Prognostics-Based Two-Operator Competition for Maintenance and Service Part Logistics

Prognostics and timely maintenance of components are critical to the continuing operation of a system. By implementing prognostics, it is possible for the operator to maintain the system in the right place at the right time. However, the complexity in the real world makes near-zero downtime difficult to achieve partly because of a possible shortage of required service parts. This is realistic and quite important in maintenance practice. To coordinate with a prognostics-based maintenance schedule, the operator must decide when to order service parts and how to compete with other operators who also need the same parts. This research addresses a joint decision-making approach that assists two operators in making proactive maintenance decisions and strategically competing for a service part that both operators rely on for their individual operations. To this end, a maintenance policy involving competition in service part procurement is developed based on the Stackelberg game-theoretic model. Variations of the policy are formulated for three different scenarios and solved via either backward induction or genetic algorithm methods. Unlike the first two scenarios, the possibility for either of the operators being the leader in such competitions is considered in the third scenario. A numerical study on wind turbine operation is provided to demonstrate the use of the joint decision-making approach in maintenance and service part logistics

Stochastic RUL calculation enhanced with TDNN-based IGBT failure modeling

Power electronics are widely used in the transport and energy sectors. Hence, the reliability of these power electronic components is critical to reducing the maintenance cost of these assets. It is vital that the health of these components is monitored for increasing the safety and availability of a system. The aim of this paper is to develop a prognostic technique for estimating the remaining useful life (RUL) of power electronic components. There is a need for an efficient prognostic algorithm that is embeddable and able to support on-board real-time decision-making. A time delay neural network (TDNN) is used in the development of failure modes for an insulated gate bipolar transistor (IGBT). Initially, the time delay neural network is constructed from training IGBTs' ageing samples. A stochastic process is performed for the estimation results to compute the probability of the health state during the degradation process. The proposed TDNN fusion with a statistical approach benefits the probability distribution function by improving the accuracy of the results of the TDDN in RUL prediction. The RUL (i.e., mean and confidence bounds) is then calculated from the simulation of the estimated degradation states. The prognostic results are evaluated using root mean square error (RMSE) and relative accuracy (RA) prognostic evaluation metrics

Prescriptive System for Reconfigurable Manufacturing Systems considering Variable Demand and Production Rates

O mercado atual é dinâmico criando a necessidade de resposta a mudanças imprevisíveis de mercado por parte das empresas de forma a permanecerem competitivas. Para lidar com a mudança de paradigma, de produção em massa para customização em massa, a flexibilidade de manufatura é crucial. A atual digitalização da indústria proporciona novas oportunidades em relação a sistemas de apoio à decisão em tempo real permitindo que as empresas tomem decisões estratégicas e obtenham vantagem competitiva e valor comercial acrescido. Nesta dissertação pretende-se implementar um Sistema Prescritivo que sugere sequências de throughputs tendo em consideração objetivos de produção semanais e falhas em equipamentos num contexto de Manufatura Reconfigurável. O Sistema Prescritivo proposto é constituído por dois módulos: Simulação do ambiente de manufatura e o optimizador. O módulo de simulação é modelado com base em teoria de grafos e o optimizador com base em Algoritmos Genéticos. O seu output é uma sequência de throughputs que equilibram da melhor forma as ações de manutenção e produtividade. De forma a avaliar os indivíduos gerados pelo algoritmo genético, estes são aplicados ao primeiro módulo e o seu impacto no sistema de produção analisado. O sistema apresentado mostra notáveis melhorias na mitigação dos efeitos de downtime das máquinas durante a produção. As métricas utilizadas na medição do desempenho do sistema são a variação na produção de peças em relação ao target, descrito nesta dissertação como diferencial, e disponibilidade de produção do sistema. Todos os testes realizados apresentam um diferencial consideravelmente melhor e em certas instâncias, a disponibilidade aumenta ligeiramente. Não obstante, ainda que os resultados obtidos nas configurações testadas sejam robustos, necessita de mais estudos de modo a que seja possível a generalização dos resultados obtidos ao longo desta dissertação.The current market is dynamic and, consequently, industries need to be able to meet unpredictable market changes in order to remain competitive. To address the change in paradigm, from mass production to mass customization, manufacturing flexibility is key. Moreover, the current digitalization opens opportunities regarding real-time decision support systems allowing the companies to make strategic decisions and gain competitive advantage and business value. The aim of this dissertation is to implement a Prescriptive System that suggests sequences of throughputs that take into consideration weekly production targets and machine failures applied to Reconfigurable Manufacturing Systems. The Prescriptive System is mainly composed of two modules: manufacturing environment simulation and optimizer. The simulation module is modeled based on graph theory and the second one on Genetic Algorithms. Its output is a sequence of throughputs that best balances maintenance actions and productivity. In order to evaluate the individuals generated by the algorithm, candidate solutions are fed to the first module and their impact on the production system assessed. The proposed Prescriptive System shows large improvements in the mitigation of machines downtime effects in productivity when compared without any optimization approach. The metrics used to measure the performance of the system are the variation of pieces produced in relation to target, named in the current dissertation as differential, and Availability of the production system. In all tests performed, the differential largely improved and, in some instances, the availability slightly increased. Despite the robust results obtained in the tested configurations, further research should be conducted in order to be able to generalize the obtained results in this dissertation to non-tested configurations

