scholarly journals A Case Driven Study of the Use of Time Series Classification for Flexibility in Industry 4.0

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7273
Author(s):  
Julien Polge ◽  
Jérémy Robert ◽  
Yves Le Traon
Keyword(s):  
Time Series ◽  
Industry 4.0 ◽  
Production Systems ◽  
Industrial Process ◽  
Cloud Manufacturing ◽  
Computer Integrated Manufacturing ◽  
Scale Model ◽  
Time Series Classification ◽  
Use Of Time ◽  
Industrial Internet

With the Industry 4.0 paradigm comes the convergence of the Internet Technologies and Operational Technologies, and concepts, such as Industrial Internet of Things (IIoT), cloud manufacturing, Cyber-Physical Systems (CPS), and so on. These concepts bring industries into the big data era and allow for them to have access to potentially useful information in order to optimise the Overall Equipment Effectiveness (OEE); however, most European industries still rely on the Computer-Integrated Manufacturing (CIM) model, where the production systems run as independent systems (i.e., without any communication with the upper levels). Those production systems are controlled by a Programmable Logic Controller, in which a static and rigid program is implemented. This program is static and rigid in a sense that the programmed routines cannot evolve over the time unless a human modifies it. However, to go further in terms of flexibility, we are convinced that it requires moving away from the aforementioned old-fashioned and rigid automation to a ML-based automation, i.e., where the control itself is based on the decisions that were taken by ML algorithms. In order to verify this, we applied a time series classification method on a scale model of a factory using real industrial controllers, and widened the variety of parts the production line has to treat. This study shows that satisfactory results can be obtained only at the expense of the human expertise (i.e., in the industrial process and in the ML process).

scholarly journals Defining SMEs’ 4.0 Readiness Indicators

2020 ◽  
Vol 10 (24) ◽  
pp. 8998
Author(s):  
Nilubon Chonsawat ◽  
Apichat Sopadang
Keyword(s):  
Decision Making ◽  
Industry 4.0 ◽  
Production Efficiency ◽  
Cloud Manufacturing ◽  
Smart Manufacturing ◽  
Increase Production Efficiency ◽  
Five Dimensions ◽  
High Stage ◽  
Industrial Internet ◽  
Infrastructure System

Industry 4.0 revolution offers smart manufacturing; it systematically incorporates production technology and advanced operation management. Adopting these high-state strategies can increase production efficiency, reduce energy consumption, and decrease manufacturer costs. Simultaneously, small and medium-sized enterprises (SMEs) were the backbone of economic growth and development. They still lack both the knowledge and decision-making to verify this high-stage technology’s performance and implementation. Therefore, the research aims to define the readiness indicators to assess and support SMEs toward Industry 4.0. The research begins with found aspects that influence the SME 4.0 readiness by using Bibliometric techniques. The result shows the aspects which were the most occurrences such as the Industrial Internet, Cloud Manufacturing, Collaborative Robot, Business Model, and Digital Transformation. They were then grouped into five dimensions by using the visualization of similarities (VOS) techniques: (1) Organizational Resilience, (2) Infrastructure System, (3) Manufacturing System, (4) Data Transformation, and (5) Digital Technology. Cronbach’s alpha then validated the composite dimensions at a 0.926 level of reliability and a significant positive correlation. After that, the indicators were defined from the dimension and aspects approach. Finally, the indicators were pilot tested by small enterprises. It appeared that 23 indicators could support SMEs 4.0 readiness indication and decision-making in the context of Industry 4.0.

Proposal of an Architecture based on RAMI 4.0 Layers for Flexible Manufacturing in Industry 4.0

2020 ◽  
Author(s):  
José Z. Neto ◽  
Joel Ravelli Jr ◽  
Eduardo P. Godoy
Keyword(s):  
Flexible Manufacturing ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Three Dimensional ◽  
Reference Architecture ◽  
Architecture Model ◽  
Industrial Internet ◽  
Integration Architecture ◽  
Rapid Changes

