•First survey of UK SMEs (n = 271, KMO = 0.701) on adoption of Industry 4.0 technologies (n = 20)•First application of Technology Acceptance Model on Industry 4.0 in SMEs•Guidance on which aspects ...should be focussed on to ensure success of Industry 4.0 in SMEs•Flexibility, cost, efficiency, quality and competitive advantage are key benefits•Financial, awareness and knowledge constraints are found to be key adoption challenges
Future industrial systems have been popularised in recent years through buzzwords such as Industry 4.0, the Internet of Things (IoT), and Cyber Physical Systems (CPS). Whilst the technologies of Industry 4.0 and likes have many conceivable benefits to manufacturing, the majority of these technologies are developed for, or by, large firms. Much of the contemporary work is therefore disconnected from the needs of small and medium-sized enterprises (SMEs), despite the fact they represent 90 % of registered companies in Europe. This study approaches the disconnect through an industrial survey of UK SMEs (n = 271, KMO = 0.701), which is the first in the UK that used to collect opinions, reinforcing the current literature on the most reported Industry 4.0 technologies (n = 20), benefits, and challenges to implementation. Flexibility, cost, efficiency, quality and competitive advantage are found to be the key benefits to Industry 4.0 adoption in SMEs. Whilst many SMEs show a desire to implement Industry 4.0 technologies for these reasons, financial and knowledge constraints are found to be key challenges.
•Roadmap for the development of industrial cyber-physical systems.•Description of 4 prototype implementations for industrial automation based on cyber-physical systems technologies.•Overview of key ...CPS challenges to increase Technology Readiness Levels.
Cyber-Physical Systems (CPS) is an emergent approach that focuses on the integration of computational applications with physical devices, being designed as a network of interacting cyber and physical elements. CPS control and monitor real-world physical infrastructures and thus is starting having a high impact in industrial automation. As such design, implementation and operation of CPS and management of the resulting automation infrastructure is of key importance for the industry. In this work, an overview of key aspects of industrial CPS, their technologies and emerging directions, as well as challenges for their implementation is presented. Based on the hands-on experiences gathered from four European innovation projects over the last decade (i.e. SOCRADES, IMC-AESOP, GRACE and ARUM), a key challenges have been identified and a prioritization and timeline are pointed out with the aim to increase Technology Readiness Levels and lead to their usage in industrial automation environments.
Nowadays digital surveillance systems are universally installed for continuously collecting enormous amounts of data, thereby requiring human monitoring for the identification of different activities ...and events. Smarter surveillance is the need of this era through which normal and abnormal activities can be automatically identified using artificial intelligence and computer vision technology. In this paper, we propose a framework for activity recognition in surveillance videos captured over industrial systems. The continuous surveillance video stream is first divided into important shots, where shots are selected using the proposed convolutional neural network (CNN) based human saliency features. Next, temporal features of an activity in the sequence of frames are extracted by utilizing the convolutional layers of a FlowNet2 CNN model. Finally, a multilayer long short-term memory is presented for learning long-term sequences in the temporal optical flow features for activity recognition. Experiments<xref ref-type="fn" rid="fn1"> 1 1
https://github.com/Aminullah6264/Activity_Rec_ML-LSTM .
are conducted using different benchmark action and activity recognition datasets, and the results reveal the effectiveness of the proposed method for activity recognition in industrial settings compared with state-of-the-art methods.
This paper reviews literature that addresses applications of resilience engineering principles to various fields. Recently the concept has attracted great attention from a technical and industrial ...perspective. The primary focus of this paper is to review the resilience engineering applications to industrial systems with the purpose of applying them to the chemical industry. A systematic review is performed to classify peer-reviewed journal papers that are associated with resilience engineering applications into three categories: industrial systems, ecological systems, and interlinked systems. The literature in the category of industrial systems is further divided based on the type of approaches such as field studies, case studies, methodologies, and mathematical modeling. After thoroughly analyzing the literature, four key research areas are identified: Considering socio-technical factors for resilience assessment efficiently; Inculcating the possibility of multiple disasters in resilience assessment; Design optimization for resilience enhancement; Efficient restoration strategies. All these research areas have not been explored exclusively for chemical facilities to a great extent. It is concluded that if these research areas are addressed appropriately, it would help in triggering the research pertaining to the application of resilience engineering principles in chemical facilities.
