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基于自适应阶次双谱切片和故障特征能量比分析的行星齿轮箱故障诊断

Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis.

作者信息

Shen Zhaoyang, Shi Zhanqun, Zhen Dong, Zhang Hao, Gu Fengshou

机构信息

Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China.

Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK.

出版信息

Sensors (Basel). 2020 Apr 24;20(8):2433. doi: 10.3390/s20082433.

Abstract

The vibration of a planetary gearbox (PG) is complex and mutually modulated, which makes the weak features of incipient fault difficult to detect. To target this problem, a novel method, based on an adaptive order bispectrum slice (AOBS) and the fault characteristics energy ratio (FCER), is proposed. The order bispectrum (OB) method has shown its effectiveness in the feature extraction of bearings and fixed-shaft gearboxes. However, the effectiveness of the PG still needs to be explored. The FCER is developed to sum up the fault information, which is scattered by mutual modulation. In this method, the raw vibration signal is firstly converted to that in the angle domain. Secondly, the characteristic slice of AOBS is extracted. Different from the conventional OB method, the AOBS is extracted by searching for a characteristic carrier frequency adaptively in the sensitive range of signal coupling. Finally, the FCER is summed up and calculated from the fault features that were dispersed in the characteristic slice. Experimental data was processed, using both the AOBS-FCER method, and the method that combines order spectrum analysis with sideband energy ratio (OSA-SER), respectively. Results indicated that the new method is effective in incipient fault feature extraction, compared with the methods of OB and OSA-SER.

摘要

行星齿轮箱(PG)的振动复杂且相互调制,这使得早期故障的微弱特征难以检测。针对这一问题,提出了一种基于自适应阶次双谱切片(AOBS)和故障特征能量比(FCER)的新方法。阶次双谱(OB)方法在轴承和定轴齿轮箱的特征提取中已显示出有效性。然而,PG的有效性仍有待探索。开发FCER以汇总因相互调制而分散的故障信息。在该方法中,首先将原始振动信号转换到角度域。其次,提取AOBS的特征切片。与传统的OB方法不同,AOBS是通过在信号耦合的敏感范围内自适应搜索特征载波频率来提取的。最后,根据分散在特征切片中的故障特征汇总并计算FCER。分别使用AOBS-FCER方法以及将阶次谱分析与边带能量比相结合的方法(OSA-SER)对实验数据进行处理。结果表明,与OB和OSA-SER方法相比,新方法在早期故障特征提取方面是有效的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac0d/7219498/0426309b564b/sensors-20-02433-g001.jpg

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