Description
8440-2256 WOODWARD generator set speed control board
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.
8440-2256 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 8440-2256 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 8440-2256？
What is 8440-2256 used for?
8440-2256 Customs Code
2 Leveraging big data tool chainsAfter the data collected from the manufacturing product value chain is stored in the database, a data analysis system is required to analyze the data. The manufacturing data analysis system framework is shown in Figure 1. Data is first extracted, transformed, and loaded (ETL) from different databases into a distributed file system, such as Hadoop Distributed File System (HDFS) or a NoSQL database (such as MongoDB). Next, machine learning and analytics tools perform predictive modeling or descriptive analytics. To deploy predictive models, the previously mentioned tools are used to convert models trained on historical data into open, encapsulated statistical data mining models and associated metadata called Predictive Model Markup Language (PMML), and Stored in a scoring engine. New data from any source is evaluated using models stored in the scoring engine [9].A big data software stack for manufacturing analytics can be a mix of open source, commercial, and proprietary tools. An example of a manufacturing analytics software stack is shown in Figure 2. It is known from completed projects that existing stack vendors do not currently offer complete solutions. Although the technology landscape is evolving rapidly, the best option currently is modularity with a focus on truly distributed components, with the core idea of success being a mix of open source and commercial components [10].In addition to the architecture presented here, there are various commercial IoT platforms. These include GE”s Predix ( www.predix.com ), Bosch”s IoT suite (www.bosch-iot-suite.com), IBM”s Bluemix ( www.ibm.com/cloud-computing/ ), ABB based on Microsoft Azure IoT services and people platform (https://azure.microsoft.com) and Amazon’s IoT cloud (https://aws.amazon.com/iot). These platforms offer many standard services for IoT and analytics, including identity management and data security, which are not covered in the case study here. On the other hand, the best approaches offer flexibility and customizability, making implementation more efficient than standard commercial solutions. But implementing such a solution may require a capable data science team at the implementation site. The choice comes down to several factors, non-functional requirements, cost, IoT and analytics.
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