Axle-Box Bearing Fault Diagnosis Based on Multiband Weighted Envelope Spectrum
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摘要:
为增强复杂噪声干扰下轴箱轴承故障检测的鲁棒性,基于循环谱分析并考虑轴承故障信息分布差异和阈值降噪,对轴箱轴承故障诊断的包络谱构造方法进行了研究. 首先,提出频域信噪比作为轴承故障信息量化的新测度,用于评估谱相干中不同谱频带内的轴承故障相关信息;其次,构造以谱频率为变量的故障特征信息分布函数,并自适应确定信息阈值来辨识谱相干中故障信息丰富和干扰噪声主导的谱频率分量,进一步基于故障特征信息分布函数和信息阈值设计权重函数;最后,由谱相干和权重函数生成融合多带信息的多带加权包络谱,通过分析谱中的轴承故障特征频率来检测轴箱轴承的不同故障. 铁路轴箱轴承实验数据的分析结果表明:相比于基于谱相干的典型包络谱方法,多带加权包络谱能够在复杂噪声干扰下准确识别轴箱轴承的外圈、滚动体和内圈故障,并能取得更高的性能量化指标(频域信噪比和负熵).
Abstract:To enhance the robustness of axle-box bearing fault detection under complex interference noise, the envelope spectrum construction method for axle-box bearing fault diagnosis is investigated by cyclic spectral analysis and considering the distribution difference of bearing fault information and threshold denoising. Firstly, the frequency domain signal-to-noise ratio is proposed as a new measure for bearing fault information quantification to evaluate the fault-related information in different spectral frequency bands of the spectral coherence. Secondly, a fault characteristic information distribution function with spectral frequency as the variable is constructed and an information threshold is adaptively determined to identify the spectral frequency components that are rich in fault information and dominated by interference noise in the spectral coherence; further, a weight function based on the fault characteristic information distribution function and the information threshold is designed. Finally, a multiband weighted envelope spectrum is generated from the spectral coherence with the weight function and is used to detect different axle-box bearing faults by analyzing the bearing fault characteristic frequencies. The analysis results of the experimental data of railway axle-box bearings show that compared with typical spectral coherence-based envelope spectrum methods, the multiband weighted envelope spectrum can accurately detect the faults of the outer race, rolling element and inner race of the axle-box bearing under complex interference noise and can achieve higher performance quantification indicators (frequency domain signal-to-noise ratio and negentropy).
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表 1 不同轴承实验信号包络谱的FDSNR
Table 1. FDSNR of envelope spectra of different bearing experimental signals
故障类型 EES MWES CIES WES 外圈故障 1.912 4.775 3.107 2.384 滚动体故障 1.966 3.604 2.860 3.059 内圈故障 1.507 2.949 1.515 1.714 表 2 不同轴承实验信号包络谱的负熵
Table 2. Negentropy of envelope spectra of different bearing experimental signals
故障类型 EES MWES CIES WES 外圈故障 0.040 0.136 0.068 0.046 滚动体故障 0.059 0.176 0.104 0.116 内圈故障 0.062 0.201 0.064 0.072 -
[1] 刘志亮,潘登,左明健,等. 轨道车辆故障诊断研究进展[J]. 机械工程学报,2016,52(14): 134-146. doi: 10.3901/JME.2016.14.134LIU Zhiliang, PAN Deng, ZUO Mingjian, et al. A review on fault diagnosis for rail vehicles[J]. Journal of Mechanical Engineering, 2016, 52(14): 134-146. doi: 10.3901/JME.2016.14.134 [2] 王曦,侯宇,孙守光,等. 高速列车轴承可靠性评估关键力学参量研究进展[J]. 力学学报,2021,53(1): 19-34.WANG Xi, HOU Yu, SUN Shouguang, et al. Advances in key mechanical parameters for reliability assessment of high-speed train bearings[J]. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(1): 19-34. [3] 张卫华,李权福,宋冬利. 关于铁路机车车辆健康管理与状态修的思考[J]. 中国机械工程,2021,32(4): 379-389.ZHANG Weihua, LI Quanfu, SONG Dongli. Thoughts on health management and condition-based maintenance of rolling stocks[J]. China Mechanical Engineering, 2021, 32(4): 379-389. [4] LEI Y G, LI N P, GUO L, et al. Machinery health prognostics: a systematic review from data acquisition to RUL prediction[J]. Mechanical Systems and Signal Processing, 2018, 104: 799-834. doi: 10.1016/j.ymssp.2017.11.016 [5] 王志伟. 服役环境下高速列车齿轮及轴承系统动力学建模及耦合振动分析[D]. 成都: 西南交通大学, 2019. [6] ANTONI J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing, 2007, 21(1): 108-124. [7] SMITH W A, BORGHESANI P, NI Q, et al. Optimal demodulation-band selection for envelope-based diagnostics: a comparative study of traditional and novel tools[J]. Mechanical Systems and Signal Processing, 2019, 134: 106303.1-106303.24. [8] ANTONI J. Cyclic spectral analysis of rolling-element bearing signals: facts and fictions[J]. Journal of Sound and Vibration, 2007, 304(3/4/5): 497-529. [9] RANDALL R B, ANTONI J, CHOBSAARD S. The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals[J]. Mechanical Systems and Signal Processing, 2001, 15(5): 945-962. doi: 10.1006/mssp.2001.1415 [10] ANTONI J, XIN G, HAMZAOUI N. Fast computation of the spectral correlation[J]. Mechanical Systems and Signal Processing, 2017, 92: 248-277. doi: 10.1016/j.ymssp.2017.01.011 [11] WANG D, ZHAO X J, KOU L L, et al. A simple and fast guideline for generating enhanced/squared envelope spectra from spectral coherence for bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2019, 122: 754-768. doi: 10.1016/j.ymssp.2018.12.055 [12] MAURICIO A, SMITH W A, RANDALL R B, et al. Improved envelope spectrum via feature optimisation-gram (IESFOgram): a novel tool for rolling element bearing diagnostics under non-stationary operating conditions[J]. Mechanical Systems and Signal Processing, 2020, 144: 106891.1-106891.14. doi: 10.1016/j.ymssp.2020.106891 [13] CHEN B Y, CHENG Y, ZHANG W H, et al. Optimal frequency band selection using blind and targeted features for spectral coherence-based bearing diagnostics: a comparative study[J]. ISA Transactions, 2022, 127: 395-414. doi: 10.1016/j.isatra.2021.08.025 [14] MAURICIO A, GRYLLIAS K. Cyclostationary-based multiband envelope spectra extraction for bearing diagnostics: the combined improved envelope spectrum[J]. Mechanical Systems and Signal Processing, 2021, 149: 107150.1-107150.13. [15] ZHANG B Y, MIAO Y H, LIN J, et al. Weighted envelope spectrum based on the spectral coherence for bearing diagnosis[J]. ISA Transactions, 2022, 123: 398-412. doi: 10.1016/j.isatra.2021.05.012 [16] DUAN J, SHI T L, DUAN J, et al. A narrowband envelope spectra fusion method for fault diagnosis of rolling element bearings[J]. Measurement Science and Technology, 2018, 29(12): 125106.1-125106.16. [17] ANTONI J. The infogram: Entropic evidence of the signature of repetitive transients[J]. Mechanical Systems and Signal Processing, 2016, 74: 73-94. doi: 10.1016/j.ymssp.2015.04.034