scholarly journals Fault Dynamic Modeling and Characteristic Parameter Simulation of Rolling Bearing with Inner Ring Local Defects

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhang Fengling ◽  
Zhang Yuwei ◽  
Guan Jiaoyue ◽  
Tian Jing ◽  
Wang Yingjie
Keyword(s):  
Dynamic Model ◽  
Engineering Application ◽  
Characteristic Parameter ◽  
Rolling Bearing ◽  
Mean Value ◽  
Radial Clearance ◽  
Characteristic Parameters ◽  
Bearing Fault ◽  
Bearing Condition Monitoring ◽  
Local Defects

To further study the fault mechanism and fault features of rolling bearings, a two-DOF rolling bearing fault dynamic model with inner ring local defects considering the bearing radial clearance and time-varying displacement excitation is established based on Hertz contact theory. By comparing the simulated fault signal with bearing fault test data in the time domain and frequency domain, the accuracy of the established fault dynamic model is verified. Finally, the change rules of the characteristic parameters of the bearing inner ring fault signal, including effective value, absolute mean value, square root amplitude, peak value, kurtosis factor, pulse factor, peak factor, and shape factor, are simulated by the fault dynamic model. The results highlight that the proposed fault dynamic model is in good agreement with the experimental results. The model can simulate the fault signal characteristic parameters with the change of defect width, external load, and rotating speed effectively. The study in this paper is of engineering application value for bearing condition monitoring and fault diagnosis.

Feature Extraction of Rolling Bearing Fault Diagnosis

2012 ◽  
Vol 190-191 ◽  
pp. 993-997
Author(s):  
Li Jie Sun ◽  
Li Zhang ◽  
Yong Bo Yang ◽  
Da Bo Zhang ◽  
Li Chun Wu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Attribute Reduction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Characteristic Parameters ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Experimental Trials

Mechanical equipment fault diagnosis occupies an important position in the industrial production, and feature extraction plays an important role in fault diagnosis. This paper analyzes various methods of feature extraction in rolling bearing fault diagnosis and classifies them into two big categories, which are methods of depending on empirical rules and experimental trials and using objective methods for screening. The former includes five methods: frequency as the characteristic parameters, multi-sensor information fusion method, rough set attribute reduction method, "zoom" method and vibration signal as the characteristic parameters. The latter includes two methods: sensitivity extraction and data mining methods to select attributes. Currently, selection methods of feature parameters depend heavily on empirical rules and experimental trials, thus extraction results are be subjected to restriction from subjective level, feature extraction in the future will develop toward objective screening direction.

Poor Information Analysis of Rolling Bearing Friction Torque Characteristic Parameter Based on Phase Space

2011 ◽  
Vol 382 ◽  
pp. 167-171
Author(s):  
Lei Lei Gao ◽  
Xin Tao Xia
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Characteristic Parameter ◽  
Rolling Bearing ◽  
Friction Torque ◽  
Point Estimation ◽  
Experimental Result ◽  
Bayesian Probability ◽  
Characteristic Parameters ◽  
Bearing Friction

The friction torque of rolling bearings belongs to an information poor system with unknown probability distributions and trends. This counteracts dynamical assessment for the characteristics of the rolling bearing friction torque as a time series. For this reason, the chaos theory is employed to recover the original dynamic characteristics of a friction torque time series by means of the phase space reconstruction theory. The dynamical Bayesian probability density function of the characteristic parameters of the friction torque is constructed by the information poor theory based on the phase space. The method for point estimation, interval estimation, and trend estimation of the characteristic parameters is proposed in this paper. The investigation shows that the error between the calculated result and the experimental result is very small.

scholarly journals Investigation on the Effects of Structural Dynamics on Rolling Bearing Fault Diagnosis by Means of Multibody Simulation

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Reza Golafshan ◽  
Georg Jacobs ◽  
Matthias Wegerhoff ◽  
Pascal Drichel ◽  
Joerg Berroth
Keyword(s):  
Fault Detection ◽  
Mechanical Systems ◽  
Rolling Bearing ◽  
Multibody Simulation ◽  
Bearing Fault ◽  
Transfer Path ◽  
Bearing Fault Diagnosis ◽  
Bearing Condition Monitoring ◽  
Complex Mechanical Systems

The present study aims to combine the fields modal analysis and signal processing and to show the use of Frequency Response Function (FRF), as a vibration transfer path, in enhancing reliability and abilities of the next generation vibration-based rolling bearing condition monitoring (CM) systems in complex mechanical systems. In line with this purpose, the hereby-presented paper employs an appropriate numerical model, that is, Multibody Simulation (MBS) of a vehicle’s drivetrain as a manner for numerical modal and structural analyses. For this, first, the principles of vibration-based bearing fault detection are reviewed and presented. Following that, a summary of MBS modelling and validating strategies are given. Then, the validated MBS model is used as a case study for further investigations. The results can confirm existence of challenges in fault detection of rolling bearings, in particular in complex mechanical systems. In further discussions, the capability of FRFs in fault localization and determination of ideal sensor positions is discussed in some detail. Finally, concluding remarks and suggestions for future works are summarized.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method
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