The Industry 4.0 (I4.0) together with the Industrial Internet of Things (IIoT) enable business productivity to be improved through rapid changes in production scope in an increasingly volatile market. This technology innovation is perceived by integrating manufacturing systems, managing business rules, and decentralizing computing resources, enabling rapid changes in production systems. The Reference Architecture Model for Industry 4.0 (RAMI 4.0) is a three-dimensional layer model to support I4.0 applications. One of the major challenges for adopting RAMI 4.0 is the development of solutions that support the functionality of each layer and the necessary interactions between the elements of each layer. This paper focuses on the proposal of architecture for flexible manufacturing in I4.0 using all the Information Technology (IT) Layers of the RAMI 4.0. In order to enable a standardized and interoperable communication, the architecture used the OPC-UA protocol to connect the low layers elements in the factory perspective and REST APIs to connect the high layers in the business perspective. The integration architecture creates an online interface to provide the client the ability to enter, view, and even modify an order based on their needs and priorities, enabling the industry to implement rapid changes to adapt to the marketplace.

scholarly journals Formal Tools for Management of Manufacturing Systems: A Multi Agents System Approach

2019 ◽  
Vol 13 (5) ◽  
pp. 691-699
Author(s):  
Doriana M. D’Addona ◽  
Alessandro A. Bruzzone ◽  
◽  
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Cloud Manufacturing ◽  
Technological Innovations ◽  
Real Sector ◽  
Multi Agent System ◽  
Industrial Internet ◽  
Multi Agent ◽  
Multi Agents

To overcome the consequences of the 2008 crisis on the real sector, especially manufacturing, Industry 4.0 gives guidelines to drive production by emphasizing technological innovations, such as industrial internet, cloud manufacturing, etc. The proposed paper focuses on cognitive manufacturing within the framework of the emergent synthesis paradigm. Specifically, the structuring process by which the manufacturing assets are organized to provide the finished goods is analyzed. The study is carried out by considering the analogies between manufacturing and other inventive processes supported by formal tools such as formal languages, semantic webs, and multi agent system.

scholarly journals Active Learning Framework for Time-Series Classification of Vibration and Industrial Process Data

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sergio Martin del Campo Barraza ◽  
William Lindskog ◽  
Davide Badalotti ◽  
Oskar Liew ◽  
Arash Toyser
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Active Learning ◽  
Condition Monitoring ◽  
Industrial Process ◽  
Time Series Classification ◽  
Process Data ◽  
Learning Framework ◽  
Monitoring Applications

Data-based models built using machine learning solutions are becoming more prominent in the condition monitoring, maintenance, and prognostics fields. The capacity to build these models using a machine learning approach depends largely in the quality of the data. Of particular importance is the availability of labelled data, which describes the conditions that are intended to be identified. However, properly labelled data that is useful in many machine learning strategies is a scare resource. Furthermore, producing high-quality labelled data is expensive, time-consuming and a lot of times inaccurate given the uncertainty surrounding the labeling process and the annotators.  Active Learning (AL) has emerged as a semi-supervised approach that enables cost and time reductions of the labeling process. This approach has had a delayed adoption for time series classification given the difficulty to extract and present the time series information in such a way that it is easy to understand for the human annotator who incorporates the labels. This difficulty arises from the large dimensionality that many of these time series possess. This challenge is exacerbated by the cold-start problem, where the initial labelled dataset used in typical AL frameworks may not exist. Thus, the initial set of labels to be allocated to the time series samples is not available. This last challenge is particularly common on many condition monitoring applications where data samples of specific faults or problems does not exist. In this article, we present an AL framework to be used in the classification of time series from industrial process data, in particular vibration waveforms originated from condition monitoring applications. In this framework, we deal with the absence of labels to train an initial classification model by introducing a pre-clustering step. This step uses an unsupervised clustering algorithm to identify the number of labels and selects the points with a stronger group belonging as initial samples to be labelled in the active learning step. Furthermore, this framework presents two approaches to present the information to the annotator that can be via time-series imaging and automatic extraction of statistical features. Our work is motivated by the interest to facilitate the effort required for labeling time-series waveforms, while maintaining a high level of accuracy and consistency on those labels. In addition, we study the number of time-series samples that require to be labelled to achieve different levels of classification accuracy, as well as their confidence intervals. These experiments are carried out using vibration signals from a well-known rolling element bearing dataset and typical process data from a production plant.   An active learning framework that considers the conditions of the data commonly found in maintenance and condition monitoring applications while presenting the data in ways easy to interpret by human annotators can facilitate the generation reliable datasets. These datasets can, in turn, assist in the development of data-driven models that describe the many different processes that a machine undergoes.

