Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture

Cachada, Ana; Barbosa, José; Leitno, Paulo; Gcraldcs, Carla A. S.; Deusdado, Leonel; Costa, Jacinta; Teixeira, Carlos; Teixeira, João Marcelo X. N.; Moreira, António H. J.; Miguel, Pedro Antonio Sánchez; Romero, Luís

doi:10.1109/etfa.2018.8502489

Cited by 154 publications

(79 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Data collection must take place often in (near) real time, which is why control tools that are capable of collecting data automatically from several components and systems are useful (Aivaliotis et al 2017). Data must not only be recorded but also processed and analyzed (Cachada et al 2018). Appropriate tools not only process sensor data but also take the maintenance history, operational data, design and application into account (Darwanto et al 2012).…”

Section: Predictive Maintenance and The Related Literaturementioning

confidence: 99%

Predictive maintenance as an internet of things enabled business model: A taxonomy

et al. 2020

View full text Add to dashboard Cite

Predictive maintenance (PdM) is an important application of the Internet of Things (IoT) discussed in many companies, especially in the manufacturing industry. PdM uses data, usually sensor data, to optimize maintenance activities. We develop a taxonomy to classify PdM business models that enables a comparison and analysis of such models. We use our taxonomy to classify the business models of 113 companies. Based on this classification, we identify six archetypes using cluster analysis and discuss the results. The “hardware development”, “analytics provider”, and “all-in-one” archetypes are the most frequently represented in the study sample. For cluster analysis, we use a visualization technique that involves an autoencoder. The results of our analysis will help practitioners assess their own business models and those of other companies. Business models can be better differentiated by considering the different levels of IoT architecture, which is also an important implication for further research.

show abstract

Section: Predictive Maintenance and The Related Literaturementioning

confidence: 99%

Predictive maintenance as an internet of things enabled business model: A taxonomy

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The classes of the ontology model are inspired by system architecture for the intelligent and predictive maintenance of an asset. 37 The ontology model not only captures domain knowledge and maintains the semantics of asset functions during operation but also inherits the basic characteristics of an asset DT for further simulations and analysis. The ontology model proposed is the generic representation of an asset DT during its operational phase.…”

Section: Dt Ontology Modelmentioning

confidence: 99%

Data management for developing digital twin ontology model

Singh

Shehab

Higgins

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Digital Twin (DT) is the imitation of the real world product, process or system. Digital Twin is the ideal solution for data-driven optimisations in different phases of the product lifecycle. With the rapid growth in DT research, data management for digital twin is a challenging field for both industries and academia. The challenges for DT data management are analysed in this article are data variety, big data & data mining and DT dynamics. The current research proposes a novel concept of DT ontology model and methodology to address these data management challenges. The DT ontology model captures and models the conceptual knowledge of the DT domain. Using the proposed methodology, such domain knowledge is transformed into a minimum data model structure to map, query and manage databases for DT applications. The proposed research is further validated using a case study based on Condition-Based Monitoring (CBM) DT application. The query formulation around minimum data model structure further shows the effectiveness of the current approach by returning accurate results, along with maintaining semantics and conceptual relationships along DT lifecycle. The method not only provides flexibility to retain knowledge along DT lifecycle but also helps users and developers to design, maintain and query databases effectively for DT applications and systems of different scale and complexities.

show abstract

“…É um ponto crucial para assegurar a eficiência da produção na indústria, pois a ocorrência de perturbações inesperadas leva à degradação do desempenho do sistema, ocasionando perda de produtividade e oportunidades de negócios. Porém, normalmente os custos associados às tarefas de manutenção em uma empresa de produção são extremamente significativos (Cachada 2018).…”

Section: Filosofias De Manutenção Industrialunclassified

Manutenção Industrial Eficiente: Um Estudo de Caso Aplicando Termografia.

Dinardi

Costa

Torné

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

View full text Add to dashboard Cite

The industry has constantly allied to the maintenance of its plant as a measure of optimization of profits, costs and development of advantages. The thermography proposes to be an improved tool in predictive maintenance. The aim of this paper is to study the applications of thermography in an industrial installation, verifying the time and efficiency of the tool. In an analysis of a system with a monthly average capacity of 165,652 kWh, thermal images were captured and correlated with the industrial maintenance philosophies and the maintenance sector reports of the plant. Performance was obtained with excellent agility in the method of monitoring and recording the main electrical system of the plant with the description of the defects, faults and conditioners discriminated using the thermography. For future work it is recommended to collect and cross-measure measurements of defective equipment, obtained by invasive methods and by thermographic method, to measure statistical reliability and operation data.

show abstract

Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture

Cited by 154 publications

References 15 publications

Predictive maintenance as an internet of things enabled business model: A taxonomy

Predictive maintenance as an internet of things enabled business model: A taxonomy

Data management for developing digital twin ontology model

Manutenção Industrial Eficiente: Um Estudo de Caso Aplicando Termografia.

Contact Info

Product

Resources

About