A description of an on-line statistical quality control package

A description of an on-line statistical quality control package

Copyright Wesr 0 LatByette, IFAC Real Indiana, Time ProgrannninK lOtl5 1985 A DESCRIPTION OF AN ON-LINE STATISTICAL QUALITY CONTROL PACKAGE J...

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Copyright Wesr







ProgrannninK lOtl5



ABSTRACT Control charting and other statistical quality control techniques have been used by quality control departments in an off-line mode for many years, resulting in significant reductions in process SETPOIKT has developed an on-line variability. statistical quality control package which employs these same techniques in real time. This package provides real-time statistical quality control functions such as control charting and alarming. Keywords : Control engineering computer minicomputers; applications; on-line operation; process control; quality control. INTRODUCTION In any given scheme of production and all measurable characteristics of a inspection, product will vary as a result of chance. This is inherent in all processes. Usually, any deviation pattern is the result of some outside this “stable” outside cause. Statistical quality control techniques have been devised to single out these outside causes. Once known, these causes can be corrected. Traditional methods of statistical quality control have experienced a very slow “turn around” after a batch of product is time. For instance, made, an inspector randomly inspects the batch. A quality control group, in turn, analyses the Some time later (possibly several inspection data. batches later) the quality control group reports back to the production group, telling them about the bad products they have been producing. Perhaps, even during this elapsed time, more bad products have been made. The costs of this time lag can be enormous. Today, statistical quality control techniques can be applied In real time. In the above example, the production group (using real time statistical quality control techniques) may discover a problem before bad products are actually made. Now, the production group can identify operating trends that will cause their process to go out of statistical control before it actually happens. With this advance notice, they stand a good chance of correcting the problem, and of course minimizing any costs associated with the problem. The mathematical foundation of statistical quality control is well established. The end user need not be overwhelmed by mathematics involved in applying statistical quality control to a process. Real time systems perform the statistical calculations and present the results in a meaningful manner. These systems can even analyze the data and alert the production personnel when the process is in statistical alarm.


SETPOINT’s On-Line Statistical Quality Control Package operates in real time. The data to be analyzed must be gathered and manipulated in a real time environment. To accomplish this, the statistical quality control software package is “layered” on top of an existing data acquisition and process control system. Both this base system and the layered statistical quality control software are resident in the same computer. Both packages are written in FORTRANand run on a variety of minicomputers - 16 and 32 bit - from well-known computer manufacturers. The functional layering of the package is shown in Fig. 1. Note the operations personnel use the same peripheral devices (consoles and printers) for both base system access and statistical quality To the operator, both control system access. systems have the same characteristics -- the statistical quality control system is integrated well with the underlying data acquisition and process control system. CONTROLCHARTDISPLAYS Practically any variable can be shown on the control chart displays. The control chart display is activated by stroking the control chart key. The user selects the particular control chart by then stroking a process unit key and a secondary key. This method for identifying control charts, plots, throughout the system graphics, etc. is consistent -- a user-defined number of process unit keys are available, along with a user-defined number of secondary keys. Each process unit key - secondary key combination represents a uniqua selection. The description of each process unit key, the description of the secondary key, and the key placement (on the keyboard) are all user-definable. The user can change data For instance, at will. a given originally show VARIABLE X. If preferred, the user can move to

on a control chart control chart may VARIABLE Y is the variable name

field on the screen and change it. Changes such as these are controlled by the use of three levels of passwords. The entire operator console subsystem consistently uses the password authorization system. The user can move to a field on the display and toggle between the X BAR control chart and the R control chart. The time context of the data being plotted can be altered -- for instance. a user can call up a plot that shows data from any arbitrary time, provided that data is available. The current X BAR BAR and R BAR values are also shown on the display -- this gives the user a powerful “what-if” tool to use in managing the process. The user can alter the number of samples or the observations per sample for the current display, if desired. If a particular variable is being monitored in real time and is in statistical alarm, that alarm will be displayed.

J. R. Rushing. Jr.


Examples of X BAR and R control charts are shown in Fig. 2 and Fig. 3. These are computer-generated hardcopies of actual control charts.

a tabular display of values and timestamps is available. Plot directories with titles are automatically maintained and are always available. Figure 6 shows a standard trend plot.

The user can optionally view the data in a tabular form. This form is initiated by one additional keystroke from any control chart. It shows the numeric observations and timestamp for each point plotted on the control chart. An example of this is shown in Fig. 4 -- note it corresponds to the values on the X BAR control chart shown previously.

X/Y scatter plots are available to plot one Included in the scatter variable against another. plot display, is the ability to calculate a least This provides an squares approximation curve fit. Figure 7 easy way to check correlations for data. shows an X/Y scatter plot.


Users define color process graphics for their process. The graphic displays include background figures such as mixing tanks, crude and vacuum units, control schemes and anything else imaginable. “Live” process data can be added to these background Each unique process graphic is assigned figures. to a process unit key - secondary key sequence, A directory similar to control charts and plots. of existing process graphics is maintained and always accessible.

