Description
- BENTLY 3500/40M 176449-01 gateway is designed with multi protocol compatibility and modularity, seamlessly connecting industrial systems,
- operating at a wide temperature range of -40 ° C to 85 ° C, supporting 8-channel rotating equipment monitoring,
- remote configuration to improve efficiency, and ensuring stable power and automation.
It can connect the Bently Nevada 3500 system with other control systems or host computers to achieve data interconnectivity.
thus achieving integration with other industrial control systems.
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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