Description
hardware flow control. It is an ideal choice in the field of industrial automation.
(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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DS200TCCBG1B analog I/O expansion card
DS200TCCBG1A extended analog I/O card
DS200TCCAG2B turbine simulation plate
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DS200TCCAG1B I/O analog card
DS200TCCAG1A input/output analog board
DS200TBSAG1A drive sensor card
DS200TBQGG1A Turbine terminal card
DS200TBQEG1B simulation module
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DS200TBCAG2AAB General Electric analog I/O terminal board
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DS200TBCAG1A Analog I/O terminal board
DS200SVMAG1 voltage monitoring board
DS200SVAAG1A Shunt isolator card
DS200STCAG1A turbine communication board
DS200STBAG1A relay module
DS200SSRAG1A solid state relay board card
DS200SSHVMG1A High voltage module
DS200SSBCG1A General Electric circuit board
DS200SSBBG1A board GE EX2000
DS200SSBAG1B turbine buffer plate
DS200SPCBG1AAA multi-bridge signal processing board
DS200SNPAH1ABA printed circuit board
DS200SLCCG4REG General Electric communication card
DS200SLCCG4A interface communication card
DS200SLCCG3A Interface Communication SLCC card
DS200SLCCG2A communication card
DS200SLCCG2A LAN communication module
DS200SLCCG1A Electrical communication board
DS200SLCCG1ABA communication control card
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