The control limit displays show statistical information about a particular alarmed variable. Values are shown for the upper and lower control limits, the upper and lower process capability limits, and the operator-entered target high and low limits. An example control limit chart is shown in Fig. 5. The user has the option of entering a manual set of X BAR BAR and R BAR values and having the system calculate the appropriate control limits. After the control limits are calculated based on these manual entries, the user can request that the new limits be used for statistical control purposes. Control limits may be recalculated based only on the real-time data, much the same as using manual entries. The user can request these limits be used These provisions for statistical quality control. allow the operator the flexibility of doing some selecting the appropriate “what-if” calculations, and continuing control with those results. results, The user may view and modify the high and low These values are for the operator’s target limits. on the reference only, and have no affect statistical quality control package. Alarm messages can be suppressed on a point basis. This allows alarm messages to be displayed on the control chart, but eliminates the printing of the message when the alarm condition is detected. Again, modifications of certain the proper password clearance.





Nethods for cataloging background data exist. This allows the user to define a library of graphic to figures and symbols, then to use that library build process graphics using those figures and symbols. “Live” data on the process graphics may include process inputs, manual entries, and Status indicators depict the calculated outputs. Data entry can be status of particular variables. Figures may enabled/disabled on a point basis. change colors and shapes based on discrete values. These graphic features make it very easy to meet any customized needs. Process graphics are assigned as accessible/not accessible on a console number basis. The user creates, deletes, and modifies process In modifying a process graphic, any data graphics. (background or “live”) can be added, changed, or Process graphics refresh on a periodic deleted. basis -- this interval is specified for each process graphic by the user. Examples of four user-defined process are shown in Figs. 8, 9, 10, and 11.


USER-DEFINEDREPORTS The system allows data (process inputs, manual and calculated outputs) to be displayed on entries, other types of plot displays. These other plots are accessed in much the same way as control charts -the plot key is stroked, followed by a process unit key and secondary key. As in control charts, the process unit key - secondary key sequence indicate The user, with appropriate password a unique plot. clearance, can define which variables are to be displayed on each unique plot. Both standard trend These plots and X/Y scatter plots are available. plots are extremely useful for visually monitoring They can also prove useful the process behavior. in analyzing process upsets and in tuning control loops. Standard trend plots are very much like Up to four variables are plotted control charts. The upper on the Y axis against time on the X axis. and lower scale for each of the four individual variables can be defined, independent of the other This makes it very easy to analyze three variables. different variables to varying resolutions. The user can select the type of plot to view -- trend, Also, 12 minute, hourly, shift or daily averages. Once the plot the beginning time can be selected. the plot will continue to has been displayed, The interval for refreshing refresh periodically. vi11 depend upon the type of average that is Similar to control charts, currently being viewed.

Users may define reports with no programming Any value, including historical data, can effort. be placed on a report, in a user-specified position. A number of calculations, such as totaling, can be Reports can performed when the report is printed. be activated on a periodic basis, or upon request. ADVANCED CONTROLFEATURES An extensive set of advanced control The proven algorithms is available for use. algorithms handle even the toughest of process Control algorithms can be control problems. The algorithms available are: cascaded. 1. Setpoint Output - used to output regulatory control.



2. PID, I, NPID, DPID - proportionalintegral-derivative control equation, integral-only form, non-linear form, and deadbeat form; all allow AUTO-ON, TRACKING, and BUMPLESSTRANSFERoptions. 3. Lead/Lag - used for process disturbances. 4. Delay

- used for





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5. High/Low Select iowest value. 6. Sum/Distribution values.

- used to select - used to couple




or decouple

Discrete values can be input and/or output on value a periodic basis or on demand. Discrete processing includes change-of-state detection. Change-of-state detection can be used to activate user programs or reports, and change figure colors This tremendous f lexlbility or shapes on displays. is very useful in both discrete and batch processes. ORIGIN OF DATA Data used for analysis may originate from Manual entries by operators are several sources. accepted, process inputs may be used, and program These types are not mutually outputs may be used. an installation may have exclusive . For instance, many process inputs and a few manual entries, and no program outputs. Any combination is allowable. Entry validation, limit checking, and alarming Each support,exists for all values in the system. type of data is maintained by the system and is completely accessible by the statistical quality The system allows any of this control functions. data to be displayed on user-defined process and all data is accessible by graphics or reports, user-written programs. A variety of process input-output systems ara currently supported. These systems are well known in the industry, and range from relatively simple analog instruments to programmable controllers, and on to large distributed control instrumentation systems. Development is ongoing to support additional devices. !lODES OF STATISTICAL APPLICATION Statistical quality control data may be used in two ways. The first, and more critical method is a true real-time monitor. The second way is to allow manual entries, process values, and program data to be viewed on control charts. In general, these two types are differentiated by referring to them as either “alarmed” or “non-alarmed” data. In the first method, data is monitored either on a periodic basis or at the detection of a signif icant event. Regardless of the reason of the statistical quality control activation, information is updated at that time. The data is checked for statistical alarm conditions -- any alarm occurring is logged to the appropriate logging device. These alarms are accessible at the operator consoles and require operator acknowledgement. Any active control chart plot is updated on a user-specified frequency. The non-alarmed method allows the operator to view the immediate historical operations through the If the current operation in a statistical context. data originates from process values, the control chart can be driven from either trend, 12 minute, hourly, shift, or daily averages from the base process control system historical data base. The size of the process history data base is user-selectable. Also, any operator-entered or program-entered data can be viewed on control charts. The alarmed data previously mentioned is a subset of the non-alarmed data described here. STATISTICAL QUALITY CONTROLCALCULATIONS Several parameters are calculated for the data. For both alarmed and non-alarmed data, X BAR BAR and R BAR are calculated, as well as upper and lower 1, 3, and 7 point control limits. Alarmed data variables also have a process capability limit which is calculated. Operators are allowed to enter upper




