CASe study

Orbital Sets New State-of-the-Art Benchmark with 427% Faster Asset Failure Prediction

12 Jun 2024

Goals

Demonstrate that a decentralised deep learning approach for asset failure prediction can be achieved while maintaining data privacy, security, and reduced operational costs.

Develop and evaluate this approach in a digital twin environment for pipeline failure prediction up to 96 hours before onset.

Benchmark and compare this approach to current machine learning methods deployed via Cloud in the industry on the following metrics: Model Accuracy, False Positive Prediction and Prediction performance

Results

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%

Earlier predictions compared to other models

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%

Overall accuracy

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%

Higher accuracy at each day of prediction

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© Applied Computing Technologies 2025

Applied Computing Technologies is a remote first company headquartered in London, UK

© Applied Computing Technologies 2025

Applied Computing Technologies is a remote first company headquartered in London, UK

© Applied Computing Technologies 2025

Applied Computing Technologies is a remote first company headquartered in London, UK