Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis

2013 ◽  
Vol 332 (8) ◽  
pp. 2081-2097 ◽  
Author(s):  
Feiyun Cong ◽  
Jin Chen ◽  
Guangming Dong ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Rolling Element Bearings ◽  
Vibration Model ◽  
Rolling Element ◽  
Rotor Bearing ◽  
Bearing System

scholarly journals Diagnosing faults in rolling-element bearings of rotor systems equipped with vibration dampers

2020 ◽  
Vol 12 (4) ◽  
pp. 168781402091541
Author(s):  
Vladas Vekteris ◽  
Andrius Trumpa ◽  
Vytautas Turla ◽  
Vadim Mokšin ◽  
Gintas Viselga ◽  
...  
Keyword(s):  
Rolling Element Bearings ◽  
Rotor Systems ◽  
Milk Processing ◽  
Processing Machinery ◽  
Rolling Element ◽  
Rotor Bearing ◽  
Fault Diagnostic ◽  
Bearing System ◽  
Vibration Dampers

This article considers problems arising from conventional techniques used to diagnose faults in the rolling-element bearings of rotor-bearing systems, with dampers used in centrifugal milk processing machinery. Such machines include milk separators and related processing machinery. The article asserts that where the rotor-bearing system is equipped with vibration dampers, conventional fault diagnostic measurements produce inadequate results. Hence, for rotor-bearing systems of this type, this article suggests a different way to diagnose faults in bearings and monitor conditions.

scholarly journals Nonlinear dynamic mechanism of rolling element bearings with an internal clearance in a rotor-bearing system

2016 ◽  
Vol 8 (11) ◽  
pp. 168781401667958 ◽  
Author(s):  
Guofang Nan ◽  
Min Tang ◽  
Eryun Chen ◽  
Ailing Yang
Keyword(s):  
Nonlinear Dynamic ◽  
Dynamic Mechanism ◽  
Rolling Element Bearings ◽  
Rolling Element ◽  
Rotor Bearing ◽  
Bearing System

Nonlinear Parameter Estimation in Rotor-Bearing System Using Volterra Series and Method of Harmonic Probing

2003 ◽  
Vol 125 (3) ◽  
pp. 299-306 ◽  
Author(s):  
Animesh Chatterjee ◽  
Nalinaksh S. Vyas
Keyword(s):  
Volterra Series ◽  
Parametric Identification ◽  
Harmonic Amplitude ◽  
Rolling Element Bearings ◽  
Identification Procedure ◽  
Rolling Element ◽  
Rotor Bearing ◽  
Bearing Stiffness ◽  
Recursive Evaluation ◽  
Bearing System

Volterra series provides a structured analytical platform for modeling and identification of nonlinear systems. The series has been widely used in nonparametric identification through higher order frequency response functions or FRFs. A parametric identification procedure based on recursive evaluation of response harmonic amplitude series is presented here. The procedure is experimentally investigated for a rotor-bearing system supported in rolling element bearings. The estimates of nonlinear bearing stiffness obtained from experimentation have been compared with analytical values and experimental results of previous works.

A NEW GEARBOX FAULT DIAGNOSIS METHOD BASED ON LUCY–RICHARDSON DECONVOLUTION

2015 ◽  
Vol 39 (3) ◽  
pp. 593-603
Author(s):  
Xinghui Zhang ◽  
Jianshe Kang ◽  
Hongzhi Teng ◽  
Jianmin Zhao
Keyword(s):  
Fault Diagnosis ◽  
Experimental Studies ◽  
Experimental Tests ◽  
Rolling Element Bearings ◽  
Envelope Analysis ◽  
Rolling Element ◽  
Negative Effect ◽  
Diagnosis Method ◽  
Good Identification ◽  
Incipient Fault Diagnosis

Gear and bearing faults are the main causes of gearbox failure. Till now, incipient fault diagnosis of these two components has been a problem and needs further research. In this context, it is found that Lucy–Richardson deconvolution (LRD) proved to be an excellent tool to enhance fault diagnosis in rolling element bearings and gears. LRD’s good identification capabilities of fault frequencies are presented which outperform envelope analysis. This is very critical for early fault diagnosis. The case studies were carried out to evaluate the effectiveness of the proposed method. The results of simulated and experimental studies show that LRD is efficient in alleviating the negative effect of noise and transmission path. The results of simulation and experimental tests demonstrated outperformance of LRD compared to classical envelope analysis for fault diagnosis in rolling element bearings and gears, especially when it is applied to the processing of signals with strong background noise.

Two-step fault diagnosis framework for rolling element bearings with imbalanced data based on GSA-WELM and GSA-ELM

2017 ◽  
Vol 232 (16) ◽  
pp. 2937-2947 ◽  
Author(s):  
Yuan Lan ◽  
Xiaohong Han ◽  
Weiwei Zong ◽  
Xiaojian Ding ◽  
Xiaoyan Xiong ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Gravitational Search Algorithm ◽  
Imbalanced Data ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Misclassification Cost ◽  
Weighted Extreme Learning Machine ◽  
Rolling Element ◽  
Learning Machine

Rolling element bearings constitute the key parts on rotating machinery, and their fault diagnosis is of great importance. In many real bearing fault diagnosis applications, the number of fault data is much less than the number of normal data, i.e. the data are imbalanced. Many traditional diagnosis methods will get low accuracy because they have a natural tendency to favor the majority class by assuming balanced class distribution or equal misclassification cost. To deal with imbalanced data, in this article, a novel two-step fault diagnosis framework is proposed to diagnose the status of rolling element bearings. Our proposed framework consists of two steps for fault diagnosis, where Step 1 makes use of weighted extreme learning machine in an effort to classify the normal or abnormal categories, and Step 2 further diagnoses the underlying anomaly in detail by using preliminary extreme learning machine. In addition, gravitational search algorithm is applied to further extract the significant features and determine the optimal parameters of the weighted extreme learning machine and extreme learning machine classifiers. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our Institute, and the empirical results show that our approach is really fast and can achieve the diagnosis accuracies more than 96%.

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