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Nonlinear sparse mode decomposition and its application in planetary gearbox fault diagnosis
Mechanism and Machine Theory ( IF 5.2 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.mechmachtheory.2020.104082
Haiyang Pan , Jinde Zheng , Yu Yang , Junsheng Cheng

Abstract Traditional time-frequency analysis methods, including empirical mode decomposition (EMD), local characteristic-scale decomposition (LCD) and variable mode decomposition (VMD), have some limitations in nonlinear signal analysis. When the signal has strong noise, traditional time-frequency analysis methods will force the signal to be decomposed into several inaccurate components, and the achieved components usually suffer from the end effect problem. Considering the above pressing challenge, a new signal decomposition algorithm, nonlinear sparse mode decomposition (NSMD), is proposed in this protocol. The core of NSMD is that the local narrowband signal disappears under the action of the singular local linear operator, so the singular local linear operator can be applied to extract the local narrowband component of the detected signal. Meanwhile, the obtained local narrowband signal can be superposed as the basic signal to close to the original signal, realizing the adaptive decomposition of the signal with good robustness and adaptability. The analysis results of simulation signals and planetary gearbox fault signals indicate that the proposed NSMD method is effective for raw vibration signals.

中文翻译:

非线性稀疏模式分解及其在行星齿轮箱故障诊断中的应用

摘要 传统的时频分析方法,包括经验模态分解(EMD)、局部特征尺度分解(LCD)和可变模态分解(VMD),在非线性信号分析中存在一定的局限性。当信号具有强噪声时,传统的时频分析方法会迫使信号被分解为若干个不准确的分量,所得到的分量通常会出现端效应问题。考虑到上述紧迫的挑战,该协议提出了一种新的信号分解算法,非线性稀疏模式分解(NSMD)。NSMD的核心是局部窄带信号在奇异局部线性算子的作用下消失,因此可以应用奇异局部线性算子来提取检测信号的局部窄带分量。同时,可以将得到的局部窄带信号作为基本信号进行叠加,接近原始信号,实现了对信号的自适应分解,具有良好的鲁棒性和适应性。仿真信号和行星齿轮箱故障信号的分析结果表明,所提出的 NSMD 方法对原始振动信号是有效的。
更新日期:2021-01-01
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