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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8280-418 single-enginefor boatscontrol unit
8280-417 DSLC/MSLC Gateway Controller
8280-416 DSLC/MSLC Gateway Control
8280-415 Governor Digital Speed Controller
8280-414 Shared Digital Speed Controller
8280-413 digital speed control
8280-413 woodward controller
8280-412 723PLUS Controller
8280-411 Digital Controllers for the 723Plus Series
8280-410 723PLUS Digital Industrial Speed Controller
8280-339 Woodward 723PLUS
8280-338 Low voltage controller
8280-219 woodward speed controller
8280-208 723 Digital Marine Speed Control
8280-207 woodward governor
8280-206 723 Speed Control/Generation
8280-1173 digitalfor boatswith speed control
8280-1129 digital governor
8280-1109 723PLUS Digital Marine Speed Control Model
8280-1099 723PLUS Standard Generator Control
8280-1076 load sharing control
8280-1056 Digital control unit for redundant load distribution
8280-1042 Single Engine Digital Speed Controller
8280-1009 Numerical Control Model
8280-1001 723PLUS Digital Controller
8262-092 digital control
8237-1278 versatile Woodward DSLC/MSLC Gateway Control
8237-1277 gateway controller
8230-3012 723PLUS Generator Control
8230-3011 WOODWARD digital controller
9907-171 operator control panel
9907-170 Woodward 505E Microprocessor Based Control Unit
9907-169 Digital Control of Turbines
9907-166 Woodward 505E 32-bit microprocessor
9907-167 505E Series Digital Control Equipment
9907-165 32-bit microcontroller
8200-1302 505 Digital Governor
9907-1183 32-bit microcontroller
8200-1300 Gas turbines provide digital control
9907-164 505 and 505E Governor Control Units
9907-163 Governor Control Unit
9907-162 Controls for 505 and 505E Models
FBM24 PM900HT Contact/DC Input
FBM22 PM900HS Auto/Manual Station
FBM21 PM700TW 240 Vac Input Expander
FBM20 PM700QV 240 Vac input
FBM18 PM400YV Smart Transmitter I/O
FBM17 PM400YT 0-10 Vdc, Contact/DC I/O
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