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Vibrations and Acoustics for Condition Monitoring in Non-Stationary Operations
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Vibrations and Acoustics for Condition Monitoring in Non-Stationary Operations

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 5554

Special Issue Editor

Prof. Dr. Jiawei Xiang

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China
Interests: fault diagnosis; structural health monitoring; signal processing; finite element analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Condition monitoring (COM) in the context of Industry 4.0 refers to the use of advanced technologies and data analytics to monitor the health and performance of industrial equipment and systems in real-time. It is a key component of the fourth industrial revolution, which emphasizes the integration of digital technologies and automation into industrial processes. CM offers significant economic benefits to industries. By continuously monitoring key parameters and analyzing data, potential faults or abnormalities can be detected early, allowing for proactive maintenance interventions. This can prevent unplanned downtime, minimize repair costs, and extend the lifespan of the machinery. Additionally, condition monitoring enables data-driven decision making, the optimization of maintenance strategies, and resource allocation. Overall, it enhances operational efficiency, improves safety, and ensures the smooth operation of critical systems in industrial settings.

While COM has become an invaluable tool for ensuring the proper functioning and optimal performance of industrial machinery, it faces challenges when dealing with non-stationary operations. In reality, many industrial systems operate under non-stationary conditions due to factors such as changes in the production process, varying workloads, and environmental influences. These non-stationary operations pose a challenge to the development of effective condition monitoring techniques, as traditional stationary statistical methods may not be suitable for such dynamic systems.

To foster knowledge exchange and explore the latest advancements in condition monitoring techniques for non-stationary operations, we are announcing a Special Issue: Vibrations and acoustics for condition monitoring in non-stationary operations. We invite researchers and experts in this field to contribute their insights, ideas, and research findings to enhance our understanding and develop innovative solutions for monitoring non-stationary operations.

Topics

  • AI and deep learning-based methods for defect identification;
  • The dynamic analysis of complex systems;
  • Digital twin technology-assisted condition monitoring;
  • Vibration and acoustic emission analysis for non-stationary machinery;
  • AI for defect diagnosis under varying speeds and loads.

Prof. Dr. Jiawei Xiang
Guest Editor

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.

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Published Papers (4 papers)

