Fast, efficient and sustainable evaluation of measurement data presents a huge challenge for every industry – especially for innovation-driven sectors that deploy extensive testing such as the automobile, aircraft and mechanical engineering industries, and also for research and development. Vehicle developers are particularly keen to seek out new approaches in this area.
As sensor technology continues to evolve, more complex metrological questions are being asked – and thanks to new possibilities for digitalizing measurement data and configuring measurement chains, it is easier and faster to find answers and solutions. Huge quantities of valuable measurement data are now being generated, but development times are also becoming shorter: these combined pressures are creating the need for fast, efficient, and sustainable methods of evaluation and analysis.
How can companies merge and evaluate data that originates from vast numbers of different sensors, channels or networks so as to maximize the benefit? And how can they utilize this data as the basis for new and practically relevant insights so they can develop innovations to meet the challenges of the future?
Post-processing of this sort is made possible by high-performance software to analyze measurement data: it stores and organizes the results so they are quickly available for a variety of calculations, analyses and comparisons. The basis for potential interpretations of this data is enhanced by the widest possible range of search options, and compatibility with numerous different data formats. Clear visualization and graphic presentations are equally important criteria. Trends, structures and outliers can be identified more quickly, helping users to gain in-depth understanding of the relevant relationships.
'Data Mining' goes one step further: this method is based on analytical software that automatically reveals underlying correlations and patterns with the help of integrated application-specific algorithms – in other words, artificial intelligence (AI).
Relevance of evaluating measurement data in: