Title of article :
Predictable Maintenance: A Bayesian Network-based Model
Author/Authors :
Partovi ، Mohammad School of Industrial Engineering, College of Engineering - University of Tehran , Amra ، Mohsen Department of Industrial Engineering - Islamic Azad University, South-Tehran Branch , Pahlevanzadeh ، Mohammadjavad Department of Management and Accounting - College of Farabi - University of Tehran , Alwardi ، Abbas Industrial Engineering Department - Faculty of Industry, Management and Accounting - Shahabdanesh University , Fathi ، Mohammad Reza Department of Management and Accounting - College of Farabi - University of Tehran
Abstract :
Industries increasing progress and complexity has made maintenance and repair tasks very challenging, complex, and time-consuming. Maintenance is one of the important sectors in several industries, and improvement in this sector can have excellent results. This paper develops a new maintenance prediction model based on Bayesian networks (BN) capabilities. The models include several variables that experts determine and their influence on each other s-called conditional probability tables-which are learned from historical data. The model is implemented in an automobile repair department case study to show its performance. The model is evaluated through a sensitivity analysis, and the results show the proficiency of the proposal mode.
Keywords :
Maintenance , Prediction , Bayesian Networks , Conditional Probability
Journal title :
International Journal of Reliability, Risk and Safety: Theory and Application
Journal title :
International Journal of Reliability, Risk and Safety: Theory and Application
