scholarly journals Predictive Maintenance and Sensitivity Analysis for Equipment with Multiple Quality States

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiao Wang ◽  
Deyi Xu ◽  
Na Qu ◽  
Tianqi Liu ◽  
Fang Qu ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Preventive Maintenance ◽  
Simulation Method ◽  
Predictive Maintenance ◽  
Time Interval ◽  
Discrete State ◽  
Markov Decision ◽  
Optimal Maintenance ◽  
Test Objective ◽  
Equipment System

This paper discusses the predictive maintenance (PM) problem of a single equipment system. It is assumed that the equipment has deteriorating quality states as it operates, resulting in multiple yield levels represented as system observation states. We cast the equipment deterioration as discrete-state and continuous-time semi-Markov decision process (SMDP) model and solve the SMDP problem in reinforcement learning (RL) framework using the strategy-based method. In doing so, the goal is to maximize the system average reward rate (SARR) and generate the optimal maintenance strategy for given observation states. Further, the PM time is capable of being produced by a simulation method. In order to prove the advantage of our proposed method, we introduce the standard sequential preventive maintenance algorithm with unequal time interval. Our proposed method is compared with the sequential preventive maintenance algorithm in a test objective of SARR, and the results tell us that our proposed method can outperform the sequential preventive maintenance algorithm. In the end, the sensitivity analysis of some parameters on the PM time is given.

MAINTENANCE OPTIMIZATION OF EQUIPMENT BY LINEAR PROGRAMMING

2005 ◽  
Vol 20 (1) ◽  
pp. 183-193 ◽  
Author(s):  
Archana Jayakumar ◽  
Sohrab Asgarpoor
Keyword(s):  
Linear Programming ◽  
Preventive Maintenance ◽  
Mathematical Optimization ◽  
Optimal Solution ◽  
Cost Effective ◽  
Maintenance Policy ◽  
Markov Decision ◽  
Random Failure ◽  
Optimal Maintenance ◽  
Optimal Maintenance Policy

Optimal levels of preventive maintenance performed on any system ensures cost-effective and reliable operation of the system. In this paper a component with deterioration and random failure is modeled using Markov processes while incorporating the concept of minor and major preventive maintenance. The optimal mean times to preventive maintenance (both minor and major) of the component is determined by maximizing its availability with respect to mean time to preventive maintenance. Mathematical optimization programs Maple 7 and Lingo 7 are used to find the optimal solution, which is illustrated using a numerical example. Further, an optimal maintenance policy is obtained using Markov Decision Processes (MDPs). Linear Programming (LP) is utilized to implement the MDP problem.

An Optimal Maintenance Policy for a Queueing System Server Under Periodic Observation

1997 ◽  
Vol 04 (04) ◽  
pp. 357-367 ◽  
Author(s):  
Junji Koyanagi ◽  
Hajime Kawai
Keyword(s):  
Preventive Maintenance ◽  
Queueing System ◽  
Observation Time ◽  
Maintenance Policy ◽  
Corrective Maintenance ◽  
Markov Decision ◽  
Poisson Stream ◽  
Optimal Maintenance ◽  
Optimal Maintenance Policy ◽  
Time Of Failure

This paper describes an optimal maintenance policy for an M/M/1 queueing system server. Customers arrive at the system in a Poisson stream and are served by the exponential server. After a random time, the server is interrupted by a failure and this failure is detected through regularly timed observations. We begin corrective maintenance when we detect the failure. Through the failure of the server, we lose the customers in the system at the time of failure, as well as the customers that arrive between the failure and the completion of corrective maintenance. However, it is possible to avoid the failure and subsequent corrective maintenance by performing preventive maintenance at observation time. It is true that customers in the system at the start of preventive maintenance and those that arrive prior to its completion are lost. Since the queueing system should serve as many customers as possible, our objective is to minimize the number of lost customers. We then formulate this problem as a semi-Markov decision process and prove the switch curve structure of the optimal policy.

scholarly journals Sensitivity Analysis of Sentinel-1 Backscatter to Oil Palm Plantations at Pluriannual Scale: A Case Study in Gabon, Africa

2021 ◽  
Vol 13 (11) ◽  
pp. 2075
Author(s):  
J. David Ballester-Berman ◽  
Maria Rastoll-Gimenez
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Oil Palm ◽  
Time Interval ◽  
Potential Candidate ◽  
Initial Growth ◽  
Natural Forests ◽  
Wet Season ◽  
Radar Signatures ◽  
The Times

