Analysis of ISO VG 68 bearing oil for condition monitoring collected from an externally pressurized ball bearing system

2021 ◽  
Author(s):  
Dileswar Pradhan ◽  
Ajit Kumar Mishra
Keyword(s):  
Condition Monitoring ◽  
Ball Bearing ◽  
Bearing System

Parameters research on time-varying stiffness of the ball bearing system without race control hypothesis

2021 ◽  
pp. 095440622110179
Author(s):  
Yongzhen Liu ◽  
Yimin Zhang
Keyword(s):  
Ball Bearing ◽  
Combined Loading ◽  
Time Varying ◽  
Noticeable Effect ◽  
Rotating Speed ◽  
Bearing Stiffness ◽  
Vibration Mechanism ◽  
Control Hypothesis ◽  
Full Consideration ◽  
Bearing System

When the ball bearing serving under the combined loading conditions, the ball will roll in and out of the loaded zone periodically. Therefore the bearing stiffness will vary with the position of the ball, which will cause vibration. In order to reveal the vibration mechanism, the quasi static model without raceway control hypothesis is modeled. A two-layer nested iterative algorithm based on Newton–Raphson (N-R) method with dynamic declined factors is presented. The effect of the dispersion of bearing parameters and the installation errors on the time-varying carrying characteristics of the ball-raceway contact and the bearing stiffness are investigated. Numerical simulation illustrates that besides the load and the rotating speed, the dispersion of bearing parameters and the installation errors have noticeable effect on the ball-raceway contact load, ball-inner raceway contact state and bearing stiffness, which should be given full consideration during the process of design and fault diagnosis for the rotor-bearing system.

An analysis method to determine how changes in dynamic complexity impact on rotor-bearing systems

2020 ◽  
pp. 095440622095488
Author(s):  
Mian Jiang ◽  
Shuangqi Liu ◽  
Yuhua Wang
Keyword(s):  
Condition Monitoring ◽  
Lubricating Oil ◽  
Analysis Method ◽  
Dynamic Complexity ◽  
Rotor Systems ◽  
Methodological Choices ◽  
Rotor Bearing ◽  
Nonlinearity Measures ◽  
Monitoring Performance ◽  
Bearing System

Condition monitoring performance and diagnosis of rotor-bearing systems depend not only on the methods used, but also on the dynamic complexity of the system itself. Thus, it is important to analyze how changes in parameters under various working conditions impact on dynamic complexity. Most of previous research efforts on this topic have been focused on the analysis of nonlinear dynamics of rotor-bearing systems with different parameters. In this paper, a nonlinearity quantification based analysis method is presented to determine how parameter dynamics impact the complexity of rotor-bearing systems. The dynamic complexity of rotor system is estimated using defined nonlinearity measures. To validate this method, a sliding rotor-bearing system with a loose pedestal is used. The estimates (nonlinearity degrees) and the states of motion are matched with increasing rotational speeds. It is then investigated, how the eccentricities, lubricating oil viscosities, and bearing clearances impacted the dynamic complexity at several critical rotational speeds. These results can guide methodological choices for condition monitoring and diagnosis of rotor systems.

Nonlinear Vibration Prediction of a Highly Flexible Rotor Supported by an Axial Groove Journal Bearing Considering Journal Angular Whirling Motion

2019 ◽  
Author(s):  
Nuntaphong Koondilogpiboon ◽  
Tsuyoshi Inoue
Keyword(s):  
Nonlinear Vibration ◽  
Ball Bearing ◽  
Journal Bearing ◽  
Flexible Rotor ◽  
Large Disk ◽  
Subcritical Bifurcation ◽  
Whirling Motion ◽  
Vibration Prediction ◽  
Rotor Bearing ◽  
Bearing System

Abstract In this study, the difference in dynamic behavior of the rotor-bearing system supported by the bearing model that considers both lateral and angular whirling motions of the journal (model A), and the model that considers only lateral whirling motion (model B) is investigated. The rotor model consists of a slender shaft, a large disk and two small disks supported by a self-aligning ball bearing and an axial groove journal bearing of L/D = 0.6. Three positions of the large disk: 410, 560, and 650 mm measured from the ball bearing, are investigated. Numerical integration of the rotor-bearing system which is modally reduced to the 1st forward mode is performed at above the onset speed of instability until either a steady state journal orbit or contact between the journal and the bearing occurs to identify the bifurcation type. Numerical results using model A indicate subcritical bifurcation with the contact between the journal and the inboard side of the bearing in all three large disk positions, whereas those of model B indicate subcritical bifurcation when the large disk position is at 410 mm, and supercritical bifurcation is observed in the other two cases. Lastly, the experiments at the same three large disk positions are performed. Subcritical bifurcation with the contact between the journal and the inboard side of the bearing is observed in all large disk positions, which conforms with the calculation result of model A. As a result, model A is essential in nonlinear vibration analysis of a highly flexible rotor system.

Multivariable Trend Analysis Using Neural Networks for Intelligent Diagnostics of Rotating Machinery

1997 ◽  
Vol 119 (2) ◽  
pp. 378-384 ◽  
Author(s):  
S. Zhang ◽  
R. Ganesan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Trend Analysis ◽  
Condition Monitoring ◽  
Diagnostic Procedure ◽  
On Line ◽  
Neural Network Algorithm ◽  
Intelligent Diagnostics ◽  
Bearing System ◽  
Self Organizing

The objective of this paper is the development of an efficient intelligent diagnostic procedure that considers several diagnostic indices for the quantification of developing faults and for monitoring machine condition. In this procedure, the condition monitoring is performed based on the on-line vibration measurements, and further, the fault quantification is formulated into a multivariate trend analysis. Self-organizing neural networks are then deployed to perform the multivariable trending of the fault development. The attributes for the disordering of “knots” in the trend analysis are determined. The disordering of neural network units is then eliminated by suitably altering the self-organizing neural network algorithm. Applications of this diagnostic procedure to the condition monitoring and life estimation of a bearing system are fully developed and demonstrated. The efficiency and advantages of the intelligent diagnostic procedure in precisely monitoring and quantifying the fault development are systematically brought out considering this bearing system.

Modeling and Simulation of 5 and 11 DOF Ball Bearing System with Localized Defect

2013 ◽  
Vol 42 (1) ◽  
pp. 20120345 ◽  
Author(s):  
J. M. Marín ◽  
H. Rubio ◽  
J. C. García-Prada ◽  
O. Reinoso
Keyword(s):  
Modeling And Simulation ◽  
Ball Bearing ◽  
Bearing System
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