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.
SR469-P1-HI-A20-E GE 469 Motor management relay
DS200SIOBH1A input/output control card
DS200SIOCG1A Instantaneous overcurrent module
DS200SIOBG1AAA input and output control module
DS200SHVMG1A high voltage module
DS200SHVIG1BDH PLC module card
DS200SHVIG1B high voltage interface board
DS200SHVIG1A adjustable speed driver
SPSED01 ABB SOE DI module
DS200SHCBG1A Shunt connection card
DS200SHCAG1B analog expansion module
DS200SHCAG1BAA card splitter connection
DS200SDCIG2A Power control card
DS200SDCIG2A power control card
DS200SDCIG1A Power supply panel
DS200SDCCG5A driver control card
DS200SDCCG4A turbine drive control card
DS200SDCCG3A Drive control board GE
DS200SDCCG2A drive control card
DS200SDCCG1ACA Steam turbine control drive control card
DS200SDCCG1A drive control board
DS200SDCCF1ANC circuit board
DS200SBCAG1A static brake plate
DS200RTBAG5A relay terminal module
DS200RTBAG4RHC printed circuit board
DS200RTBAG4A power excitation board
DS200RTBAG3A relay board
DS200RTBAG2A General Electric relay wiring panel
DS200RTBAG2AFB excitation system control board
DS200RTBAG1A Relay terminal card
DS200QTBAG1B analog terminal board
DS200QTBAG1A terminal module
DS200PTCTG2B signal regulator
DS200PTCTG1B signal regulator card
DS200PTCTG1A signal regulator module
DS200PTBAG1B module card
DS200PTBAG1A expansion module
DS200PLIBG2A distributed module
DS200PLIBG1A Logical interface board
DS200PLFMG1A Redundant module
DS200PCCAG9A power supply control board
DS200PCCAG8A driver module
DS200PCCAG7A input/output module
DS200GSNAG1A high frequency power supply board
DS200GSIAG1CDC GE digital I/O board
DS200GSIAG1CBA analog input submodule
DS200GSIAG1C main communication module
Reviews
There are no reviews yet.