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.
https://www.xmamazon.com
https://www.xmamazon.com
https://www.plcdcs.com/
www.module-plc.com/
https://www.ymgk.com
IS200EROCH1A Excitation Regulator TAB
IS200ERIOH1A I/O module
IS200ERGTH1A Ground detection board
IS200ERDDH1A dynamic discharge panel
IS200ERBPG1A Midplane module
IS200EPSMG2A Power module
IS200EPSMG1A exciter power module
IS200EPDMG1B Power distribution module of the exciter
IS200EPDMG1A Power distribution module
IS200EPCTG1A exciter PT/CT plate
IS200EMIOH1A I/O module
IS200EMCSG1A Conduction sensor card
IS200EISBH1A Exciter ISBus board
IS200EHPAG2D pulse amplifier module
IS200EHPAG1C excitation high voltage pulse amplifier
IS200EHPAG1D high voltage gate pulse amplifier
IS200EHPAG1B high voltage pulse amplifier board
IS200EHPAG1A pulse amplifier board
IS200EHFCH2A excitation fan control
IS200EHFCH1A exciter fan control board
IS200EGPAG1B Temperature monitoring module
IS200EGPAG1A Temperature monitoring module
IS200EGDMH1A Ground detection board
IS200EDSLH2A transmission control unit
IS200EDSLH1A dual voltage regulator
IS200EDFFH3A DC feedback board
IS200EDFFH2A excitation module
IS200EDFFH1A Interface board
IS200EDEXG2B degaussing module
IS200EDEXG2A exciter board
IS200EDEXG1A communication card
IS200EDEXG1B excitation module
IS200EDCFG1B I/O terminal board
IS200EDCFG1A DC feedback board
IS200ECTXG1A Exciter CT expansion board
IS200ECTBG1A Output and input modules
IS200ECTBG2A contact terminal board
IS200EBRGH2A Interface board
IS200EBRGH1A Excitation bridge interface board
IS200EBKPG1C Backplane control board
Reviews
There are no reviews yet.