Description
IS200DAMDG1A 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) Data collection and traceability issues. Data collection issues often occur, and many assembly lines lack “end-to-end traceability.” In other words, there are often no unique identifiers associated with the parts and processing steps being produced. One workaround is to use a timestamp instead of an identifier. Another situation involves an incomplete data set. In this case, omit incomplete information parts or instances from the forecast and analysis, or use some estimation method (after consulting with manufacturing experts).(3) A large number of features. Different from the data sets in traditional data mining, the features observed in manufacturing analysis may be thousands. Care must therefore be taken to avoid that machine learning algorithms can only work with reduced datasets (i.e. datasets with a small number of features).(4) Multicollinearity, when products pass through the assembly line, different measurement methods are taken at different stations in the production process. Some of these measurements can be highly correlated, however many machine learning and data mining algorithm properties are independent of each other, and multicollinearity issues should be carefully studied for the proposed analysis method.(5) Classification imbalance problem, where there is a huge imbalance between good and bad parts (or scrap, that is, parts that do not pass quality control testing). Ratios may range from 9:1 to even lower than 99,000,000:1. It is difficult to distinguish good parts from scrap using standard classification techniques, so several methods for handling class imbalance have been proposed and applied to manufacturing analysis [8].(6) Non-stationary data, the underlying manufacturing process may change due to various factors such as changes in suppliers or operators and calibration deviations in machines. There is therefore a need to apply more robust methods to the non-stationary nature of the data. (7) Models can be difficult to interpret, and production and quality control engineers need to understand the analytical solutions that inform process or design changes. Otherwise the generated recommendations and decisions may be ignored.
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