•Different aspects of resilience and applications in process industries were reviewed.•Key research areas and approaches in resilience engineering have been identified.•Resilience of chemical plants was addressed by possible applications.•Effects of human factors on resilience engineering have been reviewed.•Limitations and future research directions of resilience were outlined.
Integrating digital twin technology in Cyber–Physical Systems and Internet of Things can boost their intelligence. Given the current maturity of digital twin technology (yet in progress), improving ...the models that these systems use is typically achieved off-line, requiring the system to stop and reconfigure to run each new model version. In fact, most works use cloud back-ends to run heavy machine learning algorithms, imposing strict requirements on the data exchange between the physical system and the cloud.
We address the online improvement of digital twin models in cyber–physical systems by supporting model refinement without disrupting nor stopping the normal operation of the system. This improves the dynamicity of the system that may turn into a major competitive advantage in a number of industrial scenarios. Precisely, we exploit the collaborative expectation of next generation cyber–physical systems based on highly-connected cells enabled by 5G and 6G networking; and on top of these, we design a shared space properly managed to deliver the needed temporal behaviour required by cyber–physical systems.
For this, we present the design of CoTwin framework as a middleware that allows cells to collectively improve digital twin models seamlessly. CoTwin manages the interaction of cells with a blockchain-based collaborative space offering a built-in trusted storage model. We integrate neural network algorithms as they provide fast execution that meet the time-sensitivity requirements of cyber–physical systems. Our contribution is validated by means of its implementation and deployment on an actual blockchain network, and an exhaustive set of experiments to analyse the resulting overhead and temporal behaviour. Results show that CoTwin achieves stable execution times across all its functional pieces; and it exhibits stable service time for large sets of cells.
•Design of a shared space for collaborative training and improvement of digital twin models•A middleware to manage the trained digital twin models in the blockchain network•Formal specification of middleware, processes, and involved smart contracts•Framework that meets temporal requirements of cyber-physical systems and IoT•Experimental validation that yields low overhead of all relevant operations
•Review of methods used for heat integration of Organic Rankine Cycle.•Integration of Organic Rankine Cycle with industrial waste heat.•Influence of working fluid selection, architecture and low ...temperature waste heat.•Problems in integration with continuous and batch industrial processes.
Production systems represent a significant source of waste heat. The waste heat cannot be reused often. Many optimization methods can give a solution for waste heat recovery. However, the results do not depend only on the method. The low-temperature waste heat makes difficulties for its recovery within the processes. Organic Rankine Cycle units can be used for low-temperature heat transformation into electricity. Linking the Organic Rankine Cycle within the heat integrated system is not simple. This depends on the influence of a few important factors. The process parameters of the working medium, the physical and chemical characteristics of the working fluid, the continuity of heat supply, and the temperature level of waste heat are necessary conditions that must be included in optimization. The optimization method should determine the optimal operating point of the Organic Rankine Cycle. The displacement of the operating point leads to decrease in the effective transformation of heat into electricity. These problems are analyzed through a review of the methods and approaches used for the integration of Organic Rankine Cycle in thermal process systems. These include Pinch technology, Non-Linear Programming, Multiple Integer Linear Programming, Genetic Algorithm, Artificial Neural Network and many different approaches for polygeneration systems. All methods were compared and systematized in a general scheme for integration of an Organic Rankine Cycle with low-temperature industrial waste heat supply. This work also includes experience in implemented and designed projects of an integrated Organic Rankine Cycle.