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Research

17 pages, 6790 KiB  
Article
An Improved Method for Detecting Crane Wheel–Rail Faults Based on YOLOv8 and the Swin Transformer
by Yunlong Li, Xiuli Tang, Wusheng Liu, Yuefeng Huang and Zhinong Li
Sensors 2024, 24(13), 4086; https://doi.org/10.3390/s24134086 - 24 Jun 2024
Viewed by 960
Abstract
In the realm of special equipment, significant advancements have been achieved in fault detection. Nonetheless, faults originating in the equipment manifest with diverse morphological characteristics and varying scales. Certain faults necessitate the extrapolation from global information owing to their occurrence in localized areas. [...] Read more.
In the realm of special equipment, significant advancements have been achieved in fault detection. Nonetheless, faults originating in the equipment manifest with diverse morphological characteristics and varying scales. Certain faults necessitate the extrapolation from global information owing to their occurrence in localized areas. Simultaneously, the intricacies of the inspection area’s background easily interfere with the intelligent detection processes. Hence, a refined YOLOv8 algorithm leveraging the Swin Transformer is proposed, tailored for detecting faults in special equipment. The Swin Transformer serves as the foundational network of the YOLOv8 framework, amplifying its capability to concentrate on comprehensive features during the feature extraction, crucial for fault analysis. A multi-head self-attention mechanism regulated by a sliding window is utilized to expand the observation window’s scope. Moreover, an asymptotic feature pyramid network is introduced to augment spatial feature extraction for smaller targets. Within this network architecture, adjacent low-level features are merged, while high-level features are gradually integrated into the fusion process. This prevents loss or degradation of feature information during transmission and interaction, enabling accurate localization of smaller targets. Drawing from wheel–rail faults of lifting equipment as an illustration, the proposed method is employed to diagnose an expanded fault dataset generated through transfer learning. Experimental findings substantiate that the proposed method in adeptly addressing numerous challenges encountered in the intelligent fault detection of special equipment. Moreover, it outperforms mainstream target detection models, achieving real-time detection capabilities. Full article
(This article belongs to the Special Issue Vibrations and Acoustics for Condition Monitoring in Non-Stationary Operations)
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20 pages, 6168 KiB  
Article
A New Denoising Method for Belt Conveyor Roller Fault Signals
by Xuedi Hao, Jiajin Zhang, Yingzong Gao, Chenze Zhu, Shuo Tang, Pengfei Guo and Wenliang Pei
Sensors 2024, 24(8), 2446; https://doi.org/10.3390/s24082446 - 11 Apr 2024
Cited by 1 | Viewed by 809
Abstract
In the process of the intelligent inspection of belt conveyor systems, due to problems such as its long duration, the large number of rollers, and the complex working environment, fault diagnosis by acoustic signals is easily affected by signal coupling interference, which poses [...] Read more.
In the process of the intelligent inspection of belt conveyor systems, due to problems such as its long duration, the large number of rollers, and the complex working environment, fault diagnosis by acoustic signals is easily affected by signal coupling interference, which poses a great challenge to selecting denoising methods of signal preprocessing. This paper proposes a novel wavelet threshold denoising algorithm by integrating a new biparameter and trisegment threshold function. Firstly, we elaborate on the mutual influence and optimization process of two adjustment parameters and three wavelet coefficient processing intervals in the BT-WTD (the biparameter and trisegment of wavelet threshold denoising, BT-WTD) denoising model. Subsequently, the advantages of the proposed threshold function are theoretically demonstrated. Finally, the BT-WTD algorithm is applied to denoise the simulation signals and the vibration and acoustic signals collected from the belt conveyor experimental platform. The experimental results indicate that this method’s denoising effectiveness surpasses that of traditional threshold function denoising algorithms, effectively addressing the denoising preprocessing of idler roller fault signals under strong noise backgrounds while preserving useful signal features and avoiding signal distortion problems. This research lays the theoretical foundation for the non-contact intelligent fault diagnosis of future inspection robots based on acoustic signals. Full article
(This article belongs to the Special Issue Vibrations and Acoustics for Condition Monitoring in Non-Stationary Operations)
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20 pages, 50195 KiB  
Article
Topology Optimization Design Method for Acoustic Imaging Array of Power Equipment
by Jun Xiong, Xiaoming Zha, Xuekai Pei and Wenjun Zhou
Sensors 2024, 24(7), 2032; https://doi.org/10.3390/s24072032 - 22 Mar 2024
Viewed by 908
Abstract
Acoustic imaging technology has the advantages of non-contact and intuitive positioning. It is suitable for the rapid positioning of defects such as the mechanical loosening, discharge, and DC bias of power equipment. However, the existing research lacks the optimization design of microphone array [...] Read more.
Acoustic imaging technology has the advantages of non-contact and intuitive positioning. It is suitable for the rapid positioning of defects such as the mechanical loosening, discharge, and DC bias of power equipment. However, the existing research lacks the optimization design of microphone array topology. The acoustic frequency domain characteristics of typical power equipment are elaborately sorted out. After that, the cut-off frequencies of acoustic imaging instruments are determined, to meet the needs of the full bandwidth test requirements. Through a simulation calculation, the circular array is demonstrated to be the optimal shape. And the design parameters affect the imaging performance of the array to varying degrees, indicating that it is difficult to obtain the optimal array topology by an exhaustive method. Aimed at the complex working conditions of power equipment, a topology optimization design method of an acoustic imaging array for power equipment is proposed, and the global optimal solution of microphone array topology is obtained. Compared with the original array, the imaging performance of the improved LF and HF array is promoted by 54% and 49%, respectively. Combined with the simulation analysis and laboratory test, it is verified that the improved array can not only accurately locate the single sound source but also accurately identify the main sound source from the interference of the contiguous sound source. Full article
(This article belongs to the Special Issue Vibrations and Acoustics for Condition Monitoring in Non-Stationary Operations)
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13 pages, 17799 KiB  
Article
Torsional Vibration Analysis Using Rotational Laser Vibrometers
by Steven Chatterton, Ludovico Dassi, Edoardo Gheller, Tommaso Ghisi, Andrea Vania and Paolo Pennacchi
Sensors 2024, 24(6), 1788; https://doi.org/10.3390/s24061788 - 10 Mar 2024
Viewed by 2327
Abstract
Torsional vibration is a critical phenomenon in rotor dynamics. It consists of an oscillating movement of the shaft and causes failures in multiple oscillating fields of application. This type of vibration is more difficult to measure than lateral vibration. Torsional vibrometers are generally [...] Read more.
Torsional vibration is a critical phenomenon in rotor dynamics. It consists of an oscillating movement of the shaft and causes failures in multiple oscillating fields of application. This type of vibration is more difficult to measure than lateral vibration. Torsional vibrometers are generally invasive and require a complicated setup, as well as being inconvenient for field measurements. One of the most reliable, non-invasive, and transportable measuring techniques involves the laser torsional vibrometer. For this research, two laser heads with different measurement capabilities were utilized. An experimental test rig was used to perform a relative calibration of the two laser vibrometers. The frequency of the acting force and the rotation speed of the shaft vary in the same range, which is commonly found in rotating machines. Finally, experimental measurements of torsional vibrations using laser vibrometers were compared with numerical results from a 1D finite element model of the same test rig. The main outcome of this paper is the definition of a reliable measuring procedure to exploit two laser vibrometers for detecting torsional mode-shapes and natural frequencies on real machines. The relative calibration of two different measuring heads is described in detail, and the procedure was fundamental to properly correlate measuring signals in two machine sections. A good correspondence between the numerical and experimental results was found. Full article
(This article belongs to the Special Issue Vibrations and Acoustics for Condition Monitoring in Non-Stationary Operations)
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