Data architecture and model design for Industry 4.0 components integration in cyber-physical production systems

2020 ◽  
pp. 095440542097946
Author(s):  
Vincent Havard ◽  
M’hammed Sahnoun ◽  
Belgacem Bettayeb ◽  
Fabrice Duval ◽  
David Baudry
Keyword(s):  
Data Model ◽  
Industry 4.0 ◽  
Production Systems ◽  
Industrial Internet Of Things ◽  
Digital Twins ◽  
Industrial Internet ◽  
Data Architecture ◽  
Important Challenge ◽  
Cyber Physical Production Systems ◽  
High Level

In the context of Industry 4.0, Cyber-Physical Production Systems (CPPS) and digital twins are key technologies for the management of huge amount of data generated by Industrial Internet of things (IIoT) devices. However, the interoperability and flexibility of different components is still an important challenge so as to integrate them in the process and fit all industrial specific needs. Thus, the main contribution of this paper is to propose a database architecture and a data model associated allowing multiple agents to work collaboratively and synchronously to perform high-level tasks. Therefore, it fulfils requirements and needs of Industry 4.0: interoperability, scalability, flexibility and resilience. The proposed architecture and model are implemented on a cyber-physical production system (CPPS) which is used in order to show and discuss several use cases examples.

Domestic Engineering - Industry 4.0 Technology Transition Problems

2019 ◽  
pp. 11-20
Author(s):  
E. N. Lapteva ◽  
O. V. Nasarochkina
Keyword(s):  
Additive Manufacturing ◽  
Industry 4.0 ◽  
Technology Development ◽  
Engineering Industry ◽  
Problem Analysis ◽  
Industrial Internet Of Things ◽  
Technology Transition ◽  
Industrial Internet ◽  
Qualified Personnel ◽  
Virtual And Augmented Reality

The paper deals with problem analysis due to domestic engineering transition to the Industry 4.0 technology. It presents such innovative technologies as additive manufacturing (3D-printing), Industrial Internet of Things, total digitization of manufacturing (digital description of products and processes, virtual and augmented reality). Among the main highlighted problems the authors include a lack of unification and standardization at this stage of technology development; incompleteness of both domestic and international regulatory framework; shortage of qualified personnel.

A Frequent Pattern Based Time Series Classification Framework

2010 ◽  
Vol 32 (2) ◽  
pp. 261-266
Author(s):  
Li Wan ◽  
Jian-xin Liao ◽  
Xiao-min Zhu ◽  
Ping Ni
Keyword(s):  
Time Series ◽  
Frequent Pattern ◽  
Time Series Classification ◽  
Classification Framework

Emerging Industrial Revolution: Symbiosis of Industry 4.0 and Circular Economy: The Role of Universities

2020 ◽  
Vol 25 (3) ◽  
pp. 505-525 ◽  
Author(s):  
Seeram Ramakrishna ◽  
Alfred Ngowi ◽  
Henk De Jager ◽  
Bankole O. Awuzie
Keyword(s):  
Economic Growth ◽  
Circular Economy ◽  
Industry 4.0 ◽  
Business Models ◽  
Industrial Revolution ◽  
New Technologies ◽  
Production Systems ◽  
Fourth Industrial Revolution ◽  
New Jobs

Growing consumerism and population worldwide raises concerns about society’s sustainability aspirations. This has led to calls for concerted efforts to shift from the linear economy to a circular economy (CE), which are gaining momentum globally. CE approaches lead to a zero-waste scenario of economic growth and sustainable development. These approaches are based on semi-scientific and empirical concepts with technologies enabling 3Rs (reduce, reuse, recycle) and 6Rs (reuse, recycle, redesign, remanufacture, reduce, recover). Studies estimate that the transition to a CE would save the world in excess of a trillion dollars annually while creating new jobs, business opportunities and economic growth. The emerging industrial revolution will enhance the symbiotic pursuit of new technologies and CE to transform extant production systems and business models for sustainability. This article examines the trends, availability and readiness of fourth industrial revolution (4IR or industry 4.0) technologies (for example, Internet of Things [IoT], artificial intelligence [AI] and nanotechnology) to support and promote CE transitions within the higher education institutional context. Furthermore, it elucidates the role of universities as living laboratories for experimenting the utility of industry 4.0 technologies in driving the shift towards CE futures. The article concludes that universities should play a pivotal role in engendering CE transitions.

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