Control warning limit

Statistical Factors for the Upper Warning Limit and Upper Control Limit... 

Interpreting Control Charts The purpose of a control chart is to determine if a system is in statistical control. This determination is made by examining the location of individual points in relation to the warning limits and the control limits, and the distribution of the points around the central line. If we assume that the data are normally distributed, then the probability of finding a point at any distance from the mean value can be determined from the normal distribution curve. The upper and lower control limits for a property control chart, for example, are set to +3S, which, if S is a good approximation for O, includes 99.74% of the data. The probability that a point will fall outside the UCL or LCL, therefore, is only 0.26%. The... 

QCC contain - as quality target values Q - standard or reference values, x0, resp. optimum values as well as their limits. The inner pair of limits are called warning limits and the outer pair control limits (action limits). When... 

When using control charts, you should take action on any points which fall outside the action limits and be alert when points exceed the warning limits. There are three other situations which normally indicate a problem with the system, as follows ... 

FIGURE 1.5 An example of a control chart showing a device, procedure, process, or method that is in statistical control because the numerical values are consistently between the warning limits. 

A process, such as a manufacturing process, may also be monitored with a control chart. In this case, the desirable value, warning limits, and action limits for the product of the manufacturing process is determined over time using materials and equipment that the scientists and engineers are confident provide an accurate picture of the product. 

Warning limits are the maximum and minimum values within which a single control sample result is normally expected to lie. 

The narrower limits are usually known as the warning limits. Failure to meet these limits implies that the method must be investigated and any known weakness, such as unstable reagents, temperature control, etc., should be rectified. However, results obtained at the same time as the control result can still be accepted. Probably the first step in a case like this is to repeat the control analysis. If the original result was a valid random point about the mean then the repeat result should be nearer to the mean value. If the repeat analysis shows no improvement or the original control result lay outside the wider control limits (known as action limits) then it must be assumed that all the results are wrong. The method must be investigated, the fault rectified and the analysis of samples and controls repeated. 

As stated above, an occasional point outside the warning limits is expected. However, if there begins to be some consistency with points outside the warning limits, there is sufficient cause for some evaluation of the situation. Perhaps some component of the system is out of calibration, or perhaps some bias has been introduced inadvertently. Figure 5.12 is an example of such a control chart. 

Figure 5.11 A control chart showing that a process is under statistical control because all the points are between the two warning limits.

Figure 5.12 A control chart showing a consistent pattern outside the upper warning limit after Day 24, indicating that an evaluation of the situation is warrented. It may indicate a component of the system is out of calibration, etc.

Define statistical control, control chart, warning limits, action limits. 

Assuming a standard deviation of 0.0002, add warning limits at 2 standard deviations and action limits at 3 standard deviations to your control chart. Is there any day that you would take the balance out of service and perhaps call in a service agent Explain. 

Two aspects are important for IQC (1) the analysis of control materials such as reference materials or spiked samples to monitor trueness and (2) replication of analysis to monitor precision. Of high value in IQC are also blank samples and blind samples. Both IQC aspects form a part of statistical control, a tool for monitoring the accuracy of an analytical system. In a control chart, such as a Shewhart control chart, measured values of repeated analyses of a reference material are plotted against the run number. Based on the data in a control chart, a method is defined either as an analytical system under control or as an analytical system out of control. This interpretation is possible by drawing horizontal lines on the chart x(mean value), x + s (SD) and x - s, x + 2s (upper warning limit) and x-2s (lower warning limit), and x + 3s (upper action or control limit) and x- 3s (lower action or control limit). An analytical system is under control if no more than 5% of the measured values exceed the warning limits [2,6, 85]. 

Figure 4.11. Shewhart means plot of the duplicate analysis of a certified reference material, twice per day for 20 days. Each point is the mean of the day s four results. Warning limits (UWL and LWL) are at the global mean 2xs/i/4 and control (action) limits (UCL and LCL) at the global mean 3xs/ /4, where s is the standard deviation of all the data.

Two consecutive points lie between a warning limit and its control limit (P=. 0021). 

The average range of the data is multiplied by D to give the lower control limit (Dq ooi). lower warning limit (/I(i.(i25). upper warning limit ( >0.975) and upper control limit ( >0.999). Adapted from Oakland (1992). 

Figure 4.15. Shewhart range plot of data in figure 4.11. The range is of the four points taken each day. Dashed lines are control limits and dotted lines are warning limits.

An example of this scenario is shown in spreadsheet 4.2 and figure 4.18. Here the motor octane number of a standard sample of 95 octane fuel is measured each month in duplicate. The Shewhart means chart never goes over the warning limit, let alone the control limit (see figure 4.19), but something is clearly wrong. The seven-in-a-row rule would be triggered, but CuSum also reveals that the system is out of control at about the same time. 

When control charts are employed for process control, two sets of control limits are frequently used x A2R (action limits) and xT 2/3 A2R (warning limits). When the process exceeds the action limits, corrective steps are necessary. When the process exceeds only the warning limits, the user is alerted that the process may be malfunctioning. 

The upper and lower warning limits (UWL and LWL) are drawn at 2s above and below, respectively, of the mean recovery. The upper and lower control limits (UCL and LCL) are defined at 3s value about the mean. If ary data point falls outside UCL or LCL, an error in analysis is inferred that must be determined and corrected. The recoveries should fall between both the warning limits (UWL... 

Precision control charts may, alternatively, be constructed by plotting the RPDs of duplicate analysis measured in each analytical batch against frequency of analysis (or number of days). The mean and the standard deviation of an appropriate number (e g., 20) of RPDs are determined. The upper and lower warning limits and the uppper and lower control limits are defined at 2 and 3.v, respectively. Such a control chart, however, would measure only the quality of precision in the analysis. This may be done as an additional precision check in conjunction with the spike recovery control chart. 

Graphical plot of test results with respect to time or sequence of measurement upon which control and warning limits are set to guide in detecting whether the system is in a state of control. Volume 1(10). 

An example of a recovery control chart is shown in Figure 4.7. The mean recovery of individual measurements is represented by the centreline. The upper warning limit (UWL) and the lower warning limit (LWL) are calculated as plus/minus two standard deviations (mean recovery + 2s) and correspond to a statistical confidence interval of 95 percent. The upper control limit (UCL) and the lower control limit (LCL) are calculated as plus/minus three standard deviations (mean recovery 3s), and represent a statistical confidence interval of 99 percent. Control limits vary from laboratory to laboratory as they depend on the analytical procedure and the skill of the analysts. 

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