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Application of Artificial Intelligence in Test Benches

Application of Artificial Intelligence in Test Benches

AI in the Test Environment – Leveraging Test Rig Data Intelligently

Modern test benches generate large volumes of data from sensors, control systems, and test automation. This data provides a detailed representation of real system behavior but is often only partially evaluated. Using data analytics and machine learning, correlations, deviations, and patterns can be systematically identified. This enables more targeted test planning, early detection of anomalies, and automated evaluation processes.

Against the backdrop of increasing system complexity, growing variant diversity, and shorter development cycles, AI helps to make testing processes more efficient and to deliver reliable results faster.

Typical applications of AI in the test field

  • Automated analysis of large test data sets

    Machine learning methods can automatically analyse large data sets from test campaigns and identify patterns.

    Examples include:

    • classification of test results

    • identification of anomalous signal behaviour

    • analysis of complex system responses

  • Anomaly detection

    AI can be used to detect unusual or critical conditions of a test item at an early stage.

    Typical applications include:

    • detection of malfunctions

    • identification of abnormal operating conditions

    • support in failure analysis

  • Optimisation of test programs

    By analysing historical test data, AI-based methods can help to make test programmes more efficient.

    Possible applications include:

    • prioritisation of relevant tests

    • reduction of redundant test cases

    • optimisation of test sequences

  • Forecasting system states

    Data-driven models can be used to identify trends in the behaviour of a test item.

    Examples include:

    • prediction of wear

    • estimation of remaining useful life

    • support of endurance testing

Our services

We support our customers in integrating AI-based analytics methods into their test infrastructure. In this context, the use of AI enables comprehensive support across the entire testing process − from design and operation through to automated evaluation:

  • Intelligent design and specification of test systems

    AI supports the specification of future-proof testing systems and thus helps to safeguard investments. We use our IABG AI tools to analyze interviews and research standards, among other things. They are already creating clear added value, particularly in the area of consulting in test bench construction - efficient test solutions.

  • Intelligent parameterisation of test systems

    Learning algorithms can be used to derive optimum process and control parameters. In addition, test bench parameters can be consistently derived from test specimen, test and field data. As part of our individual test bench solutions, we integrate these approaches specifically into our software solutions for test automation. In this way, we enable consistent, data-based parameterization, increase process stability and create the basis for adaptive, future-proof test systems.

  • Digital test specimen models

    Test specimen models are continuously trained during the ongoing test. The resulting digital twins precisely reproduce the real behavior and make it possible to analyze test item properties in virtual test environments or to reproduce subsequent failures and deviations in the field. We integrate these functions on a turnkey basis as part of our software solutions for test automation.

  • Data-driven analysis and quality assurance

    Anomaly detection detects deviations in measurement data both in real time and in post-processing. Automated plausibility checks increase the validity of the measurement results and ensure data quality throughout the entire testing process. We integrate these functions on a turnkey basis as part of our software solutions for test automation.

To ensure that test rigs operate reliably, integrate seamlessly into existing processes and are optimally tailored to the respective application, we embed AI-based analytical methods directly into the data and automation architecture. This enables test results to be evaluated and utilised already during ongoing test execution.

We consider AI to be an integral part of a digitalised test environment. In addition to intelligent analytical methods, this includes test automation, structured data management, as well as the use of digital twins and simulation-based approaches.

In safety-critical applications in particular, such AI processes must be traceable, transparent and validatable. This is where our customers benefit from our expertise in the safeguarding, evaluation and validation of AI systems.

Your advantages at a glance

✅ Faster evaluation of large test campaigns

✅ Better use of existing test data

✅ Early detection of critical conditions

✅ More efficient test programs

✅ Support of development and qualification processes

Please take benefit from our expertise!



We are looking forward to your inquiry!

Dr. Timo Jungblut
Head of Test Systems
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