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Applied Sciences
Special Issues
Machine Learning in Vibration and Acoustics 2.0
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Machine Learning in Vibration and Acoustics 2.0

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 5177

Special Issue Editors

Dr. Chengjin Qin

E-Mail Website
Guest Editor
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: PHM; machine learning; vibration and acoustics; signal processing; dynamics analysis and control
Special Issues, Collections and Topics in MDPI journals
Dr. Liang Yu

E-Mail Website
Guest Editor
School of Civil Aviation, Northwestern Polytechnical University, Xi'an 710072, China
Interests: machine learning; acoustic distributed and multisensor intelligent processing; vibration and acoustics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The reliability and quality requirements of modern industry equipment and products continue to increase. As we all know, vibration and sound contain rich information about the operation processes of equipment and products often used to monitor and analyze the state of systems. Over the past two decades, machine learning has been widely used in various fields of engineering due to its ability to learn complex problems. For this Special Issue, we are interested in articles on the latest research progress and achievements concerning machine learning in vibration and acoustics. Potential topics include, but are not limited to, the following:

  • Advanced vibration and sound data mining technology;
  • Advanced condition monitoring based on vibration and sound;
  • Advanced machine-learning-based diagnosis and health assessment methods;
  • PHM based on vibration and acoustic information;
  • Acoustic distributed and multisensor intelligent processing;
  • Acoustic measurements and array signal processing;
  • Aeroacoustic signal processing;
  • Aeroengine acoustic testing and signal processing;
  • Aeroacoustic detection and security;
  • VR/AR/MR/CR technologies for the visual reconstruction and control of the sound field.

Dr. Chengjin Qin
Dr. Liang Yu
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. Applied Sciences 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 2400 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.

Published Papers (5 papers)

