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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YOKOGAWA CP134E-64
YOKOGAWA CP132E-32
YOKOGAWA PS63A
YOKOGAWA CP99AA
YOKOGAWA FC11A
YOKOGAWA PW504
YOKOGAWA CP313D
YOKOGAWA PS501
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YOKOGAWA PS35A
YOKOGAWA ADM12
YOKOGAWA PM1C
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YOKOGAWA ES1B
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YOKOGAWA PS40B
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YOKOGAWA PS33A
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YOKOGAWA CPL-6
YOKOGAWA AAM21
YOKOGAWA PW402 S2
YOKOGAWA 2302-32-VLE-2
YOKOGAWA 230311
YOKOGAWA 8596020000
YOKOGAWA 8662570000
YOKOGAWA 8662560000
YOKOGAWA AIP121-S00
YOKOGAWA AIP171
YOKOGAWA AIP578
YOKOGAWA AIP591
YOKOGAWA ALR121-S00
YOKOGAWA AMM42
YOKOGAWA ANR10D
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YOKOGAWA AVR10D-Q22020
YOKOGAWA CP345
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YOKOGAWA CP451-10
YOKOGAWA CP451-50
YOKOGAWA CP451-51
YOKOGAWA CP461-50
YOKOGAWA DR1030B60
YOKOGAWA EB501
YOKOGAWA F3BU06-0N
YOKOGAWA F3LC21-1N
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ABB REF620E_F
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YOKOGAWA F3PU10-0N
YOKOGAWA F3SP21-0N
YOKOGAWA F3WD64-3N
YOKOGAWA F3XD64-3N
YOKOGAWA F3YD64-1A
YOKOGAWA LR 4220E
YOKOGAWA NFAI143-H00
YOKOGAWA PSCAMAAN A5E00239363/04
YOKOGAWA PSCAMAAN16404-500/3
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