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Acoustic Sensing for Condition Monitoring
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Acoustic Sensing for Condition Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 25 October 2025 | Viewed by 1130

Special Issue Editors

Dr. Pan Dou

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, 28 Xianning Road West, Xi'an 710049, China
Interests: ultrasonic measurement; acoustic emission technology; mechanical health monitoring
Prof. Dr. Tonghai Wu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, 28 Xianning Road West, Xi\'an 710049, China
Interests: machinery health monitoring; sensor technology; tribology; artificial intelligence; image processing; diagnostics; prognostics; system control
Special Issues, Collections and Topics in MDPI journals
Dr. Longbiao He

E-Mail Website
Guest Editor
National Institute of Metrology (NIM), 18, Bei San Huan Dong Lu, Beijing 100029, China
Interests: acoustic sensors; calibration; acoustic emission technology; ultrasound technology; material processing

Special Issue Information

Dear Colleagues,

Acoustic sensing has emerged as a powerful tool for condition monitoring in various engineering fields, including mechanical health monitoring (MHM) and structural health monitoring (SHM). Techniques such as ultrasonic sensing and acoustic emission (AE) enable real-time, non-invasive monitoring, making them valuable for detecting damage, wear, fatigue, and other faults. These methods are widely applied in industries such as civil infrastructure, aerospace, automotives, and manufacturing to enhance safety, reliability, and predictive maintenance.

However, effective acoustic sensing faces challenges such as signal noise, environmental interference, and complex data interpretation. Recent advances in signal processing, sensor fusion, machine learning, and distributed sensing networks have significantly improved the reliability and efficiency of acoustic-based condition monitoring systems.

This Special Issue aims to gather cutting-edge research on acoustic sensing technologies for condition monitoring, covering both methodological innovations and practical applications. We invite original contributions focusing on novel sensing techniques, real-world case studies, and advanced data analysis methods. Authors are encouraged to share their code in public repositories (e.g., GitHub) where applicable.

Dr. Pan Dou
Prof. Dr. Tonghai Wu
Dr. Longbiao He
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ultrasonic sensing
  • acoustic emission sensing
  • calibration
  • structural health monitoring (SHM)
  • mechanical health monitoring (MHM)
 

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Published Papers (2 papers)

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Research

29 pages, 40108 KiB  
Article
Decomposing and Modeling Acoustic Signals to Identify Machinery Defects in Industrial Soundscapes
by Christof Pichler, Markus Neumayer, Bernhard Schweighofer, Christoph Feilmayr, Stefan Schuster and Hannes Wegleiter
Sensors 2025, 25(16), 4923; https://doi.org/10.3390/s25164923 - 9 Aug 2025
Viewed by 220
Abstract
Acoustic sound-based condition monitoring (ASCM) systems, which typically utilize machine learning algorithms on established audio features, have demonstrated effectiveness under controlled conditions. However, their application in real-world industrial environments presents significant challenges due to complex and variable soundscapes with high noise and limited [...] Read more.
Acoustic sound-based condition monitoring (ASCM) systems, which typically utilize machine learning algorithms on established audio features, have demonstrated effectiveness under controlled conditions. However, their application in real-world industrial environments presents significant challenges due to complex and variable soundscapes with high noise and limited fault data. The presence of random interfering sounds and variability in operating conditions can lead to lower performance and high false-positive rates. To overcome these limitations, we propose a fault detection method that leverages the underlying physical characteristics of the sound signals. By investigating the components of the acoustic signal, we found that fault-related sounds can be modeled as exponentially decaying oscillations. This insight allows for the development of a physically based signal model, setting our approach apart from purely data-driven methods. Using this model, we developed a robust detection method based on a Generalized Likelihood Ratio Test (GLRT). The effectiveness of this approach was validated using both synthetic and real-world data from a steel industry facility. Our results demonstrate that the proposed model-based approach provides superior performance compared to standard audio features, particularly in high-noise conditions. On real-world data, the GLRT-based approach outperformed all audio features, as clearly shown by the Receiver Operating Characteristic (ROC) analysis. Specifically, the Partial Area Under the Curve (pAUC) of the GLRT is more than twice that of the best-performing audio feature, demonstrating good detection at significantly lower-false-positive rates compared to audio features. Furthermore, simulations showed that our method maintains robust detection down to a Signal-to-Noise Ratio (SNR) of −13 dB, significantly outperforming audio feature-based detection, which was limited to approximately −10 dB. The physically informed nature of our model not only provides a more reliable and robust solution but also enables the method to be generalized to other industrial scenarios with similar fault properties, offering broader applicability for reliable acoustic condition monitoring. Full article
(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)
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24 pages, 6218 KiB  
Article
The Design and Data Analysis of an Underwater Seismic Wave System
by Dawei Xiao, Qin Zhu, Jingzhuo Zhang, Taotao Xie and Qing Ji
Sensors 2025, 25(13), 4155; https://doi.org/10.3390/s25134155 - 3 Jul 2025
Viewed by 464
Abstract
Ship seismic wave signals represent one of the most critical physical field characteristics of vessels. To achieve the high-precision detection of ship seismic wave field signals in marine environments, an underwater seismic wave signal detection system was designed. The system adopts a three-stage [...] Read more.
Ship seismic wave signals represent one of the most critical physical field characteristics of vessels. To achieve the high-precision detection of ship seismic wave field signals in marine environments, an underwater seismic wave signal detection system was designed. The system adopts a three-stage architecture consisting of watertight instrument housing, a communication circuit, and a buoy to realize high-capacity real-time data transmissions. The host computer performs the collaborative optimization of multi-modal hardware architecture and adaptive signal processing algorithms, enabling the detection of ship targets in oceanic environments. Through verification in a water tank and sea trials, the system successfully measured seismic wave signals. An improved ALE-LOFAR (Adaptive Line Enhancer–Low-Frequency Analysis) joint framework, combined with DEMON (Demodulation of Envelope Modulation) demodulation technology, was proposed to conduct the spectral feature analysis of ship seismic wave signals, yielding the low-frequency signal characteristics of vessels. This scheme provides an important method for the covert monitoring of shallow-sea targets, providing early warnings of illegal fishing and ensuring underwater security. Full article
(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)
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