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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IC800SSI107RD2-CE GE controller
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IC800SSI104D2-CE GE controller
IC695GCG001 GE Communication gateway
IC695ETM001 GE Single slot module
IC695EDS001 GE External station module
IC695CRU320 GE Redundant processor
IC695CPU320 GE The central processing unit
IC695CPU315 GE Central processing unit
IC695CPU310 GE Programmable automation controller
IC695CPK400 GE Programmable automation controller
IC695CPE400 GE Central processing unit (CPU) module
IC695CPE330 GE Automation controller
IC695CPE310 GE Central processor module
IC695CPE305 GE Central processing unit
IC695CPE302 GE controller
IC693DSM302 GE Motion controller
IC693DNS201 GE Communication module
IC693DNM200 GE Main control module
IC693CPU374 GE Programmable Logic Controller (PLC) module
IC693CPU372 GE Ethernet communication module
IC693CPU370 GE Programmable logic controller
IC693CPU367 GE Central processing unit
IC693CPU366 GE Network CPU main module
IC693CPU364 GE Single slot central processing unit
IC693CPU363 GE Programmable logic controller
IC693CPU360 GE The CPU module is embedded in the backplane
IC693CPU352 GE Single slot CPU module manufactured by PLC system
IC693CPU351 GE CPU module
IC693CPU350 GE controller
IC693CPU341 GE Single-slot CPU
IC693CPU340 GE Expansion base plate
IC693CPU331 GE The CPU module is embedded in the backplane
IC693CPU323 GE Base Turbo CPU in slot 10
IC693CPU321 GE 10-slot I/O backboard with embedded CPU
IC693CPU313 GE Embedded CPU
IC693CPU311 GE 5 Slot Embedded CPU base rack
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DSQC545 ABB Optical fiber point sensor
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DSMB-01C ABB Power strip
DSDX452 Remote input ABB
DSDI-110AV1-3BSE018295R1 ABB Digital input pad
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