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.

Risk-Based Maintenance Planning for Deteriorating Pressure Vessels With Multiple Defects

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Shane Haladuick ◽  
Markus R. Dann
Keyword(s):  
Decision Analysis ◽  
Failure Modes ◽  
Oil And Gas ◽  
Pressure Vessels ◽  
Optimal Decision ◽  
Optimal Time ◽  
Analysis Framework ◽  
Simple Method ◽  
Industrial Facilities ◽  
Multiple Defects

Pressure vessels are subject to deterioration processes, such as corrosion and fatigue, which can lead to failure. Inspections and repairs are performed to mitigate this risk. Large industrial facilities (e.g., oil and gas refineries) often have regularly scheduled shutdown periods during which many components, including the pressure vessels, are disassembled, inspected, and repaired if necessary. This paper presents a decision analysis framework for the risk-based maintenance (RBM) planning of corroding pressure vessels. After a vessel has been inspected, this framework determines the optimal maintenance time of each defect, where the optimal time is the one that minimizes the total expected cost over the lifecycle of the vessel. The framework allows for multiple defects and two failure modes (leak and burst), and accounts for the dependent failure events. A stochastic gamma process is used to model the future deterioration growth to determine the probability of vessel failure. The novel growth model presents a simple method to predict both the depth and length of each corrosion defect to enable burst analysis. The decision analysis framework can aid decision makers in deciding when a repair or replacement should be performed. This method can be used to immediately inform the decision maker of the optimal decision postinspection. A numerical example of a corroding pressure vessel illustrates the method.

Uncertainty Modeling and Probabilities in Engineering Decision Analysis

2003 ◽  
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

The present paper initially gives a basic introduction on the interpretation of uncertainty and probability in engineering decision analysis and explains 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 pre-posterior decision problems in accordance with the Bayesian decision theory. Finally an input is given to an ongoing discussion concerning the correctness and consistency of the uncertainty modeling applied in most recent reliability updating analysis for structural re-qualification and inspection and maintenance planning. To this end an outline is given in regard to the appropriate uncertainty treatment in the probabilistic modeling for different types of decision problems.

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.

Optimized inchworm motion planning for a novel in-pipe robot

2013 ◽  
Vol 228 (7) ◽  
pp. 1248-1258 ◽  
Author(s):  
Qingyou Liu ◽  
Yonghua Chen ◽  
Tao Ren ◽  
Ying Wei
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Strain Energy ◽  
Modern Society ◽  
Robot Design ◽  
Stroke Length ◽  
Velocity Pattern ◽  
Optimization Result ◽  
Gradient Based ◽  
Inspection And Maintenance

Modern society is fueled by very comprehensive grids of gas and liquid supply pipelines. The frequent inspection and maintenance of such pipeline grids is not a trivial task. It has been demonstrated that such task is best performed by using in-pipe robots. In this paper, a novel inchworm robot design and its optimized motion planning are presented. The proposed design uses a helical drive for both gripping and locomotion of the robot. The extension and retraction between inchworm segments are facilitated by conic springs as they can store strain energy. The proposed inchworm robot can also be made very compact without sacrificing stroke length as conic springs can be easily designed with telescopic feature. To generate inchworm motion, a sinusoidal velocity pattern is planned for each segment. The frequency of the velocity pattern is optimized using a genetic algorithm (GA). The optimization result from the GA method has been validated using a traditional gradient based method.

Modern Approaches to Optimal Decision Analysis

2002 ◽  
pp. 607-642
Author(s):  
Byron K. Williams ◽  
James D. Nichols ◽  
Michael J. Conroy
Keyword(s):  
Decision Analysis ◽  
Optimal Decision
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