Description
DS200ADMAH1A 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.
3.2 Machine learningAs the functionality of distributed computing tools such as Spark MLLib (http://spark.apache.org/mllib) and SparkR (http://spark.apache.org/docs/latest/index.html) increases, it becomes It is easier to implement distributed and online machine learning models, such as support vector machines, gradient boosting trees and decision trees for large amounts of data. Test the impact of different machine parameters and process measurements on overall product quality, from correlation analysis to analysis of variance and chi-square hypothesis testing to help determine the impact of individual measurements on product quality. This design trains some classification and regression models that can distinguish parts that pass quality control from parts that do not. The trained models can be used to infer decision rules. According to the highest purity rule, purity is defined as Nb/N, where N is the number of products that satisfy the rule and Nb is the total number of defective or bad parts that satisfy the rule.Although these models can identify linear and nonlinear relationships between variables, they do not represent causal relationships. Causality is critical to determining the true root cause, using Bayesian causal models to infer causality across all data.3.3 VisualizationA visualization platform for collecting big data is crucial. The main challenge faced by engineers is not having a clear and comprehensive overview of the complete manufacturing process. Such an overview will help them make decisions and assess their status before any adverse events occur. Descriptive analytics uses tools such as Tableau (www.tableau.com) and Microsoft BI (https://powerbi.microsoft.com/en-us) to help achieve this. Descriptive analysis includes many views such as histograms, bivariate plots, and correlation plots. In addition to visual statistical descriptions, a clear visual interface should be provided for all predictive models. All measurements affecting specific quality parameters can be visualized and the data on the backend can be filtered by time.
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