Feature Analysis Mechanical Fault Signals Based on Correlation Dimension and Complexity

Bingcheng Wang, Z. Ren
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Abstract

In connection with the nonlinear dynamic characteristics shown from the performance of fault rotating mechanical system, based on the research and analysis, correlation dimension and complexity can be used to characterize the system state of motion. The authors propose the analysis method of correlation dimension and complexity to the signal feature of mechanical fault. Using theory of phase space reconstruction, simulating fault signal of rotating machine is reconstructed. In order to reconstruct the phase space which can be adequately reflect the movement characteristics of the system, the time delay and embedding dimension are discussed emphatically, on this basis, the correlation dimension are calculated. From the analysis and calculation on simulation of different fault signals, it shows that under different rotating machinery fault conditions, its correlation dimension, and complexity are significantly different, which verifies that the these nonlinear feature quantities are effective parameters for fault information and they are excellent parameters in terms of extraction and recognition of fault feature. Studies have shown that, these nonlinear feature quantities can reflect the nonlinearity of the system. If combine these parameters, supplemented mutually, verifies mutually, it will be more conducive to recognize and analyze fault signal recognition, enhance the reliability, and thus to study the fault diagnosis of complexity rotating machinery in a more effective way.
基于关联维数和复杂度的机械故障信号特征分析
针对故障旋转机械系统表现出的非线性动态特征,在研究分析的基础上,可以利用相关维数和复杂度来表征系统的运动状态。提出了机械故障信号特征的相关维数和复杂度分析方法。利用相空间重构理论,对旋转机械的模拟故障信号进行了重构。为了重建能充分反映系统运动特征的相空间,重点讨论了时间延迟和嵌入维数,在此基础上计算了相关维数。通过对不同故障信号的仿真分析和计算,表明在不同的旋转机械故障条件下,其相关维数和复杂度存在显著差异,验证了这些非线性特征量是获取故障信息的有效参数,是提取和识别故障特征的优良参数。研究表明,这些非线性特征量可以反映系统的非线性。如果将这些参数结合起来,相互补充,相互验证,将更有利于对故障信号的识别和分析,提高可靠性,从而更有效地研究复杂旋转机械的故障诊断。
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