scholarly journals Process-Driven and Flow-Based Processing of Industrial Sensor Data

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5245
Author(s):  
Klaus Kammerer ◽  
Rüdiger Pryss ◽  
Burkhard Hoppenstedt ◽  
Kevin Sommer ◽  
Manfred Reichert
Keyword(s):  
Sensor Data ◽  
Technical Solution ◽  
Manufacturing Companies ◽  
Industrial Internet Of Things ◽  
Capture Process ◽  
Processing Pipeline ◽  
Digital Services ◽  
Industrial Internet ◽  
Sensor Processing ◽  
Recorded Data

For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.

scholarly journals Advances in Future Internet and the Industrial Internet of Things

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 244 ◽  
Author(s):  
Jong Park
Keyword(s):  
Internet Of Things ◽  
Communication Networks ◽  
Future Internet ◽  
Resource Provisioning ◽  
Sensor Data ◽  
Third Wave ◽  
Industrial Internet Of Things ◽  
Privacy And Security ◽  
Industrial Internet ◽  
Use Of Internet

After the emergence of the Internet and mobile communication networks, the IoT has been considered as the third wave of information technology. The Industrial Internet of Things (IIoT) is the use of Internet of Things (IoT) technologies in manufacturing. IIoT incorporates machine learning and big data technology, sensor data, and machine-to-machine (M2M) communications that have existed in industrial areas for years. In the future, people and objects will be connected at any time, any place, with anything and anyone and will utilize any network and services. IIoT is creating a new world in which people and businesses can manage their assets in more informed ways and can make more opportune and better-informed decisions. Many advanced IIoT and 5G technologies have been successfully applied in everyday life, but there are still many practical problems tackled by traditional methods which are generally difficult to experimentally solve in the advanced Industrial Internet of Things. Therefore, in this special issue, we accepted five articles in three different dimensions: communication networks, optimized resource provisioning and data forwarding, privacy and security.

HOW THE INDUSTRIAL INTERNET OF THINGS CHANGES BUSINESS MODELS IN DIFFERENT MANUFACTURING INDUSTRIES

2016 ◽  
Vol 20 (08) ◽  
pp. 1640015 ◽  
Author(s):  
CHRISTIAN ARNOLD ◽  
DANIEL KIEL ◽  
KAI-INGO VOIGT
Keyword(s):  
Internet Of Things ◽  
Business Models ◽  
Manufacturing Companies ◽  
Industrial Internet Of Things ◽  
Study Approach ◽  
Current State ◽  
Industrial Internet ◽  
Information And Communication ◽  
Multiple Case ◽  
Industry Sectors

The Industrial Internet of Things (IIoT) poses large impacts on business models (BM) of established manufacturing companies within several industries. Thus, this paper aims at analyzing the influence of the IIoT on these BMs with particular respect to differences and similarities dependent on varying industry sectors. For this purpose, we employ an exploratory multiple case study approach based on semi-structured expert interviews in 69 manufacturing companies from the five most important German industries. Owing the lack of previous research, our study contributes to the current state of management literature by revealing the following valuable insights with regard to industry-specific BM changes: The machine and plant engineering companies are mainly facing changing workforce qualifications, the electrical engineering and information and communication technology companies are particularly concerned with the importance of novel key partner networks, and automotive suppliers predominantly exploit IIoT-inherent benefits in terms of an increasing cost efficiency.

scholarly journals Visibility of resources and assets in the shipyard through industrial internet of things

2021 ◽  
Vol 11 (2) ◽  
pp. 88-101
Author(s):  
Ibrahim Cil ◽  
Fahri Arisoy ◽  
Hilal Kilinc
Keyword(s):  
Internet Of Things ◽  
Transformation Process ◽  
Digital Transformation ◽  
Sensor Data ◽  
Cyber Physical System ◽  
Industrial Internet Of Things ◽  
Use Of Data ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Other Information

Industrial Internet of Things is becoming one of the fundamental technologies with the potential to be widely used in shipyards as in other industries to increase information visibility. This article aims to analyze how to develop an industrial IoT-enabled system that provides visibility and tracking of assets at SEDEF Shipyard, which is in the digital transformation process. The research made use of data from previous studies and by using content analysis, the findings were discussed. Industrial IoT enables the collection and analysis of data for more informed decisions.  Based on the findings, sensor data in the shipyard are transmitted to the cloud via connected networks. These data are analysed and combined with other information and presented to the stakeholders. Industrial IoT enables this data flow and monitors processes remotely and gives the ability to quickly change plans as needed. Keywords: Shipyard, Industrial Internet of Things, Cyber-Physical System, Visibility, Assets tracking;        

scholarly journals Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2376
Author(s):  
Pavol Tanuska ◽  
Lukas Spendla ◽  
Michal Kebisek ◽  
Rastislav Duris ◽  
Maximilian Stremy
Keyword(s):  
Neural Networks ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Assembly Line ◽  
Assembly Process ◽  
Manufacturing Companies ◽  
Industrial Internet Of Things ◽  
Smart System ◽  
Industrial Internet ◽  
Unique Approach

One of the big problems of today’s manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.

