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Bearing fault detection and recognition methodology based on weighted multiscale entropy approach

机译：基于加权多尺度熵方法的轴承故障检测与识别方法

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摘要

In the present study, a new bearing fault detection and recognition methodology is proposed based on complementary ensemble empirical mode decomposition method (CEEMD) and a newly developed weighted multiscale entropy method. The need for this methodology is felt due to the inability of the existing multiscale entropy methods in correctly identifying the nature of the signal, particularly in the initial scales. The implication of this drawback is strongly perceived in the experimental analysis in the present work. Vibration signals acquired from test machines/working machines have a substantial presence of noise which severely affects the consistency and reliability of the extracted features. Therefore, for effective implementation and comparing the efficiency of the proposed methods, the original signal is firstly processed with CEEMD. The processing of the signal includes its decomposition into several modes thereafter reconstructing a new signal from the modes chosen through Hurst exponent threshold analysis. From the reconstructed signals, the faulty feature vectors are extracted by the weighted multiscale entropy methods. The capabilities of the proposed method are intensively tested through simulation and experimental analysis. From the analysis of simulated signals, it is demonstrated that the drawback prevailing in the established entropy methods have strongly been mitigated by the newly developed weighted entropy methods. On the experimental front, an impressive improvement is observed by the proposed methods both qualitatively (in indicating the faulty system from the normal system) and quantitatively (in recognizing the fault type and severity by the support vector machine classifier). Apart from the analysis of vibration signals, the versatility of the proposed method is also verified on the acoustic signals acquired under similar experimental conditions.
机译：在本研究中，提出了一种基于互补集合经验模型分解方法（CEEMD）和新开发的加权多尺度熵方法的新轴承故障检测和识别方法。由于现有的多尺度熵方法无法正确识别信号的性质，特别是在初始尺度中，因此感受到对该方法的需求。在本作实验分析中，强烈地认为该缺点的含义在实验分析中受到强烈地感知。从测试机器/工作机器获取的振动信号具有大量存在噪声，其严重影响提取特征的一致性和可靠性。因此，为了有效实现和比较所提出的方法的效率，首先用CeEMD处理原始信号。该信号的处理包括其分解成几种模式，此后从通过HUSST指数阈值分析所选择的模式重建新信号。从重建信号中，故障特征向量由加权多尺度熵方法提取。通过模拟和实验分析强烈地测试所提出的方法的能力。从模拟信号的分析中，证明了新开发的加权熵方法强烈减轻了建立的熵方法中的缺点。在实验前，通过定性（指示来自正常系统的故障系统）和定量（通过支持向量机分类器的故障类型和严重性）来观察到令人印象深刻的改进。除了振动信号的分析外，还在在类似实验条件下获取的声学信号上验证了所提出的方法的多功能性。

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