9,182 research outputs found
Continuous maintenance and the future – Foundations and technological challenges
High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security
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
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
Prescriptive System for Reconfigurable Manufacturing Systems considering Variable Demand and Production Rates
O mercado atual é dinâmico criando a necessidade de resposta a mudanças imprevisÃveis 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 constituÃdo 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 indivÃduos 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 possÃvel 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
Machine Learning Approach for Degradation Path Prediction Using Different Models and Architectures of Artificial Neural Networks
Degradation and failure prediction has become more and more crucial for maintenance planning and scheduling, the decision-making process, and many other areas of manufacturing systems. This paper presents an approach where different artificial neural network models were developed to predict the degradation path of a machine component using different architectures, including fully connected networks (FCN) and arbitrarily connected networks (ACN). These models were trained using the Neuron-by-Neuron (NBN) training algorithm with forward-backward computations, where NBN is an improved form of the Levenberg-Marquardt (LM) algorithm, combined with FCN and ACN architectures, which can be trained efficiently, it can give more accurate predictions with a fewer number of neurons used. The developed models were evaluated using the statistical performance measure of the sum of squared error (SSE). The results show that the used networks are successfully able to predict the degradation path; the 8-neurons model of FCN architecture and the 3-neurons model of ACN architecture with tanh (mbib) hidden layers activation function and linear function (mlin) of the outputs have the lowest prediction error (SSE) among all the developed models. The use of such architectures combined with NBN training algorithm can easily model manufacturing systems with complex component structures that provide a vast amount of data
Investigation of degradation and upgradation models for flexible unit systems: a systematic literature review
Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS.The project is funded by the Department of Science and Technology, Science & Engineering
Research Board (DST-SERB), Statutory Body Established through an Act of Parliament: SERB Act
2008, Government of India with Sanction Order No ECR/2016/001808, and also by FCT—Fundação
para a Ciência e Tecnologia through the R&D Units Project Scope: UIDB/00319/2020
State of the art in simulation-based optimisation for maintenance systems
Recently, more attention has been directed towards improving and optimising maintenance in manufacturing systems using simulation. This paper aims to report the state of the art in simulation-based optimisation of maintenance by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. The authors investigate application areas and published real case studies as well as researched maintenance strategies and policies. Much of the research in this area is focusing on preventive maintenance and optimising preventive maintenance frequency that will lead to the minimum cost. Discrete event simulation was the most reported technique to model maintenance systems whereas modern optimisation methods such as Genetic Algorithms was the most reported optimisation method in the literature. On this basis, the paper identifies the current gaps and discusses future prospects. Further research can be done to develop a framework that guides the experimenting process with different maintenance strategies and policies. More real case studies can be conducted on multi-objective optimisation and condition based maintenance especially in a production context
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