Improvement of Gas Turbine Availability Using Reliability Modeling Based on Fuzzy System

  • Nadji Hadroug
  • Ahmed Hafaifa
  • Abdellah Kouzou
  • Ahmed Chaibet
Conference paper
First Online:
Part of the Applied Condition Monitoring book series (ACM, volume 10)

Abstract

The development of the reliability approaches mainly aims at finding the probability that the studied system or a part of it will perform the required function without interruption or failure under the actual stated conditions of defects for a determined period. In this work, the analysis of the effects and consequences resulting from the failures that can affect the industrial system itself and its environment is proposed, where an application on gas turbine system is presented. The main objective is to identify the impact of the gas turbine rotor vibration on the turbine itself and on the operator based on reliability modeling. Indeed, this approach will allow to discover accurately the causes of this kind of vibration. In this paper, a fuzzy modeling method to optimize reliability and availability of the gas turbine is proposed, with the main aim to improve the system exploitation and monitoring.

Keywords

Availability Gas turbines Monitoring system Reliability Optimization Industrial installation 

Nomenclature

\( R(t) \)

Reliability function

\( F(t) \)

Failure time distribution function

\( f(t) \)

Probability density function

\( h(t) \)

Instantaneous failure rate

\( \beta \)

Shape parameter in Weibull distribution

\( \eta \)

Scale parameter in Weibull distribution

\( \lambda \)

Failure rate

\( \mu \)

Average

\( \sigma \)

Standard deviation

ANFIS

Adaptive neuro-fuzzy inference system

MTBF

Mean time between failure

MTTR

Mean time to repair

\( \gamma \)

Positional parameter in Weibull distribution

\( \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{a} \) and \( \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{b} \)

Estimated parameters in regression equations

\( n_{i} \)

Numbers of failure

\( t_{i} \)

Uptime between damage (in hours)

RMSE

Root-mean-square error

CV

Coefficient of variation

MTTF

Mean time to failure

\( x \)

Variable

\( E \)

Variables set

\( \mu_{A} \)

Fuzzy membership function

\( A \)

Fuzzy set

\( \alpha \)

Degree of appurtenance in fuzzy set

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Nadji Hadroug
    • 1
  • Ahmed Hafaifa
    • 1
  • Abdellah Kouzou
    • 1
  • Ahmed Chaibet
    • 2
  1. 1.Applied Automation and Industrial Diagnostics Laboratory, Faculty of Science and TechnologyUniversity of DjelfaDZAlgeria
  2. 2.Aeronautical Aerospace Automotive Railway Engineering SchoolESTACAParisFrance

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