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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P3403893-0351 | BENTLY | Vibration Transmitter
2TLA020070R1800 | ABB | Programmable Safety Controller
VN115/87-EMC | VISATRON | Oil Mist Detector
IC693DSM302-AE | GE | 90-30 Series Motion Controller
S72402-NANANA-NA-030 | Kollmorgen |server Driver
IC693MDL740D | GE | Positive Logic Output Module
IC670CHS102 | GE | I/O terminals
IC693PWR330C | GE | High Capacity Power Module
IC693MDL241D | GE | Discrete Input Module
IC693MDL730E | GE | Discrete Output Module
AI801 3BSE020512R1EBP | ABB | Analog input 8 channels
DI801 3BSE020508R1 | ABB | digital input module
DO801 3BSE020510R1 | ABB | digital output module
4PP420.1043-75 | B&R | power panel
2198-C1015-ERS | Allen-Bradley | server Driver
IC693ALG220 | GE | Analog input module Spot goods
IC690ACC901 | GE | With RS-485/RS-232 cable
X20PS3300 | B&R | power module Spot goods
X20CP1486 | B&R | I/O processor
X20PS9400 | B&R | power module Discount
8U-SDOX01 | HONEYWELL | DO Relay Extension (Uncoated)
8U-TDODB1 | HONEYWELL | DO 24V Bussed IOTA Redundant (Uncoated)
8U-TDODA1 | HONEYWELL | DO 24V Bussed IOTA (Uncoated)
8U-TAOXB1 | HONEYWELL | AO IOTA Redundant (Uncoated)
8U-TAOXA1 | HONEYWELL | AO IOTA (Uncoated)
8U-PAONA1 | HONEYWELL | Analog Output w/o HART
8U-PAOHA1 | HONEYWELL | Analog Output HART (Uncoated)
8U-TAIMA1 | HONEYWELL | Low-level AI IOTA (Uncoated)
8U-TAIXB1 | HONEYWELL | AI IOTA Redundant (Uncoated)
8U-TAIXA1 | HONEYWELL | AI IOTA (Uncoated)
8U-PAINA1 | HONEYWELL | High-level AI w/o HART, Single-ended (Uncoated)
8U-PAIHA1 | HONEYWELL | High-level AI HART, Single-ended (Uncoated)
8U-TAIDB1 | HONEYWELL | AI IOTA Redundant (Uncoated)
8U-TAIDA1| HONEYWELL | AI IOTA (Uncoated)
8C-TDODB1 | HONEYWELL | DO 24V Bussed IOTA Redundant (Coated)
8U-PAIH54 | HONEYWELL | High-level AI HART, Differential / Singleended (Uncoated)
8C-TDODA1 | HONEYWELL | DO 24V Bussed IOTA (Coated)
8C-PDODA1 | HONEYWELL | DO 24V Bussed Out (Coated)
8C-TDILA1 | HONEYWELL | DI 24V IOTA (Coated)
8C-PDIPA1 | HONEYWELL | Digital Input 24V Pulse Accumulation (Coated)
8C-PDISA1 | HONEYWELL | Digital Input Sequence of Events (Coated)
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