Optimisation of Capacitated Planned Preventive Maintenance in Multiple Production Lines Using Optimisation-in-the-Loop Simulation

In a mass customisation manufacturing system, the production schedule is tailored to the customer's specifications. However, the production system must be accompanied by an effective maintenance program to ensure that the production lines operate as intended. The purpose of this study is to optimise planned preventive maintenance across multiple production lines. An optimised Weibull distribution is proposed to model the machine's Mean Time Between Failures (MTBF), and the total expected maintenance cost is calculated using this distribution, taking into account the probability of the machines remaining operational and failing. Because the optimised Weibull distribution is a continuous distribution, in order to simulate the continuous time domain, it will be divided into several sub-systems and optimised using Bayesian optimisation during simulation. The maintenance scheduling is carried out by considering available time capacity after production scheduling was arranged. The study's findings indicate that the proposed method successfully optimised the planned maintenance schedule without interfering production activity with total cost for the proposed maintenance planning as low as IDR 50.017,75/maintenance unit time

Optimisation of Capacitated Planned Preventive Maintenance in Multiple Production Lines Using Optimisation-in-the-Loop Simulation

In a mass customisation manufacturing system, the production schedule is tailored to the customer's specifications. However, the production system must be accompanied by an effective maintenance program to ensure that the production lines operate as intended. The purpose of this study is to optimise planned preventive maintenance across multiple production lines. An optimised Weibull distribution is proposed to model the machine's Mean Time Between Failures (MTBF), and the total expected maintenance cost is calculated using this distribution, taking into account the probability of the machines remaining operational and failing. Because the optimised Weibull distribution is a continuous distribution, in order to simulate the continuous time domain, it will be divided into several sub-systems and optimised using Bayesian optimisation during simulation. The maintenance scheduling is carried out by considering available time capacity after production scheduling was arranged. The study's findings indicate that the proposed method successfully optimised the planned maintenance schedule without interfering production activity with total cost for the proposed maintenance planning as low as IDR 50.017,75/maintenance unit time

A Data-Driven Predictive Model of Reliability Estimation Using State-Space Stochastic Degradation Model

The concept of the Industrial Internet of Things (IIoT) provides the foundation to apply data-driven methodologies. The data-driven predictive models of reliability estimation can become a major tool in increasing the life of assets, lowering capital cost, and reducing operating and maintenance costs. Classical models of reliability assessment mainly rely on lifetime data. Failure data may not be easily obtainable for highly reliable assets. Furthermore, the collected historical lifetime data may not be able to accurately describe the behavior of the asset in a unique application or environment. Therefore, it is not an optimal approach anymore to conduct a reliability estimation based on classical models. Fortunately, most of the industrial assets have performance characteristics whose degradation or decay over the operating time can be related to their reliability estimates. The application of the degradation methods has been recently increasing due to their ability to keep track of the dynamic conditions of the system over time. The main purpose of this study is to develop a data-driven predictive model of reliability assessment based on real-time data using a state-space stochastic degradation model to predict the critical time for initiating maintenance actions in order to enhance the value and prolonging the life of assets. The new degradation model developed in this thesis is introducing a new mapping function for the General Path Model based on series of Gamma Processes degradation models in the state-space environment by considering Poisson distributed weights for each of the Gamma processes. The application of the developed algorithm is illustrated for the distributed electrical systems as a generic use case. A data-driven algorithm is developed in order to estimate the parameters of the new degradation model. Once the estimates of the parameters are available, distribution of the failure time, time-dependent distribution of the degradation, and reliability based on the current estimate of the degradation can be obtained

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

Reliability Modeling and Optimization Strategy for Manufacturing System Based on RQR Chain

Accurate and dynamic reliability modeling for the running manufacturing system is the prerequisite to implement preventive maintenance. However, existing studies could not output the reliability value in real time because their abandonment of the quality inspection data originated in the operation process of manufacturing system. Therefore, this paper presents an approach to model the manufacturing system reliability dynamically based on their operation data of process quality and output data of product reliability. Firstly, on the basis of importance explanation of the quality variations in manufacturing process as the linkage for the manufacturing system reliability and product inherent reliability, the RQR chain which could represent the relationships between them is put forward, and the product qualified probability is proposed to quantify the impacts of quality variation in manufacturing process on the reliability of manufacturing system further. Secondly, the impact of qualified probability on the product inherent reliability is expounded, and the modeling approach of manufacturing system reliability based on the qualified probability is presented. Thirdly, the preventive maintenance optimization strategy for manufacturing system driven by the loss of manufacturing quality variation is proposed. Finally, the validity of the proposed approach is verified by the reliability analysis and optimization example of engine cover manufacturing system

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

Toward intelligent cyber-physical systems: Digital twin meets artificial intelligence

Industry 4.0 aims to support smarter and autonomous processes while improving agility, cost efficiency, and user experience. To fulfill its promises, properly processing the data of the industrial processes and infrastructures is required. Artificial intelligence (AI) appears as a strong candidate to handle all generated data, and to help in the automation and smartification process. This article overviews the digital twin as a true embodiment of a cyber-physical system (CPS) in Industry 4.0, showing the mission of AI in this concept. It presents the key enabling technologies of the digital twin such as edge, fog, and 5G, where the physical processes are integrated with the computing and network domains. The role of AI in each technology domain is identified by analyzing a set of AI agents at the application and infrastructure levels. Finally, movement prediction is selected and experimentally validated using real data generated by a digital twin for robotic arms with results showcasing its potential.This work has been (partially) funded by the H2020 EU/TW joint action 5G-DIVE (Grant #859881) and the H2020 5Growth project (Grant #856709). It has also been funded by the Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52/AEI/10.13039/501100011033)