Predictive maintenance on sensorized stamping presses by time series segmentation, anomaly detection, and classification algorithms

Sheet metal forming tools, like stamping presses, play an ubiquitous role in the manufacture of several products. With increasing requirements of quality and efficiency, ensuring maximum uptime of these tools is fundamental to marketplace competitiveness. Using anomaly detection and predictive maintenance techniques, it is possible to develop lower risk and more intelligent approaches to maintenance scheduling, however, industrial implementations of these methods remain scarce due to the difficulties of obtaining acceptable results in real-world scenarios, making applications of such techniques in stamping processes seldom found. In this work, we propose a combination of two distinct approaches: (a) time segmentation together with feature dimension reduction and anomaly detection; and (b) machine learning classification algorithms, for effective downtime prediction. The approach (a)+(b) allows for an improvement rate up to 22.971% of the macro F1-score, when compared to sole approach (b). A ROC AUC index of 96% is attained by using Randomized Decision Trees, being the best classifier of twelve tested. An use case with a decentralized predictive maintenance architecture for the downtime forecasting of a stamping press, which is a critical machine in the manufacturing facilities of Bosch Thermo Technology, is discussed.publishe

An improved quantitative recurrence analysis using artificial intelligence based image processing applied to sensor measurements

Artificial intelligence has been widely used in reliability analysis for industrial equipment. The gear transmission systems are the most common components in mining machines. A simple fault in the gearbox may break down the mining machine for couple of days, resulting in enormous economic loss. Condition monitoring techniques can prevent unscheduled failures in the gear transmission systems. Although many techniques have been developed for gearbox fault diagnosis, one challenging task that the condition monitoring still faces is how to extract quantitative fault indicators. To this end, this paper proposes an improved quantitative recurrence analysis (IQRA) based on artificial intelligence theory. This new method takes advantages of chaos and fractal properties of the gear transmission system to obtain the recurrence of the system. The characteristics of different gear faults can be observed through the visualization of recurrence. Quantitative parameters can be then calculated from the recurrence plots. Experimental data acquired from a gearbox under variable working conditions was used to evaluate the proposed method. The analysis results demonstrate that the proposed IQRA method is able to effectively quantify different the gear faults.This project was supported by the National Science Foundation of China (NSFC) (51775546), Priority Academic Program Development of Jiangsu Higher Education Institutions, and Yingcai project of CUMT (YG2017001).http://wileyonlinelibrary.com/journal/cpe2020-05-25hj2019Electrical, Electronic and Computer Engineerin

Audio-based signal extraction techniques for stamping tool condition monitoring

This thesis developed blind signal separation techniques to extract wear related information from the signal mixtures. Extracted signal analysis demonstrated that there is a significant qualitative association between the emitted audio and the wear progression of sheet metal stamping tools and this is the first study that identifies such correlation.<br /

Organizational knowledge in the making : history, breakdowns and narratives

The present study looks at the dynamics whereby organisational knowledge comes into existence and is eventually crystallised into stable structures of signification through processes of utilisation and institutionalisation. Recent years have seen an astounding explosion of writing about organisational knowledge. In different versions, organisational theorists have been paying increasing attention to the idea of the firm as a body of knowledge, stressing in turn the ability of firms to create, manage and transfer knowledge as a critical success factor. However, the current debate on the topic has highlighted the difficulty of documenting empirically the process of creation, accumulation and maintenance of knowledge in organisations. This, of course, begs the question: how is it possible to relate an empirical study to the theoretical debate on knowledge in organisations? More specifically, how does a particular knowledge system emerge and become stabilised? How does it evolve over time? In this study, we argue that the lack of attention to knowledge as an empirical phenomenon can be traced back to the assumptions underlying the mainstream knowledge-based theories of the firm, which emphasise the instrumental, functional character of knowledge in organisations. In contrast to the functionalist view of knowledge, we contend that mainstream assumptions need to be combined with those perspectives focusing on the social construction of knowledge and highlighting its contentious, provisional nature. Given the problems identified at both theoretical and methodological levels, the present study proposes a framework for studying knowledge as an empirical phenomenon based on three methodological lenses, which are echoed in the title of this work: history, breakdowns and narratives. The three lenses have to be seen as bringing into focus the tacit features of knowledge and organisation. The empirical core of the research is evidenced by three in-depth case studies conducted at Fiat Auto Italy. The findings of the study provide the backbone for constructing a theoretical model of knowledge in organisations. The model links the content, process, and context of knowledge-related phenomena in a coherent classificatory system. More generally, the empirical research highlights the systemic, institutionalised, and multi-faceted nature of knowledge in organisations

