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Control charts are graphical plots used in production control to determine whether quality and manufacturing processes are being controlled under stable conditions. (ISO 7870-1) [1] The hourly status is arranged on the graph, and the occurrence of abnormalities is judged based on the presence of data that differs from the conventional trend or deviates from the control limit line.
The resulting plots are analyzed as for other control charts, using the rules that are deemed appropriate for the process and the desired level of control. At the least, any points above either upper control limits or below the lower control limit are marked and considered a signal of changes in the underlying process that are worth further ...
Simple example of a process control chart, tracking the etch (removal) rate of Silicon in an ICP Plasma Etcher at a microelectronics waferfab. [1] Time-series data shows the mean value and ±5% bars. A more sophisticated SPC chart may include "control limit" & "spec limit" % lines to indicate whether/what action should be taken.
The above eight rules apply to a chart of a variable value. A second chart, the moving range chart, can also be used but only with rules 1, 2, 3 and 4. Such a chart plots a graph of the maximum value - minimum value of N adjacent points against the time sample of the range.
One example for state-based control may be that transitions from one state to another can only occur when the logic has checked the process conditions are correct to move to that state. However, in the event of a failed instrument if the condition can be independently verified operations may force the transition to the new state.
Example of a "performance seeking" control-flow diagram. [1] A control-flow diagram (CFD) is a diagram to describe the control flow of a business process, process or review. Control-flow diagrams were developed in the 1950s, and are widely used in multiple engineering disciplines.
There are distribution-free control charts for both Phase-I analysis and Phase-II monitoring. One of the most notable distribution-free control charts for Phase-I analysis is RS/P chart proposed by G. Capizzi and G. Masaratto. RS/P charts separately monitor location and scale parameters of a univariate process using two separate charts.
For example, if the dimension of the state space is greater than the dimension of the output, then there will be a set of possible state configurations for each individual output. That is, the system can have significant zero dynamics , which are trajectories of the system that are not observable from the output.