This study aims to reflect on a possible symbiosis between the fashion system and unrelated companies focused on reducing textile waste. Recent developments in the fashion system, in terms of the ...organizational network, highlight the importance of collaboration, or vice versa of competition enhancement mechanisms. The highly innovative and creative skills and tasks concentrate much of the value in the creation phase, characteristic of emerging sectors such as creative ones. The methodology will be carried out through a review of the reference literature, with a critical, constructive, and real analysis on strategies for the construction of this symbiosis. It is expected to contribute to a reflection on the development of collaboration and cooperation skills in an interdisciplinary, or even transdisciplinary approach, for the training and preparation of fashion design professionals, requiring a greater commitment from Academia in the creation of interactions and interrelationships with still very different disciplinary sectors.
•This paper explores the use of TCNs for predicting failures in industrial machines.•It presents comparative experiments between TCNs and CNN/LSTM networks.•Models are evaluated under different ...conditions and their performances are presented.•Results show that TCNs can outperform LSTMs/CNNs for long time sequence forecasting.
Cyber-physical systems (CPS) are an indispensable aspect of the modern age’s data driven industrial systems. These systems can be controlled and monitored with the help of computer-oriented devices and software that are responsible for integrating the physical environment with cyber frameworks. Owing to the nature of operations in any physical process industry, it becomes imperative to deal with potential failures before they occur. To avoid downtime and losses, predictive maintenance is one relevant policy that utilizes prior information and domain knowledge to help in scheduling operations and maintenance. Predictive maintenance (PdM) in industrial applications is known to improve the efficiency, lifetime, and reliability of the machines and thereby reducing the maintenance cost. With the advances in machine learning approaches in cyber physical systems, reliable predictions can be performed to significantly reduce downtime and operational losses associated with the physical processes. In this paper, usefulness of Temporal Convolutional Networks (TCNs) is investigated with the aim of forecasting the remaining useful life (RUL) for Turbofan engines. This paper demonstrates the effectiveness of using TCNs for prognosis under various evaluation conditions and also provides comparison of their performance with hybrid architectures like CNN-LSTM networks and meta-heuristically optimized LSTM networks. The proposed methods were able to achieve upto 94.47% accuracy in case of binary classification tasks and upto 98.7% precision in case of multi-label classification. The cumulative results in accordance to elaborated test cases are presented with the conclusion of the study.
With the prevalence of web techniques and Internet-of-Things networks, an increasing number of developers build software by invoking existing application programming interfaces (APIs), especially in ...industrial systems. As the number of existing APIs in industrial systems is large, it is critical to recommend suitable APIs from big APIs data to developers in industrial software development. There have been some approaches proposed for APIs recommendation, but the existing approaches focus on the utilization of historical invocation records but ignore the exploitation of other information in the development process. We find that this ignored information can be mined as cognitive knowledge to learn the behavior rules of developers. In this article, we propose a holistic personalized recommendation framework that contains two individual models and one ensemble model, which are based on joint matrix factorization and cognitive knowledge mining. In the two individual models, we study the hidden relationships among users, which are mined from the APIs following records. We also study the hidden relationships among APIs, which are mined from the content information. We also propose an ensemble model. We crawled a large real-word dataset and conducted sufficient experiments, and compared our framework with well-known existing methods. The experimental results demonstrate that our framework achieves the best performance.
Editorial, Vol. 6, No. 2 De Gloria, Alessandro
International journal of serious games,
06/2019, Letnik:
6, Številka:
2
Journal Article
Recenzirano
Odprti dostop
This edition of the International Journal of Serious Games is mostly dedicated to a special issue on “Gamification of Industrial Systems”, guest edited by Samir Garbaya and Theo Lim. The special ...issue aims at investigating new emerging needs for gamification in industry. The issue features six articles, that address important challenging issues through conducting pilot studies and developing gamified industrial applications. The issue is completed by a regular paper by Batista et Al.
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