Correction of prediction model output for structural design and risk-based inspection and maintenance planning

2015 ◽  
Vol 97 ◽  
pp. 114-125 ◽  
Author(s):  
Nabile Hifi ◽  
Nigel Barltrop
Keyword(s):  
Prediction Model ◽  
Structural Design ◽  
Maintenance Planning ◽  
Model Output ◽  
Inspection And Maintenance

scholarly journals Maintenance Planning of Offshore Wind Turbine Using Condition Monitoring Information

2009 ◽  
Author(s):  
Jose´ G. Rangel-Rami´rez ◽  
John D. So̸rensen
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Wind Farm ◽  
Offshore Structures ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Maintenance Planning ◽  
Offshore Wind Turbines ◽  
Inspection And Maintenance ◽  
Monitoring Information

Deterioration processes such as fatigue and corrosion are typically affecting offshore structures. To “control” this deterioration, inspection and maintenance activities are developed. Probabilistic methodologies represent an important tool to identify the suitable strategy to inspect and control the deterioration in structures such as offshore wind turbines (OWT). Besides these methods, the integration of condition monitoring information (CMI) can optimize the mitigation activities as an updating tool. In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT incorporating CMI, addressing this analysis to fatigue prone details in welded steel joints at jacket or tripod steel support structures for offshore wind turbines. The increase of turbulence in wind farms is taken into account by using a code-based turbulence model. Further, additional modes t integrate CMI in the RBI approach for optimal planning of inspection and maintenance. As part of the results, the life cycle reliabilities and inspection times are calculated, showing that earlier inspections are needed at in-wind farm sites. This is expected due to the wake turbulence increasing the wind load. With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents.

CORRECTION OF PREDICTION MODEL OUTPUT–APPLICATION TO GENERAL CORROSION MODEL

2021 ◽  
Vol 156 (A4) ◽  
Author(s):  
N Hifi ◽  
N Barltrop
Keyword(s):  
Prediction Model ◽  
Reliability Analysis ◽  
Model Calibration ◽  
Structural Reliability ◽  
Prediction Models ◽  
General Corrosion ◽  
Model Output ◽  
Reliability Models ◽  
Corrosion Model ◽  
Recorded Data

This paper applies a newly developed methodology to calibrate the corrosion model within a structural reliability analysis. The methodology combines data from experience (measurements and expert judgment) and prediction models to adjust the structural reliability models. Two corrosion models published in the literature have been used to demonstrate the technique used for the model calibration. One model is used as a prediction for a future degradation and a second one to represent the inspection recorded data. The results of the calibration process are presented and discussed.

scholarly journals Genetic Algorithm for Inspection and Maintenance Planning of Deteriorating Structural Systems: Application to Pressure Vessels

2018 ◽  
Vol 3 (3) ◽  
pp. 32 ◽  
Author(s):  
Shane Haladuick ◽  
Markus Dann
Keyword(s):  
Genetic Algorithm ◽  
Complex Systems ◽  
Decision Analysis ◽  
Pressure Vessels ◽  
Optimal Decision ◽  
Maintenance Planning ◽  
Plan Optimization ◽  
Planning Decision ◽  
Inspection And Maintenance ◽  
Maintenance Plan

For engineering systems, decision analysis can be used to determine the optimal decision from a set of options via utility maximization. Applied to inspection and maintenance planning, decision analysis can determine the best inspection and maintenance plan to follow. Decision analysis is relatively straightforward for simple systems. However, for more complex systems with many components or defects, the set of all possible inspection and maintenance plans can be very large. This paper presents the use of a genetic algorithm to perform inspection and maintenance plan optimization for complex systems. The performance of the genetic algorithm is compared to optimization by exhaustive search. A numerical example of life cycle maintenance planning for a corroding pressure vessel is used to illustrate the method. Genetic algorithms are found to be an effective approach to reduce the computational demand of solving complex inspection and maintenance optimizations.

On the Treatment of Uncertainties and Probabilities in Engineering Decision Analysis

2005 ◽  
Vol 127 (3) ◽  
pp. 243-248 ◽  
Author(s):  
Michael Havbro Faber
Keyword(s):  
Decision Theory ◽  
Decision Analysis ◽  
Uncertainty Modeling ◽  
Bayesian Decision Theory ◽  
Decision Problems ◽  
Maintenance Planning ◽  
Bayesian Decision ◽  
Generic Character ◽  
Different Types ◽  
Inspection And Maintenance

In the present paper an introduction is initially given on the interpretation of uncertainty and probability in engineering decision analysis and it is explained how, in some cases, uncertainties may change type depending on the “scale” of the applied modeling and as a function of time. Thereafter it is attempted to identify and outline the generic character of different engineering decision problems and to categorize these as prior, posterior, and preposterior decision problems, in accordance with the Bayesian decision theory. Finally, input is given to an ongoing discussion concerning the correctness and consistency of uncertainty modeling applied in the most recent reliability updating analysis for structural requalification and inspection and maintenance planning. To this end an outline is given in regard to appropriate uncertainty treatment in the probabilistic modeling for different types of decision problems.

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