Rapid test data anomaly detection relies on AI algorithm

19 April 2024

The Anomaly Detector from Monolith is said to be the first AI powered software designed to rapidly identify a wide range of issues in test data.

It does this by automating the process of raw test data inspection to look for potential errors or abnormalities across hundreds of test channels.

Failing to recognise issues with test data in a timely manner can result in months of wasted testing and potential product delays.

Data anomalies caused by measurement or sensor errors, user errors, system malfunctions, or incorrect usage of the system during testing, can now be found quickly and more efficiently thanks to Monolith’s self-learning algorithms.

Anomaly Detector AI has been tested in real-world applications with existing customers, predominantly in automotive, motorsports and industrial segments. In working directly with customers, the Monolith team was able to create a deep learning algorithm that finds many types of anomalies within test results and across hundreds of channels based on complex system behaviour. Users can tune the anomaly detector for speed or depth of inspection, as well as for prevalence or severity of anomalies. Using a two-dimensional heat-map display, engineers can quickly peruse the results and rapidly recognise which tests or channels are showing questionable results to prioritise next steps. 


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