39,828 research outputs found
Towards In-Transit Analytics for Industry 4.0
Industry 4.0, or Digital Manufacturing, is a vision of inter-connected
services to facilitate innovation in the manufacturing sector. A fundamental
requirement of innovation is the ability to be able to visualise manufacturing
data, in order to discover new insight for increased competitive advantage.
This article describes the enabling technologies that facilitate In-Transit
Analytics, which is a necessary precursor for Industrial Internet of Things
(IIoT) visualisation.Comment: 8 pages, 10th IEEE International Conference on Internet of Things
(iThings-2017), Exeter, UK, 201
Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm
Industry 4.0 aims at achieving mass customization at a
mass production cost. A key component to realizing this is accurate
prediction of customer needs and wants, which is however a
challenging issue due to the lack of smart analytics tools. This
paper investigates this issue in depth and then develops a predictive
analytic framework for integrating cloud computing, big data
analysis, business informatics, communication technologies, and
digital industrial production systems. Computational intelligence
in the form of a cluster k-means approach is used to manage
relevant big data for feeding potential customer needs and wants
to smart designs for targeted productivity and customized mass
production. The identification of patterns from big data is achieved
with cluster k-means and with the selection of optimal attributes
using genetic algorithms. A car customization case study shows
how it may be applied and where to assign new clusters with
growing knowledge of customer needs and wants. This approach
offer a number of features suitable to smart design in realizing
Industry 4.0
Internet of Things Based Smart Health Monitoring of Industrial Standard Motors
The Industry 4.0 vision provides recommendations how companies can ease the challenges. In an industrial environment, it is beneficial to have a predictive approach to make smart industry using IoT. The Predictive approach includes automating the maintenance activities of machines which help to deliver safety, performance, customer experience, capacity, cost efficiency and sustainability of the key business assets. It helps to improve work force safety which reduces the need to access the infrastructure, develop technologies to enable activities to be remotely controlled from safe areas and automate processes to remove manual tasks and helps to increase infrastructure reliability. It also improves the precision and accuracy of data collection, introducing data analytics, removing human bias, improving reproducibility. This will improve information about asset condition, inform inspection and repair schedules based on asset risks. By implementing predictive and preventive maintenance, one can improve equipment life and avoid any unplanned maintenance activity and thus reducing unscheduled downtime. We in this work have an unit which could be easily attached to the motor units and this does not demand any wiring to carried out. The sensor monitor signals from the motor, accurately measuring key parameters at regular interval of time, as desired. And, the data is sent to the cloud, which in our case is adafruit. From there, the data is analysed and it produces meaningful information. The server then sends alert message to the users about critical data of machine. This will help in fixing any technical issue with ease without incurring much delay
Continuous maintenance and the future – Foundations and technological challenges
High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security
Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment
Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in the entire ship design, manufacturing and operation process to achieve these goals. This paper highlights smart design, manufacturing and operation as the way forward in an industry 4.0 (i4) era from designing for better energy efficiency to more intelligent ships and smart operation through-life. The paper (i) draws parallels between ship design, manufacturing and operation processes, (ii) identifies key challenges facing such a temporal (lifecycle) as opposed to spatial (mass) products, (iii) proposes a closed-loop ship lifecycle framework and (iv) outlines potential future directions in smart design, manufacturing and operation of ships in an industry 4.0 value chain so as to achieve more energy-efficient vessels. Through computational intelligence and cyber-physical integration, we envision that industry 4.0 can revolutionise ship design, manufacturing and operations in a smart product through-life process in the near future
Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments
Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment
Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review
The digital transformation of manufacturing (a phenomenon also known as "Industry 4.0" or "Smart Manufacturing") is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field
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