Integrated maintenance and mission planning using remaining useful life information

The modern world requires high reliability and availability with minimum ownership cost for complex industrial systems (high-value assets). Maintenance and mission planning are two major interrelated tasks affecting availability and ownership cost. Both tasks play critical roles in cost savings and effective utilization of the assets, and cannot be performed without taking each other into consideration. Maintenance schedule may make an asset unavailable or too risky to use for a mission. Mission type and duration affect the health of the system, which affects the maintenance schedule. This article presents a mathematical formulation for integrated maintenance and mission planning for a fleet of high-value assets, using their current and forecast health information. An illustrative example for a fleet of unmanned aerial vehicles is demonstrated and evolutionary-based solutions are presented

Predictive Maintenance on the Machining Process and Machine Tool

This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces

Techniques and Emerging Trends for State of the Art Equipment Maintenance Systems - A Bibliometric Analysis

The increasing interconnection of machines in industrial production on one hand, and the improved capabilities to store, retrieve, and analyze large amounts of data on the other, offer promising perspectives for maintaining production machines. Recently, predictive maintenance has gained increasing attention in the context of equipment maintenance systems. As opposed to other approaches, predictive maintenance relies on machine behavior models, which offer several advantages. In this highly interdisciplinary field, there is a lack of a literature review of relevant research fields and realization techniques. To obtain a comprehensive overview on the state of the art, large data sets of relevant literature need to be considered and, best case, be automatically partitioned into relevant research fields. A proper methodology to obtain such an overview is the bibliometric analysis method. In the presented work, we apply a bibliometric analysis to the field of equipment maintenance systems. To be more precise, we analyzed clusters of identified literature with the goal to obtain deeper insight into the related research fields. Moreover, cluster metrics reveal the importance of a single paper and an investigation of the temporal cluster development indicates the evolution of research topics. In this context, we introduce a new measure to compare results from different time periods in an appropriate way. In turn, among others, this simplifies the analysis of topics, with a vast amount of subtopics. Altogether, the obtained results particularly provide a comprehensive overview of established techniques and emerging trends for equipment maintenance systems

Predicting Failure Probability in Industry 4.0 Production Systems: A Workload-Based Prognostic Model for Maintenance Planning

Maintenance of equipment is a crucial issue in almost all industrial sectors as it impacts the quality, safety, and productivity of any manufacturing system. Additionally, frequent production rescheduling due to unplanned and unintended interruptions can be very time consuming, especially in the case of centrally controlled systems. Therefore, the ability to estimate the likelihood that a monitored machine will successfully complete a predefined workload, taking into account both historical data from the machine’s sensors and the impending workload, may be essential in supporting a new approach to scheduling activities in an Industry 4.0 production system. This study proposes a novel approach for integrating machine workload information into a well-established PHM algorithm for Industry 4.0, with the aim of improving maintenance strategies in the manufacturing process. The proposed approach utilises a logistic regression model to assess the health condition of equipment and a neural network computational model to estimate its failure probability according to the scheduled workloads. Results from a prototypal case study showed that this approach leads to an improvement in the prediction of the likelihood of completing a scheduled job, resulting in improved autonomy of CPSs in accepting or declining scheduled jobs based on their forecasted health state, and a reduction in maintenance costs while maximising the utilisation of production resources. In conclusion, this study is beneficial for the present research community as it extends the traditional condition-based maintenance diagnostic approach by introducing prognostic capabilities at the plant shop floor, fully leveraging the key enabling technologies of Industry 4.0