Optimization of maintenance policy under warranty length‐based demand with consideration of both manufacturer and buyer satisfaction

2020 ◽  
Vol 36 (4) ◽  
pp. 586-603
Author(s):  
Ali Salmasnia ◽  
Maryam Baratian
Keyword(s):  
Maintenance Policy ◽  
Warranty Length

Availability analysis and maintenance optimization for multiple failure mode systems considering imperfect repair

2021 ◽  
pp. 1748006X2110127
Author(s):  
Qingan Qiu ◽  
Baoliang Liu ◽  
Cong Lin ◽  
Jingjing Wang
Keyword(s):  
Failure Modes ◽  
Maintenance Cost ◽  
Imperfect Maintenance ◽  
Maintenance Policy ◽  
Cost Rate ◽  
Availability Analysis ◽  
Long Run ◽  
Optimal Maintenance ◽  
Maintenance Policies ◽  
Periodic Inspections

This paper studies the availability and optimal maintenance policies for systems subject to competing failure modes under continuous and periodic inspections. The repair time distribution and maintenance cost are both dependent on the failure modes. We investigate the instantaneous availability and the steady state availability of the system maintained through several imperfect repairs before a replacement is allowed. Analytical expressions for system availability under continuous and periodic inspections are derived respectively. The availability models are then utilized to obtain the optimal inspection and imperfect maintenance policy that minimizes the average long-run cost rate. A numerical example for Remote Power Feeding System is presented to demonstrate the application of the developed approach.

scholarly journals Prognostic and Classification of Dynamic Degradation in a Mechanical System Using Variance Gamma Process

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 254
Author(s):  
Marwa Belhaj Salem ◽  
Mitra Fouladirad ◽  
Estelle Deloux
Keyword(s):  
Mechanical System ◽  
Dynamic Environment ◽  
Machine Learning Algorithms ◽  
Maintenance Policy ◽  
Degradation Behaviour ◽  
Pump System ◽  
Variance Gamma ◽  
Variance Gamma Process ◽  
Operating Environments ◽  
Finite State

Recently, maintaining a complex mechanical system at the appropriate times is considered a significant task for reliability engineers and researchers. Moreover, the development of advanced mechanical systems and the dynamics of the operating environments raises the complexity of a system’s degradation behaviour. In this aspect, an efficient maintenance policy is of great importance, and a better modelling of the operating system’s degradation is essential. In this study, the non-monotonic degradation of a centrifugal pump system operating in the dynamic environment is considered and modelled using variance gamma stochastic process. The covariates are introduced to present the dynamic environmental effects and are modelled using a finite state Markov chain. The degradation of the system in the presence of covariates is modelled and prognostic results are analysed. Two machine learning algorithms k-nearest-neighbour (KNN) and neural network (NN) are applied to identify the various characteristics of degradation and the environmental conditions. A predefined degradation threshold is assigned and used to propose a prognostic result for each classification state. It was observed that this methodology shows promising prognostic results.

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