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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MOTOROLA MVME162-10
MOTOROLA MVME162-13
MOTOROLA MVME162-210
MOTOROLA MVME162-212
MOTOROLA MVME162-223
MOTOROLA MVME162-512
MOTOROLA 01-W3960B/61C
MOTOROLA MVME162-522A
MOTOROLA MVME162-522A 01-W3960B/61C
MOTOROLA MVME162PA-344
MOTOROLA MVME162PA-344E
MOTOROLA MVME172-533
MOTOROLA MVME172PA-652SE 0767252
MOTOROLA MVME172PA-652SE
ABB 3BSE024387R4
ABB PFCA401SF
ABB PFCA401SF 3BSE024387R4
MOTOROLA MVME188A
MOTOROLA MVME2100
MOTOROLA MVME2301-900
MOTOROLA MVME2400
MOTOROLA MVME2431
MOTOROLA MVME2432
MOTOROLA MVME2434
MOTOROLA MVME300
MOTOROLA MVME333-2
MOTOROLA MVME335
MOTOROLA MVME5100
MOTOROLA MVME5500
MOTOROLA MVME5500-0161
MOTOROLA MVME55006E-0163
MOTOROLA MVME55006E-0163R
MOTOROLA MVME705B
MOTOROLA MVME712/M
MOTOROLA MVME712A/AM
MOTOROLA MVME715P
MOTOROLA TMCP700 W33378F
MOTOROLA MVME172PA-652SE
MOTOROLA VME172PA-652SE MVME172PA-652SE
MOTOROLA VME172PA-652SE
ABB 3BSE030221R2
ABB CI854A-EA
ABB CI854A-EA 3BSE030221R2
ABB 3BSE025961R1
ABB CI854K01
ABB CI854K01 3BSE025961R1
ABB 3BSE018144R1
ABB CI857K01
ABB CI857K01 3BSE018144R1
ABB 3BSE018135R1
ABB CI858K01
ABB CI858K01 3BSE018135R1
ABB 3BSE032444R1
ABB CI860K01
ABB CI860K01 3BSE032444R1
ABB 3BSE048845R2
ABB CI868K01-eA
ABB CI868K01-eA 3BSE048845R2
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