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Detection of rub-impact fault for rotor-stator systems: A novel method based on adaptive chirp mode decomposition

机译:转子定子系统擦伤断层的检测:一种基于自适应啁啾模式分解的新方法

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摘要

For rotating machineries, rub-impact between the surfaces of the rotor and the stator during the rotation is a common and severe fault. The rub-impact fault will lead to a frequency-modulated (FM) vibration signal with a fast fluctuating instantaneous frequency (IF). It is difficult to extract the fast fluctuating IF feature due to the limited resolution of the current time-frequency (TF) methods. The recently proposed variational nonlinear chirp mode decomposition (VNCMD) method shows promising advantages in analyzing strongly FM signals. However, the VNCMD solves a joint-estimation problem which requires the prior information of the number of the signal components and may cause instability issues. In this paper, a tractable version of the VNCMD, called adaptive chirp mode decomposition (ACMD), is introduced to extract the fast fluctuating IF of the vibration signal from the rub-impact rotor. The ACMD employs a greedy algorithm to catch each signal component individually. Moreover, using the estimated instantaneous amplitude and the IF by ACMD, we obtain a high-resolution adaptive TF spectrum which can clearly represent the rub-impact feature of the vibration signal. The effectiveness of the fault detection method based on ACMD is demonstrated by dynamic simulations at first. Then, the method is applied to vibration signals of a heavy oil catalytic cracking machine set indicating its usefulness in early fault detection and multi-feature extraction. (C) 2018 Elsevier Ltd. All rights reserved.
机译:对于旋转机械,在转子的表面和定子之间的旋转之间的冲击是常见而严重的故障。摩擦冲击故障将导致频率调制(FM)振动信号,具有快速波动的瞬时频率(IF)。由于当前时间频率(TF)方法的分辨率有限,难以提取快速波动。最近提出的变分非线性啁啾模式分解(VNCMD)方法显示了在分析强FM信号方面的有希望的优点。但是,VNCMD解决了一个关节估计问题,该问题需要信号分量的数量的先前信息,可能导致不稳定问题。在本文中,引入了VNCMD的vncmd的易诊版本,以提取来自摩擦碰撞转子的振动信号的快速波动。 ACMD采用贪婪算法单独捕获每个信号组件。此外,使用估计的瞬时幅度和ACMD,我们获得高分辨率自适应TF光谱,其可以清楚地表示振动信号的摩擦撞击特征。首先通过动态仿真对基于ACMD的故障检测方法的有效性。然后,将该方法应用于重油催化裂化机组的振动信号,其指示其在早期故障检测和多种特征提取中的用途。 (c)2018年elestvier有限公司保留所有权利。

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