Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps

Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which have the potential for causing a great impact on the effectiveness and performance of the overall process and the sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.Postprint (published version

Comparative study of Sustainability Metrics for Face Milling AISI 1045 in different Machining Centers

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)The objective of this study is to compare a set of sustainability metrics between different manufacturing resources applied to high performances machining centers. The research compares distributed scenarios in order to find the optimal conditions that allow the minimum consumed power and the minimum roughness when performing face milling operations of AISI 1045 steel. The set of experiments for the surface machining was carried out considering different path strategies in three main directions for two dimensional movements of the tool. The selected experiments considered the main axis movement, the perpendicular axis movement and a 45 degrees movement. Besides, it was considered the feed rate speed and the cutting depth. The design of experiments was developed with the Taguchi method considering an orthogonal matrix of L27 design type, and three levels of experimental design, and the analysis of variance and noise signal were performed. The methodology to determine the lowest power consumed and the best surface quality allowed to establish the working condition in the most sustainable machining. The results show how the cutting parameters influence in each manufacturing resource

Energy rating of a water pumping station using multivariate analysis

Among water management policies, the preservation and the saving of energy demand in water supply and treatment systems play key roles. When focusing on energy, the customary metric to determine the performance of water supply systems is linked to the definition of component-based energy indicators. This approach is unfit to account for interactions occurring among system elements or between the system and its environment. On the other hand, the development of information technology has led to the availability of increasing large amount of data, typically gathered from distributed sensor networks in so-called smart grids. In this context, data intensive methodologies address the possibility of using complex network modeling approaches, and advocate the issues related to the interpretation and analysis of large amount of data produced by smart sensor networks. In this perspective, the present work aims to use data intensive techniques in the energy analysis of a water management network. The purpose is to provide new metrics for the energy rating of the system and to be able to provide insights into the dynamics of its operations. The study applies neural network as a tool to predict energy demand, when using flowrate and vibration data as predictor variables

HR Metrics and Strategy

[Excerpt] The idea that an organization\u27s people represent a key strategic resource is widely accepted. The business press is filled with examples of top executives proclaiming how important it is to engage people\u27s minds and spirits in the quest for competitive advantage (Boudreau & Ramstad, 1997; Boudreau, 1996). There is also mounting scientific evidence that certain bundles of high-performance work practices (e.g., performance-contingent pay, team-based work structures, selective recruitment and hiring, extensive training, etc.) are associated with higher organizational financial performance (Becker & Huselid, forthcoming; Ichniowski, Arthur, MacDuffie, Welbourne & Andrews)

Wireless industrial monitoring and control networks: the journey so far and the road ahead

While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks

Quality assessment technique for ubiquitous software and middleware

The new paradigm of computing or information systems is ubiquitous computing systems. The technology-oriented issues of ubiquitous computing systems have made researchers pay much attention to the feasibility study of the technologies rather than building quality assurance indices or guidelines. In this context, measuring quality is the key to developing high-quality ubiquitous computing products. For this reason, various quality models have been defined, adopted and enhanced over the years, for example, the need for one recognised standard quality model (ISO/IEC 9126) is the result of a consensus for a software quality model on three levels: characteristics, sub-characteristics, and metrics. However, it is very much unlikely that this scheme will be directly applicable to ubiquitous computing environments which are considerably different to conventional software, trailing a big concern which is being given to reformulate existing methods, and especially to elaborate new assessment techniques for ubiquitous computing environments. This paper selects appropriate quality characteristics for the ubiquitous computing environment, which can be used as the quality target for both ubiquitous computing product evaluation processes ad development processes. Further, each of the quality characteristics has been expanded with evaluation questions and metrics, in some cases with measures. In addition, this quality model has been applied to the industrial setting of the ubiquitous computing environment. These have revealed that while the approach was sound, there are some parts to be more developed in the future

Attributing Benefits to Voluntary Programs in EPA’s Office of Resource Conservation and Recovery: Challenges and Options

This paper reviews the economic justification for voluntary environmental programs to derive defensible measures of their positive social outcomes. We consider ideal experimental and statistical designs to detect and attribute benefits. We also explore a set of more practical approaches to benefit attribution that take into account the data gaps and statistical challenges that often make more rigorous approaches infeasible.voluntary programs, cost–benefit assessment, program evaluation

Human emotional response to steering wheel vibration in automobiles

This is the post-print (final draft post-refereeing) version of the final published paper that is available from the link below. Copyright @ 2013 Inderscience Enterprises Ltd.This study investigates what form of correlation may exist between measures of the valence and the arousal dimensions of the human emotional response to steering wheel vibration and the vibration intensity metrics obtained by means of the unweighted and the frequency weighted root mean square (rms). A laboratory experiment was performed with 30 participants who were presented 17 acceleration time histories in random order and asked to rate their emotional feelings of valence and arousal using a self-assessment manikin (SAM) scale. The results suggest a highly linear correlation between the unweighted, Wh weighted and Ws weighted vibration intensity metrics and the arousal measures of the human emotional response. The results also suggest that while vibration intensity plays a significant role in eliciting emotional feelings, there are other factors which influence the human emotional response to steering wheel vibration such as the presence of high peaks or high frequency band amplitudes