Realidade aumentada na indústria 4.0 – análise bibliométrica sobre orientações, aplicações e implementações / Augmented reality in industry 4.0 - bibliometric analysis on guidelines, applications and implementations

Considerando a necessidade de implantações de tecnologias da informação e comunicação desenvolvidas para a indústria 4.0 propostas para serem utilizadas na área de manutenção industrial, esta pesquisa tem como propósito realizar a busca de informações a respeito de publicações que possam orientar a utilização e a implantação de Realidade Aumentada nas indústrias brasileiras que necessitam se adequar as novas exigências de competitividade mundial, imprescindíveis a sua sobrevivência futura, de forma a acompanhar a evolução que se encontra em curso. Assim, Mediante a investigação desenvolvida em duas das bases de repositórios mais bem conceituadas no ambiente da pesquisa internacional (Web of Science e Scopus), dez artigos foram selecionados e resumidos obtendo como resultado relatos que salientam a importância crescente do tema explorado concluindo que futuramente essa tecnologia será um importante diferencial para o rápido alcance dos resultados almejados

Viable system architecture applied to maintenance 4.0

Câmara, R. A., Mamede, H. S., & Dos Santos, V. D. (2019). Viable system architecture applied to maintenance 4.0. In A. P. Abraham, J. Roth, & L. Rodrigues (Eds.), Multi Conference on Computer Science and Information Systems, MCCSIS 2019 - Proceedings of the International Conferences on Big Data Analytics, Data Mining and Computational Intelligence 2019 and Theory and Practice in Modern Computing 2019 (pp. 127-134). (Multi Conference on Computer Science and Information Systems, MCCSIS 2019 - Proceedings of the International Conferences on Big Data Analytics, Data Mining and Computational Intelligence 2019 and Theory and Practice in Modern Computing 2019). IADIS Press.Disruptive requirements that currently drive the so-called Industry 4.0 (I4.0) are increasingly present in today's industries, where factories are forced to innovate in search of improvement in the quality of manufacturing of products aligned with the reductions of: manufacturing time, environmental and cost impacts with the manufacturing process. For this, an Information Systems (IS) architecture is proposed to reduce the negative impacts on an industrial operation caused by manual configuration failures in manufacturing systems, machines that are worn out in the production process and unstable integrations between industrial subsystems. The suggested SI model uses the Viable Systems Model adapted to Maintenance 4.0 technologies (Cyber-physical Systems (CPS), Manufacturing Execution Systems (MES), Data Mining and Digital Manufacturing concepts/technologies) with the goal to create an automatic purchase flow to replace parts by mitigating impending failures in industrial equipment through data mining and predictive analysis.publishersversionpublishe

A review on predictive maintenance technique for nuclear reactor cooling system using machine learning and augmented reality

Reactor TRIGA PUSPATI (RTP) is the only research nuclear reactor in Malaysia. Maintenance of RTP is crucial which affects its safety and reliability. Currently, RTP maintenance strategies used corrective and preventative which involved many sensors and equipment conditions. The existing preventive maintenance method takes a longer time to complete the entire system’s maintenance inspection. This study has investigated new predictive maintenance techniques for developing RTP predictive maintenance for primary cooling systems using machine learning (ML) and augmented reality (AR). Fifty papers from recent referred publications in the nuclear areas were reviewed and compared. Detailed comparison of ML techniques, parameters involved in the coolant system and AR design techniques were done. Multiclass support vector machines (SVMs), artificial neural network (ANN), long short-term memory (LSTM), feed forward back propagation (FFBP), graph neural networks-feed forward back propagation (GNN-FFBP) and ANN were used for the machine learning techniques for the nuclear reactor. Temperature, water flow, and water pressure were crucial parameters used in monitoring a nuclear reactor. Image marker-based techniques were mainly used by smart glass view and handheld devices. A switch knob with handle switch, pipe valve and machine feature were used for object detection in AR markerless technique. This study is significant and found seven recent papers closely related to the development of predictive maintenance for a research nuclear reactor in Malaysia

One datum and many values for sustainable Industry 4.0: a prognostic and health management use case

Industrial context of today, driven by the Industry 4.0 paradigm, is overwhelmed by data. Decreasing cost of innovative technologies, and recent market dynamics have pushed and pulled respectively for those architectures and practices in which data are the masters. While advancing, we have to take care of waste, even though intangibility of data makes them hardly connected to waste. In this paper we are going to reflect on data intensive context of today, focusing on the industrial sector. A smart approach for fully exploiting data collecting infrastructures is proposed, and its declination in a prognostic and health management (PHM) use case set inside an automatic painting system is presented. The contributions of this papers are mainly two: first of all, the general conceptual take-away of "data re-use" is presented and discussed. Moreover, a PHM solution for painting system's number plates, based on optical character recognition (OCR), is proposed and tested as a proof-of-concept for the "data re-use" concept. Summarizing, the already-in-use data sharing principle for achieving transparency and integration inside Industry 4.0, is presented as complementary with the proposed "data re-use", in order to develop a really sustainable shift toward the future

Maturity level of predictive maintenance application in small and medium-sized industries: Case of Morocco

