Condition monitoring of ball bearings using estimated AR models as logistic regression features

2019 ◽  
Author(s):  
Matteo Barbieri ◽  
Roberto Diversi ◽  
Andrea Tilli
Keyword(s):  
Logistic Regression ◽  
Condition Monitoring ◽  
Ball Bearings ◽  
Ar Models

Detectivity: A combination of Hjorth’s parameters for condition monitoring of ball bearings

2022 ◽  
Vol 164 ◽  
pp. 108247
Author(s):  
Marco Cocconcelli ◽  
Matteo Strozzi ◽  
Jacopo Cavalaglio Camargo Molano ◽  
Riccardo Rubini
Keyword(s):  
Condition Monitoring ◽  
Ball Bearings

scholarly journals Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring

2019 ◽  
Vol 66 (10) ◽  
pp. 8136-8147 ◽  
Author(s):  
Osama Abdeljaber ◽  
Sadok Sassi ◽  
Onur Avci ◽  
Serkan Kiranyaz ◽  
Abdelrahman Aly Ibrahim ◽  
...  
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Ball Bearings

Condition monitoring studies on ball bearings considering solid contaminants in the lubricant

2010 ◽  
Vol 224 (8) ◽  
pp. 1727-1748 ◽  
Author(s):  
V Hariharan ◽  
P S S Srinivasan
Keyword(s):  
Particle Size ◽  
Condition Monitoring ◽  
Experimental Tests ◽  
Solid Particles ◽  
Contaminant Concentration ◽  
Ball Bearings ◽  
Rolling Element Bearings ◽  
Rolling Bearings ◽  
Rolling Element ◽  
Vibration Signatures

Rolling element bearings are common in any rotating machinery. They are subject to failure under continuous running. Therefore they have received a great deal of attention in the field of condition monitoring. In rolling element bearings, contamination of lubricant grease by solid particles is one of the several reasons for an early bearing failure. In this context, this article investigates the effect of contamination of lubricant by solid particles on the dynamic behaviour of rolling bearings. Silica powder at three concentration levels and different particle sizes was used to contaminate the lubricant. Experimental tests have been performed on the ball bearings lubricated with grease, and the trends in the amount of vibration affected by the contamination of the grease were determined. The contaminant concentration as well as the particle size is varied. Vibration signatures were analysed in terms of root mean square (RMS) values. From the results, some fruitful conclusions are made about the bearing performance. The effects of contaminant and the bearing vibration are studied for both good and defective bearings. The results show significant variation in the RMS velocity values on varying the contaminant concentration and particle size.

EXPERIMENTAL STUDY AND LOGISTIC REGRESSION MODELING FOR MACHINE CONDITION MONITORING THROUGH MICROCONTROLLER-BASED DATA ACQUISITION SYSTEM

2009 ◽  
Vol 08 (02) ◽  
pp. 177-192 ◽  
Author(s):  
JULIE ZHANG ◽  
HONG NIE
Keyword(s):  
Experimental Study ◽  
Logistic Regression ◽  
Data Acquisition ◽  
Condition Monitoring ◽  
Data Acquisition System ◽  
Regression Modeling ◽  
Acquisition System ◽  
Machine Condition ◽  
Logistic Regression Modeling ◽  
Machine Condition Monitoring

Machine condition monitoring plays an important role in machining performance. A machine condition monitoring system will provide significant economic benefits when applied to machine tools and machining processes. By applying Taguchi design method, real-time pilot experimental study was conducted on a CNC machining center for monitoring the end mill cutting operations through the vibration data collection via a microcontroller-based data acquisition system. Featured machining signals were identified through data analyses and regression models were established that incorporates different combinations of featured machining signals and machining parameters in using logistic regression modeling approach. The onsite tests show that the developed logistic models including the featured machining signals can correctly distinguish worn and new cutting tools. Therefore, they can help construct decision-making mechanism for machine condition monitoring.

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