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Non-normal Distribution Selection where the system is constantly being corrected
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Non-normal Distribution Selection where the system is constantly being corrected
Non-normal Distribution Selection where the system is constantly being corrected
Non-normal Distribution Selection where the system is constantly being corrected
Non-normal Distribution Selection where the system is constantly being corrected
Non-normal Distribution Selection where the system is constantly being corrected
Non-normal Distribution Selection where the system is constantly being corrected
Non-normal Distribution Selection where the system is constantly being corrected
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  Post Number #1  
Old 8th February 2018, 11:59 AM
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rmf180

 
 
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Please Help! Non-normal Distribution Selection where the system is constantly being corrected

Most things in life follow the normal distribution left to their own nature. However, GD&T features and others which drive toward zero are by design not normal (log-normal, exponential, etc). Does anyone have a list beyond these few as to the appropriate non-normal distribution?

The specific example I am working with is a water heater is controlled by PID system (non-normal or tampering) which constantly corrects for "error" from set point. Heated water from this system is cooling molds which contain internal thermocouples. The process is monitored and each cycle evaluates the temperature and dispositions product based upon alarm limits.

What distribution would be appropriate for a system where the system is constantly being corrected?

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Old 8th February 2018, 12:48 PM
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Look! re: Non-normal Distribution Selection where the system is constantly being corrected

I responded to this question on Quality Forum Online.
Thanks to Miner for your informative Post and/or Attachment!
  Post Number #3  
Old 9th February 2018, 08:08 PM
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Re: Non-normal Distribution Selection where the system is constantly being corrected

Quote:
In Reply to Parent Post by rmf180 View Post

What distribution would be appropriate for a system where the system is constantly being corrected?
If the process constantly adjusts generating a random curve, it tends to be normal by overcontrol.

If the process adjusts between limits generating a sawtooth curve, it tends to be continuous uniform distribution.
  Post Number #4  
Old 10th February 2018, 08:32 AM
isolytical

 
 
Total Posts: 23
Re: Non-normal Distribution Selection where the system is constantly being corrected

Ad hoc application of distributions to data is not the best use of time. Visualize the data by plotting it first. From the description, the data may have a time related function so a run sequence plot is the first plot. This gives an idea about variation and outliers. Next plot the lag-1 autocorrelation plot to check for randomness. And lastly, make a probability plot, normal and any other distribution surmised from the previous plots.
These plots will show whether the process is in control before any distribution is applied. And included is the added advantage of possibly identifying the distribution.
  Post Number #5  
Old 10th February 2018, 12:28 PM
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Re: Non-normal Distribution Selection where the system is constantly being corrected

Actually, you should consider expected distributions prior to analyzing your data. First you should prepare your total variance equation to determine what variances you expect, and what distributions they are likely to generate. The process will be the result of the sum of those distributions. Effort to reduce the effects of distributions from gage, measurement and other minor process variances to prevent masking of the the main variances is key.

Then do your time-ordered sequence chart. Look for signs of a function (variation as a function of time, such as a rate; e.g. tool wear rate, heating rate, etc.), such as runs in one direction or another throughout the chart. If you are not getting your expected distribution, you may need to go back to the equation and either find or reduce the effect of the other variances before you can ever claim control or determine randomness and independence. After all, you can often be in control and not random and independent if you can identify your process output as a function. And, if that is the case, you can not use Shewhart charts to monitor that kind of process. If will yield signals exactly opposite of the true process condition.

Of course, curve fitting is a far more straight forward analysis of the resulting distribution than probability plot.

Rummaging through the data as is without this thought process will not be the best use of time.

Last edited by bobdoering; 10th February 2018 at 12:39 PM.
  Post Number #6  
Old 10th February 2018, 01:59 PM
isolytical

 
 
Total Posts: 23
Re: Non-normal Distribution Selection where the system is constantly being corrected

  Post Number #7  
Old 10th February 2018, 02:46 PM
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Re: Non-normal Distribution Selection where the system is constantly being corrected

Quote:
In Reply to Parent Post by isolytical View Post

I know, right? Your approach that assumes a homogeneous singular distribution is not only vintage, but the assumption is false for most industrial processes. Yeah, I remember back in the day when I didn't know any better either. Good times.
Thanks to bobdoering for your informative Post and/or Attachment!
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