Improvement of Measurement Contribution for Asset Characterization in Complex Engineering Systems by an Iterative Methodology

Improvement of Measurement Contribution for Asset Characterization in Complex Engineering Systems by an Iterative Methodology

Giulio D'Emilia (University of L'Aquila, L'Aquila, Italy), Antonella Gaspari (University of L'Aquila, L'Aquila, Italy), and Diego Pascual Galar (Lulea University of Technology, Lulea, Sweden)
DOI: 10.4018/IJSSMET.2018040104
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The evolution of systems based on the integration of Internet of Things (IoT) and Cloud computing technologies requires resolute and trustable management approaches, to let the industrial assets thrive and avoid losses in efficiency and, thus, profitability. In this article, a methodology based on the evaluation of the measurement uncertainty is proposed, which is able to suggest possible improvement paths and reliable decisions. The approach is based on the identification of subsequent tasks that should be fulfilled, also in a recursive way. Its application in the field, for the identification of the vibration and acoustic emission signatures of highly-performance machining tools, allows directing future actions to increase the potentiality of proper management of the information provided by measurements. In a complex scenario, characterized by many devices and instruments, the compliance with the procedures for measurement accuracy has proven to be a useful support.
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1. Introduction

Exciting opportunities are arising from the evolution of the systems in the current industrial scenario. The integration of Internet of Things (IoT) (Lee & Lee, 2015) and Cloud computing technologies (Bayramustaa & Nasir, 2016) leads us to the Internet of Everything (IoE), a network of networks where billions of objects are connected, providing an informative base without precedent (Botta et al., 2016). This trend is based on a large amount of data and returns the same uncountable quantity of data, which is called “big data” (Wamba et al., 2015; Jin et al., 2015; Krimpmann & Stühmeier, 2017; Wahi et al., 2015). Suitable information gathered by this huge mass of data is traditionally extracted by means of the use of data mining techniques (Aggarwal, 2015), artificial intelligent tools (Galar, 2015), decision support systems and knowledge-based systems.

The impact on the manufacturing companies is remarkable, since it allows adding value to the core products by integrating customers-oriented services including different opportunities among many: higher quality of products, new functionalities, reliability and maintenance procedures, life-cycle assessment and global environmental effects, new managerial solutions and strategies (Marques & Cuhna, 2014, Géczy et al., 2012).

In past years, the development of enterprise information systems (EIS) have allowed management to give rise to an informative base useful to decision-making (D’Emilia et al., 2015a). The ability of an organization to take all input data and convert them into knowledge is referred as Business Intelligence (BI) (Aufaure et al., 2015). On the other hand, the development of industrial automation, in rapid evolution, helped the industries to increase their productivity, setting up complex control and monitoring systems, aiming at a real-time management of the activities and achieving a real competitive advantage (Mehta et al., 2015; Patra et al., 2016).

Nowadays, the possibility of applying new intelligent technologies, by integrating the informative content coming from disparate devices seems to make this goal possible, through the convergence of the physical world and the digital world into the so-called cyber-physical systems (Thramboulidis, 2015). This new trend for industrial systems is known as “Industry 4.0” or “Industrie 4.0”, in German (Toroa et al., 2015; Weyer et al., 2015). The adoption of the methods typically used in engineering tasks underline the possibility of merging them with “good” measurements.

In this context, the international reference standards offer a basic support to set corporate activities into a well-defined framework, in terms of the requirements for the implementation of integrated management systems. Quality (ISO 9001, 2009), Environmental (ISO 14001, 2015) Energy (ISO 50001, 2011) and Asset (ISO 55001, 2014) management systems are perhaps the most known international standards that companies adopt for benchmarking, marketing and self-organization purposes. Nevertheless, the quality of the achievable results and outcomes cannot be granted automatically and tacitly, since many aspects that could lead to variability may occur, due to the structure of these systems themselves. From the informative flow point of view, each node of the network requires specific procedures for assuring proper exchange of information.

Like and more than other branches of knowledge, measurement topics are stressed, to compete to the compliance of this requirement. This is primarily because we must measure to get experimental, objective and reliable information and because measurement results cannot be provided in a deterministic and univocal way.

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