Integrated production quality and condition-based maintenance optimisation for a stochastically deteriorating manufacturing system

This paper investigates the problem of optimally integrating production quality and condition-based maintenance in a stochastically deteriorating single- product, single-machine production system. Inspections are periodically performed on the system to assess its actual degradation status. The system is considered to be in ‘fail mode’ whenever its degradation level exceeds a predetermined threshold. The proportion of non-conforming items, those that are produced during the time interval where the degradation is beyond the specification threshold, are replaced either via overtime production or spot market purchases. To optimise preventive maintenance costs and at the same time reduce production of non-conforming items, the degradation of the system must be optimally monitored so that preventive maintenance is carried out at appropriate time intervals. In this paper, an integrated optimisation model is developed to determine the optimal inspection cycle and the degradation threshold level, beyond which preventive maintenance should be carried out, while minimising the sum of inspection and maintenance costs, in addition to the production of non-conforming items and inventory costs. An expression for the total expected cost rate over an infinite time horizon is developed and solution method for the resulting model is discussed. Numerical experiments are provided to illustrate the proposed approach

Modelo de apoio à decisão para a manutenção condicionada de equipamentos produtivos

Doctoral Thesis for PhD degree in Industrial and Systems EngineeringIntroduction: This thesis describes a methodology to combine Bayesian control chart and CBM (Condition-Based Maintenance) for developing a new integrated model. In maintenance management, it is a challenging task for decision-maker to conduct an appropriate and accurate decision. Proper and well-performed CBM models are beneficial for maintenance decision making. The integration of Bayesian control chart and CBM is considered as an intelligent model and a suitable strategy for forecasting items failures as well as allow providing an effectiveness maintenance cost. CBM models provides lower inventory costs for spare parts, reduces unplanned outage, and minimize the risk of catastrophic failure, avoiding high penalties associated with losses of production or delays, increasing availability. However, CBM models need new aspects and the integration of new type of information in maintenance modeling that can improve the results. Objective: The thesis aims to develop a new methodology based on Bayesian control chart for predicting failures of item incorporating simultaneously two types of data: key quality control measurement and equipment condition parameters. In other words, the project research questions are directed to give the lower maintenance costs for real process control. Method: The mathematical approach carried out in this study for developing an optimal Condition Based Maintenance policy included the Weibull analysis for verifying the Markov property, Delay time concept used for deterioration modeling and PSO and Monte Carlo simulation. These models are used for finding the upper control limit and the interval monitoring that minimizes the (maintenance) cost function. Result: The main contribution of this thesis is that the proposed model performs better than previous models in which the hypothesis of using simultaneously data about condition equipment parameters and quality control measurements improve the effectiveness of integrated model Bayesian control chart for Condition Based Maintenance.Introdução: Esta tese descreve uma metodologia para combinar Bayesian control chart e CBM (Condition- Based Maintenance) para desenvolver um novo modelo integrado. Na gestão da manutenção, é importante que o decisor possa tomar decisões apropriadas e corretas. Modelos CBM bem concebidos serão muito benéficos nas tomadas de decisão sobre manutenção. A integração dos gráficos de controlo Bayesian e CBM é considerada um modelo inteligente e uma estratégica adequada para prever as falhas de componentes bem como produzir um controlo de custos de manutenção. Os modelos CBM conseguem definir custos de inventário mais baixos para as partes de substituição, reduzem interrupções não planeadas e minimizam o risco de falhas catastróficas, evitando elevadas penalizações associadas a perdas de produção ou atrasos, aumentando a disponibilidade. Contudo, os modelos CBM precisam de alterações e a integração de novos tipos de informação na modelação de manutenção que permitam melhorar os resultados.Objetivos: Esta tese pretende desenvolver uma nova metodologia baseada Bayesian control chart para prever as falhas de partes, incorporando dois tipos de dados: medições-chave de controlo de qualidade e parâmetros de condição do equipamento. Por outras palavras, as questões de investigação são direcionadas para diminuir custos de manutenção no processo de controlo.Métodos: Os modelos matemáticos implementados neste estudo para desenvolver uma política ótima de CBM incluíram a análise de Weibull para verificação da propriedade de Markov, conceito de atraso de tempo para a modelação da deterioração, PSO e simulação de Monte Carlo. Estes modelos são usados para encontrar o limite superior de controlo e o intervalo de monotorização para minimizar a função de custos de manutenção.Resultados: A principal contribuição desta tese é que o modelo proposto melhora os resultados dos modelos anteriores, baseando-se na hipótese de que, usando simultaneamente dados dos parâmetros dos equipamentos e medições de controlo de qualidade. Assim obtém-se uma melhoria a eficácia do modelo integrado de Bayesian control chart para a manutenção condicionada

