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Electronics
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Applications of Artificial Intelligence in Mechanical Engineering
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Applications of Artificial Intelligence in Mechanical Engineering

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2364

Special Issue Editors

Dr. Haibo Huang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Interests: vehicle big data analysis; noise and vibration control; intelligent driving
Dr. Yawen Wang

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
Interests: gear noise/vibration; structural dynamics; vibro-acoustics; active noise and vibration control, automotive NVH (noise, vibration & harshness); electro-mechanical system dynamics; data-driven condition monitoring and prognostics
Special Issues, Collections and Topics in MDPI journals
Dr. Shaogan Ye

E-Mail Website
Guest Editor
Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, China
Interests: noise and vibration control in fluid power; artificial intelligence in fault diagnosis of fluid power components and systems
Dr. Jun Wu

E-Mail Website
Guest Editor
Department of Biology, University of Oxford, Oxford, OX1 2JD, UK
Interests: mechanical vibration; dynamics and control

Special Issue Information

Dear Colleagues,

With the rapid development of artificial intelligence technology, we find ourselves at the forefront of a technological revolution poised to profoundly transform our understanding and application of mechanical engineering. Mechanical engineering, as a crucial discipline within the field of engineering, encompasses a wide array of domains, ranging from advanced manufacturing techniques to engineering design and reliability analysis. Each of these domains is continuously benefiting from breakthroughs in artificial intelligence. Emerging technologies such as deep learning, neural networks, and big data analytics are progressing at an unprecedented pace, presenting mechanical engineering with unprecedented opportunities and challenges. This Special Issue is dedicated to focusing on the extensive applications of artificial intelligence in the field of mechanical engineering, showcasing the latest research outcomes in this interdisciplinary domain.

Dr. Haibo Huang
Dr. Yawen Wang
Dr. Shaogan Ye
Dr. Jun Wu
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. Electronics 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.

Keywords

  • data-driven applications
  • big data analysis
  • active and passive control
  • noise, vibration and harshness
  • prognostics health management

Published Papers (2 papers)

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Research

21 pages, 15103 KiB  
Article
A Hybrid Fault Diagnosis Method for Autonomous Driving Sensing Systems Based on Information Complexity
by Tianshi Jin, Chenxi Zhang, Yikang Zhang, Mingliang Yang and Weiping Ding
Electronics 2024, 13(2), 354; https://doi.org/10.3390/electronics13020354 - 14 Jan 2024
Viewed by 955
Abstract
In the context of autonomous driving, sensing systems play a crucial role, and their accuracy and reliability can significantly impact the overall safety of autonomous vehicles. Despite this, fault diagnosis for sensing systems has not received widespread attention, and existing research has limitations. [...] Read more.
In the context of autonomous driving, sensing systems play a crucial role, and their accuracy and reliability can significantly impact the overall safety of autonomous vehicles. Despite this, fault diagnosis for sensing systems has not received widespread attention, and existing research has limitations. This paper focuses on the unique characteristics of autonomous driving sensing systems and proposes a fault diagnosis method that combines hardware redundancy and analytical redundancy. Firstly, to ensure the authenticity of the study, we define 12 common real-world faults and inject them into the nuScenes dataset, creating an extended dataset. Then, employing heterogeneous hardware redundancy, we fuse MMW radar, LiDAR, and camera data, projecting them into pixel space. We utilize the “ground truth” obtained from the MMW radar to detect faults on the LiDAR and camera data. Finally, we use multidimensional temporal entropy to assess the information complexity fluctuations of LiDAR and the camera during faults. Simultaneously, we construct a CNN-based time-series data multi-classification model to identify fault types. Through experiments, our proposed method achieves 95.33% accuracy in detecting faults and 82.89% accuracy in fault diagnosis on real vehicles. The average response times for fault detection and diagnosis are 0.87 s and 1.36 s, respectively. The results demonstrate that the proposed method can effectively detect and diagnose faults in sensing systems and respond rapidly, providing enhanced reliability for autonomous driving systems. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Mechanical Engineering)
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16 pages, 3404 KiB  
Article
Improving Electric Vehicle Structural-Borne Noise Based on Convolutional Neural Network-Support Vector Regression
by Xiaoli Jia, Lin Zhou, Haibo Huang, Jian Pang and Liang Yang
Electronics 2024, 13(1), 113; https://doi.org/10.3390/electronics13010113 - 27 Dec 2023
Viewed by 643
Abstract
In order to enhance the predictive accuracy and control capabilities pertaining to low- and medium-frequency road noise in automotive contexts, this study introduces a methodology for Structural-borne Road Noise (SRN) prediction and optimization. This approach relies on a multi-level target decomposition and a [...] Read more.
In order to enhance the predictive accuracy and control capabilities pertaining to low- and medium-frequency road noise in automotive contexts, this study introduces a methodology for Structural-borne Road Noise (SRN) prediction and optimization. This approach relies on a multi-level target decomposition and a hybrid model combining Convolutional Neural Network (CNN) and Support Vector Regression (SVR). Initially, a multi-level target analysis method is proposed, grounded in the hierarchical decomposition of vehicle road noise along the chassis parts, delineated layer by layer, in accordance with the vibration transmission path. Subsequently, the CNN–SVR hybrid model, predicated on the multi-level target framework, is proposed. Notably, the hybrid model exhibits a superior predictive accuracy exceeding 0.97, surpassing both traditional CNN and SVR models. Finally, the method and model are deployed for sensitivity analysis of chassis parameters in relation to road noise, as well as for the prediction and optimization analysis of SRN in vehicles. The outcomes underscore the high sensitivity of parameters such as the dynamic stiffness of the rear axle bushing and the large front swing arm bushing influencing SRN. The optimization results, facilitated by the CNN–SVR hybrid model, align closely with the measured outcomes, displaying a negligible relative error of 0.82%. Furthermore, the measured results indicate a noteworthy enhancement of 4.07% in the driver’s right-ear Sound Pressure Level (SPL) following the proposed improvements compared to the original state. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Mechanical Engineering)
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