递归解调同步样条线核啁啾信号提取变换:非稳态信号非线性行为估计的有用工具,并应用于风力涡轮机故障检测

Yubo Ma, Huawei Wu, Rui Yuan, Hongyu Zhong, Hong-Yi Wu
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引用次数: 0

摘要

非线性行为广泛存在于自然界和工程领域的多种信号中。尽管目前各种先进的时频(TF)分析(TFA)技术的高能量集中度确保了对信号时变分量(TVC)非线性行为的精细呈现,但仅仅考虑能量集中度这一单一方面是远远不够的,因为实际信号的构成总是更为复杂,特别是对于一些棘手的难题,如强噪声干扰和早期弱TVC等,这些不利因素给揭示实际信号TVC的非线性行为带来了巨大挑战。本文针对这一问题提出了一种新的 TFA 方法,即递归解调同步样条线核啁啾小波提取变换(RDSSCET)。所提出的 RDSSCET 是在同步花键啁啾小波提取变换(SSCET)的基础上开发的,采用了新设计的外部-内部嵌套双迭代机制,有效解决了 SSCET 在处理多分量信号时的局限性,同时还表现出卓越的高能量集中性和噪声鲁棒性。因此,所提出的 RDSSCET 在揭示 TVC 的非线性行为时,特别是对于具有强噪声干扰的弱 TVC,能产生更有利的结果。数值模拟的对比分析结果验证了 RDSSCET 的性能。通过两个真实世界的声音信号和一个风力涡轮机故障检测的实际案例,充分检验了 RDSSCET 在实际应用中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recursive demodulated synchro spline-kernelled chirplet extracting transform: a useful tool for non-linear behavior estimation of non-stationary signal and application to wind turbine fault detection
Non-linear behavior is widespread in many kinds of signals from nature and engineering fields. Although the high energy-concentration level of various advanced time-frequency (TF) analysis (TFA) techniques currently developed ensure a fine representation of non-linear behavior of time-varying component (TVC) of the signal, it is far from sufficient to solely consider the single aspect of energy-concentration level, because the actual signal composition is always more complicated, especially for some thorny difficulties such as strong noise interference and the early weak TVC, etc., these negative factors bring significant challenges to reveal the non-linear behavior of TVC of practical signals. A new TFA method aimed at this issue, called recursive demodulated synchro spline-kernelled chirplet extracting transform (RDSSCET), is proposed in this paper. The proposed RDSSCET is developed on the frame of synchro spline-kernelled chirplet extracting transform (SSCET) and a newly designed external-internal nested double iteration mechanism, which effectively addresses the limitation of SSCET in handling multicomponent signals while also exhibiting superior high energy concentration and noise robustness. As such, the proposed RDSSCET can yield a more favorable outcome when revealing the non-linear behavior of TVC, particularly for weak TVC with strong noise interference. Comparison analysis results in numerical simulations verified the performance of RDSSCET. Its effectiveness in real applications is fully tested via two real-world sound signals and a practical case of wind turbine fault detection.
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