The Gearbox Health State Monitoring Based on Wear Debris Analysis

2018 ◽  
Author(s):  
Wei Cao ◽  
Ning Wang ◽  
Han Zhang ◽  
Dong Wang ◽  
Haiwen Wang
Keyword(s):  
Wear Debris ◽  
Health State ◽  
State Monitoring ◽  
Wear Debris Analysis

Image based portable wear debris analysis tool

2015 ◽  
Vol 67 (4) ◽  
pp. 389-398 ◽  
Author(s):  
Muhammad Ali Khan ◽  
Ahmed Farooq Cheema ◽  
Sohaib Zia Khan ◽  
Shafiq-ur-Rehman Qureshi
Keyword(s):  
Real Time ◽  
Wear Debris ◽  
Machine Component ◽  
Analysis Tool ◽  
Content Type ◽  
Real Time Analysis ◽  
Distribution Shape ◽  
Basic Features ◽  
Health Diagnosis ◽  
Wear Debris Analysis

Purpose – The purpose of this paper is to show the development of an image processing-based portable equipment for an automatic wear debris analysis. It can analyze both the qualitative and quantitative features of machine wear debris: size, quantity, size distribution, shape, surface texture and material composition via color. Design/methodology/approach – It comprises hardware and software components which can take debris in near real-time from a machine oil sump and process it for features diagnosis. This processing provides the information of the basic features on the user screen which can further be used for machine component health diagnosis. Findings – The developed system has the capacity to replace the existing off-line methods due to its cost effectiveness and simplicity in operation. The system is able to analyze debris basic quantitative and qualitative features greater than 50 micron and less than 300 micron. Originality/value – Wear debris basic features analysis tool is developed and discussed. The portable and near real-time analysis offered by the discussed work can be more technically effective as compared to the existing off-line and online techniques.

Actuator Health State Monitoring & Degradation Impact Study on a 4-Mecanum Wheeled Mobile Robot Behaviour

2021 ◽  
Author(s):  
Samia Mellah ◽  
Guillaume Graton ◽  
El Mostafa El Adel ◽  
Mustapha Ouladsine ◽  
Alain Planchais
Keyword(s):  
Mobile Robot ◽  
Wheeled Mobile Robot ◽  
Health State ◽  
Impact Study ◽  
State Monitoring

Analysis of the Behaviour of Rolling Bearings in Contaminated Oil Using Some Condition Monitoring Techniques

2006 ◽  
Vol 220 (5) ◽  
pp. 447-453 ◽  
Author(s):  
T Akagaki ◽  
M Nakamura ◽  
T Monzen ◽  
M Kawabata
Keyword(s):  
Vibration Analysis ◽  
Wear Debris ◽  
Friction And Wear ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Deep Groove ◽  
Deep Groove Ball Bearing ◽  
Observation Results ◽  
Wear Debris Analysis ◽  
Worn Surfaces

Friction and wear behaviours of rolling bearing in contaminated oil containing white-fused alumina particles were studied. The friction and wear processes were monitored using wear debris analysis, such as ferrography and spectrometric oil analysis program, and vibration analysis. Test bearing was a deep groove ball bearing (6002P5); Wear debris and worn surfaces of the bearing components were observed with a scanning electron microscope (SEM). It was found that the friction coefficient in the contaminated oil became lower by about 0.001 than that in the new oil for the large contaminants. The results of wear debris analysis showed that the large contaminants caused the high wear rate in the bearing. Three types of wear debris were commonly observed: thread-like debris, cutting chip debris, and plate-like debris. On the basis of the SEM observation results of the worn surfaces, wear mechanisms of these wear debris were discussed. The results of vibration analysis showed that the probability density function of vibration waveform was normal distribution in both the new and contaminated oils. In the contaminated oil, it changed depending on the contaminant size and the runtime, i.e. the progress of wear in the bearing. The result of wear debris analysis was related to that of vibration analysis and discussed.

Induction Machine Insulation Health State Monitoring Based on Online Switching Transient Exploitation

2015 ◽  
Vol 62 (3) ◽  
pp. 1835-1845 ◽  
Author(s):  
Peter Nussbaumer ◽  
Markus A. Vogelsberger ◽  
Thomas M. Wolbank
Keyword(s):  
Induction Machine ◽  
Health State ◽  
State Monitoring
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