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2017年 第5卷 第4期    刊出日期：2017-12-25

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Deep digital maintenance

Harald Rødseth, Per Schjølberg, Andreas Marhaug

2017, 5(4):  299-310.  doi:10.1007/s40436-017-0202-9

摘要 ( 493 )   PDF (281KB) ( 190 )  

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With the emergence of Industry 4.0, maintenance is considered to be a specific area of action that is needed to successfully sustain a competitive advantage. For instance, predictive maintenance will be central for asset utilization, service, and after-sales in realizing Industry 4.0. Moreover, artificial intelligence (AI) is also central for Industry 4.0, and offers data-driven methods. The aim of this article is to develop a new maintenance model called deep digital maintenance (DDM). With the support of theoretical foundations in cyber-physical systems (CPS) and maintenance, a concept for DDM is proposed. In this paper, the planning module of DDM is investigated in more detail with realistic industrial data from earlier case studies. It is expected that this planning module will enable integrated planning (IPL) where maintenance and production planning can be more integrated. The result of the testing shows that both the remaining useful life (RUL) and the expected profit loss indicator (PLI) of ignoring the failure can be calculated for the planning module. The article concludes that further research is needed in testing the accuracy of RUL, classifying PLI for different failure modes, and testing of other DDM modules with industrial case studies.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0202-9/fulltext.html

Industry 4.0: a way from mass customization to mass personalization production

Yi Wang, Hai-Shu Ma, Jing-Hui Yang, Ke-Sheng Wang

2017, 5(4):  311-320.  doi:10.1007/s40436-017-0204-7

摘要 ( 954 )   PDF (280KB) ( 454 )  

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Although mass customization, which utilizes modularization to simultaneously increase product variety and maintain mass production (MP) efficiency, has become a trend in recent times, there are some limitations to mass customization. Firstly, customers do not participate wholeheartedly in the design phase. Secondly, potential combinations are predetermined by designers. Thirdly, the concept of mass customization is not necessary to satisfy individual requirements and is not capable of providing personalized services and goods. Industry 4.0 is a collective term for technologies and concepts of value chain organization. Based on the technological concepts of radio frequency identification, cyber-physical system, the Internet of things, Internet of service, and data mining, Industry 4.0 will enable novel forms of personalization. Direct customer input to design will enable companies to increasingly produce customized products with shorter cycle-times and lower costs than those associated with standardization and MP. The producer and the customer will share in the new value created. To overcome the gaps between mass customization and mass personalization, this paper presents a framework for mass personalization production based on the concepts of Industry 4.0. Several industrial practices and a lab demonstration show how we can realize mass personalization.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0204-7/fulltext.html

Thinking process rules extraction for manufacturing process design

Jing-Tao Zhou, Xiang-Qian Li, Ming-Wei Wang, Rui Niu, Qing Xu

2017, 5(4):  321-334.  doi:10.1007/s40436-017-0205-6

摘要 ( 447 )   PDF (254KB) ( 292 )  

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To realize the reuse of process design knowledge and improve the efficiency and quality of process design, a method for extracting thinking process rules for process design is proposed. An instance representation model of the process planning reflecting the thinking process of technicians is established to achieve an effective representation of the process documents. The related process attributes are extracted from the model to form the related events. The manifold learning algorithm and clustering analysis are used to preprocess the process instance data. A rule extraction mechanism of process design is introduced, which is based on the related events after dimension reduction and clustering, and uses the association rule mining algorithm to realize the similar process information extraction in the same cluster. Through the vectorization description of the related events, the final process design rules are formed. Finally, an example is given to evaluate the method of process design rules extraction.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0205-6/fulltext.html

Development of an industrial Internet of things suite for smart factory towards re-industrialization

C. K. M. Lee, S. Z. Zhang, K. K. H. Ng

2017, 5(4):  335-343.  doi:10.1007/s40436-017-0197-2

摘要 ( 521 )   PDF (254KB) ( 183 )  

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Re-industrialization, which supports industrial upgrading and transformation, promotes smart production and high value-added manufacturing processes, and helps to create new momentum for the economic. Under the current situation, industrialists encounter several challenges to achieve re-industrialization. Firstly, the cost and technical thresholds for industrialists to leverage emerging technologies are high. Secondly, there are huge quantities and numerous types of Internet of things (IoT) devices in smart factories, warehouses and offices. The enormous extents of data exchange and communication, management, monitoring and control of IoT devices as well as the establishment and maintenance of a reliable cloud platform hinder industrialists to implement an integrated smart production management. Therefore, to achieve re-industrialization, an industrial Internet of things (ⅡoT) suite consisting of a micro-services-based ⅡoT cloud platform and ⅡoT-based smart hub is proposed, which helps to materialize re-industrialization and to conduct industrial upgrading and transformation to achieve smart production and high value-added manufacturing processes.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0197-2/fulltext.html

The fit of Industry 4.0 applications in manufacturing logistics: a multiple case study

Jo Wessel Strandhagen, Erlend Alfnes, Jan Ola Strandhagen, Logan Reed Vallandingham

2017, 5(4):  344-358.  doi:10.1007/s40436-017-0200-y

摘要 ( 493 )   PDF (281KB) ( 200 )  

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The fourth industrial revolution, Industry 4.0, is expected to cause disruptive changes in industrial production. It is driven by rapid technological developments and the need for manufacturing companies to make themselves independent of high labor costs. Industry 4.0 concerns several aspects of industrial production, including manufacturing logistics, business models and products and services. The applications of Industry 4.0 have been vastly outlined. However, the fit of Industry 4.0 applications in different production environments is not clear. The purpose of this paper is to identify and investigate the Industry 4.0 technologies that are applicable to manufacturing logistics, and how the production environment influences the applicability of these technologies. This is done through a multiple case study of four Norwegian manufacturing companies. The findings from the study indicate that the applicability of Industry 4.0 in manufacturing logistics is dependent on the production environment. Companies with a low degree of production repetitiveness see less potential in applying Industry 4.0 technologies in manufacturing logistics, while companies with a highly repetitive production see a higher potential.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0200-y/fulltext.html