The present paper focuses on a sensitivity analysis of Sentinel-1 backscattering signatures from oil palm canopies cultivated in Gabon, Africa. We employed one Sentinel-1 image per year during the 2015–2021 period creating two separated time series for both the wet and dry seasons. The first images were almost simultaneously acquired to the initial growth stage of oil palm plants. The VH and VV backscattering signatures were analysed in terms of their corresponding statistics for each date and compared to the ones corresponding to tropical forests. The times series for the wet season showed that, in a time interval of 2–3 years after oil palm plantation, the VV/VH ratio in oil palm parcels increases above the one for forests. Backscattering and VV/VH ratio time series for the dry season exhibit similar patterns as for the wet season but with a more stable behaviour. The separability of oil palm and forest classes was also quantitatively addressed by means of the Jeffries–Matusita distance, which seems to point to the C-band VV/VH ratio as a potential candidate for discrimination between oil palms and natural forests, although further analysis must still be carried out. In addition, issues related to the effect of the number of samples in this particular scenario were also analysed. Overall, the outcomes presented here can contribute to the understanding of the radar signatures from this scenario and to potentially improve the accuracy of mapping techniques for this type of ecosystems by using remote sensing. Nevertheless, further research is still to be done as no classification method was performed due to the lack of the required geocoded reference map. In particular, a statistical assessment of the radar signatures should be carried out to statistically characterise the observed trends.

scholarly journals Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation

2007 ◽  
Vol 11 (2) ◽  
pp. 793-817 ◽  
Author(s):  
Y. Tang ◽  
P. Reed ◽  
T. Wagener ◽  
K. van Werkhoven
Keyword(s):  
Sensitivity Analysis ◽  
Model Identification ◽  
Local Analysis ◽  
Time Interval ◽  
Watershed Model ◽  
Soil Moisture Accounting ◽  
Analysis Methods ◽  
Accounting Model ◽  
Sobol’S Method ◽  
Comparative Results

Abstract. This study seeks to identify sensitivity tools that will advance our understanding of lumped hydrologic models for the purposes of model improvement, calibration efficiency and improved measurement schemes. Four sensitivity analysis methods were tested: (1) local analysis using parameter estimation software (PEST), (2) regional sensitivity analysis (RSA), (3) analysis of variance (ANOVA), and (4) Sobol's method. The methods' relative efficiencies and effectiveness have been analyzed and compared. These four sensitivity methods were applied to the lumped Sacramento soil moisture accounting model (SAC-SMA) coupled with SNOW-17. Results from this study characterize model sensitivities for two medium sized watersheds within the Juniata River Basin in Pennsylvania, USA. Comparative results for the 4 sensitivity methods are presented for a 3-year time series with 1 h, 6 h, and 24 h time intervals. The results of this study show that model parameter sensitivities are heavily impacted by the choice of analysis method as well as the model time interval. Differences between the two adjacent watersheds also suggest strong influences of local physical characteristics on the sensitivity methods' results. This study also contributes a comprehensive assessment of the repeatability, robustness, efficiency, and ease-of-implementation of the four sensitivity methods. Overall ANOVA and Sobol's method were shown to be superior to RSA and PEST. Relative to one another, ANOVA has reduced computational requirements and Sobol's method yielded more robust sensitivity rankings.

scholarly journals A dynamic Bayesian network approach for prognosis computations on discrete state systems

2017 ◽  
Vol 231 (5) ◽  
pp. 516-533 ◽  
Author(s):  
Josquin Foulliaron ◽  
Laurent Bouillaut ◽  
Patrice Aknin ◽  
Anne Barros
Keyword(s):  
Complex Systems ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Multicomponent System ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Discrete State ◽  
Virtual Maintenance

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented.

Reliability/risk centered cost effective preventive maintenance planning of generating units

2018 ◽  
Vol 35 (9) ◽  
pp. 2052-2079 ◽  
Author(s):  
Umamaheswari E. ◽  
Ganesan S. ◽  
Abirami M. ◽  
Subramanian S.
Keyword(s):  
Preventive Maintenance ◽  
Optimization Technique ◽  
Solution Space ◽  
Fuzzy Decision Making ◽  
Content Type ◽  
Multi Objective ◽  
Statistical Measures ◽  
Highly Nonlinear ◽  
Optimal Maintenance ◽  
Maintenance Schedules

