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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PDD205A1121 3BHE025336P201 ABB
PDD205A1121 3BHE025335R1121 ABB
PDD205A1121 3BHE025335R1121/3BHE025336P201REV.D
PDD205A0121 3BHE025335R0121/3BHE025336P201REVD
PDD205A0121 3BHE025336P201 ABB
PDD205A0121 3BHE025335R0121 ABB
3BHE025336P201 Central Processing Unit
3BHE025335R0121 Central Processing Unit
PDD205A0121 Central Processing Unit ABB
3BHE020P201 Central Processing Unit ABB
3BHE019633R0101 Central Processing Unit
PDD200A101 Central Processing Unit ABB
PDD200A101 3BHE020P201 ABB
PDD200A101 3BHE019633R0101 ABB
PDD200A101 3BHE019633R0101/3BHE020P201
P4LQA HENF209736R0003 Power module
P-HA-RPS-32200000 Power module ABB
OKYM175W22 Programmable controller
OCAHG 492838402 Programmable controller
OCAH 940181103 Programmable controller ABB
NU8976A99 HIER466665R0099/NU8976A
HIER466665R0099 Industrial module ABB
HIEE220295R0001 Industrial module ABB
NU8976A HIEE220295R0001 ABB
NPCT-01C 64009486D ABB
NMTU-21C 3BSE017429R1 ABB
NF93A-2 HESG440280R2 ABB
NF93A-2 HESG440280R2 HESG323662R1
NF93A-2 HESG440280R2 HESG323662R1/HESG216665/K
NBIO-21CU 3BSE017427R1 ABB
3BSE017427R1 Analog output board ABB
NBIO-21CU Analog output board ABB
NDCU-12CK Analog output board ABB
NDCU-12C Analog output board ABB
MVR0.44-10KA Analog output board ABB
MANUFACTURER Analog output board ABB
MSR04X1 Analog output board ABB
MSR04X1 MANUFACTURER ABB
MSR04X1 MANUFACTURER:ABB AGUST
HESG440588R4 Analog output board
MC91 HESG112714/B Analog output board
MC91 HESG112714/B ABB
MC91 HESG440588R4 ABB
MC91 HESG440588R4 HESG112714/B ABB
MB510 3BSE0002540R1 Analog output board
M063B003217 Analog output board ABB
3BHL000986P7000 Analog output board ABB
LXN1604-6 Analog output board ABB
LXN1604-6 3BHL000986P7000 ABB
LWN2660-6 Analog output board ABB
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