Description
hardware flow control. It is an ideal choice in the field of industrial automation.
(5) Perform predictive maintenance, analyze machine operating conditions, determine the main
causes of failures, and predict component failures to avoid unplanned downtime.
Traditional quality improvement programs include Six Sigma, Deming Cycle, Total Quality Management (TQM), and Dorian Scheinin’s
Statistical Engineering (SE) [6]. Methods developed in the 1980s and 1990s are typically applied to small amounts
of data and find univariate relationships between participating factors. The use of the MapReduce paradigm to simplify data processing in
large data sets and its further development have led to the mainstream proliferation of big data analytics [7]. Along with the development of
machine learning technology, the development of big data analytics has provided a series of new tools that can be applied to manufacturing
analysis. These capabilities include the ability to analyze gigabytes of data in batch and streaming modes, the ability to find complex multivariate
nonlinear relationships among many variables, and machine learning algorithms that separate causation from correlation.
Millions of parts are produced on production lines, and data on thousands of process and quality measurements are collected for them, which is
important for improving quality and reducing costs. Design of experiments (DoE), which repeatedly explores thousands of causes through
controlled experiments, is often too time-consuming and costly. Manufacturing experts rely on their domain knowledge to detect key
factors that may affect quality and then run
DoEs based on these factors. Advances in big data analytics and machine learning enable the detection of critical factors that effectively
impact quality and yield. This, combined with domain knowledge, enables rapid detection of root causes of failures. However,
there are some unique data science challenges in manufacturing.
(1) Unequal costs of false alarms and false negatives. When calculating accuracy, it must be recognized that false alarms
and false negatives may have unequal costs. Suppose a false negative is a bad part/instance that was wrongly predicted to
be good. Additionally, assume that a false alarm is a good part that was incorrectly predicted as bad. Assuming further that
the parts produced are safety critical, incorrectly predicting that bad parts are good (false negatives) can put human lives
at risk. Therefore, false negatives can be much more costly than false alarms. This trade-off needs to be considered when
translating business goals into technical goals and candidate evaluation methods.
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KJ1501X1-BC1 SYSTEM POWER SUPPLY | DeltaV
XVS-440-57MPI-1-1U Touch panel
A6500-UM axis relative vibration mode
A6370D monitor A6370D DCS analog
5SHX264510002 insulated gate bipolar transistor
2711P-T6C21D8S Analog resistance touch screen
PPD103-B03-10-150000 Central processing unit
V7768-320000 VMEbus single board computer
0005-4050-710 Press panel
2711-K10G9L1 Keyboard/Touch Operator interface Terminal
2711-K6C20 Operator terminal
Allen-Bradley 2711-T10C10 Touch screen man-machine interface terminal
2711-NM28 8MB Flash ATA card
2711-T10C15 Man-Machine Interface (HMI)
2711-T10G8L1 PanelView 1000 Control terminal
2711-T5A8L1 Man-machine interface (HMI) device
PFSK152 ABB PFSK 152 Signal concentrator
PFSK151 ABB PFSK 151 DSP- Signal processing
PFSK142 ABB PFSK 142 Control panel
KJ1501X1-BA2 System power supply| DeltaV
PFSK 141 ABB PFSK141 Power supply with heat sink
PFSK 129 ABB PFSK129 Terminal board
PFSK126 ABB PFSK 126 Channel control unit
PFSK 115 ABB PFSK115 Adapter stress meter STU
PFSK111 ABB PFSK 111 VDU board
PFSK 109 ABB PFSK109 Connection unit
PFSK 104 ABB PFSK104 Control Board
PFSK 102 ABB PFSK102 Reel supply device
PFSA240 ABB PFSA 240 Coil DC power supply unit
PFSA185 ABB PFSA 185 ower and media converter unit
PFSA 146 ABB PFSA146 Power supply 24V external
PFSA 145 ABB PFSA145 MAINS FILTER
PFSA101 ABB PFSA 101 Roll Supply Unit
DSPC 454 ABB DSPC454 Programmable controller
DSPC 320 ABB DSPC320 processor board
DSPC 172 ABB DSPC172 Processor module
KJ1501X1-BB1 System DC/DC power supply|| DeltaV
DSPC157 ABB DSPC 157 Processor Board
DSPC 155 ABB DSPC155 processor board
DSAI 151 ABB DSAI151 Analog input board
DSAI 301 ABB DSAI301 Analog input unit
DSAI 151 ABB DSAI151 Analog input board
DSAI 130D ABB DSAI130D Analog input board
PFSK 130 ABB PFSK130 Channel control unit
PFSK103 ABB PFSK 103 Expansion board
DSTC452 ABB DSTC 452 FSK-Mod.Serial I/O
DSPC452 ABB DSPC452 Programmable controller
DSPC 174 ABB DSPC174 Processor board
DSPC170 ABB DSPC 170 Printed circuit board
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