Production and corrective maintenance planning control problem for failure-prone manufacturing systems

2003 ◽  
Author(s):  
E.K. Boukas ◽  
A. Communal
Keyword(s):  
Control Problem ◽  
Manufacturing Systems ◽  
Maintenance Planning ◽  
Corrective Maintenance

MINIMUM AVERAGE COST PRODUCTION PLANNING IN STOCHASTIC MANUFACTURING SYSTEMS

1998 ◽  
Vol 08 (07) ◽  
pp. 1251-1276 ◽  
Author(s):  
SURESH P. SETHI ◽  
HANQIN ZHANG ◽  
QING ZHANG
Keyword(s):  
Control Problem ◽  
Production Planning ◽  
Average Cost ◽  
Production Cost ◽  
Manufacturing Systems ◽  
Production Control ◽  
Production Capacity ◽  
Production Rates ◽  
Planning Problems

Recently, the production control problem in stochastic manufacturing systems has generated a great deal of interest. The goal is to obtain production rates to minimize total expected surplus and production cost. This paper reviews the research devoted to minimum average cost production planning problems in stochastic manufacturing systems. Manufacturing systems involve a single or parallel failure-prone machines producing a number of different products, random production capacity, and constant demands.

Machine Fault Diagnosis and Prognosis using Self-Organizing Map

2014 ◽  
pp. 129-148
Author(s):  
Kesheng Wang ◽  
Zhenyou Zhang ◽  
Yi Wang
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Manufacturing Systems ◽  
Maintenance Scheduling ◽  
Self Organizing Map ◽  
Corrective Maintenance ◽  
Diagnosis And Prognosis ◽  
Machine Fault ◽  
Fault Diagnosis And Prognosis ◽  
Self Organizing

This chapter proposes a Self-Organizing Map (SOM) method for fault diagnosis and prognosis of manufacturing systems, machines, components, and processes. The aim of this work is to optimize the condition monitoring of the health of the system. With this method, manufacturing faults can be classified, and the degradations can be predicted very effectively and clearly. A good maintenance scheduling can then be created, and the number of corrective maintenance actions can be reduced. The results of the experiment show that the SOM method can be used to classify the fault and predict the degradation of machines, components, and processes effectively, clearly, and easily.

Improved Markov stable state simulation for maintenance planning

2019 ◽  
Vol 25 (2) ◽  
pp. 199-212
Author(s):  
Chibundo Princewill Nwadinobi ◽  
Bethrand Nduka Nwankwojike ◽  
Fidelis Ibiang Abam
Keyword(s):  
Manufacturing Systems ◽  
Stable State ◽  
Mechanistic Modeling ◽  
Maintenance Planning ◽  
Probability Models ◽  
Actual Performance ◽  
Content Type ◽  
Mean Time Between Failures ◽  
Relevant Variables ◽  
Mean Time

Purpose The purpose of this paper is to propose a software (Equipment State Simulator) used for predicting equipment performance parameters required for maintenance planning. Design/methodology/approach This maintenance software was developed from the derived stable state probability models using algebraic substitution and computation of total operational period, number of breakdowns, total downtime, mean time between failures and mean time to repair of equipment/component(s) at preventive maintenance and corrective maintenance states. The models were derived using mechanistic modeling technique such that all the relevant variables were accounted for. Findings Analysis of this software revealed that its predictions reckon with the actual performance of the test specimens by about 99 percent. Originality/value The research proposes a maintenance model and software for predicting state probabilities of manufacturing systems degradation. This program also predicts maintenance action(s) required by the equipment based on the predetermined alert levels.

scholarly journals On the optimal control of single-stage hybrid manufacturing systems via novel and different variants of particle swarm optimization algorithm

2005 ◽  
Vol 2005 (3) ◽  
pp. 257-279 ◽  
Author(s):  
M. Senthil Arumugam ◽  
M. V. C. Rao
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Manufacturing Systems ◽  
Hypothesis Test ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Inertia Weight

This paper presents several novel approaches of particle swarm optimization (PSO) algorithm with new particle velocity equations and three variants of inertia weight to solve the optimal control problem of a class of hybrid systems, which are motivated by the structure of manufacturing environments that integrate process and optimal control. In the proposed PSO algorithm, the particle velocities are conceptualized with the local best (orpbest) and global best (orgbest) of the swarm, which makes a quick decision to direct the search towards the optimal (fitness) solution. The inertia weight of the proposed methods is also described as a function of pbest and gbest, which allows the PSO to converge faster with accuracy. A typical numerical example of the optimal control problem is included to analyse the efficacy and validity of the proposed algorithms. Several statistical analyses including hypothesis test are done to compare the validity of the proposed algorithms with the existing PSO technique, which adopts linearly decreasing inertia weight. The results clearly demonstrate that the proposed PSO approaches not only improve the quality but also are more efficient in converging to the optimal value faster.

Sign in / Sign up

Export Citation Format

Share Document