Description
DSAO120A 3BSE018293R1 Система возбуждения DCS ABB
(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.
IC200ALG264H GE Analog Input Module
IC200ALG263D GE Analog Input Module
IC200ALG260 GE VersaMax analog input module
F650-N-F-L-F-2-G-1-HI-P-6E GE Multilin Feeder Protection & Bay Controller
DS200NATOG2A GE Voltage Feedback Scaling Board
04380A Beijer Electronics E300 Display Control Panel
469-P5-HI-A20-E SR469-CASE GE SR 469 Motor Management Relay line
04220FL11232A GE RXI CONTROLLER
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SPASO11 ABB AO Module, 14 CH, Supports 4-20mA, 1-5V
SDCS-PIN-4 ABB POWER INTERFACE BOARD
SAFT-112-POW ABB PC BOARD
RMIO-01C ABB CONTROL BOARD
REX521GHHPSH06G ABB PROTECTION UNIT
REF615E-G ABB FEEDER PROTECTION AND CONTROL RELAY
REF615D ABB Feeder protection and control
REF615A_1G-HAFNAEFCBGC1BQK11G ABB FEEDER PROTECTION AND CONTROL RELAY
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NINT-68 ABB Interface module
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FPBA-01 ABB PROFIBUS DP adapter module
DSQC345B ABB RECTIFIER UNIT
DSDP140B-57160001-ACX ABB Counter Board
CI502 1SAP220700R0001 ABB Bus-Module
A5E36717788 ABB Frequency converter IGD3 840A drive board Trigger board
129740-002 ABB On-line main board module
3HAC040658-001 ABB Motor unit MU400
3HAC16831-1 ABB Battery pack
3BDH000741R1 ABB Rectifier power supply panel
CP450-T-ETH ABB Control Panel 10.4” TFT Touc
1SNA684252R0200 ABB Serial Data Converter
81001-451-61-R Allen-Bradley Silicon controlled inverter module
81001-524-51-R Allen-Bradley Silicon controlled inverter module
81001-524-53-R Allen-Bradley Silicon controlled inverter module
81001-503-54-R Allen-Bradley Silicon controlled inverter module
81001-451-61-R Allen-Bradley Silicon controlled inverter module
81001-614-56-R Allen-Bradley Silicon controlled inverter module
81001-503-53-R Allen-Bradley Silicon controlled inverter module
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