Review on Sound-Based Industrial Predictive Maintenance: From Feature Engineering to Deep Learning

Tongzhou Ye, Tianhao Peng and Lidong Yang ()
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Tongzhou Ye: The School of Mechatronics Engineering, The Anhui University of Science and Technology, Huainan 232001, China
Tianhao Peng: The School of Mechatronics Engineering, The Anhui University of Science and Technology, Huainan 232001, China
Lidong Yang: Department of Industrial and Systems Engineering, The Research Institute for Advanced Manufacturing, The Hong Kong Polytechnic University, Hong Kong 999077, China

Mathematics, 2025, vol. 13, issue 11, 1-31

Abstract: Sound-based predictive maintenance (PdM) is a critical enabler for ensuring operational continuity and productivity in industrial systems. Due to the diversity of equipment types and the complexity of working environments, numerous feature engineering methods and anomaly diagnosis models have been developed based on sound signals. However, existing reviews focus more on the structures and results of the detection model, while neglecting the impact of the differences in feature engineering on subsequent detection models. Therefore, this paper aims to provide a comprehensive review of the state-of-the-art feature extraction methods based on sound signals. The judgment standards in the sound detection models are analyzed from empirical thresholding to machine learning and deep learning. The advantages and limitations of sound detection algorithms in varied equipment are elucidated through detailed examples and descriptions, providing a comprehensive understanding of performance and applicability. This review also provides a guide to the selection of feature extraction and detection methods for the predictive maintenance of equipment based on sound signals.

Keywords: sound signal; predictive maintenance; feature engineering; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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