一种MED最优滤波长度选择新方法及其应用

doi:10.7527/S1000-6893.2020.23658

Abstract

Abstract: Minimal entropy deconvolution (MED), an effective method widely used in the field of rotating machinery fault diagnosis, can extract weak fault impact components from noise. However, its effectiveness depends on the selection of filter length. Currently, no clear and effective method exists for the automatic selection of MED filter length, thus often requiring experience based human selection. Based on the MED algorithm and combining the autocorrelation function, a new method for MED optimal filter length selection is proposed. It constructs an energy target to measure the periodicity of the output signal and adaptively determine the filter length to improve the periodic pulse components in the weak fault signal, hence avoiding maximization of a single random pulse. The effectiveness of the method was verified in two cases:the rolling bearing test in the Cincinnati based test center; the fault test of an aero-engine rotor tester with casing simulating the case of the signal being far away from the rolling bearing. The results show that the proposed method can eliminate the effect of the transmission path and enhance the weak periodic impulses. Compared with those of the maximum correlated kurtosis deconvolution method, the diagnostic results are more advantageous.

Key words: minimal entropy deconvolution (MED), filter length, rolling bearing, weak fault, fault detection

CLC Number:

V263.6

HE Zhiyuan, CHEN Guo, HE Chao, TENG Chunyu. MED optimal filter length selection: New method and applications[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(10): 423658-423658.

References

[1] RANDALL R B, ANTONI J. Rolling element bearing diagnostics-A tutorial[J].Mechanical Systems and Signal Processing, 2011, 25(2):485-520.
[2] WIGGINS R A. Minimum entropy deconvolution[J].Geoexploration, 1978, 16(1-2):21-35.
[3] SAWALHI N, RANDALL R B, ENDO H. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis[J].Mechanical Systems and Signal Processing, 2007, 21(6):2616-2633.
[4] 王宏超, 陈进, 董广明. 基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取[J].机械工程学报, 2013, 49(1):88-94. WANG H C, CHEN J, DONG G M. Fault diagnosis method for rolling bearing's weak fault based on minimum entropy deconvolution and sparse decomposition[J].Journal of Mechanical Engineering, 2013, 49(1):88-94(in Chinese).
[5] 陈海周, 王家序, 汤宝平, 等. 基于最小熵解卷积和Teager能量算子直升机滚动轴承复合故障诊断研究[J].振动与冲击, 2017, 36(9):45-50, 73. CHEN H Z, WANG J X, TANG B P, et al. Helicopter rolling bearing hybrid faults diagnosis using minimum entropy deconvolution and Teager energy operator[J].Journal of Vibration and Shock, 2017, 36(9):45-50, 73(in Chinese).
[6] 林桐, 陈果, 滕春禹, 等. 基于机匣振动信号的滚动轴承故障协同诊断技术[J].航空动力学报, 2018, 33(10):2376-2384. LIN T, CHEN G, TENG C Y, et al. Rolling bearing collaborative fault diagnosis technology for casing vibration signal[J].Journal of Aerospace Power, 2018, 33(10):2376-2384(in Chinese).
[7] 姚成玉, 来博文, 陈东宁, 等. 基于最小熵解卷积-变分模态分解和优化支持向量机的滚动轴承故障诊断方法[J].中国机械工程, 2017, 28(24):3001-3012. YAO C Y, LAI B W, CHEN D N, et al. Fault diagnosis method based on MED-VMD and optimized SVM for rolling bearings[J].China Mechanical Engineering, 2017, 28(24):3001-3012(in Chinese).
[8] 崔伟成, 张征. 基于局部特征尺度分解与最小熵解卷积的轴承故障诊断[J].轴承, 2018(5):51-55. CUI W C, ZHANG Z. Fault diagnosis for bearings based on LCD-MED[J].Bearing, 2018(5):51-55(in Chinese).
[9] 郭家宇, 熊炘, 刘浩. 基于VMD和MED的滚动轴承故障诊断方法[J].轴承, 2018(6):50-54. GUO J Y, XIONG X, LIU H. Fault diagnosis method for rolling bearings based on VMD and MED[J].Bearing, 2018(6):50-54(in Chinese).
[10] BSRSZCZ T, SAWALHI N. Fault detection enhancement in rolling element bearings using the minimum entropy deconvolution[J].Archives of Acoustics, 2012, 37(2):131-141.
[11] CHENG Y, ZHOU N, ZHANG W H, et al. Application of an improved minimum entropy deconvolution method for railway rolling element bearing fault diagnosis[J].Journal of Sound and Vibration, 2018, 425:53-69.
[12] MIAO Y H, ZHAO M, LIN J, et al. Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings[J].Mechanical Systems and Signal Processing, 2017, 92:173-195.
[13] ABBOUD D, ELBADAOUI M, SMITH W A, et al. Advanced bearing diagnostics:A comparative study of two powerful approaches[J].Mechanical Systems and Signal Processing, 2019, 114:604-627.
[14] CHENG Y, WANG Z W, ZHANG W H, et al. Particle swarm optimization algorithm to solve the deconvolution problem for rolling element bearing fault diagnosis[J].ISA Transactions, 2019, 90:244-267.
[15] JIANG X X, XU C, SHI J J, et al. A new l₀-norm embedded MED method for roller element bearing fault diagnosis at early stage of damage[J].Measurement, 2018, 127:414-424.
[16] ZHANG L, HU N Q. Fault diagnosis of sun gear based on continuous vibration separation and minimum entropy deconvolution[J].Measurement, 2019, 141:332-344.
[17] WANG S H, XIANG J W, TANG H S, et al. Minimum entropy deconvolution based on simulation-determined band pass filter to detect faults in axial piston pump bearings[J].ISA Transactions, 2019, 88:186-198.
[18] QIU H, LEE J, LIN J, et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J].Journal of Sound and Vibration, 2006, 289(4-5):1066-1090.
[19] MCDONALD G L, ZHAO Q, ZUO M J. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection[J].Mechanical Systems and Signal Processing, 2012, 33:237-255.
[20] 宿磊, 黄海润, 李可, 等. 基于LCD-MCKD的滚动轴承故障特征提取方法[J].华中科技大学学报(自然科学版), 2019, 47(9):19-24. SU L, HUANG H R, LI K, et al. Feature extraction of fault rolling bearings based on LCD-MCKD[J].Journal of Huazhong University of Science and Technology (Natural Science Edition), 2019, 47(9):19-24(in Chinese).
[21] 李政, 张炜, 明安波, 等. 基于IEWT和MCKD的滚动轴承故障诊断方法[J]. 机械工程学报,2019,55(23):136-146. LI Z, ZHANG W, MING A B, et al. A novel fault diagnosis method based on Improved Empirical Wavelet Trans-form and Maximum Correlated Kurtosis Deconvolution for rolling element bearing[J]. Journal of Mechanical Engineering, 2019,55(23):136-146(in Chinese).

MED optimal filter length selection: New method and applications

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