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| A novel energy difference singular value ratio spectrum and its application to wheelset bearing fault diagnosis |
| HUANG Chenguang，LIN Jianhui，DING Jianming，LIU Zechao |
| State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China |
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Abstract Due to random slip of rolling elements and the cage, bearing fault signals are mostly pseudo-cyclostationary.A wheelset bearing fault diagnosis method based on energy difference singular value ratio spectrum was proposed to deal with this specific kind of signal.The singular values distribution of the bearing fault pseudo-cyclostationary signal was studied in this article; then an energy difference singular value ratio spectrum, an embedding dimension calculation method for periodic segment matrix, was proposed by combining the concepts of singular value energy difference and singular value ratio.The embedding dimension was determined by energy difference singular value ratio spectrum and the wheelset bearing vibration signal was reconstructed to a periodic segment matrix.The singular value decomposition was then performed on the matrix and the energy difference spectrum was applied to determine the effective rank order of singular value.The final stage of fault diagnosis for wheelset bearings was conducted by recovering the periodic signal and employing corresponding envelope analysis.The proposed method was verified by the vibration data of a compound-fault bearing collected from a wheelset test rig.The results show that the proposed method can effectively extract the characteristic frequencies and their harmonics of the bearing outer race, the rolling element and the cage.Comparing with the traditional Hankel-based SVD method, the proposed method has better interference robustness and performance in multi-fault signal, reveals clearer and more frequencies and corresponding harmonics in envelope analysis, which demonstrates that the reliability of fault diagnosis has been significantly improved.
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Received: 08 August 2018
Published: 15 February 2020
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[1] Ding J. Fault detection of a wheelset bearing in a high-speed train using the shock-response convolutional sparse-coding technique[J]. Measurement, 2018, 117:108-124.
[2] Duan Z, Wu T, Guo S, et al. Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review[J]. International Journal of Advanced Manufacturing Technology, 2018, 96(1-4):803-819.
[3] Kan M S, Tan A C C, Mathew J. A review on prognostic techniques for non-stationary and non-linear rotating systems[J]. Mechanical Systems & Signal Processing, 2015, 62-63:1-20.
[4] 张锐戈. 滚动轴承振动信号非平稳、非高斯分析及故障诊断研究[D]. 西安电子科技大学, 2014.
Zhang R. Vibration Signal Non-Station, Non-Gaussian Analysis and Fault Diagnosis Based on Rolling Element Bearings[D]. XIDIAN UNIVERSITY, 2014.
[5] Hu A, Lin J, Sun S, et al. A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing[J]. Shock & Vibration, 2017, 2017:1-11.
[6] 吕靖香, 余建波. 基于多层混合滤噪的轴承早期弱故障特征提取方法[J]. 振动与冲击, 2018(8).
Lu J, Yu J. Early fault diagnosis of bearings based on multilayer hybrid de-noising[J]. Journal of Vibration and Shock, 2018(8).
[7] Jiang H, Chen J, Dong G, et al. Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis[J]. Mechanical Systems & Signal Processing, 2015, 52-53(1):338-359.
[8] 何刘, 林建辉, 刘新厂,等. 万向轴动不平衡检测的改进 DTCWT-SVD 方法[J]. 振动与冲击, 2016, 35(22):142-151.
He Liu, Lin Jianhui, Liu Xinchang, et al. Detection of the dynamic imbalance of cardan shaft by applying an improved DTCWT-SVD[J]. Journal of Vibration and Shock, 2016, 35(22):142-151.
[9] 曾鸣, 杨宇, 郑近德,等. -SVD降噪算法及其在齿轮故障诊断中的应用[J]. 机械工程学报, 2015, 51(3):95-103.
Zeng Ming, Yang Yu, Zheng Jinde, et al. -SVD based denoising method and its application to gear fault diagnosis[J]. Journal of Mechanical Engineering, 2015, 51(3):95-103.
[10] Zhao X, Ye B. Selection of effective singular values using difference spectrum and its application to fault diagnosis of headstock[J]. Mechanical Systems & Signal Processing, 2011, 25(5):1617-1631.
[11] 崔伟成, 许爱强, 李伟,等. 基于拟合误差最小化原则的奇异值分解降噪有效秩阶次确定方法[J]. 振动与冲击, 2017, 36(3):132-137.
Cui Weicheng, Xu Aiqiang, Li Wei, et al. A new method for determining effective rank order of singular value decomposition denoising based on fitting error minimum principle[J]. Journal of Vibration and Shock, 2017, 36(3):132-137.
[12] Kanjilal P P, Palit S. The singular value decomposition — Applied in the modelling and prediction of quasiperiodic processes[J]. Signal Processing, 1994, 35(3):257-267.
[13] Mcfadden P D, Smith J D. Model for the vibration produced by a single point defect in a rolling element bearing[J]. Journal of Sound Vibration, 1984, 96(1):69-82.
[14] Kanjilal P P, Palit S. On multiple pattern extraction using singular value decomposition[J]. Signal Processing IEEE Transactions on, 1995, 43(6):1536-1540.
[15] Antoni J, Randall R B. A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings With Localized Faults[J]. Journal of Vibration & Acoustics, 2003, 125(3):282-289.
[16] J. Gilles. Empirical Wavelet Transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16):3999-4010.
[17] J. Gilles, K. Heal. A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentation[J]. International Journal of Wavelets, Multiresolution and Information Processing, 2014, 14(6): 1-17.
[18] 戈卢布 G H, 范洛恩 C F. 矩阵计算[M]. 袁亚湘, 译. 北京: 科学出版社, 2001.
[19] Hongxing Liu, Jian Li, Ying Zhao, et al. Improved Singular Value Decomposition technique for detecting and extracting periodic impulse component in a vibration signal[J]. Chinese Journal of Mechanical Engineering, 2004, 17(3): 340-345.
[20] Wang Dong. Some further thoughts about spectral kurtosis, spectral L2/L1 norm, spectral smoothness index and spectral Gini index for characterizing repetitive transients[J]. Mechanical Systems & Signal Processing, 2018, 108:360-368.
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