Plant Visualization Using Iot and Ar (original) (raw)
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Improving sustainability is a key concern for industrial development. Industry has recently been benefiting from the rise of IoT technologies, leading to improvements in the monitoring and breakdown prevention of industrial equipment. In order to properly achieve this monitoring and prevention, visualization techniques are of paramount importance. However, the visualization of real-time IoT sensor data has always been challenging, especially when such data are originated by sensors of different natures. In order to tackle this issue, we propose a methodology that aims to help users to visually locate and understand the failures that could arise in a production process.This methodology collects, in a guided manner, user goals and the requirements of the production process, analyzes the incoming data from IoT sensors and automatically derives the most suitable visualization type for each context. This approach will help users to identify if the production process is running as well as...
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Smart manufacturing technologies (Industry 4.0) as solutions to enhance productivity and improve efficiency are a priority to manufacturing industries worldwide. Such solutions have the ability to extract, integrate, analyze and visualize sensor and data from other legacy systems in order to enhance the operational performance. This paper proposes a solution to the challenge of real-time analysis and visualization of sensor and ERP data. Dynamic visualization is achieved using a machine learning approach. The combination of real-time visualization and machine learning allows for early detection and prevention of undesirable situations or outcomes. The prototype system has so far been tested by a smart manufacturing company with promising results.
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The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine ma...
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Manufacturing creates a lot of data, and this is increasing due to digitalization of manufacturing, industrial Internet of Things (IIoT) and needs for product traceability as well as predictive maintenance. Typically data from production material flow is not analyzed and thus the improvement potential is not found. There is need for interactive analytics tools that can turn raw data from heterogeneous data sources e.g. starting from sensor data, manufacturing IT systems, (e.g. Enterprise Resource Planning, ERP, Manufacturing Execution System, MES and Supervisory Control And Data Acquisition, SCADA), into meaningful information and predictions-and presented on easy-to-use interfaces. This paper presents a feasibility study focusing on interactive visual analytics of manufacturing data set carried out at