Composite fault diagnosis method of rolling bearing based on consistent optimization index

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (9) : 237-245.

ZHANG Long1, CAI Binghuan1, XIONG Guoliang1, HU Junfeng2

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Abstract

Cycle impact caused by rolling bearing local fault transmitted from bearing to sensor is affected by transmission path, environmental noise and accidental impact interference, which makes fault feature extraction difficult and diagnosis effect poor.Here, a composite fault diagnosis method of rolling bearing based on the maximum correlation kurtosis deconvolution and adjustable quality factor wavelet transform was proposed.The former was used to weaken the influence of transfer path, while the latter was used to suppress noise components with filtering, and their parameter optimizations consistently took the correlated kurtosis, which could consider characteristics of rolling bearing fault impact cycle, as the optimization index to ensure the overall effect of feature extraction.Meanwhile, this index could not be affected by accidental impact interference.Simulated and test signals were analyzed using the proposed method, and the results were compared to those using the common methods, such as, fast spectral kurtosis to verify the effectiveness and superiority of the proposed method in rolling bearing fault diagnosis.

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fault diagnosis / correlated kurtosis / wavelet transform / feature enhancement

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ZHANG Long1, CAI Binghuan1, XIONG Guoliang1, HU Junfeng2. Composite fault diagnosis method of rolling bearing based on consistent optimization index[J]. Journal of Vibration and Shock, 2021, 40(9): 237-245

References

［1］RAI A, UPADHYAY S H.A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings［J］.Tribology International, 2016, 9(6): 289-306.
［2］PEETERS C, GUILLAUME P, HELSEN J.A comparison of cepstral editing methods as signal pre-processing techniques for vibration-based bearing fault detection［J］.Mechanical Systems & Signal Processing, 2017,9(1): 354-381.
［3］BARSZCZ T, SAWALHI N.Fault detection enhancement in rolling element bearings using the minimum entropy deconvolution［J］.Archives of acoustics, 2012,37(2): 131-141.
［4］JIANG R, CHEN J, DONG G, et al.The weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelop spectrum［J］.Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2013, 227(5): 1116-1129.
［5］RICCI R, BORGHESANI P, CHATTERTON S, et al.The combination of empirical mode decomposition and minimum entropy deconvolution for roller bearing diagnostics in non-stationary operation［J］.in ASME 2012-International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE, 2012,67(8):156-173.
［6］WANG H, CHEN J, DONG G.Fault diagnosis method for rolling bearing’s weak fault based on minimum entropy deconvolution and sparse decomposition［J］.Journal of Mechanical Engineering, 2013, 1(6): 1-14.
［7］胡爱军，赵军，孙尚飞,等.基于谱峭度和最大相关峭度解卷积的滚动轴承复合故障特征分离方法［J］.振动与冲击, 2019, 38(4): 158-165.
HU Aijun，ZHAO Jun, SUN Shangfei, et al.A compound fault features separation method of rolling bearing based on spectral kurtosis combined with maximum correlated kurtosis deconvolution［J］.Journal of Vibration and Shock，2019, 38(4): 158-165.
［8］张龙，毛志德，熊国良,等.基于包络谱带通峭度的改进谱峭度方法及在轴承诊断中的应用［J］.振动与冲击, 2018, 37(23): 171-179.
ZHANG Long, MAO Zhide, XIONG Guoliang, et al.An improved Kurtogram based on band-pass envelope spectral Kurtosis with its application in bearing fault diagnosis［J］.Journal of Vibration and Shock, 2018, 37(23): 171-179.
［9］ANTONI J.Fast computation of the kurtogram for the detection of transient faults［J］.Mechanical Systems and Signal Processing, 2007, 21(1): 108-124.
［10］ZHANG X, KANG J, ZHAO J, et al.Rolling element bearings fault diagnosis based on correlated kurtosis kurtogram［J］.Journal of Vibroengineering, 2015, 17(6):33-46.
［11］ZHANG X, KANG J, XIAO L, et al.A new improved kurtogram and its application to bearing fault diagnosis［J］.Shock and Vibration, 2015, 20(15):87-99.
［12］LI Y, LIANG X, XU M, et al.Early fault feature extraction of rolling bearing based on ICD and tunable Q-factor wavelet transform［J］.Mechanical Systems and Signal Processing, 2017, 8(6): 204-223.
［13］SU W, WANG F, ZHANG Z, et al.Application of EMD denoising and spectral kurtosis in early fault diagnosis of rolling element bearings［J］.
Shock and Vibration, 2010, 29(3): 18-21.
［14］SHANG Z, RUI G, CHENG L, et al.Rolling bearing fault diagnosis method based on MOMEDA and IEWT［J］.International Journal of Information and Management Sciences, 2018, 29(4): 345-363.
［15］MA P, ZHANG H L, FAN W H, et al.Early fault diagnosis of bearing based on frequency band extraction and improved tunable Q-factor wavelet transform［J］.Measurement, 2019, 13(7): 189-202.
［16］MIAO Y, 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 & Signal Processing, 2017, 9(2): 173-195.
［17］MCDONALD G L, ZHAO Q, ZUO M J, et al.Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection［J］.Mechanical Systems & Signal Processing, 2012, 3(3): 237-255.
［18］CAI W, YANG Z, WANG Z, et al.A new compound fault feature extraction method based on multipoint kurtosis and variational mode decomposition［J］.Entropy, 2018, 20(7): 5-21.
［19］FENG J, JIA F, LEI Y G, et al.Early fault diagnosis of bearings using an improved spectral kurtosis by maximum correlated kurtosis deconvolution［J］.Sensors, 2015, 15(11): 29363-29377.
［20］SELESNICK I W.Wavelet transform with tunable Q-factor［J］.IEEE Transactions on Signal Processing, 2011, 59(8): 3560-3575.
［21］SELESNICK I W.Resonance-based signal decomposition: a new sparsity-enabled signal analysis method［J］.Signal Processing, 2011, 91(12): 2793-2809.
［22］ZHANG C, LI B, CHEN B, et al.Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform［J］.Mechanical Systems and Signal Processing, 2015, 6(4): 162-187.
［23］WANG H, CHEN J, DONG G.Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform［J］.Mechanical Systems and Signal Processing, 2014, 48(1/2): 103-119.