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Bearing damage assessment using Jensen-Renyi Divergence based on EEMD

机译：基于EEMD的Jensen-Renyi发散度评估轴承损坏

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摘要

An Ensemble Empirical Mode Decomposition (EEMD) and Jensen Rinyi divergence (JRD) based methodology is proposed for the degradation assessment of rolling element bearings using vibration data. The EEMD decomposes vibration signals into a set of intrinsic mode functions (IMFs). A systematic methodology to select IMFs that are sensitive and closely related to the fault is proposed in the paper. The change in probability distribution of the energies of the sensitive IMFs is measured through JRD which acts as a damage identification parameter. Evaluation of JRD with sensitive IMFs makes it largely unaffected by change/fluctuations in operating conditions. Further, an algorithm based on Chebyshev's inequality is applied to JRD to identify exact points of change in bearing health and remove outliers. The identified change points are investigated for fault classification as possible locations where specific defect initiation could have taken place. For fault classification, two new parameters are proposed: 'a value' and Probable Fault Index, which together classify the fault. To standardize the degradation process, a Confidence Value parameter is proposed to quantify the bearing degradation value in a range of zero to unity. A simulation study is first carried out to demonstrate the robustness of the proposed JRD parameter under variable operating conditions of load and speed. The proposed methodology is then validated on experimental data (seeded defect data and accelerated bearing life test data). The first validation on two different vibration datasets (inner/outer) obtained from seeded defect experiments demonstrate the effectiveness of JRD parameter in detecting a change in health state as the severity of fault changes. The second validation is on two accelerated life tests. The results demonstrate the proposed approach as a potential tool for bearing performance degradation assessment.
机译：提出了一种基于整体经验模态分解（EEMD）和詹森林尼散度（JRD）的方法，用于利用振动数据进行滚动轴承的退化评估。 EEMD将振动信号分解为一组固有模式函数（IMF）。本文提出了一种系统的方法，用于选择敏感且与故障密切相关的IMF。敏感IMF的能量概率分布的变化是通过JRD来测量的，JRD用作损伤识别参数。使用敏感的IMF对JRD进行评估使其在很大程度上不受操作条件变化/波动的影响。此外，将基于切比雪夫不等式的算法应用于JRD，以识别轴承健康状况的确切变化点并消除异常值。调查已识别的变更点，以进行故障分类，以作为可能发生特定缺陷的可能位置。对于故障分类，提出了两个新参数：“一个值”和可能的故障指数，它们一起对故障进行分类。为了使退化过程标准化，提出了置信度参数以量化轴承退化值（范围为零到1）。首先进行仿真研究，以证明所提出的JRD参数在负载和速度变化的工作条件下的鲁棒性。然后根据实验数据（种子缺陷数据和轴承寿命加速测试数据）对提出的方法进行验证。从种子缺陷实验获得的两个不同振动数据集（内部/外部）的首次验证表明，随着故障严重性的变化，JRD参数在检测健康状态变化方面的有效性。第二个验证是在两个加速寿命测试中。结果表明，该方法可作为轴承性能下降评估的潜在工具。

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