Assessment of simulation-based data augmentation technique by Uncertainty Quantification for Spacecraft Propulsion System PHM

One of the challenges of applying Prognostics and Health Management (PHM) in industrial systems is the lack of labelled training data including anomalies and faults. This study proposes training data generation by a physics-based numerical model and uncertainty quantification (UQ) considering input uncertainty and model form uncertainty, and demonstrates the proposed methodology in a spacecraft propulsion system. A one-dimensional numerical model of the spacecraft propulsion system has been developed in which ignition delay and trapped bubble dynamics are modeled. Sources of uncertainty originating in input variables of the numerical model are identified by domain experts. The probability distributions of them are modeled as uniform distributions, and training data are generated through the propagation of these probability distributions using a Monte Carlo approach. The generated training data were compared with available experimental data and showed good agreement in time-series and frequency-domain response. The 95% confidence interval (C.I.) of total uncertainty, integrating input uncertainty and model form uncertainty, was evaluated through UQ. The generated data enables the use of unsupervised methods for anomaly detection. The C.I. can be used as the normal space for anomaly detection.