scholarly journals Prediction of metal PM emission in rail tracks for condition monitoring application

2011 ◽  
Author(s):  
B. Tesfa ◽  
Fengshou Gu ◽  
A. Anyakwo ◽  
F. Al Thobiani ◽  
A. Ball
Keyword(s):  
Condition Monitoring ◽  
Monitoring Application

scholarly journals Bifurcation Approach to Condition Monitoring: Application to Pulse Energy Conversion Systems

OPSEARCH ◽  
2002 ◽  
Vol 39 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Yuri V. Kolokolov ◽  
Stanislav L. Koschinsky ◽  
Kondo H. Adjallah
Keyword(s):  
Energy Conversion ◽  
Condition Monitoring ◽  
Pulse Energy ◽  
Energy Conversion Systems ◽  
Monitoring Application

scholarly journals Novel technology concepts and architecture for on-board condition-based monitoring of railway running gear: The RUN2Rail vision

2020 ◽  
pp. 095440972095140
Author(s):  
Julián Martín Jarillo ◽  
Juan Moreno ◽  
Stefano Alfi ◽  
Sylvain Barcet ◽  
Pascal Bouvet ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Fault Classification ◽  
Rolling Stock ◽  
Novel Technology ◽  
New Concepts ◽  
Wear Damage ◽  
High Level ◽  
Railway Operation ◽  
Condition Based Monitoring ◽  
Monitoring Application

Bogies are key subsystems for rolling stock safety and, therefore, meaningful and objective data concerning their condition is of paramount importance for railway operation. These subsystems experience severe service conditions causing wear, damage and degradation of components and affecting the vibrations to which the passengers are exposed. As such, safe and reliable operation, together with a high level of comfort for the passengers, can only be assured by an in-depth, data-based and comprehensive maintenance of the bogie components. In this perspective, advanced health monitoring of the running gear plays a fundamental role as the enabler for condition-based maintenance strategies. This paper reports about work performed in the RUN2Rail project aimed at formulating new concepts for the condition monitoring of the running gear. Three case studies are addressed: wheelsets, powertrain and suspension components. For these cases, the suitable choice and location of sensors is investigated and innovative fault detection and fault classification methods are proposed and preliminarily validated by means of numerical experiments and laboratory tests. A concise outline of the impacts and benefits of each proposed condition monitoring application is also provided.

Drift detection and characterization for condition monitoring: application to dynamical systems with unknown failure modes

2013 ◽  
Vol 26 (2) ◽  
pp. 225-243 ◽  
Author(s):  
Antoine Chammas ◽  
Moussa Traore ◽  
Eric Duviella ◽  
Moamar Sayed-Mouchaweh ◽  
Stéphane Lecoeuche
Keyword(s):  
Dynamical Systems ◽  
Condition Monitoring ◽  
Failure Modes ◽  
Monitoring Application

Machine Condition Monitoring by a Novel Fractal Analysis

2012 ◽  
Vol 452-453 ◽  
pp. 1434-1440
Author(s):  
Wen Xian Yang ◽  
Jie Sheng Jiang
Keyword(s):  
Condition Monitoring ◽  
Fractal Analysis ◽  
Health Condition ◽  
Practical Calculation ◽  
Analysis Method ◽  
Operation Condition ◽  
Window Width ◽  
Machine Condition Monitoring ◽  
Monitoring Technique ◽  
Monitoring Application

The nonlinearities, induced by structural looseness or wear/fatigue of components, are good indicators of the health condition of a machine or structure. However, most existing condition monitoring techniques were initially designed for dealing with linear systems, thus unable to account for these scenarios properly. A few available nonlinear techniques are tried in condition monitoring. However, they are more or less limited owing to either intensive computation or unsatisfactory sensitivity to incipient abnormalities. In view of this, a new fractal analysis-based condition monitoring technique is researched in this paper. Firstly, a few number of fractal analysis methods with efficient computing algorithms are investigated in order to find an ideal one for condition monitoring application. Subsequently, a detailed investigation was conducted to verify the favored method and understand its instantaneous properties, robust performance against noise, and sensitivity to the abnormalities. Finally, following discussing the window width used in practical calculation, the condition monitoring technique developed based on the favored fractal analysis method is validated experimentally. Experiments show that the proposed technique does provide an efficient and successful nonlinear tool for machine operation condition and structural health condition assessment.

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