A novel Switching Unscented Kalman Filter method for remaining useful life prediction of rolling bearing

Cui, Lingli; Wang, Xin; Xu, Yonggang; Jiang, Hong; Zhou, Jianping

doi:10.1016/j.measurement.2018.12.028

Cited by 163 publications

(80 citation statements)

References 25 publications

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“…(1) When To = 248, Figure 9a,b show the relationship between M and MTEK, and the relationship between the filter length L and the TEK by iterative calculation respectively. It was found that when M = 3, the MTEK value was the largest from Figure 9a, and when L = 20, the TEK value was the largest from Figure 9b, so the optimal parameters To, Mo, and Lo were determined [248,3,20].…”

Section: Fault Type Fault Frequency Period Of Interest T* (* Is I O)mentioning

confidence: 99%

“…The operational status of the rolling element bearing often directly affects the performance of the whole machine. Consequently, the fault identification and diagnosis of rolling bearings are of great significance to ensure the safe and reliable operation of mechanical equipment [1][2][3]. Vibration signals caused by rolling bearing faults have been extensively studied and powerful diagnostic methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Cui

Yang

et al. 2019

Applied Sciences

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Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and achallenge is how to accurately separate the inner and outer race fault features from noisy compoundfaults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Qfactorsand improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, thecompound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonancecomponents of the signal (compound fault impact component and small amount of noise) are obtained,but it can only highlight the impact of compound faults, and failed to separate the inner and outerrace compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection ofparameters (the shift order M and the filter length L) based on the iterative calculation method withthe Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filteredand de-noised by the proposed method, the inner and outer race fault signals are obtained respectively.The fault characteristic frequency is consistent with the theoretical calculation value. The results showthat the proposed method can efficiently separate the mixed fault information and avoid the mutualinterference between the components of the compound fault.

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Section: Fault Type Fault Frequency Period Of Interest T* (* Is I O)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Cui

Yang

et al. 2019

Applied Sciences

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show abstract

“…Various dictionaries are utilized to gain the sparsest decomposition results of an original signal. MP has been used in many areas since it was proposed [13][14][15][16][17]. Nevertheless, the highly redundant dictionary must be selected beforehand, and the physical meaning of the obtained decomposition results are not explicit enough when MP is used; thus, the application range of MP is limited [3].…”

Section: Introductionmentioning

confidence: 99%

Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow‐Band Decomposition and MMC‐FCH

Cheng

Chen

Liu

et al. 2019

Shock and Vibration

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Adaptive sparsest narrow-band decomposition (ASNBD) method is proposed based on matching pursuit (MP) and empirical mode decomposition (EMD). ASNBD obtains the local narrow-band (LNB) components during the optimization process. Firstly, an optimal filter is designed. The parameter vector in the filter is obtained during optimization. The optimized objective function is a regulated singular local linear operator so that each obtained component is limited to be a LNB signal. Afterward, a component is generated by filtering the original signal with the optimized filter. Compared with MP, ASNBD is superior in both the physical meaning and the adaptivity. Drawbacks in EMD such as end effect and mode mixing are reduced in the proposed method because the application of interpolation function is not required. To achieve the fault diagnosis of roller bearings, raw signals are decomposed by ASNBD at first. Then, appropriate features of the decomposed results are chosen by applying distance evaluation technique (DET). Afterward, different faults are recognized by utilizing maximum margin classification based on flexible convex hulls (MMC-FCH). Comparisons between EMD and ASNBD show that the proposed method performs better in the antinoise performance, accuracy, orthogonality, and extracting the fault features of roller bearings.

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“…Gearbox is one of the most important components in mechanical industry and daily life. With constant development of modern industrial technology, fault monitoring of gearbox is more and more valued in research field [1][2][3][4][5][6][7][8][9][10]. As the key component of gearbox, the study of gear fault diagnosis method [11][12][13][14] is of great significance.…”

Section: Introductionmentioning

confidence: 99%

Improvement on Meshing Stiffness Algorithms of Gear with Peeling

et al. 2019

Self Cite

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This paper investigates the effect of a gear tooth peeling on meshing stiffness of involute gears. The tooth of the gear wheel is symmetric about the axis, and its symmetry will change after the gear spalling, and its meshing stiffness will also change during the meshing process. On this basis, an analytical model was developed, and based on the energy method a meshing stiffness algorithm for the complete meshing process of single gear teeth with peeling gears was proposed. According to the influence of the change of meshing point relative to the peeling position on the meshing stiffness, this algorithm calculates its stiffness separately. The influence of the peeling sizes on mesh stiffness is studied by simulation analysis. As a very important parameter, the study of gear mesh stiffness is of great significance to the monitoring of working conditions and the prevention of sudden failure of the gear box system.

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A novel Switching Unscented Kalman Filter method for remaining useful life prediction of rolling bearing

Cited by 163 publications

References 25 publications

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow‐Band Decomposition and MMC‐FCH

Improvement on Meshing Stiffness Algorithms of Gear with Peeling

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