Description
IS200VRTDH1DAC Product Introduction
The specific application scope of the product
will depend on the needs of system integration and industrial application, but generally speaking, this type of embedded controller module can be applied to the following categories:
manufacturing processes, etc.
monitoring and control system.
of the controller module, as well as the specific needs of the customer.
designed to manage gas or steam turbines.
It has a CIMPLICITY graphical interface and an HMI with software suitable for running heavy-duty turbines.
be installed at the bottom of the cabinet. For a small setup that is easy to serve a triple redundant system, up to three components can be installed side by side.
he board can operate within a temperature range of 0 to 65 degrees Celsius without the need for a fan for cooling. NFPA Class 1. This board can be used for two applications.
0 PrefaceGermany”s “Industry 4.0″ and the United States” “Industrial Internet” will restructure the world”s industrial layout and economic structure, bringing different challenges and opportunities to countries around the world. The State Council of China issued “Made in China 2025” as an action plan for the first ten years of implementing the strategy of manufacturing a strong country, which will accelerate the integrated development of IoT technology and manufacturing technology [1]. IoT collects data on machine operations, material usage, facility logistics, etc., bringing transparency to operators. This transparency is brought about by the application of data analytics, which refers to the use of statistical and machine learning methods to discover different data characteristics and patterns. Machine learning technology is increasingly used in various manufacturing applications, such as predictive maintenance, test time reduction, supply chain optimization, and process optimization, etc. [2-4]. The manufacturing process of enterprises has gradually developed from the traditional “black box” model to the “multi-dimensional, transparent and ubiquitous perception” model [5].1 Challenges facing manufacturing analysisThe goal of manufacturing analytics is to increase productivity by reducing costs without compromising quality:(1) Reduce test time and calibration, including predicting test results and calibration parameters;(2) Improve quality and reduce the cost of producing scrap (bad parts) by identifying the root causes of scrap and optimizing the production line on its own;(3) Reduce warranty costs, use quality testing and process data to predict field failures, and cross-value stream analysis;(4) Increase throughput, benchmark across production lines and plants, improve first-pass rates, improve first-pass throughput, and identify the cause of performance bottlenecks such as overall equipment effectiveness (OEE) or cycle time;
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