scholarly journals An impulse control approach for optimal maintenance scheduling of a gas turbine

PAMM ◽  
2016 ◽  
Vol 16 (1) ◽  
pp. 689-690
Author(s):  
Michael Gröger
Keyword(s):  
Gas Turbine ◽  
Impulse Control ◽  
Maintenance Scheduling ◽  
Control Approach ◽  
Optimal Maintenance

scholarly journals Optimization of Gas Turbine Maintenance Scheduling in PLN Tanjung Datuk Pekanbaru

2020 ◽  
Vol 64 (3) ◽  
pp. 88-93
Author(s):  
Bambang Nofri ◽  
◽  
Anita Susilawati ◽  
Romy Romy ◽  
◽  
...  
Keyword(s):  
Gas Turbine ◽  
Turbine Blade ◽  
Maintenance Scheduling ◽  
Optimal Scheduling ◽  
Turbine Engines ◽  
Optimal Time ◽  
Time To Failure ◽  
Optimal Maintenance ◽  
Critical Components ◽  
Mean Time

This study discusses determining the optimal scheduling for maintenance of gas turbine engines in PLN Tanjung Datuk Pekanbaru. The optimal maintenance scheduling is done on critical components, namely turbine blade and AVR (Automatic Voltage Regulator) using Monte Carlo simulation. The optimal scheduling maintenance scenario is done by generating random numbers from MTTF (Mean Time To Failure) and MTTR (Mean Time To Repair) values and data validity testing. The results of research for optimal checking of turbine engines are once every 10 days with the reliability of turbine engines 43%. The optimal time for repairing a gas turbine in case of damage is 1.49 hours. The checking time for critical components of the turbine blade is 9 days and AVR of 12 days. The scenario of preventive maintenance is likely need special repair or replacement periodically that is 117 days for turbine blade components and 173 days for AVR.

scholarly journals Developing an Enhanced Short-Range Railroad Track Condition Prediction Model for Optimal Maintenance Scheduling

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Xu ◽  
Chuanjun Jia ◽  
Ye Li ◽  
Quanxin Sun ◽  
Rengkui Liu
Keyword(s):  
Prediction Model ◽  
Short Range ◽  
Prediction Method ◽  
Maintenance Scheduling ◽  
Railroad Track ◽  
Track Geometry ◽  
Track Maintenance ◽  
Track Condition ◽  
Optimal Maintenance ◽  
Process Work

As railroad infrastructure becomes older and older and rail transportation is developing towards higher speed and heavier axle, the risk to safe rail transport and the expenses for railroad maintenance are increasing. The railroad infrastructure deterioration (prediction) model is vital to reducing the risk and the expenses. A short-range track condition prediction method was developed in our previous research on railroad track deterioration analysis. It is intended to provide track maintenance managers with two or three months of track condition in advance to schedule track maintenance activities more smartly. Recent comparison analyses on track geometrical exceptions calculated from track condition measured with track geometry cars and those predicted by the method showed that the method fails to provide reliable condition for some analysis sections. This paper presented the enhancement to the method. One year of track geometry data for the Jiulong-Beijing railroad from track geometry cars was used to conduct error analyses and comparison analyses. Analysis results imply that the enhanced model is robust to make reliable predictions. Our in-process work on applying those predicted conditions for optimal track maintenance scheduling is discussed in brief as well.

Prediction of Gas Turbine Performance Degradation Between Soakwashes in Natural Gas Compressor Stations

2012 ◽  
Author(s):  
R. Chatterjee ◽  
K. K. Botros ◽  
H. Golshan ◽  
D. Rogers ◽  
Z. Samoylove
Keyword(s):  
Gas Turbine ◽  
Standard Procedure ◽  
Engine Performance ◽  
Performance Degradation ◽  
Pipeline System ◽  
Turbine Engine ◽  
Wear And Tear ◽  
Optimal Maintenance ◽  
Prime Movers ◽  
Over Time

Gas Turbine (GT), like other prime movers, undergoes wear and tear over time which results in performance drop as far as available power and efficiency is concerned. In addition to routine wear and tear, the engine also undergoes corrosion, fouling etc. due to the impurities it breathes in. It is standard procedure to ‘wash’ the engine from time to time to revive it. However, it is important to establish a correct schedule for the wash to ensure optimal maintenance procedure. This calls for accurate prediction of the performance degradation of the engine over time. In this paper, a methodology is presented to predict the performance degradation in a GE LM2500 Gas Turbine engine used at one of TransCanada’s pipeline system, Canada. Emphasis is laid on analyzing the degradation of the air compressor side of the engine since it is most prone to fouling and degradation. Although the results presented are for a specific engine type, the general framework of the model could be used for other engines as well to quantify degradation over time of other components within the GT engine. The present model combines Gas Path Analysis (GPA) to evaluate the thermodynamic parameters over the engine cycle followed by parameter estimation to filter the data of possible noise due to instrumentation errors. The model helps quantify the degradation in the engine performance over time and also indicates the effectiveness of each engine wash. The analysis will lead to better scheduling of the engine wash thereby optimizing operational costs as well as engine overhaul time.

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