Description
9903-109 From Woodward, USA
Non differential regulation is mainly used for constant speed control and is suitable for single machine operation or multiple prime movers working together in an isolated power grid. Differential regulation provides more control flexibility.
further enhancing its performance and application range.
rich additional functions, and high-precision output signals. Whether it is in the fields of generator sets, compressors, pump stations, or ships and locomotives, it can effectively ensure the stable operation of equipment within the set range.
power measurement level 1, editable screen, multi interface toolkit connection, etc. All details can be found in Woodward easyYgen manual 37582A
The rated operating temperature range of this model is -20 to 70 ° C; the rated temperature range of the LT model is -40 to 70 ° C, suitable for outdoor use.
9903-109 is equipped with a monitor (not available on the 3100 model) and is designed for front panel installation.
The built-in HMI has a color LCD and soft keys (now with dedicated buttons) for direct control of the 9903-109 device. Multi level password protection can prevent unauthorized changes.
The generator set has four operating modes and the option to configure a manual circuit breaker control device.
How to use 9903-109？
What is 9903-109 used for?
9903-109 Customs Code
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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