Application of weighted contribution rate of nonlinear output frequency response functions to rotor rub-impact

Liu, Y.; Zhao, Yulai; Li, J.T.; Ma, Hui; Yang, Qiang; Yan, Xiaolu

doi:10.1016/j.ymssp.2019.106518

Cited by 86 publications

(43 citation statements)

References 21 publications

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“…In this part, the crack stiffness model is established by the neutral axis theory. [34][35][36] When the shaft is subject to stress concentration, especially in the position where the cross-section suddenly changes or material defects exist, the fatigue cracks will propagate faster on both sides of the crack edge under long-term action of the alternating load. As a result, the area of the crack element section will become an annular one.…”

Section: The Modeling Of the Cracked Rotor Systemmentioning

confidence: 99%

Dynamical analysis of hollow-shaft dual-rotor system with circular cracks

Yang

Wang

et al. 2020

Journal of Low Frequency Noise, Vibration and Active Control

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In this paper, we considered a dual-rotor system with crack in shaft. The influence of circular crack in hollow shaft on dynamical response was studied. The equations of motion of 12 elements dual-rotor system model were derived. Harmonic balance method was employed to solve the equations. The critical speed and sub-critical speed responses were investigated. It was found that the circular crack in hollow shaft had greater influence on the first-backward critical speed than the first-forward critical speed. Owing to the influence of crack, the vibration peaks occurred at the 1/2, 1/3 and 1/4 critical speeds of the rotor system, along with a reduction in sub-critical speeds and critical speeds. The deeper crack away from the bearing affected the rotor more significantly. The whirling orbits, the time-domain responses and the spectra were obtained to show the super-harmonic resonance phenomenon in hollow-shaft cracked rotor system.

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Section: The Modeling Of the Cracked Rotor Systemmentioning

confidence: 99%

Dynamical analysis of hollow-shaft dual-rotor system with circular cracks

Yang

Wang

et al. 2020

Journal of Low Frequency Noise, Vibration and Active Control

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“…However, Deep PCA is a linear stationary transformation method, when it is used for a monitoring non-linear time-varying system, its performance is greatly reduced since it does not fully take account of the characteristics of the actual system. In order to solve this problem, many non-linear time-varying fault detection methods have been proposed [27][28][29][30]. Schölkopf found that the Kernel Principal Component Analysis (KPCA) was a common nonlinear principal component analysis method when researching support vector classification algorithms [31].…”

Section: Introductionmentioning

confidence: 99%

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

et al. 2020

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The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.

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“…The model-based method is one of the early fault detection methods for gearboxes, which requires accurate prior physical models of gears [12]- [15]. However, the motions and internal forces in the gearbox are complicated, which reduces the model accuracy and extends the calculation time.…”

Section: Introductionmentioning

confidence: 99%

Gearbox Incipient Fault Detection Based on Deep Recursive Dynamic Principal Component Analysis

et al. 2020

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As a part of the energy transmission chain, gearboxes are considered as important components in rotating machines, and the gearbox failure results in costly economic losses. Therefore, it is necessary to detect the appearance of incipient gearbox faults by implementing an appropriate detected model. The incipient failure characteristics of the gearbox are weak and hidden in a set of time-varying series signals the vibration signals, which is difficult to effectively extract under the background of strong noise. The PCA method is not effective in detecting weak fault features in time-varying signals, so this paper proposes a method based on Deep Recursive Dynamic Principal Component Analysis (Deep RDPCA) to detect incipient faults in gearboxes. The proposed approach is modeled via both the deep decomposed theorems and time-varying dynamic model based on traditional PCA to extract characteristic of time-varying and weak fault information under the background of strong noise. The proposed method could get a better real-time reflection for changed system by introducing ''Moving Window'' technologies, so that the incipient fault of gearbox could be detected accurately, too. Finally, the effect of Deep RDPCA-based fault diagnosis is compared with the results of PCA, DPCA, RDPCA, Deep PCA, and Deep DPCA methods. It is concluded that the proposed method can effectively capture the time-varying relationship of process variables and accurately extract the weak fault characteristics in the vibration signal, which effectively improves the fault detection performance. INDEX TERMS Gearbox, fault diagnosis, gear failure experiment, feature extraction, Deep RDPCA.

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Application of weighted contribution rate of nonlinear output frequency response functions to rotor rub-impact

Cited by 86 publications

References 21 publications

Dynamical analysis of hollow-shaft dual-rotor system with circular cracks

Dynamical analysis of hollow-shaft dual-rotor system with circular cracks

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

Gearbox Incipient Fault Detection Based on Deep Recursive Dynamic Principal Component Analysis

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