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.
Control module DCS system spare parts DSQC266B
Control module DCS system spare parts DSQC266A
Control module DCS system spare parts DSQC260
Control module DCS system spare parts DSQC259
Control module DCS system spare parts DSQC258
Control module DCS system spare parts DSQC256A
Control module DCS system spare parts DSQC256
Control module DCS system spare parts DSQC255
Control module DCS system spare parts DSQC254
Control module DCS system spare parts DSQC252
Control module DCS system spare parts DSQC249B
Control module DCS system spare parts DSQC2498
Control module DCS system spare parts DSQC248
Control module DCS system spare parts DSQC243
Control module DCS system spare parts DSQC239 YB560103-CH
Control module DCS system spare parts DSQC239
Control module DCS system spare parts DSQC238
Control module DCS system spare parts DSQC236U
Control module DCS system spare parts DSQC236P
Control module DCS system spare parts DSQC236H
Control module DCS system spare parts DSQC236G
Control module DCS system spare parts DSQC236D
Control module DCS system spare parts DSQC236C
Control module DCS system spare parts DSQC236B
Control module DCS system spare parts DSQC236A
Control module DCS system spare parts DSQC2360
Control module DCS system spare parts DSQC235B
Control module DCS system spare parts DSQC235A
Control module DCS system spare parts DSQC233
Control module DCS system spare parts DSQC230
Control module DCS system spare parts DSQC228
Control module DCS system spare parts DSQC224
Control module DCS system spare parts DSQC223YB 560103-BD/4
Control module DCS system spare parts DSQC223
Control module DCS system spare parts DSQC215
Control module DCS system spare parts DSQC211
Control module DCS system spare parts DSQC210
Control module DCS system spare parts DSQC209-9H
Control module DCS system spare parts DSQC209
Control module DCS system spare parts DSQC208A
Control module DCS system spare parts DSQC208
Control module DCS system spare parts DSQC206
Control module DCS system spare parts DSQC205
Control module DCS system spare parts DSQC205
Control module DCS system spare parts DSQC202
Control module DCS system spare parts DSQC202
Control module DCS system spare parts DSQC202
Control module DCS system spare parts DSQC201
Control module DCS system spare parts DSQC201
Control module DCS system spare parts DSQC140
Control module DCS system spare parts DSQC140
Control module DCS system spare parts DSQC129
Control module DCS system spare parts DSQC129
Control module DCS system spare parts DSQC125
Control module DCS system spare parts DSQC125
Control module DCS system spare parts DSQC124
Reviews
There are no reviews yet.