Description
IS200TBCIH1BCE 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.
(5) Perform predictive maintenance, analyze machine operating conditions, determine the main causes of failures, and predict component failures to avoid unplanned downtime.Traditional quality improvement programs include Six Sigma, Deming Cycle, Total Quality Management (TQM), and Dorian Scheinin’s Statistical Engineering (SE) [6]. Methods developed in the 1980s and 1990s are typically applied to small amounts of data and find univariate relationships between participating factors. The use of the MapReduce paradigm to simplify data processing in large data sets and its further development have led to the mainstream proliferation of big data analytics [7]. Along with the development of machine learning technology, the development of big data analytics has provided a series of new tools that can be applied to manufacturing analysis. These capabilities include the ability to analyze gigabytes of data in batch and streaming modes, the ability to find complex multivariate nonlinear relationships among many variables, and machine learning algorithms that separate causation from correlation.Millions of parts are produced on production lines, and data on thousands of process and quality measurements are collected for them, which is important for improving quality and reducing costs. Design of experiments (DoE), which repeatedly explores thousands of causes through controlled experiments, is often too time-consuming and costly. Manufacturing experts rely on their domain knowledge to detect key factors that may affect quality and then run DoEs based on these factors. Advances in big data analytics and machine learning enable the detection of critical factors that effectively impact quality and yield. This, combined with domain knowledge, enables rapid detection of root causes of failures. However, there are some unique data science challenges in manufacturing.(1) Unequal costs of false alarms and false negatives. When calculating accuracy, it must be recognized that false alarms and false negatives may have unequal costs. Suppose a false negative is a bad part/instance that was wrongly predicted to be good. Additionally, assume that a false alarm is a good part that was incorrectly predicted as bad. Assuming further that the parts produced are safety critical, incorrectly predicting that bad parts are good (false negatives) can put human lives at risk. Therefore, false negatives can be much more costly than false alarms. This trade-off needs to be considered when translating business goals into technical goals and candidate evaluation methods.
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