Analyzing and documenting measurement data in research, development and testing: the key to successful projects

The Importance of Measurement Data

Shifting forces on a gearbox: overview on the left and a cycle in detail on the right

In today’s R&D landscape, the results from test stands or test drives are of high value. They form the basis for informed decision-making, enable process and product optimization, and are often critical to the success of a project or product. However, simply recording and gathering data is not enough. To extract valuable insights from collected measurement data, precise and comprehensive analysis is essential.

Challenges in Measurement Data Analysis

Analyzing measurement data presents several challenges:

  • Large Data Volumes: Measurements increasingly involve more sensors, often recording hundreds or thousands of channels, quickly generating gigabytes or even terabytes of data that are difficult to manage.

  • Diverse File Formats: Data from various manufacturers come in many formats, complicating integration and comparative analysis.

  • Complex Relationships: Multiple measurement variables can involve intricate relationships that require custom, in-depth analysis.

  • Time Constraints: Many R&D projects are time-sensitive, demanding fast, reliable analysis without lengthy setup times.

  • Data Sharing: Measurement data often needs to be shared with suppliers, customers, or other departments for comprehensive evaluations.

Zooming into three data sets with 140 million samples each (2h recording with 20 kHz)

The Solution: Powerful Measurement Data Analysis Software

A powerful data analysis software solution can effectively address these challenges, offering benefits like:

  • Automation: Routine tasks like importing, converting, and filtering data can be automated, saving time and resources. Report templates can auto-fill with new data.

  • Visualization: Complex data can be easily understood with high-performance visualizations, like overlaying different test series.

  • Analytical Tools: Specialized functions enable statistical analysis, spectral analysis, correlation, and more—all without programming knowledge.

  • Integration: The software integrates with existing IT systems, facilitating data exchange with other applications and secure, company-wide updates.

  • Scalability: Suitable for small data reviews yet ready to handle growing data volumes and more complex tasks in the future.

Comparison of several laps on a test track; left: braking before a bend, right: gear change

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What Features Should a Good Data Analysis Software Offer?

A robust measurement data analysis software should include:

  • Intuitive User Interface: A user-friendly design allows even non-programmers to work efficiently, without extensive training.

  • Versatile Import Formats: Support for a wide range of file formats ensures flexible data integration.

  • Comprehensive Analytical Tools: A rich library of functions enables users to focus on their work instead of researching specific calculations.

  • Automation Capabilities: The software should facilitate both automatic and semi-automatic execution of recurring analyses.

  • Open Interfaces: Open interfaces allow for integration into existing IT landscapes and access to specialized software, like R, for advanced statistical analysis.

Ready-to-use example function for calculating electric power
Ready-to-use example function for calculating electric power

Conclusion

Modern measurement data analysis software is an invaluable aid for engineers in research, development and testing. It enables efficient and comprehensive analysis of measurement data and thus contributes to accelerated product development and higher quality results.

Helpful further resources on the topic of data analysis

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