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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MMII-PD-1-2-240 | GE | Multilin MCC controller
MMII-PD-1-2-120 | GE | Multilin Display motor protection system
PMA323BE | ABB | PMA323BE HIEE300308R1 Control board module
PPA322B HIEE300016R2 HIEE400235R1 Control board module
UAC389AE02 | ABB |UAC389AE02 HIEE300888R0002 Control board module
CP451-50 | Yokogawa | CP451-50 processor module
GRBTU | ABB | GRBTU 3BSE013175R1 ABB Base
3BSE005461R1 | ABB | DSPC174 3BSE005461R1 Processor board
DSPC 174 | ABB | DSPC174 3BSE005461R1 Processor board
7DI140.70 | B&R |7DI140.70 Digital input module
7AO352.70 | B&R |7AO352.70 analog output module
7AI261.7 | B&R | 7AI261.7 Analog input module
IOP114 | METSO | IOP114 Input module
1747-L553/A | AB | 1747-L553/A SLC 5/05 processor
1747-L552/A | ABB |1747-L552/A SLC 5/05 processor
216AB61 | ABB | 216AB61 HESG324013R100 Output module
216EA61b | ABB |216EA61b HESG324015R1 Output module
PFEA112-65 | ABB | PFEA112-65 3BSE050091R65 tension electronic sensor
216NG63 | ABB | 216NG63 HESG441635R1 Output module
F404002A | STEIN| F404002A VISTA bus interface module
PM866AK01 | ABB | PM866K01 3BSE050198R1 Processor unit
3500/42M 176449-02 | BENTLY | 3500/42M 176449-02 Preprocessor/seismic monitor
REU615E_D | ABB | REU615E_D voltage protection measurement and control device
2713P-T12WD | Alleny-Bradle | 2713P-T12WD1 Analog resistance touch screen
5069-AENTR | Allen-Bradley | 5069-AENTR Compact 5000 Ethernet communication adapter
SPIET800 | ABB | SPIET800 Ethernet CIU transmission module
PR9376/010-021 | EPRO | PR9376/010-021 Speed sensor
PR 9376/010-01 | EPRO | Speed sensor PR9376/010-01
PR9376/010-001 | EPRO | PR9376/010-001 Speed sensor
PR9350/12 | EPRO | PR9350/12 Thermal expansion sensor
PR9350/08 | EPRO | PR9350/08 Thermal expansion sensor
PR9350/06 | EPRO | PR9350/06 Thermal expansion sensor
PR9350/04 | EPRO | PR9350/04 Thermal expansion sensor
PR9350/02 | EPRO | PR9350/02 Thermal expansion sensor
PR9350/01 | EPRO | PR9350/01 Thermal expansion sensor
MMS 6772 | EPRO | MMS6772 Dual current module
MMS 6771 | EPRO | MMS6771 Shaft vibration monitoring panel
MMS 3910/410-01 | EPRO | MMS3910/410-01 Dual current module
MMS 3910/311-32 | EPRO | MMS3910/311-32 Dual current module
MMS 3910/311-16 | EPRO | MMS3910/311-16 Rotational speed redundant module
MMS 3910/311-08 | EPRO | MMS3910/311-08 Dual current module
MMS 3910/311-04 | EPRO | MMS3910/311-04 Rotational speed redundant module
MMS3910/311-01 | EPRO | Speed direction module MMS3910/311-01
MMS3910/311-02 | EPRO | MMS3910/311-02 Limiting latching module
MMS 3311 | EPRO | MMS3311 speed transmitter
MMS 6822 | EPRO | MMS6822 Communication board
MMS 6831 | EPRO | Communication board MMS6831
MMS 6418 | EPRO | MMS6418 expansion monitoring board
MMS 6410 | EPRO | MMS6410 expansion monitoring board
MMS 6312 | EPRO | MMS6312 speed monitoring board
MMS 6310 | EPRO | MMS6310 Key phase monitoring board
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