Why the evaluation has "so few" values is often seen as a problem and is one of the recurring questions on the hotline. Defined as outliers, the measured values can be excluded from the evaluation.
How to deal with outliers is an ongoing issue. It is up to the customer to decide whether and how to handle it. This is a multi-faceted and complex issue, which is covered in detail in the description of the evaluation strategy, but the focus here should be on general understanding. This explanation may need to be read iteratively several times. And it is recommended that it is discussed in the project or in a training session.
Outliers in the machine and the process capability analysis
The technical possibilities are the same in both modules, the Sample Analysis and the Process Capability Analysis. But the philosophical approach is different.
Outliers in process capability analysis are perfectly normal for many customers. Continuous data flow, automatic recording mechanisms, problems with incorrect measured values can occur anywhere. But not when it comes to sample analysis, to the analysing machine capabilities! A machine capability should be performed under control! So can there be outliers? Not really. There is a reason why the possibilities are still there: IF outliers are found, i.e. it must be assumed that no control of the data recording has taken place, the customer can then define in the machine capability that, no matter how good the process would still be towards the end, the process is output as conditionally capable or even as not capable BECAUSE outliers were found.
Types of outliers
A distinction is made between "outliers based on limit values" and "mathematical outliers"
AND
"Automatic outlier consideration during evaluation" and "manual outlier consideration"
Outliers based on limit values
A value "x" is above or below the specified limits and is therefore defined as an outlier. Various options are available in the "Preparation" tab of the evaluation strategy.
These are explained in the evaluation strategy and are therefore only briefly mentioned here. Plausibility limits and scrap limits must be entered manually in the characteristics mask. Natural boundaries cannot be technically exceeded. The "X% tolerance" option should be used with caution. While measured values of 500% out of tolerance might be considered as an outlier in classical industry, it can be considered as a valid measured value in the electrical industry. However, all this information depends to a greater or lesser extent on what is entered in the characteristics mask. In other words, a conscious action.
Link to: Evaluation Strategy
Mathematical outliers
Manual outlier consideration - Manual consideration/elimination of outliers
The term "selection" is used here instead of "outliers". This refers to the explanation of "removing" values from the value chart using the "Select" function. Link to: Working with the Graphics - Select Functions
The use of select functions should also be reconsidered, with a concept of authorisation as to who should have the right to do this:
User rights for manual removal of statistical outliers
The outliers can be removed statistically using the test procedure, for example with the Hampel test. Link to: Test Procedures
The use of test procedure should also be controlled by setting user permissions.
