How to handle outliers is an ongoing issue. Whether as a request or as a question to the helpdesk, because it is seen as a problem that the evaluation has "so few" measured values. Therefore, before the outlier treatment is actually performed, it is up to the customer to decide whether and how to handle it. This is a multi-faceted and complex issue, which is already covered in detail in the evaluation strategy description, but the focus here should be on general understanding. This article may need to be read iteratively several times. And it is recommended that this 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
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