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Research

18 pages, 4958 KiB  
Article
An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks
by Mentari Putri Jati, Muhammad Irfan Luthfi, Cheng-Kai Yao, Amare Mulatie Dehnaw, Yibeltal Chanie Manie and Peng-Chun Peng
Appl. Sci. 2024, 14(7), 2855; https://doi.org/10.3390/app14072855 - 28 Mar 2024
Viewed by 391
Abstract
This study proposes the utilization of an optical fiber vibration sensor for detecting the superposition of extremely close frequencies in vibration signals. Integration of deep neural networks (DNN) proves to be meaningful and efficient, eliminating the need for signal analysis methods involving complex [...] Read more.
This study proposes the utilization of an optical fiber vibration sensor for detecting the superposition of extremely close frequencies in vibration signals. Integration of deep neural networks (DNN) proves to be meaningful and efficient, eliminating the need for signal analysis methods involving complex mathematical calculations and longer computation times. Simulation results of the proposed model demonstrate the remarkable capability to accurately distinguish frequencies below 1 Hz. This underscores the effectiveness of the proposed image-based vibration signal recognition system embedded in DNN as a streamlined yet highly accurate method for vibration signal detection, applicable across various vibration sensors. Both simulation and experimental evaluations substantiate the practical applicability of this integrated approach, thereby enhancing electric motor vibration monitoring techniques. Full article
(This article belongs to the Special Issue Machine Learning in Vibration and Acoustics 2.0)
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11 pages, 2477 KiB  
Article
Static Sound Event Localization and Detection Using Bipartite Matching Loss for Emergency Monitoring
by Chanjun Chun, Hyung Jin Park and Myoung Bae Seo
Appl. Sci. 2024, 14(4), 1539; https://doi.org/10.3390/app14041539 - 14 Feb 2024
Viewed by 605
Abstract
In this paper, we propose a method for estimating the classes and directions of static audio objects using stereo microphones in a drone environment. Drones are being increasingly used across various fields, with the integration of sensors such as cameras and microphones, broadening [...] Read more.
In this paper, we propose a method for estimating the classes and directions of static audio objects using stereo microphones in a drone environment. Drones are being increasingly used across various fields, with the integration of sensors such as cameras and microphones, broadening their scope of application. Therefore, we suggest a method that attaches stereo microphones to drones for the detection and direction estimation of specific emergency monitoring. Specifically, the proposed neural network is configured to estimate fixed-size audio predictions and employs bipartite matching loss for comparison with actual audio objects. To train the proposed network structure, we built an audio dataset related to speech and drones in an outdoor environment. The proposed technique for identifying and localizing sound events, based on the bipartite matching loss we proposed, works better than those of the other teams in our group. Full article
(This article belongs to the Special Issue Machine Learning in Vibration and Acoustics 2.0)
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18 pages, 8074 KiB  
Article
An Optimized Fractional-Order PID Horizontal Vibration Control Approach for a High-Speed Elevator
by Rui Tang, Chengjin Qin, Mengmeng Zhao, Shuang Xu, Jianfeng Tao and Chengliang Liu
Appl. Sci. 2023, 13(12), 7314; https://doi.org/10.3390/app13127314 - 20 Jun 2023
Cited by 2 | Viewed by 982
Abstract
Due to factors such as uneven guide rails and airflow disturbance in the hoistway, high-speed elevators may experience significant vibrations during operation. This paper proposes an optimized fractional-order PID (FOPID) method to suppress vibrations of high-speed elevators. First, an accurate horizontal vibration model [...] Read more.
Due to factors such as uneven guide rails and airflow disturbance in the hoistway, high-speed elevators may experience significant vibrations during operation. This paper proposes an optimized fractional-order PID (FOPID) method to suppress vibrations of high-speed elevators. First, an accurate horizontal vibration model is established for the elevator car, in which the car frame and body are separate. Then, taking the control cost and the system performance as objective functions, we obtained an optimized FOPID controller based on multi-objective genetic algorithm optimization. Finally, the effectiveness of the controller in reducing elevator vibration was verified through numerical simulation. The results indicate that the horizontal acceleration controlled by the FOPID controller is reduced by about 68% compared to the case without a controller and about 25% compared to the conventional PID controller. Full article
(This article belongs to the Special Issue Machine Learning in Vibration and Acoustics 2.0)
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19 pages, 3822 KiB  
Article
A Hybrid Deep Learning Framework Based on Diffusion Model and Deep Residual Neural Network for Defect Detection in Composite Plates
by Tianrui Huang, Yang Gao, Zhenglin Li, Yue Hu and Fuzhen Xuan
Appl. Sci. 2023, 13(10), 5843; https://doi.org/10.3390/app13105843 - 09 May 2023
Cited by 2 | Viewed by 1663
Abstract
The establishment of a structural health monitoring (SHM) system for the damage and defects of composite structures is of great theoretical and engineering value to ensure their production and operational safety. Advanced machine learning technologies, such as deep learning, have become one of [...] Read more.
The establishment of a structural health monitoring (SHM) system for the damage and defects of composite structures is of great theoretical and engineering value to ensure their production and operational safety. Advanced machine learning technologies, such as deep learning, have become one of the main driving forces for state monitoring and predictive analysis of these structures. However, it is difficult to obtain sufficient data to train the deep learning model, which may fail to build an accurate and efficient SHM model. To overcome this problem, a new method based on Lamb waves and the diffusion model (DM) is proposed to realize the identification and classification of different defects for carbon-fiber-reinforced polymer (CFRP) structures. In this study, DM is used as the generation model of data enhancement, and the optimized and improved DDPM model is constructed in this experiment. The deep residual neural network (DenseNet) is used to identify and classify the defect features from the Lamb wave signals. Experimental and test results show that the deep learning framework designed in this study based on DenseNet classification and DDPM data enhancement can accurately detect and classify damage signals of common defects in CFRP composite plates. Full article
(This article belongs to the Special Issue Machine Learning in Vibration and Acoustics 2.0)
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19 pages, 1234 KiB  
Article
Dynamic Offloading Loading Optimization in Distributed Fault Diagnosis System with Deep Reinforcement Learning Approach
by Liang Yu, Qixin Guo, Rui Wang, Minyan Shi, Fucheng Yan and Ran Wang
Appl. Sci. 2023, 13(7), 4096; https://doi.org/10.3390/app13074096 - 23 Mar 2023
Cited by 1 | Viewed by 986
Abstract
Artificial intelligence and distributed algorithms have been widely used in mechanical fault diagnosis with the explosive growth of diagnostic data. A novel intelligent fault diagnosis system framework that allows intelligent terminals to offload computational tasks to Mobile edge computing (MEC) servers is provided [...] Read more.
Artificial intelligence and distributed algorithms have been widely used in mechanical fault diagnosis with the explosive growth of diagnostic data. A novel intelligent fault diagnosis system framework that allows intelligent terminals to offload computational tasks to Mobile edge computing (MEC) servers is provided in this paper, which can effectively address the problems of task processing delays and enhanced computational complexity. As the resources at the MEC and intelligent terminals are limited, performing reasonable resource allocation optimization can improve the performance, especially for a multi-terminals offloading system. In this study, to minimize the task computation delay, we jointly optimize the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection under a dynamic environment with stochastic task arrivals. The challenging dynamic joint optimization problem is formulated as a reinforcement learning (RL) problem, which is designed as the computational offloading policies to minimize the long-term average delay cost. Two deep RL strategies, deep Q-learning network (DQN) and deep deterministic policy gradient (DDPG), are adopted to learn the computational offloading policies adaptively and efficiently. The proposed DQN strategy takes the MEC selection as a unique action while using the convex optimization approach to obtain the local content splitting ratio and the transmission/computation power allocation. Simultaneously, the actions of the DDPG strategy are selected as all dynamic variables, including the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection. Numerical results demonstrate that both proposed strategies perform better than the traditional non-learning schemes. The DDPG strategy outperforms the DQN strategy in all simulation cases exhibiting minimal task computation delay due to its ability to learn all variables online. Full article
(This article belongs to the Special Issue Machine Learning in Vibration and Acoustics 2.0)
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