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.

Path planning algorithms are indispensable for controlling micro-/nanorobots through complex and unknown environments in the biomedical and medical fields. With the tasks performed becoming more compl...

The positioning of equipment sound frequency ridges is the foundation in monitoring the state of mine equipment. However, existing models are difficult to identify equipment sound frequency ridges from sounds generated by mine equipment under the environment with too complex noise. Facing to the mine equipment, this paper proposed an equipment sound frequency ridges positioning model (namely, HVDG-RSAGE model) which contains the Horizontal Visibility Directed Graph conjoin the Resampling graph SAmpling and aggreGatE. Firstly, the mine equipment sound is captured by the microphone, and then a suggested preprocessing decomposes the sound into frequency components. Secondly, a horizontal visibility directed criterion is proposed to create horizontal visibility directed graph which can describe the relative position relationship of frequency components. Finally, the Resampling graph SAmpling and aggreGatE, an embedding model included a resampling module, is proposed to identify the position of equipment frequency ridges, which can avoid model overfitting. The experiments show that the HVDG-RSAGE model can improve the accuracy positioning of equipment sound frequency ridges.