Experimental Investigation of the Impact of Goal-Oriented Mental Imagery on Reward Perception

Aims: Recent studies have shown that mood can bias perceived reward value, with this effect being strongest in individuals with more mood instability. Spontaneous use of mental imagery has been highlighted as an important feature in generating and maintaining mood symptoms in bipolar disorder. We examined whether mental imagery influencing motivation biases perceived reward value during learning, and to what extent effects are modulated by mood symptoms. Method: 50 healthy participants completed a brief, online-based manipulation in which they generated mental images related to goal-attainment and goal-failure with a view to increasing and decreasing motivation, respectively. We quantified the efficacy of this manipulation on mood and motivation, as well as on the perception of reward stimuli encountered in two learning blocks. Participants performed each block under one of the two types of imagery, thus using a within-participants design. To test for bias in perceived reward value, participants were subsequently asked to indicate their preference in pairwise choices between all stimuli encountered. Trait mood instability (HPS), propensity towards imagery (SUIS), and depression symptoms (PHQ-9) were included in analyses to test for modulatory effects on biased preference. Results: Goal-oriented mental imagery effectively impacted subjective motivation, with higher ratings in the goal-attainment imagery block, compared to goal-failure. Depression symptoms, but not mood instability, were observed to have a modulating effect on change in motivational state. The degree to which momentary motivation was impacted by imagery was positively associated with bias in perceived reward value, and further modulated by depression symptoms. Conclusions: Our findings indicate that goal-oriented mental imagery is effective in impacting motivational state in healthy individuals reporting more depression symptoms, and that motivational state in turn modulates reward perception. Insights are offered to aid development of interventions using mental imagery as an emotional and motivational “amplifier” to improve depressed mood

Condition-based maintenance of multi-component systems with degradation state-rate interactions

This paper presents an approach to optimise condition-based maintenance (CBM) of multi-component systems where the state of certain components could affect the rate of degradation of other components, i.e., state-rate degradation interactions. We present a real example of an industrial cold box in a petrochemical plant, where data collected on fouling of its tubes show that the extent of fouling of one tube affects the rate of fouling of other tubes due to overloading. A regression model is used to characterise the state-rate degradation interactions for this example. Further, we optimise the condition-based maintenance policy for this system using simulated annealing. The outcomes of the case study demonstrate that modelling degradation interactions between components in the system can have significant positive impact on CBM policy of the system. The paper therefore tackles a problem that has not been addressed in the literature, paving way for further developments in this important area of research with practical applications.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.ress.2015.11.01

What are the effects of the reliability model uncertainties in the maintenance decisions?

Most of the works proposed for the design of reliability test plans  are  devoted  to  the  guaranty  of  the  reliability performance  of  a  product  but  scarce  of  them  tackles maintenance  issues.  On  the  other  hand,  classical maintenance  optimization  criteria  rarely  take  into  account the variability of the failure parameters due to lack of data, especially when the data collection in the operating phase is expensive.  The  objective  of  this  paper  is  to  highlight through a numerical experiment the impact of the test plan design  defined  here  by  the  number  of  the  products  to  be tested and the test duration on the performance of a classical condition-based maintenance (CBM) policy