and lower target limits on the control charts -these values are provided only for operator reference and have no effect on the statistical The alarmed data variables quality control package. have alarm processing done each time a new X BAR or R is available.

DEFININGALARHEDVARIABLES Alarmed variables are defined to the statistical quality control package through the use The user merely selects of an interactive program. the option wanted (such as ADD, HODIFY, DELETE, or LIST) and continues to answer the questions asked. Each of the options are described further. ADD-ing Alarmed Variables The user supplies the name of the data field The number of observations per to be monitored. sample is selected, normally between one and fifteen. If exact values (not averages) are to be used, the number of observations per sample would If the number of observations is greater be one. than one, the software package calculates the values of X BAR and R from the observation data. Observation values can be grouped in records by the base process control system, or as single entities. The user identifies the method to be used. Next, the user selects the number of samples to be used in calculated X BAR BAR and R BAR. Normally, this count is between twenty-five and one hundred fifty. Since the system may not have enough observations initially to calculate control limits, the user may enter initial control limits. The user supplies tha name of a discrete value in the base process control system -- the discrete value is used to indicate the current alarm state of this particular alarmed If the user statistical quality control variable. wants the X BAR value to be physically stored in the base process control system, the targeted value may be identified. After the entry of the previous parameters, an alarmed statistical quality control variable is created. MODIFY-ing Alarmed Variables If no data observations have been made for a particular alarmed variable, any of the parameters (as described above) may be altered. If observations have been made since the variable was configured, the modifications are limited to those things that pertain to alarming and to calculation storage. That is, the option for storing the sample mean to the process data base, and the identity of the discrete value used for alarming are the only things that can be changed. Of course, if observation data already exists, the variable can be deleted and reconfigured. DELETE-ing Alarmed Variables Alarmed variables are deleted from the statistical quality control package by name. When a valid name has been entered, the user is shown that record’s definition. If the user confirms the delete action, that variable is removed. LIST-ing

Alarmed Variables

The user can list the record definition data of statistical quality control alarmed variables. A single variable can be listed by requesting it by n*me. Optionally, all alarmed variable definitions can be listed. ADDITIONALFEATURES Alarm and message logs exist for capturing significant changes during process operation. These logs are accessible from the operator console subsystem and are also logged to hardcopy devices.


J. R. Kusllin~, Jr.

An alarm summary display is shown in Fig. 12 and a message history display is shown in Fig. 13. Loop summaries provide an overview This allows the defined control loops. view one display to check on many control this is especially helpful in analyzing conditions and checkinn related 100~s. summary display is sho& in Fig. 14:

CONCLUSION SETPOINT has a field proven package with many features useful in ,applying statistical quality _. control. SETPOINTs experience with real-time systems has been combined with proven statistical quality control methods to provide a software system that is applicable.

of any operator to loops -alarm A 1OOD

REFERENCES Extensive support exists for user programs. Libraries of FORTRANsub-programs are available. The system provides constructs for user-program Any user programs activation and synchronization. have full access to the process data base and the statistical quality control system data base. Anything depicted on an operator’s console (graphics displays, plots, control charts, alarm can be directed to a hardcopy device logs, etc.) This provides an effective with a single keystroke. means for the operator to capture information of specific interest.








A printed


Grant, E. L. and Leavenworth, R. S., Statistical Quality Control, J. V. Brown, editor, McGraw-Hill, New York (1980).


Llansa, J. I., “A Process Distributed or Centralized p 515 (1983).


Shewhart, W. A., Statistical Viewpoint of Quality Control editor, Department ofmiture, D.C. (1939).





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Control Language for Architectures,” ISA, Method From the --W. E. Deming, Washington,















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Fig. 12

Alarm history display.

Fig. 13

Fig. 14

A message history display.

Loop summary display.