Logistics 4.0 and emerging sustainable business models

Jan Ola Strandhagen, Logan Reed Vallandingham, Giuseppe Fragapane, Jo Wessel Strandhagen, Aili Biriita Hætta Stangeland, Nakul Sharma

2017, 5(4):  359-369.  doi:10.1007/s40436-017-0198-1

摘要 ( 583 )   PDF (281KB) ( 405 )  

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The drive towards Logistics 4.0 as an element of Industry 4.0 gives possibilities for new business models. Instant information exchange, automated solutions and real-time big data analysis are among the features of Logistics 4.0 paving the way for new business models. The role and importance of information are changing as we can see today. The demand for sustainability of business creates on the other hand new requirements to the operations of manufacturing and logistics. This paper addresses these challenges, illustrates current trends, and offers a model to understand and relate the different elements of business operations.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0198-1/fulltext.html

A novel method for the evaluation of fashion product design based on data mining

Bao-Rui Li, Yi Wang, Ke-Sheng Wang

2017, 5(4):  370-376.  doi:10.1007/s40436-017-0201-x

摘要 ( 547 )   PDF (282KB) ( 332 )  

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It is difficult to qualitatively evaluate the design effects of product appearance. Electroencephalograph (EEG) and eye-tracking data can serve as reflection of the subconscious activities of human beings. The application of advanced neuroscience technology in industrial operation management has become a new research hot spot. This study uses EEG equipment and an eye-tracking device to record a subject's brain activity and eye-gaze data, and then uses data mining methods to analyze the correlation between the two types of signals. The fuzzy theory is then applied to create a fuzzy comprehensive evaluation model. The neural attributes are used to quantify the factors affected by product appearance and evaluation indicators. We use women's shirts as research subjects for a case study. The EEG Emotiv device and Tobii mobile eye-tracking glasses are used to record a subject's brain activity and eye-gaze data in order to quantify the evaluation factors related to product appearance. This method not only scientifically evaluates the uniqueness of product appearance but also provides an objective reference for improving product appearance design.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0201-x/fulltext.html

Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario

Zhe Li, Yi Wang, Ke-Sheng Wang

2017, 5(4):  377-387.  doi:10.1007/s40436-017-0203-8

摘要 ( 448 )   PDF (282KB) ( 571 )  

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Fault diagnosis and prognosis in mechanical systems have been researched and developed in the last few decades at a very rapid rate. However, owing to the high complexity of machine centers, research on improving the accuracy and reliability of fault diagnosis and prognosis via data mining remains a prominent issue in this field. This study investigates fault diagnosis and prognosis in machine centers based on data mining approaches to formulate a systematic approach and obtain knowledge for predictive maintenance in Industry 4.0 era. We introduce a system framework based on Industry 4.0 concepts, which includes the process of fault analysis and treatment for predictive maintenance in machine centers. The framework includes five modules:sensor selection and data acquisition module, data preprocessing module, data mining module, decision support module, and maintenance implementation module. Furthermore, a case study is presented to illustrate the application of the data mining methods for fault diagnosis and prognosis in machine centers as an Industry 4.0 scenario.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0203-8/fulltext.html

An approach to net-centric control automation of technological processes within industrial IoT systems

Nikita Voinov, Igor Chernorutsky, Pavel Drobintsev, Vsevolod Kotlyarov

2017, 5(4):  388-393.  doi:10.1007/s40436-017-0195-4

摘要 ( 440 )   PDF (281KB) ( 288 )  

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The use of industrial internet networks with netcentric control is the driving trend behind the future material manufacturing of goods and services. The promising future of this approach is provided by these complex net-centric systems functioning with high reliability. The problem of intelligent net-centric control and reliable network functioning is fundamental, with the additional requirement that the system should preserve stakeholder security and privacy according to policies. The issue is that such systems are characterized by complex multi-parameter operability modes controlled by various criteria. This study considers an approach to providing reliable management of complicated Internet of things (IoT) systems. This is achieved by solving multi-criteria tasks over many processes of various physical natures. Corresponding methods of hierarchical decomposition of multi-criteria tasks, process levels of multi-criteria optimization, specifics of aggregation levels, and the master equation of the optimization process are described.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0195-4/fulltext.html

Impact of Industry 4.0 in service oriented firm

Wladimir Bodrow

2017, 5(4):  394-400.  doi:10.1007/s40436-017-0196-3

摘要 ( 365 )   PDF (279KB) ( 241 )  

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The first part of this study is devoted to concepts, approaches and some examples of the fourth industrial revolution. The basis of this revolution also well known as Industry 4.0 builds so called cyber-physical systems. They contain the integrated smart software systems including the internet address to enable the communication with environment as for product itself as for means of production and employees. All these enable the next level of efficiency and flexibility for both organizing and controlling of the value-creation chain over the whole lifecycle of products. In the first three chapters several Internet references and documents published by German Federal Ministry for Economic Affairs and Energy were used. Because of multiple cross references in documents, this report is written without detailed references in each paragraph of mentioned chapters. The last three chapters of the research presented undertake the short review of interdependencies between the Industry 4.0 and the well-known approach of computer-supported-cooperative-work established in the late 1980s. Long list of publications can be found in Wikipedia, and in many proceedings of ACM conferences on computer supported cooperative work (CSCW).

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0196-3/fulltext.html