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.
DS200GSIAG1B robot control card
DS200GSIAG1ACA simulates input and output
DS200GSIAG1A data processing module
DS200GLAAG1A wire adapter board
DS200GLAAG1A lead adapter card
DS200GGXCG1A General Electric MARK V board
DS200GGXAG1ADB Redundant power module
DS200GGXAG1A communication module
DS200GGIAG1B main control panel
DS200GGDAG1AGD power amplifier board
DS200GGDAG1A power amplifier
DS200GDPAG1AHE analog output module
DS200GDPAG1AGC grid driver board
DS200GDPAG1AFB high frequency power supply board
DS200GDPAG1A analog output module
DS200GASCF1A servo drive
DS200FSAAG2A secure CPU module
DS200FSAAG1A power amplifier
DS200FPSAG1ABB Signal receiving board
DS200FPSAG1A Power module
DS200FHVAG2ADA analog input module
DS200FHVAG2A control system
DS200FHVAG1ADA High voltage door interface card
DS200FGPAG1AKD pulse amplifier board
DS200FECBG1A excitation card GE drive system
DS200FCSAG2ACB control current sensing interface board
DS200FCRRG2A PLC controller
DS200FCRRG1ACA circuit control card
DS200FCRRG1A Mainboard interface board
DS200FCRLG1AFC ignition circuit card
DS200FCRLG1A DI/DO control card
DS200FCGDH1B DCS control module
DS200EXPSG1ACB Turbine power supply card
DS200EXPSG1ABB PC board communication adapter
DS200EXPSG1A Turbine power supply card
DS200EXDEG1A GE excitation control board
DS200EVIAG1B control system
DS200DTBDG1A CPU module
DS200EVIAG1B Main board Interface board
DS200DTBCG1A DI/DO control card
DS200DTBAG1A system spare parts
DS200DTBBG1A controller main unit
DS200DSPDF1A input control board
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