In order to remain competitive in the long term and to push the company's efficiency to its limits, entrepreneurs are more and more open to the idea of integrating into Industry 4.0 aiming mainly at filling the important downtimes and the associated productivity losses by implementing predictive maintenance. This concept, common in developed countries, is much less widespread in Morocco and even less in small and medium-sized Moroccan companies. The objective of this article is to study the maturity level of predictive maintenance in Moroccan small and medium-sized enterprises, through a questionnaire validated by experts and made available to several companies. Valid data from 115 companies throughout the kingdom operating in different sectors were collected and processed by descriptive and factorial analysis under SPSS software. The results obtained show that only 33% of our sample were able to implement predictive maintenance, and that the expected benefits of this approach are the minimization of downtime at 96.5% and the increase in productivity at 94.8%, The main challenges observed are the lack of team motivation and a corporate culture unsuited to digitalization, which represents 42.277% of the total variance, lack of financial resources at 12.916% of the total variance and lack of data protection at 11.644% of the total variance. This analysis indicates that the level of maturity regarding the application of predictive maintenance in Moroccan small and medium-sized companies is low, these rates can be used to improve the root causes

Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios

In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.This work was funded in part by the European Commission Horizon 2020 5G-PPP Program under Grant Agreement Number H2020-ICT-2020-2/101017226: “6G BRAINS: Bringing Reinforcement learning Into Radio Light Network for Massive Connections” and the EU Horizon INCODE project Programming Platform for Intelligent Collaborative Deployments over Heterogeneous Edge IoT Environments (HORIZON-CL4-2022-DATA-01-03/101093069)

Dynamic planning of mobile service teams’ mission subject to orders uncertainty constraints

This paper considers the dynamic vehicle routing problem where a fleet of vehicles deals with periodic deliveries of goods or services to spatially dispersed customers over a given time horizon. Individual customers may only be served by predefined (dedicated) suppliers. Each vehicle follows a pre-planned separate route linking points defined by the customer location and service periods when ordered deliveries are carried out. Customer order specifications and their services time windows as well as vehicle travel times are dynamically recognized over time. The objective is to maximize a number of newly introduced or modified requests, being submitted dynamically throughout the assumed time horizon, but not compromising already considered orders. Therefore, the main question is whether a newly reported delivery request or currently modified/corrected one can be accepted or not. The considered problem arises, for example, in systems in which garbage collection or DHL parcel deliveries as well as preventive maintenance requests are scheduled and implemented according to a cyclically repeating sequence. It is formulated as a constraint satisfaction problem implementing the ordered fuzzy number formalism enabling to handle the fuzzy nature of variables through an algebraic approach. Computational results show that the proposed solution outperforms commonly used computer simulation methods

Tendencias en Instrumentación y Control de Procesos

Este documento presenta las tendencias relacionadas con la Instrumentación y el Control de Procesos. Se presentan las políticas estatales que sirven de apoyo a la incorporación de estas tecnologías, por parte del Sena y de las empresas del sector industrial. A través de revisión bibliográfica se explora el grado de desarrollo a nivel mundial de este campo, y cómo se pueden implementar a nivel local. Al final del documento se enumeran algunas actividades que se pueden desarrollar en el centro de formación para facilitar a instructores y aprendices conocer las tecnologías para aplicar en negocios de economía naranja o industria 4.0

Aplicación de un modelo estocástico para el Análisis RAM de Máquinas Rotatorias en la Industria 4.0

The application of Industry 4.0 concepts promoted through predictive maintenance of an industrial asset shapes the long-term operational management of a factory. The analysis of historical asset data provides the opportunity to apply techniques such as data modeling, which define the behavior of machines over time. This paper presents an analysis of reliability, availability and maintainability (RAM) of a group of industrial fans that are part of a clinker manufacturing process, from a perspective that relates historical vibration data with the states taken by the machines classified according to the ISO 14694 standard. For this purpose, time series are characterized for each fan, and descriptive metrics are obtained, which enable the application of a type of integrated autoregressive model of moving average, to predict the conditions that the equipment will take in the next twelve months, associating them to the calculation of the RAM indicators, which define the decision making in the operational framework of the plant.La aplicación de conceptos de la Industria 4.0 promovidas a través del mantenimiento predictivo de un activo industrial, marca la forma de la gestión operativa de una fábrica a largo plazo. El análisis de la data histórica de los activos brinda la oportunidad de aplicar técnicas como el modelamiento de datos, que definen el comportamiento de las máquinas a través del tiempo. Este paper presenta un análisis de confiabilidad, disponibilidad y mantenibilidad (RAM) de un grupo de ventiladores industriales que forman parte de un proceso de fabricación de clinker, desde una perspectiva que relaciona datos históricos de vibración con los estados que toman las máquinas, clasificados según el estándar ISO 14694. Para ello, se caracterizan series temporales por cada ventilador, y se obtienen métricas descriptivas, que habilitan la aplicación de un tipo de modelo autorregresivo integrado de media móvil, para predecir las condiciones que tomarán los equipos en los siguientes doce meses asociándolas al cálculo de los indicadores RAM, que definen la toma de decisiones en el marco operativo de la planta