Industry 4.0 has recently become an ubiquitous topic of study both by academics and practitioners. Beyond the technology application domain, one major issue that most organizations have to face when structuring their ...
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Operation and maintenance are important administrative and operational functions of the production, as they can guarantee the required demand. Together, they have the responsibilities of doing right (quality advantage...
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Operation and maintenance are important administrative and operational functions of the production, as they can guarantee the required demand. Together, they have the responsibilities of doing right (quality advantage), fast (speed advantage), timely (reliability advantage), quick setup (flexibility advantage) and affordability. In addition, they are close to several important activities such as: safety, environment, productivity, marketing, quality, reliability and accounting systems. With this new vision, changes and opportunities are being presented to these sectors of operation and maintenance. In the new paradigm, the maintenance and operation teams are motivated to strategically guarantee the full operation of the production system and not only to preserve the equipment. And, thus, to develop an efficient strategy, providing the sustainability and the increase of the assets of the company, reaching the goals of the planning and control of the production established by the company. The main objective of this work is to analyze a proposal to meet the required production demand through the alignment of production control planning strategies and maintenance planning and control. The analysis of the research points to some significant contributions, for example, to the areas of strategy and logistics of organizations that work mainly in environments with a high level of competitiveness. Through a case study, the analysis pointed to the positive impact of alignment on parameters such as: sustainability of the competitive strategy, productivity, costs, reliability and availability of physical assets, and flexibilization of the productive organizational system.
In the business environment, decision making is a differential when the team responsible for it does it right. This implies some assumptions, such as knowledge of the activity, internal and external variables that imp...
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In the business environment, decision making is a differential when the team responsible for it does it right. This implies some assumptions, such as knowledge of the activity, internal and external variables that impact the company’s business, as well as the quality of the workforce. Thus, when the maintenance issue is addressed, be it in industrial or service companies, the expected results will be translated by the satisfaction indexes of its users. This work aims to propose a framework with the elements that lead to the proper decision making in maintenance for bus companies. For this, the multicriteria decision-making method chosen was PROMETHEE. The PROMETHEE method has the flexibility to treat data both qualitatively and quantitatively, offering different scenarios to managers on the best decision to make. It is known that companies seek to minimize their maintenance costs and maximize the quality of service provided.
In this paper, we address a decision-making problem related to the requirement of costly equipment by medical diagnostic services in a segmented public healthcare system comprising several institutions and private pro...
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By associating active learning methodologies and digital media as a form to improve educational quality, the diversity of experimentations in engineering education has been motivating researches in many areas with pos...
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By associating active learning methodologies and digital media as a form to improve educational quality, the diversity of experimentations in engineering education has been motivating researches in many areas with possibilities of increasing both students' knowledge and skills. Control engineering is an area where educational laboratories are usually costly, thus the use of virtual laboratories for remote experimentation became an interesting approach. In this work, it is presented a remote experimentation procedure for the ball and beam system, which is a nonlinear and unstable open loop process. Considering the advanced control topic, multiobjective optimization was introduced in the experiment in order to tune Proportional-Integral-Derivative (PID) controllers. Through the description of the system model, the multiobjective optimization technique, and the experiment configuration, students can simulate and test their controllers remotely in a real plant.
Paper depicts an architecture expansion for communication and data management that can be used in Remote Experimentation and Game-Based Learning. This architecture is compliant with iLab Shared Architecture and is bas...
Paper depicts an architecture expansion for communication and data management that can be used in Remote Experimentation and Game-Based Learning. This architecture is compliant with iLab Shared Architecture and is based upon open source software and hardware, in particular low cost platforms such as the Raspberry Pi 3. It consists of three main entities: user-side interface, experiment-side (lab) application and, interconnecting both, the cloud server enhanced with the messaging intermediator. Architecture takes into account typical requirements for educational institutions, such as open-source technologies, remote systems usage management (service brokerage) and security. In trial implementations, three single-user, remotely operated experiments have been enabled using ELSA-Py, in which students will be able to practice concepts of lean production (LyMIE), object position control (Windball) and image calibration (TabRecon). The integration has been tested and the performance of ELSA-Py architecture as a tool do deploy educational remotely operated systems is discussed.
In the current industrial revolution, additive manufacturing (AM) embodies a promising technology that can enhance the effectiveness, adaptability, and competitiveness of supply chains (SCs). Moreover, it facilitates ...
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For damage detection, this research article discusses an easy-to-compute damage index derived from the governing dynamic of the structure that has potential practical application in Structural Health Monitoring (SHM)....
For damage detection, this research article discusses an easy-to-compute damage index derived from the governing dynamic of the structure that has potential practical application in Structural Health Monitoring (SHM). The research uses simplified structural models to explore the sensitivity of the index to damages, to compare the index performance with a traditional but popular damage detection method, and to understand the local/global predictive capability of the index. The research uses two simple models, namely, single- and two-degree-of-freedom systems. The results suggest that the damage index is local, that can only monitor damages occurring near the points of measurements, but it is sensitive to damages, unlike the natural frequency, which is global but less sensitive.
Understanding the reliability of engineering methods is crucial for its adoption and deployment. This research focuses on the reliability of the Power Spectral Density (PSD) method via the use of the F statistic for d...
Understanding the reliability of engineering methods is crucial for its adoption and deployment. This research focuses on the reliability of the Power Spectral Density (PSD) method via the use of the F statistic for damage detection. To the author best knowledge, the method is rather classic but its realibility has not been discussed in the context of a large data size. Priory, the research anticipates that the accuracy is a function of the damage level. In this study, we evaluate 3500 cases with five levels of structural integrity, namely, healthy condition and damaged conditions with 1%, 5%, 10%, and 20% damage levels. The dataset is established via a numerical analysis of a seven degree-of-freedom system loaded with a concentrated dynamic force with random magnitude. A spring on the system is reduced in its stiffness to simulate damages. Our significant findings are the following: it is challenging for the PSD-based method to differentiate the healthy condition from the damaged conditions when the damage level is small. However, the reliability is high at 95% probability when the structural integrity has dropped by five percent.
Real-world classification problems generally deal with imbalanced data, where one class represents the majority of the data set. The present work deals with event detection on a drinking-water quality time series, whe...
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Real-world classification problems generally deal with imbalanced data, where one class represents the majority of the data set. The present work deals with event detection on a drinking-water quality time series, where the presence of a quality event is the minority class. In order to solve such problems, supervised learning algorithms are recommended. Researchers have also used multi-objective optimization (MOO) in order to generate diverse models to build ensembles of classifiers. Although MOO has been used for ensemble member generation, there is a lack on it's application for member selection, which is usually done by selecting a specific subset from the resulting models, or by using meta-algorithms, such as boosting. The proposed work comprises the application of MOO design in the whole process of ensemble generation. To do so, one multi-objective problem (MOP) is defined for the creation of a set of non-dominated solutions with Pareto-optimal support vector machines (SVM). After that, a second MOP is defined for the selection of such SVMs as members of an ensemble. Such methodology is compared to other member selection methods, such as: the single best classifier, an ensemble composed of the full set of non-dominated solutions, and the selection of a specific subset from the Pareto front. Results show that the proposed method is suitable for the creation of ensembles, achieving the highest classification scores.
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