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.
https://www.xmamazon.com
https://www.xmamazon.com
https://www.plcdcs.com/
www.module-plc.com/
https://www.ymgk.com
05701-A-0361 Backplane Serial communication controller and monitor
810-800081-022 LAM Circuit board module
05074-A-0122 05704-A-0121 05704-A-0131 Relay interface card
810-066590-004 LAM Circuit board module
T8403C Trusted TMR 24Vdc digital input module
05701-A-0325 DC input card
T9110 AADvance controller
T9451 AADvance controller Controller module
T9402 AADvance controller
T8311 Trusted TMR expander Interface
T8151B Trusted ® Communication Interface Adapter
T8310 Trusted TMR expander Interface
05704-A-0145 Four-channel controller card 4-20 MA input
GE IS215VCMIH2BB IS200VCMIH2BCC Mark VI System board components
E1740A Agilent Time interval analyzer
GE IS215VCMIH2CA IS200VCMIH2CAA VME communication card
E1406A Agilent Time interval analyzer
05704-A-0144 Four channel control card catalytic input
FBM233 P0926GX FBM233 Field device system integration module
IS420UCSBH1A Mark VIe series UCSB controller
DDC779BE02 3BHE006805R0002 Control panel and control system
MMS6120 Dual channel bearing vibration measurement module MMS 6120
24765-02-01 Housing expansion sensor assembly
CI526 3BSE006085R1 Interface Module
3BSE005831R1 PM632 Processor Unit
3BSE004773R1 CS513K02 MasterBus 300E communication interface
PU512V1 3BSE004736R1 Real Time Accelerator (RTA) Module
3BSE004726R1 DSTD197 Connection Unit 8 ch, 120V
3BSE004723R1 DSTD190 Connection Unit 32 Ch
3BSE004382R1 DSRF185 ABB
DSDX180 3BSE003859R1 Digital In / Out Module
3BSE003832R1 SC510 Submodule Carrier without CPU
3BSE003829R1 CI532V04 AC410 » Communication Modules
3BSE003827R1 CI532V02 MODBUS Interface, 2 ch
SA167 3BSE003390R1 Power Supply Unit AC 115V / DC
SA168 3BSE003389R1 Power Supply Unit
07KR51-230VAC 1SBP260511R1001 Controller Basic Unit
07KP93 GJR5253200R1161 Communication Processor
ABB 07KP90 GJR5251000R0303 Communications Module
07KP53 1SBP260162R1001 MODBUS Coupler (07KP53)
07DC92 GJR5252200R0101 Configurable Digital I/O Module
07DC91 GJR5251400R0202 Digital I/O module
07CR41-c12 1SBP260020R1001 Advant Controller Basic Unit
07CR41 1SBP260020R1001 07CR41 Advant Controller Basic Unit – 24VDC
07AI91 GJR5251600R0202 Analog I/O, module
07AC91 GJR5252300R0101 ABB Advant OCS Analog I/O Unit
HIEE320693R0001 NU8976A Power module
HIER466665R0099 NU8976A99 Excitation module
Reviews
There are no reviews yet.