Description
IS200TBAIH1CDC 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.
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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