The resonance effect induced by the variable compliance vibration for an elastic rotor supported by roller bearings

Yi, Jun; Liu, Heng; Martínek, Radek; Li, Meng; Jing, Minqing

doi:10.1177/1464419314540901

Cited by 8 publications

(8 citation statements)

References 24 publications

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“…The simplified damping model can simulate the squeeze film effect exerted by seals. Yi 22 found two types of resonance related to the VC vibration in low-speed region. All the models above are related to the VC vibration of rotor-bearing system, but the torsional DOF of the rotor is not considered in them.…”

Section: Introductionmentioning

confidence: 99%

The effect of coupled lateral and torsional vibrations of an unbalanced Jeffcott rotor supported by roller bearings

Liu

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part K:

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In this paper, an unbalanced Jeffcott rotor supported by the roller bearings is modelled, and the dynamic differential equations are obtained by the Lagrange's equations. The fourth order Runge-Kutta method is employed to simulate this system in two cases. Simulation results show the combined effect of the coupled lateral and torsional vibrations and the variable compliance vibration of the rotor. In the case of that only a large and constant radial force is applied, the sum and difference between variable compliance and rotational frequency components of the torsional vibrations of the disk are induced because of the roller bearings' rotation. And when they are near to the natural frequency of the torsional vibration, the resonance peaks appear. In the case of that a large and constant radial force and a harmonic torque are simultaneously applied, the two components, the sum and difference between rotational frequency and the frequency of the external torque, of the lateral vibrations of the disk are induced. When the frequency of the external harmonic torque approximatively equal to a special value, the two frequency band lines, variable compliance and the sum of rotational frequency and the frequency of external torque, intersect near the natural frequency of the lateral vibration, and the vibration may aggravate.

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Section: Introductionmentioning

confidence: 99%

The effect of coupled lateral and torsional vibrations of an unbalanced Jeffcott rotor supported by roller bearings

Liu

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part K:

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“…As for the latter, the components of rolling bearings, i.e., rolling element, inner raceway, outer raceway, and cage interact to generate complex vibration. The sources of vibration include variable compliance [4] and geometrical imperfections (e.g., surface roughness [5] and waviness [6,7]), and some of these show typical effects in a spectrum [8]. For example, the increased amplitude at ball or roller pass frequency indicates emerging clearances in bearings or loss of loading, and that of ball spin or at cage rotational frequency gives a signature of cage slipping or collisions.…”

Section: Introductionmentioning

confidence: 99%

An Improved Scheme for Vibration-Based Rolling Bearing Fault Diagnosis Using Feature Integration and AdaBoost Tree-Based Ensemble Classifier

2020

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Bearings are key components in modern power machines. Effective diagnosis of bearing faults is crucial for normal operation. Recently, the deep convolutional neural network (DCNN) with 2D visualization technology has shown great potential in bearing fault diagnosis. Traditional DCNN-based fault diagnosis mostly adopts a single learner with one input and is time-consuming in sample and network construction to obtain a satisfied performance. In this paper, a scheme combining diverse DCNN learners and an AdaBoost tree-based ensemble classifier is proposed to improve the diagnosis performance and reduce the requirement of sample and network construction simultaneously. In this scheme, multiple types of samples can be constructed independently and employed for diagnosis simultaneously; next, the same number of DCNN learners are built for underlying features extraction and the obtained results are integrated and finally fed into the ensemble classifier for fault diagnosis. An illustration based on the Case Western Reserve University datasets is given, which proves the superiority of the proposed scheme in both accuracy and robustness. Herein, we present a universal scheme to improve the diagnosis performance, and give an example for practical application, where the signal preprocessing and image sample construction methods can also be applied in other vibration-based analysis.

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“…10 Repeated interaction of rolling elements with faults that are available in the outer race or inner race brings an unpredictable noise and vibration spectra, which needs a unique assessment using advanced signal processing and artificial intelligence tools. 11,12 A brief exploration has been done over the various methods utilized to achieve the objective of performance assessment. Prabhakar et al 13 evaluated discrete wavelet transform for the assessment of numerous defects in ball bearings.…”

Section: Introductionmentioning

confidence: 99%

Elucidation of ball bearing performance utilizing product functions of vibration signals and locality sensitive discriminant analysis

Tiwari

Upadhyay

2019

Proceedings of the Institution of Mechanical Engineers, Part K:

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Dimensionality is a decent term utilized for the dimension-related issues of feature vectors for the condition assessment of bearings. It has been a challenging task while dealing with the characteristic information conservation of the sampled data. Feature vectors with higher dimensions provide an accurate description of the condition, while lower dimension vectors are easy to be classified. These conditions make the dimensionality of feature space a big challenge in the performance evaluation of critical machine parts like bearings. This paper presents a judicious and wise application of a linear approach, locality sensitive discriminant analysis along with the rational use of local mean decomposition in performance degradation assessment. The combination effectively solves the problems of insufficient training samples and large dimensionality of fault features, which imparts excessive noise and causes loss of competent hidden characteristics. In such cases, local structures of feature space are more crucial than the global one. These obstacles can be answered by imparting locality sensitive discriminant analysis as an initial operation. Locality sensitive discriminant analysis is a linear dimensionality reduction tool, which explores the precise projections that amplify the margin between data points and prepare a conservation aid to preserve faulty information of bearings. The steps are followed to achieve the same: decomposition of vibration signal into product functions; calculation of fault features; third, higher dimensionality of the features is reduced with the implementation of locality sensitive discriminant analysis and some prime features are selected with the proposed criterion; the reduced features are further clustered and a trained model of assessment is prepared. Finally, health indicator is calculated from the trained model and test features. The proposed technique is verified on two bearing datasets. The superiority of the technique has been envisaged by comparing the method with different similar assessment methods i.e. time domain features, linear discriminant analysis, and principal component analysis.

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The resonance effect induced by the variable compliance vibration for an elastic rotor supported by roller bearings

Cited by 8 publications

References 24 publications

The effect of coupled lateral and torsional vibrations of an unbalanced Jeffcott rotor supported by roller bearings

The effect of coupled lateral and torsional vibrations of an unbalanced Jeffcott rotor supported by roller bearings

An Improved Scheme for Vibration-Based Rolling Bearing Fault Diagnosis Using Feature Integration and AdaBoost Tree-Based Ensemble Classifier

Elucidation of ball bearing performance utilizing product functions of vibration signals and locality sensitive discriminant analysis

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