Purpose Finding the optimal maintenance schedules is the primitive aim of preventive maintenance scheduling (PMS) problem dealing with the objectives of reliability, risk and cost. Most of the earlier works in the literature have focused on PMS with the objectives of leveling reserves/risk/cost independently. Nevertheless, very few publications in the current literature tackle the multi-objective PMS model with simultaneous optimization of reliability, and economic perspectives. Since, the PMS problem is highly nonlinear and complex in nature, an appropriate optimization technique is necessary to solve the problem in hand. The paper aims to discuss these issues. Design/methodology/approach The complexity of the PMS problem in power systems necessitates a simple and robust optimization tool. This paper employs the modern meta-heuristic algorithm, namely, Ant Lion Optimizer (ALO) to obtain the optimal maintenance schedules for the PMS problem. In order to extract best compromise solution in the multi-objective solution space (reliability, risk and cost), a fuzzy decision-making mechanism is incorporated with ALO (FDMALO) for solving PMS. Findings As a first attempt, the best feasible maintenance schedules are obtained for PMS problem using FDMALO in the multi-objective solution space. The statistical measures are computed for the test systems which are compared with various meta-heuristic algorithms. The applicability of the algorithm for PMS problem is validated through statistical t-test. The statistical comparison and the t-test results reveal the superiority of ALO in achieving improved solution quality. The numerical and statistical results are encouraging and indicate the viability of the proposed ALO technique. Originality/value As a maiden attempt, FDMALO is used to solve the multi-objective PMS problem. This paper fills the gap in the literature by solving the PMS problem in the multi-objective framework, with the improved quality of the statistical indices.

scholarly journals Fractional-Order Modelling and Optimal Control of Cholera Transmission

2021 ◽  
Vol 5 (4) ◽  
pp. 261
Author(s):  
Silvério Rosa ◽  
Delfim F. M. Torres
Keyword(s):  
Mathematical Model ◽  
Optimal Control ◽  
Sensitivity Analysis ◽  
Fractional Order ◽  
Control Measures ◽  
Time Interval ◽  
Variable Order ◽  
Effectiveness Analysis ◽  
The Cost ◽  
Derivative Order

A Caputo-type fractional-order mathematical model for “metapopulation cholera transmission” was recently proposed in [Chaos Solitons Fractals 117 (2018), 37–49]. A sensitivity analysis of that model is done here to show the accuracy relevance of parameter estimation. Then, a fractional optimal control (FOC) problem is formulated and numerically solved. A cost-effectiveness analysis is performed to assess the relevance of studied control measures. Moreover, such analysis allows us to assess the cost and effectiveness of the control measures during intervention. We conclude that the FOC system is more effective only in part of the time interval. For this reason, we propose a system where the derivative order varies along the time interval, being fractional or classical when more advantageous. Such variable-order fractional model, that we call a FractInt system, shows to be the most effective in the control of the disease.

scholarly journals Preventive Maintenance Interval Prediction: a Spare Parts Inventory Cost and Lost Earning Based Model

2015 ◽  
Vol 5 (3) ◽  
pp. 811-817
Author(s):  
O. A. Adebimpe ◽  
V. Oladokun ◽  
O. E. Charles-Owaba
Keyword(s):  
Preventive Maintenance ◽  
Spare Parts ◽  
Manufacturing Firms ◽  
Inventory Cost ◽  
Manufacturing Firm ◽  
Interval Prediction ◽  
Spare Parts Inventory ◽  
Optimal Maintenance

In this paper, some preventive maintenance parameters in manufacturing firms were identified and used to develop cost based functions in terms of machine preventive maintenance. The proposed cost based model considers system’s reliability, cost of keeping spare parts inventory and lost earnings in deriving optimal maintenance interval. A case of a manufacturing firm in Nigeria was observed and the data was used to evaluate the model.

scholarly journals IoT-based Running Time Monitoring System for Machine Preventive Maintenance Scheduling

ELKHA ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 33
Author(s):  
Erwin Sitompul ◽  
Agus Rohmat
Keyword(s):  
Monitoring System ◽  
Preventive Maintenance ◽  
Real Life ◽  
Remote Access ◽  
Time Interval ◽  
Monitoring And Control ◽  
Running Time ◽  
Damage And Failure ◽  
Simulation Purpose ◽  
And Control

Machines are valuable assets that need to be protected from damage and failure through proper maintenance measures. This paper proposes a system that automatically monitors the running time of machines and sends notifications regarding their preventive maintenance (PM) schedules. The system core consists of a programmable logic controller (PLC) and a human machine interface (HMI). The HMI is connected to an online platform via internet connection provided by a router, so that the monitoring result can be accessed via Android smartphone or laptop/PC. This IoT-based running time monitoring system (IRTMS) will be particularly helpful in implementation at an production site that consists of multiple various machines. The PM items of a machine may vary from cleaning, changing single component, to an overhaul, each with different time interval. By using the IRTMS, the user will have an overview of the PM schedules anytime and anywhere. The preparation of material, components, or tools can be known ahead of time. For simulation purpose, a prototype is constructed by using components as used in industrial real-life condition. Four output connections are provided to simulate the simultaneous monitoring of four machines. The IRTMS prototype is tested and completely successful on doing the running time monitoring, the running time reset, the PM notifications, and the remote access for monitoring and control.

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