ESTIMATION OF NON-LINEAR STIFFNESS PARAMETERS OF ROLLING ELEMENT BEARINGS FROM RANDOM RESPONSE OF ROTOR-BEARING SYSTEMS

1995 ◽  
Vol 187 (2) ◽  
pp. 229-239 ◽  
Author(s):  
R. Tiwari ◽  
N.S. Vyas
Keyword(s):  
Rolling Element Bearings ◽  
Random Response ◽  
Rolling Element ◽  
Non Linear ◽  
Rotor Bearing

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.

Sensitivity Analysis of an Information Maximization Approach to the Blind Separation of the Vibration Responses of Defective Rolling Element Bearings

2005 ◽  
Author(s):  
C. Yiakopoulos ◽  
I. Antoniadis
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Linear Function ◽  
Network Structure ◽  
Vibration Response ◽  
Rolling Element Bearings ◽  
Rolling Element ◽  
Non Linear ◽  
Vibration Responses ◽  
Neural Network Structure

Vibration response of rotating machines is typically mixed and corrupted by a variety of interfering sources and noise, leading to the necessity for the isolation of the useful signal components. A relevant frequently encountered industrial case is the need for the separation of the vibration responses of the same type of bearings inside the same machine. For this purpose, a Blind Source Separation procedure has been successfully applied, based on the maximization of the information transferred in a neural network structure. Thus, a key element for the success of the proposed procedure is the non-linear function used in this single layer Neural Network structure. However, since the vibration response of defective rolling element bearings is characterized by signals with super-Gaussian distributions, a sensitivity analysis of this non-linear function is necessary. First, this analysis is performed in a set of numerical experiments, based on dynamic models of defective bearings. Finally, the same analysis is applied in an experimental test rig.

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