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System Development & Application
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 6
1807-1814

ARViTrans method for machine sound anomaly detection

Chen Longa,b
Guo Fabina,b
Huang Xiaoweia,b
Lu Yashia,b
a. School of Artificial Intelligence & Big Data, b. Industrial Equipment States Evaluation & Fault Prediction Technology R&D Center, Hefei University, Hefei 230601, China
DOI: 10.19734/j.issn.1001-3695.2024.10.0365

Abstract

In order to solve the problems that the existing machine sound anomaly detection methods only focus on the single features of the time, frequency or channel dimensions, ignoring the mutual connection between the spectral features and the time series information, and the initial feature loss leads to inaccurate fitting of the sample data distribution, thus causing a high anomaly missed detection rate and false alarm rate, this paper proposed ARViTrans, a machine sound anomaly detection method that integrated attention mechanisms and skip connections. Firstly, this paper proposed a three-dimensional efficient coordinate attention mechanisms to collaboratively capture the time domain, frequency domain and channel dimension features through the decoupling operation of the feature space. Secondly, it used MobileViT as the backbone network and designed the RES-MoViT module to replace the original MobileViT module. Skip connections captured the information between the input and output and better fit the sample data distribution. The gradient reflux reduced the repeated learning of similar feature parameters and improved the parameter utilization efficiency. Finally, it compared the experimental results on the MIMII dataset with the AE and MobileNetV2 of the DCASE Task2 baseline system. The AUC improves by 10.14% and 10.26%, respectively. The pAUC improves by 13.40% and 6.50%, respectively. The experimental results indicate that the proposed method can effectively capture the mutual connection between features of different dimensions while maintaining a low model complexity, improve the accuracy of anomaly detection and reduce the false alarm rate.

Foundation Support

国家自然科学基金资助项目(61806068)
安徽省自然科学基金资助项目(KJ2021ZD0118)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.10.0365
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 6
Section: System Development & Application
Pages: 1807-1814
Serial Number: 1001-3695(2025)06-028-1807-08

Publish History

[2025-06-05] Printed Article

Cite This Article

陈龙, 郭法滨, 黄小伟, 等. 一种用于机器声音异常检测的ARViTrans方法 [J]. 计算机应用研究, 2025, 42 (6): 1807-1814. (Chen Long, Guo Fabin, Huang Xiaowei, et al. ARViTrans method for machine sound anomaly detection [J]. Application Research of Computers, 2025, 42 (6): 1807-1814. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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