Experience with advanced in-service condition monitoring techniques for GIS and transformers

1998 ◽  
Author(s):  
J. Pearson ◽  
B.F. Hampton ◽  
M.D. Judd ◽  
B. Pryor ◽  
P.F. Coventry
Keyword(s):  
Condition Monitoring ◽  
Service Condition ◽  
Monitoring Techniques

scholarly journals Condition Monitoring of Blade in Turbomachinery: A Review

2014 ◽  
Vol 6 ◽  
pp. 210717 ◽  
Author(s):  
Ahmed M. Abdelrhman ◽  
Lim Meng Hee ◽  
M. S. Leong ◽  
Salah Al-Obaidi
Keyword(s):  
Condition Monitoring ◽  
Fatigue Failure ◽  
Signal Analysis ◽  
Health Condition ◽  
Diagnostic Methods ◽  
Monitoring Techniques ◽  
Analysis Methods

Blade faults and blade failures are ranked among the most frequent causes of failures in turbomachinery. This paper provides a review on the condition monitoring techniques and the most suitable signal analysis methods to detect and diagnose the health condition of blades in turbomachinery. In this paper, blade faults are categorised into five types in accordance with their nature and characteristics, namely, blade rubbing, blade fatigue failure, blade deformations (twisting, creeping, corrosion, and erosion), blade fouling, and loose blade. Reviews on characteristics and the specific diagnostic methods to detect each type of blade faults are also presented. This paper also aims to provide a reference in selecting the most suitable approaches to monitor the health condition of blades in turbomachinery.

Condition Monitoring Techniques for Low-Speed Machines

2014 ◽  
pp. 69-119
Author(s):  
Zhaklina Stamboliska ◽  
Eugeniusz Rusiński ◽  
Przemyslaw Moczko
Keyword(s):  
Condition Monitoring ◽  
Low Speed ◽  
Monitoring Techniques

Effect of Condition Monitoring on Risk Mitigation for Steam Turbines in the Forest Products Industry

2015 ◽  
Author(s):  
Bin Zhou ◽  
Kumar Bhimavarapu
Keyword(s):  
Condition Monitoring ◽  
Domain Knowledge ◽  
Risk Mitigation ◽  
Forest Products ◽  
Event Tree ◽  
Steam Turbines ◽  
Event Tree Analysis ◽  
Monitoring Techniques ◽  
Mechanical Breakdown ◽  
Semi Empirical

Industry has been implementing condition monitoring for turbines to minimize losses and to improve productivity. Deficient conditions can be identified before losses occur by monitoring the equipment parameters. For any loss scenario, the effectiveness of monitoring depends on the stage of the loss scenario when the deficient condition is detected. A scenario-based semi-empirical methodology was developed to assess various types of condition monitoring techniques, by considering their effect on the risk associated with mechanical breakdown of steam turbines in the forest products (FP) industry. A list of typical turbine loss scenarios was first generated by reviewing loss data and leveraging expert domain knowledge. Subsequently, condition monitoring techniques that can mitigate the risk associated with each loss scenario were identified. For each loss scenario, an event tree analysis was used to quantitatively assess the variations in the outcomes due to condition monitoring, and resultant changes in the risk associated with turbine mechanical breakdown. An application was developed following the methodology to evaluate the effect of condition monitoring on turbine risk mitigation.

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