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Early fault detection and fault prognosis are crucial to ensure efficient andsafe operations of complex engineering systems such as the Spallation NeutronSource (SNS) and its power electronics (high voltage converter modulators).Following an advanced experimental facility setup that mimics SNS operatingconditions, the authors successfully conducted 21 fault prognosis experiments,where fault precursors are introduced in the system to a degree enough to causedegradation in the waveform signals, but not enough to reach a real fault. Ninedifferent machine learning techniques based on ensemble trees, convolutionalneural networks, support vector machines, and hierarchical voting ensembles areproposed to detect the fault precursors. Although all 9 models have shown aperfect and identical performance during the training and testing phase, theperformance of most models has decreased in the prognosis phase once they gotexposed to real-world data from the 21 experiments. The hierarchical votingensemble, which features multiple layers of diverse models, maintains adistinguished performance in early detection of the fault precursors with 95%success rate (20/21 tests), followed by adaboost and extremely randomized treeswith 52% and 48% success rates, respectively. The support vector machine modelswere the worst with only 24% success rate (5/21 tests). The study concludedthat a successful implementation of machine learning in the SNS or particleaccelerator power systems would require a major upgrade in the controller andthe data acquisition system to facilitate streaming and handling big data forthe machine learning models. In addition, this study shows that the bestperforming models were diverse and based on the ensemble concept to reduce thebias and hyperparameter sensitivity of individual models.

Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning