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.
GCD207B101 3BHE024642R0101 ABB Processor module
GDD360C 3BHE047217R0101 ABB Processor module
F7123 HIMA 4 Channel Power Distribution Module
DO880-1 3BSE028588R1 ABB 16 channel 24 V digital output module
DMV2400A-CPCI Data acquisition card module
ECU01 ECU01.5 EMG control panel
PMC422FP Ramix Eight Port Serial Controller
CPCI-680 FORCE PowerCore CompactPCI CPU Module
0504994880 ABB SIB V Option Board
2880065-01 PRO-FACE MST Touch screen panel
05701-A-0511 HONEYWELL Frame module
05701-A-0361 HONEYWELL Engineering Card
5X00063G01 Westinghouse COMPANION TO HART ANALOG OUTPUT IO MODULES
05701-A-0351 HONEYWELL Control Card, Single Channel
05701-A-0325 HONEYWELL DC Input Card
05701-A-0326 HONEYWELL FIELD INTERFACE CARD
5SHX1960L0004 ABB IGCT Module
SST-PFB3-VME-2-E SST Network Interface Card
TSXSCP114 Schneider Electric PCMCIA Card for Type III
MS-NAE5510-1 Johnson Network Engine
FBM211 P0914TN FOXBORO Input Interface Module
05701-A-0301 HONEYWELL Single Channel Control Card 4 – 20mA
ETT-VGA-0045 UNIOP HMI Touch Screen Front Overla
CP461-50 Yokogawa Processor Module
12149 ASSY display panel
11994R13 ASSY Communition Module
11993R2 ASSY Analog control card
136188-02 Bently Nevada ETHERNET/RS232 MODBUS I/O MODULE
140XCP51000 Schneider DUMMY MODULE WITH COVER
140XBP00400 Schneider 4-Slot Backplane
140CPU11302 Schneider PROCESSOR 256K RAM 8K USER LOGIC 1XMB
MPC4 200-510-076-114 Vibro Meter Machinery Protection Card meggit
IOCN 200-566-000-112 Meggitt Vibro Meter
7264 AMCI SSI Interface Module
AIP830-111 YOKOGAWA Operating keyboard
REF601 CE446BB1NH ABB Feeder protection
3500/60 163179-01 Bently Nevada Temperature Monitors
IC660BBA104 6231BP10910 GE Analog I/O Block
135473-01 Bently Nevada Proximitor/Seismic Monitor Module
136711-01 Bently Nevada I/O Module With Internal Barriers And Internal Terminations
FEM100 P0973CA FOXBORO Fieldbus Expansion Module
3500-25 149369-01 Bently Nevada Enhanced Keyphasor Module
3500-05-01-02-00-00-01 Bently Nevada 3500/05 System Rack
PDP403 METSO DISTRIBUTED PROCESSING UNIT
PDP401 METSO Distributed Processing Unit Module Card
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