Recurrence Behavior Statistics of Blast Furnace Gas Sensor Data in Industrial Internet of Things

2020 ◽  
Vol 7 (6) ◽  
pp. 5666-5676
Author(s):  
Yingqi Li ◽  
Di Cai ◽  
Jialin Wang ◽  
Xiaochuan Sun ◽  
Zhigang Li ◽  
...  
Keyword(s):  
Blast Furnace ◽  
Internet Of Things ◽  
Gas Sensor ◽  
Sensor Data ◽  
Industrial Internet Of Things ◽  
Blast Furnace Gas ◽  
Industrial Internet ◽  
Recurrence Behavior

Determinants of Industrial Internet of Things Adoption in German Manufacturing Companies

2019 ◽  
Vol 16 (06) ◽  
pp. 1950038 ◽  
Author(s):  
Christian Arnold ◽  
Kai-Ingo Voigt
Keyword(s):  
Logistic Regression ◽  
Internet Of Things ◽  
Technology Management ◽  
Significant Influence ◽  
Environmental Uncertainty ◽  
Relative Advantage ◽  
Survey Questionnaire ◽  
Manufacturing Companies ◽  
Industrial Internet Of Things ◽  
Industrial Internet

This study aims at examining factors that determine the adoption of the Industrial Internet of Things (IIoT) by manufacturing companies applying the technology–organization–environment framework. Data of 197 German manufacturers are gathered by means of a survey questionnaire and tested using a logistic regression. This paper contributes to academic discussion by revealing determinants, which have a significant influence on the adoption of the IIoT: relative advantage associated with the IIoT, support by a company’s top management, high levels of competition, and environmental uncertainty. The study provides important implications, both for research and practitioners, related to technology management in the context of the IIoT.

Order Fulfilment Progress Estimation for Collaborative Manufacturing Enabled by Industrial Internet of Things

2020 ◽  
Author(s):  
Chen Peng ◽  
Zheng Zhang ◽  
Tao Peng ◽  
Renzhong Tang ◽  
Xiaoliang Zhao
Keyword(s):  
Internet Of Things ◽  
Order Fulfillment ◽  
Manufacturing Companies ◽  
Industrial Internet Of Things ◽  
Production Chain ◽  
Collaborative Manufacturing ◽  
Make To Order ◽  
Industrial Internet ◽  
Order Fulfilment

Abstract It has been recognized by manufacturing companies that working collaboratively is the way to advance their competiveness. Order fulfillment estimation addresses the issue of uncertainty from vendors. It is significant for collaborative manufacturing, which enhances companies’ responsiveness to market dynamics. In a data-rich scenario, order fulfillment estimation can be performed based on information extracted from data acquisition devices, such as smart sensors. The analysis result should serve the decisions-making of the production planning, and an indicator should be passed along the production chain even to its end customer for collaborative purpose. In the meanwhile, the manufacturer’s sensitive or confidential information is excluded to avoid risks. This article studies a method to effectively evaluate the order fulfillment process in an Industrial Internet of Things (IIoT) facilitated make-to-order production system. An order fulfilment progress (OFP) indicator is proposed to dynamically represent the fulfillment progress, and its estimation mathematical models are proposed. To improve the practicability of the OFP indicator in production, the influence of abnormal event scenarios are discussed to modify the OFP. A case study presented in this research demonstrates the proposed indicator with consideration of job in process (JIP) is promising comparing to conventional indicators that are represented by the proportion of finished over total products.

scholarly journals OPAL—The Toolbox for the Integration and Analysis of IoT in a Semantically Annotated Way

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4002
Author(s):  
Philipp Hertweck ◽  
Tobias Hellmund ◽  
Jürgen Moßgraber
Keyword(s):  
Data Storage ◽  
Heterogeneous Data ◽  
Software Components ◽  
Industrial Internet Of Things ◽  
Digital Services ◽  
Industrial Internet ◽  
Broad Variety ◽  
Execution Environment ◽  
Access Data ◽  
Software Toolbox

Industrial Internet of Things (IIoT) applications are being used more and more frequently. Data collected by various sensors can be used to provide innovative digital services supporting increasing efficiency or cost reduction. The implementation of such applications requires the integration and analysis of heterogeneous data coming from a broad variety of sensors. To support these steps, this paper introduces OPAL, a software toolbox consolidating several software components for the semantically annotated integration and analysis of IoT-data. Data storage is realized in a standardized and INSPIRE-compliant way utilizing the SensorThings API. Supporting a broad variety of use cases, OPAL provides several import adapters to access data sources with various protocols (e.g., the OPC UA protocol, which is often used in industrial environments). In addition, a unified management and execution environment, called PERMA, is introduced to allow the programming language independent integration of algorithms.

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