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Integrated ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection

机译：集成的整体噪声重构经验模式分解，用于机械故障检测

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摘要

A new branch of fault detection is utilizing the noise such as enhancing, adding or estimating the noise so as to improve the signal-to-noise ratio (SNR) and extract the fault signatures. Hereinto, ensemble noise-reconstructed empirical mode decomposition (ENEMD) is a novel noise utilization method to ameliorate the mode mixing and denoised the intrinsic mode functions (IMFs). Despite the possibility of superior performance in detecting weak and multiple faults, the method still suffers from the major problems of the user-defined parameter and the powerless capability for a high SNR case. Hence, integrated ensemble noise-reconstructed empirical mode decomposition is proposed to overcome the drawbacks, improved by two noise estimation techniques for different SNRs as well as the noise estimation strategy. Independent from the artificial setup, the noise estimation by the minimax thresholding is improved for a low SNR case, which especially shows an outstanding interpretation for signature enhancement. For approximating the weak noise precisely, the noise estimation by the local reconfiguration using singular value decomposition (SVD) is proposed for a high SNR case, which is particularly powerful for reducing the mode mixing. Thereinto, the sliding window for projecting the phase space is optimally designed by the correlation minimization. Meanwhile, the reasonable singular order for the local reconfiguration to estimate the noise is determined by the inflection point of the increment trend of normalized singular entropy. Furthermore, the noise estimation strategy, i.e. the selection approaches of the two estimation techniques along with the critical case, is developed and discussed for different SNRs by means of the possible noise-only IMF family. The method is validated by the repeatable simulations to demonstrate the synthetical performance and especially confirm the capability of noise estimation. Finally, the method is applied to detect the local wear fault from a dual-axis stabilized platform and the gear crack from an operating electric locomotive to verify its effectiveness and feasibility.
机译：故障检测的一个新分支是利用噪声，例如增强，添加或估计噪声，以提高信噪比（SNR）并提取故障特征。本文中，集成噪声重构经验模式分解（ENEMD）是一种新型的噪声利用方法，可以改善模式混合并去除固有模式函数（IMF）。尽管在检测弱故障和多重故障方面可能具有优异的性能，但该方法仍存在用户自定义参数和在高SNR情况下无能为力的主要问题。因此，提出了集成的集成噪声重构经验模式分解来克服该缺点，通过针对不同SNR的两种噪声估计技术以及噪声估计策略来改进该缺陷。独立于人工设置，针对低SNR的情况，改进了通过minimax阈值​​进行的噪声估计，这尤其显示了签名增强的出色解释。为了精确地近似微弱噪声，针对高SNR情况，提出了使用奇异值分解（SVD）通过局部重新配置进行噪声估计的方法，该方法对于减少模式混合特别有用。其中，通过最小化相关性来最佳地设计用于投影相空间的滑动窗口。同时，局部归一化估计噪声的合理奇异阶由归一化奇异熵增量趋势的拐点确定。此外，借助于可能的纯噪声IMF系列，针对不同的SNR，开发并讨论了噪声估计策略，即两种估计技术的选择方法以及临界情况。通过可重复的仿真验证了该方法的有效性，以证明其综合性能，尤其是证实了噪声估计的能力。最后，将该方法应用于双轴稳定平台的局部磨损故障检测以及运行中的电力机车的齿轮裂纹，以验证其有效性和可行性。

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