Smart manufacturing: uma análise da tecnologia LIDAR para qualidade do produto na indústria automotiva

The concept of Industry 4.0 first appeared in 2011, in an article published by the German government as a high-tech strategy for 2020. In recent years, this term has been widely discussed, referring to a complex and flexible system, which involves the digitization of manufacturing, engineering and automation technology. With this, the industry increasingly seeks to explore 'Smart Manufacturing', which is nothing more than the adoption of new technologies and manufacturing theories to help industries adapt to changes and raise product quality. Furthermore, after the third industrial revolution, the explosion of artificial intelligence (AI) and machine learning (ML) self learning algorithms laid a solid foundation for an increasingly connected industry. Using this solid foundation, the objective of the monograph is, based on the current state of the literature, to review and present the use of systems based on self learning (Artificial Intelligence/Machine Learning) in conjunction with the LIDAR (Light Detection and Ranging) sensor within the concepts proposed in Industry 4.0 and 'Smart Manufacturing'. Thus, in addition to being a basis for future work, this monograph presents as an opportunity and challenge, a new way of evaluating product quality in the manufacturing process of the automotive industry.UFU - Universidade Federal de UberlândiaTrabalho de Conclusão de Curso (Graduação)O conceito de Indústria 4.0 apareceu pela primeira vez em 2011, em um artigo publicado pelo governo alemão como estratégia de alta tecnologia para 2020. Nos últimos anos, esse termo tem sido amplamente discutido, se referindo a um sistema complexo e flexível, que envolve a digitalização da tecnologia de manufatura, engenharia e automação. Com isso, a indústria busca cada vez mais explorar a ‘Smart Manufacturing’, que nada mais é que a adoção de novas tecnologias e teorias de fabricação para ajudar as indústrias a se adaptarem as mudanças e elevar a qualidade do produto. Além disso, após a terceira revolução industrial, a explosão dos algoritmos de autoaprendizagem de Inteligência Artificial (IA) e Machine Learning (ML) lançaram uma base sólida para receber uma indústria cada vez mais conectada. Usando desta base sólida, o objetivo da monografia é, com base no estado atual da literatura, revisar e apresentar o uso de sistemas baseados em autoaprendizagem (Inteligência Artificial/Machine Learning) em conjunto com o sensor LIDAR (Light Detection and Ranging) dentro dos conceitos propostos na Indústria 4.0 e ‘Smart Manufacturing’. Dessa forma, além de base para futuros trabalhos, esta monografia apresenta como oportunidade e desafio, uma nova forma de avaliação da qualidade do produto no processo de fabricação da indústria automotiva

Dual Eye-Tracking Methods for the Study of Remote Collaborative Problem Solving

Applied eye-tracking has been extensively used for the study of psychological processes. More recently, some researchers have used this technique to study the interaction between people by tracking and analyzing eye-movements of two persons synchronously. However, this is generally accomplished by observing people in simple controlled settings. In this thesis, we use a similar methodology, dual eye-tracking, to study people in more natural, semantically richer, tasks with the aim of identifying dual eye-movement patterns which reflect collaborative processes. Eye-tracking, and more globally eye-movements analyses, is a complex domain involving several methodological issues, which have not yet been satisfactorily solved. This work is also an attempt to offer solutions to several of these issues. The first part of this thesis is dedicated to the improvement of the methodology of eye-tracking data analysis. We present several developments pertaining to the general methodology of eye-tracking. More specifically, we identify problems and offer solutions to the following aspects: fixation identification, systematic position errors correction and hit detection. We also tackle more specific questions concerning the method dual eye-tracking. We present issues that arise in dual eye-tracking data collection and analysis and propose some solutions. On the technical side, we deal with the question of the synchronous recording of two streams of eye-movements and on the analytical side, we extend gaze cross-recurrence, a measure of eye-movements coupling, to complex realistic collaborative tasks. The second part is devoted to experimental studies of collaboration through the use of dual eye-tracking methods. We first present four exploratory studies which allowed us to set up the stage by identifying interesting phenomena and experimental difficulties. The main results of these experiments revolve around the relationship between gaze and speech. In these respects, we extended some results found in the literature to more natural settings and we developed a computational model to make actual predictions about dialogue and visual references. Finally, the main study of this thesis is about computer program understanding. This study is composed of two experiments: a solo programming experiment, from which we identified gaze patterns of a single programmer and a pair-programming task in which we explored how gaze patterns during program comprehension are affected by collaboration

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier