Academic Journal

Weak Signal Extraction in Noise Using Variable-Step Gaussian-Sinusiodal Filter

التفاصيل البيبلوغرافية
العنوان: Weak Signal Extraction in Noise Using Variable-Step Gaussian-Sinusiodal Filter
المؤلفون: Haiyang Lou, Rujiang Hao, Jianchao Zhang
المصدر: Machines, Vol 12, Iss 9, p 601 (2024)
بيانات النشر: MDPI AG, 2024.
سنة النشر: 2024
المجموعة: LCC:Mechanical engineering and machinery
مصطلحات موضوعية: weak signal extraction, variable-step Gaussian-Sinusoidal filter, rotating coordinate transformation, Mechanical engineering and machinery, TJ1-1570
الوصف: When analyzing vibration or acoustic signals in machinery, noise interference within the characteristic signals can significantly distort the results. This issue is particularly pronounced in complex environments, where mechanical signals are often overwhelmed by noise, making it extremely difficult or even impossible to determine the operational status of mechanical equipment by the analysis of characteristic signals. Existing methods for analyzing weak signals in the presence of strong Gaussian noise have limitations in their effectiveness. This paper proposes an innovative approach that utilizes a Variable-Step Gaussian-Sinusoidal Filter (VSGF) combined with rotational coordinate transformation to extract weak signals from strong noise backgrounds. The proposed method improves noise reduction capabilities and frequency selectivity, showing significant improvements over traditional Gaussian filters. Experimental validation demonstrates that the signal detection accuracy of the proposed method is 10–15% higher than that of conventional Gaussian filters. This paper presents a detailed mathematical analysis, experimental validation, and comparisons with other methods to demonstrate the effectiveness of the proposed approach.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2075-1702
Relation: https://www.mdpi.com/2075-1702/12/9/601; https://doaj.org/toc/2075-1702
DOI: 10.3390/machines12090601
URL الوصول: https://doaj.org/article/ee5dd8a2f8b748ff8949dbad4530215b
رقم الانضمام: edsdoj.5dd8a2f8b748ff8949dbad4530215b
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20751702
DOI:10.3390/machines12090601