TPT May 2024

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TUBE INSPECTION & TESTING

Deep learning Anomaly Detector algorithm MONOLITH, an artificial intelligence software provider to engineering teams, has developed the industry’s first AI powered Anomaly Detector software that can quickly discover a broad 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. with existing customers, predominantly in automotive, motorsports and industrial segments. In working directly with customers, the Monolith team was able to create a unique 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 an intuitive 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. Monolith www.monolithai.com

The impact of failing to recognise issues with test data in a timely manner can be huge, including months of wasted testing and potential product delays and recalls, leading to unnecessary costs, studies have found. 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 self learning algorithms. Dr Richard Ahlfeld, Monolith CEO and founder, said: “Bad data leads to wrong decisions and massive time wasted among highly valuable engineering resources. If discovered too late, these errors can also lead to schedule delays, or worse, quality issues that are released with the product leading to potentially expensive and reputation harming recalls.” “We’ve spent more than two years working directly with our customers to design, test and tune our new Anomaly Detector so engineers can rapidly find errors in engineering data. In that time, we’ve not only developed a unique deep learning algorithm that can find multiple types of anomalies, but we’ve also packaged it in a no-code user experience built specifically for engineering domain experts. This is useful AI developed by engineers for engineers.” Two years in real-world development with Monolith customers Monolith has developed and tested its new Anomaly Detector AI in real-world applications

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