Orbital Sets New State-of-the-Art Benchmark with 427% Faster Asset Failure Prediction
Pipeline failure prediction
Digital twin environment
12 Jun 2024
Earlier predictions compared to other models
Overall accuracy
Higher accuracy at each day of prediction
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 cloud-deployed machine learning methods in the industry on the following metrics: model accuracy, false-positive rate and prediction performance
Modern refinery optimisation relies heavily on advanced modelling techniques. However, failure identification remains a critical bottleneck due to inherent limitations in existing approaches. Traditional models often operate under steady-state assumptions, making them ill-suited for the dynamic, transient behaviours observed in real-world operations.
Despite the routine generation of ~2.5 exabytes of data, much of it remains underutilised, as prevailing methods lack the flexibility to handle continuous, unlabelled, high-frequency signals. Models typically require time-intensive step-testing for recalibration, sometimes taking months, further delaying fault detection. Their restricted operational scope means they perform well only within narrow, pre-defined regimes, leaving many process anomalies undetected or mischaracterised.