complex background noise accurately but also directly address key challenges such as resource constraints, real-time performance, and energy efficiency at the edge.