Description
5464-444 Controller debugger generator WOODWARD
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.
5464-444 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 5464-444 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 5464-444？
What is 5464-444 used for?
5464-444 Customs Code
3 Case Studies on Reducing Scrap RatesAny product assembled or produced in a factory goes through a series of quality tests to determine whether it needs to be scrapped. High scrap rates are caused by the opportunity cost of not delivering products to customers in a timely manner, wasted personnel time, wasted non-reusable parts, and equipment overhead expenses. Reducing scrap rates is one of the main issues manufacturers need to address. Ways to reduce scrap include identifying the root causes of low product quality.3.1 Data processingRoot cause analysis begins by integrating all available data on the production line. Assembly lines, workstations, and machines make up the industrial production unit and can be considered equivalent to IoT sensor networks. During the manufacturing process, information about process status, machine status, tools and components is constantly transferred and stored. The volume, scale, and frequency of factory production considered in this case study necessitated the use of a big data tool stack similar to the one shown in Figure 2 for streaming, storing, preprocessing, and connecting data. This data pipeline helps build machine learning models on batch historical data and streaming real-time data. While batch data analytics helps identify issues in the manufacturing process, streaming data analytics gives factory engineers regular access to the latest issues and their root causes. Use Kafka (https://kafka.apache.org) and Spark streaming (http://spark.apache.org/streaming) to transmit real-time data from different data sources; use Hadoo (http://hadoop.apache.org ) and HBase (https://hbase.apache.org) to store data efficiently; use Spark (http://spark.apache.org) and MapReduce framework to analyze data. The two main reasons to use these tools are their availability as open source products, and their large and active developer network through which these tools are constantly updated.
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