Measurement System Analysis with Report Analyzer
When manufacturing and developing measurement systems, it must be ensured that the respective measurement/test process is suitable for the intended application. Such a measurement process depends on many influencing factors. These include, among others, the operating personnel, the environment, the evaluation method, the measurement object, and the recording device. To determine whether a measurement/test process is capable under these influencing factors, a Measurement System Analysis (MSA) can be conducted using the Report Analyzer. In this context, systematic measurement deviations or the variability of the measuring instrument are assessed. The software offers the option of importing data from various sources and then analysing it. The data can be grouped, presented, and analyzed according to user specifications. By preparing the data, it should be possible to draw conclusions, for example, regarding the causes of errors in failures or possible trends in the measurement values. The evaluation or analysis should be divided into several sub-analyses, including
the statistical evaluation of the measurement data in terms of measurement instrument capability and suitability for test processes,
the statistical error distribution,
the correlation analysis (similarity analysis) of measurements,
the curve comparison of measurements
and the trend analysis of measurement value courses.
Measurement System Analysis with Report Analyzer
When manufacturing and developing measurement systems, it must be ensured that the respective measurement/test process is suitable for the intended application. Such a measurement process depends on many influencing factors. These include, among others, the operating personnel, the environment, the evaluation method, the measurement object, and the recording device. To determine whether a measurement/test process is capable under these influencing factors, a Measurement System Analysis (MSA) can be conducted using the Report Analyzer. In this context, systematic measurement deviations or the variability of the measuring instrument are assessed. The software offers the option of importing data from various sources and then analysing it. The data can be grouped, presented, and analyzed according to user specifications. By preparing the data, it should be possible to draw conclusions, for example, regarding the causes of errors in failures or possible trends in the measurement values. The evaluation or analysis should be divided into several sub-analyses, including
the statistical evaluation of the measurement data in terms of measurement instrument capability and suitability for test processes,
the statistical error distribution,
the correlation analysis (similarity analysis) of measurements,
the curve comparison of measurements
and the trend analysis of measurement value courses.
Module
Import
Import of test reports or test executions according to the IRS XML format. Additionally, other formats from different sources, such as databases, can be imported. A plugin structure also allows for the processing of custom formats or sources.
Grouping
In order to enable a comparison of analysis data based on certain characteristics, test executions are divided into groups. Defined groupings, sortings, and applied filters can be stored as predefined sets and can be reapplied through a suitable quick selection. Grouping allows results to be compared with each other, such as temperature classes in climate tests or measurement trends in long-term tests. Measurements on different test benches can also be evaluated separately, which is especially helpful in the development of measurement and testing systems to uncover potential errors.
History & Statistics
This type of analysis conducts and visualizes statistical value analyses. Moving limits can be displayed and limit violations highlighted. In addition, statistical values are determined, such as mean value, standard deviation, specification limits, measurement capability indices, and process capability indices. Various chart types, e.g., scatter or line charts with variable axes (time, index, or serial number, etc.), are available for visualization. A distribution curve overlaid with a histogram and the relevant key figures can also be displayed.
Statistical Error Distribution
In this sub-analysis, the statistical error distribution is determined and visualized on two levels. The first level is the test execution level, which evaluates the results of the test executions of a group. From the error distribution of the first level, conclusions can be drawn about how the results are distributed across groups. The second level is the test step level. Here, the results of the test steps of a group of test executions are evaluated and compared with those of other groups.
Curve Comparison
The primary goal of the curve comparison function is to show relationships between different measurements. Through the representation type “Measurement over Measurement,” conclusions can be drawn about the dependence of both measurements. Another representation type is the value trend. Here, the focus is on comparing multiple measurements from a group by jointly displaying them in a chart.
Similarity Analysis
The similarity analysis allows for the determination of linear relationships between measurements. With the help of correlation analysis, it can thus be determined whether one test step is strongly related to another.
Trend Analysis
The trend analysis aims to identify trends in the dispersion or mean value of a measurement series. Since the data to be analyzed consists of very large datasets, it is necessary to automatically recognize and prioritize or evaluate the trends.
Module
Import
Import of test reports or test executions according to the IRS XML format. Additionally, other formats from different sources, such as databases, can be imported. A plugin structure also allows for the processing of custom formats or sources.
Grouping
In order to enable a comparison of analysis data based on certain characteristics, test executions are divided into groups. Defined groupings, sortings, and applied filters can be stored as predefined sets and can be reapplied through a suitable quick selection. Grouping allows results to be compared with each other, such as temperature classes in climate tests or measurement trends in long-term tests. Measurements on different test benches can also be evaluated separately, which is especially helpful in the development of measurement and testing systems to uncover potential errors.
History & Statistics
This type of analysis conducts and visualizes statistical value analyses. Moving limits can be displayed and limit violations highlighted. In addition, statistical values are determined, such as mean value, standard deviation, specification limits, measurement capability indices, and process capability indices. Various chart types, e.g., scatter or line charts with variable axes (time, index, or serial number, etc.), are available for visualization. A distribution curve overlaid with a histogram and the relevant key figures can also be displayed.
Statistical Error Distribution
In this sub-analysis, the statistical error distribution is determined and visualized on two levels. The first level is the test execution level, which evaluates the results of the test executions of a group. From the error distribution of the first level, conclusions can be drawn about how the results are distributed across groups. The second level is the test step level. Here, the results of the test steps of a group of test executions are evaluated and compared with those of other groups.
Curve Comparison
The primary goal of the curve comparison function is to show relationships between different measurements. Through the representation type “Measurement over Measurement,” conclusions can be drawn about the dependence of both measurements. Another representation type is the value trend. Here, the focus is on comparing multiple measurements from a group by jointly displaying them in a chart.
Similarity Analysis
The similarity analysis allows for the determination of linear relationships between measurements. With the help of correlation analysis, it can thus be determined whether one test step is strongly related to another.
Trend Analysis
The trend analysis aims to identify trends in the dispersion or mean value of a measurement series. Since the data to be analyzed consists of very large datasets, it is necessary to automatically recognize and prioritize or evaluate the trends.