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Journal of Zhejiang University SCIENCE A

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Time-synchronous-averaging-spectrum based on super-resolution analysis and application in bearing fault signal identification

Author(s): Zengle REN, Yuan WANG, Huiyue TANG, Xin'an CHEN, Wei FENG
Affiliation(s): Guangdong Provincial Key Laboratory of Construction Robotics and Intelligent Construction, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; more
Corresponding email(s): wei.feng@siat.ac.cn
Key Words: Time-synchronous-averaging (TSA); Spectrum; Quasiperiodic signal processing (QSP); Super-resolution analysis; Bearing fault detection

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Abstract: Time-synchronous-averaging (TSA) is based on the idea of denoising by averaging, and it extracts the periodic components of a quasiperiodic signal and keeps the extracted waveform undistorted. This paper studies the mathematical properties of TSA, where three propositions are given to reveal the nature of TSA. This paper also proposes a TSA-spectrum based on super-resolution analysis and it decomposes a signal without using any base function. In contrast to discrete Fourier transform spectrum (DFT-spectrum), which is a spectrum in frequency domain, TSA-spectrum is a period-based spectrum, which can present more details of the cross effects between different periodic components of a quasiperiodic signal. Finally, a case study is carried out using bearing fault analysis to illustrate the performance of TSA-spectrum, where the rotation speed fluctuation of the shaft is estimated, which is about 0.12 ms difference. The extracted fault signals are presented and some insights are provided. We believe that this paper can provide new motivation for TSA-spectrum to be widely used in applications involving quasiperiodic signal processing (QSP).

基于超分辨率分析的同步平均频谱及其在轴承故障信号识别中的应用

作者：任增乐¹，王源²，汤辉玥³，陈欣安⁴，冯伟^1,5,6
机构：¹广东省建筑机器人与智能施工重点实验室，中国科学院深圳先进技术研究院，中国深圳，518055；²深圳市埃伯瑞科技有限公司，中国深圳，518038；³国家超级计算深圳中心，中国深圳，518055；⁴北京交通大学，轨道交通控制与安全国家重点实验室，中国北京，100044；⁵中国科学院大学，中国北京，100190；⁶深圳理工大学，中国深圳，518107
目的：本文旨在介绍一种基于超分辨率分析的时间同步平均（TSA）谱技术，用于提升准周期信号处理中的故障特征提取能力，尤其是在低信噪比的环境。
创新点：1.提出一种新颖的TSA谱分析方法；该方法基于超分辨率分析，无需借助基函数即可实现信号的周期分解，为准周期信号处理提供一种新的分析视角，可增强信号特征的解析度和敏感度。2.通过案例研究证明，改进的TSA谱能够有效识别轴承故障信号中的微弱周期性变化，揭示传统频域分析难以捕捉的交叉效应细节，为提高机械设备故障诊断的准确性和可靠性提供新的理论和技术支持。
方法：1.探讨时间同步平均法的数学原理，通过提出三个核心命题，深刻揭示TSA技术的基本性质；这些命题为理解TSA如何有效降噪并保持周期成分不失真提供理论支撑；通过理论推导，进一步阐明TSA如何从准周期信号中提取周期性成分而不改变原始波形结构。2.创新性地引入基于超分辨率分析的TSA频谱方法，可不依赖任何基函数直接对信号进行分解（图2和3）。3.为验证上述方法的有效性，选取具有代表性的西储大学轴承测试数据集进行应用实验；通过分析振动信号（图7和8），展示TSA如何在存在噪声的条件下，通过调节操作周期参数，显著提升信号提取能力。
结论：1.成功构建了一种基于超分辨率分析的TSA频谱方法，为分析准周期信号提供了一种新颖视角。2.通过数学推导和理论证明，确立了TSA的三个关键性质，揭示了其在保持周期信号结构完整性的同时，有效降噪并提取周期成分的能力；这一创新技术在实际应用中，特别是在旋转机械如轴承的故障诊断上，展示了卓越的性能，有效识别了不同类型的故障信号，包括滚珠与内外圈接触点的撞击信号。3.通过与DFT谱的对比，展现了TSA在处理信号的长周期（低频）成分时的独特优势：TSA谱作为周期域的表示，能更细致地揭示准周期信号中不同周期成分间的交叉效应，进而比DFT谱在呈现信号周期性细节上更为全面。这一发现强调了TSA在分析具有复杂周期结构信号时的补充作用，特别是在低信噪比环境中，其在保持信号周期性完整的同时，显著提升了信号特征的提取精度，进而为信号处理领域提供了新的研究方向和工具。

关键词组：时间同步平均；频谱；准周期信号处理；超分辨分析；轴承故障检测

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基于超分辨率分析的同步平均频谱及其在轴承故障信号识别中的应用

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