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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EPRO PR6423/10R-030+CON021
EPRO PR6423/10R-111+CON031
EPRO PR6423/11R-131+CON031
EPRO PR6423/013-030+CON021
EPRO PR6423/010-010+CON021
EMERSON Detection module A6120
EMERSON 9199-00002 A6120
EMERSON Detection module CSI3120
EMERSON A3120/022-000 CSI3120
EMERSON A6110
EMERSON A6110 9199-00001
EMERSON A6140
EMERSON A6140 9199-00058
EMERSON A6210
EMERSON A6220
EMERSON A6312/06
EMERSON A6370D
EMERSON A6410
EMERSON A6410 9199-00005
EMERSON A6500-RC
EMERSON A6500-UM
EMERSON A6740
EMERSON A6824
EMERSON A6824 9199-00090
EMERSON A6824R
EMERSON A6824R 9199-00098-13
FISHER-ROSEMOUNT 01984-0605-0001
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FISHER-ROSEMOUNT KJ1501X1-BC1
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FISHER-ROSEMOUNT KJ2002X1-BA1
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FISHER-ROSEMOUNT KJ2002X1-CA1
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FISHER-ROSEMOUNT KJ3002X1-BB1
FISHER-ROSEMOUNT KJ3002X1-BC1 12P0681X072
FISHER-ROSEMOUNT KJ3002X1-BC1
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