Core Temperature Monitoring System for Prototype Fast Breeder Reactor

Sakthivel, M; Madhusoodanan, K.

doi:10.13182/nse11-07

Cited by 6 publications

(1 citation statement)

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“…Traditional core temperature anomaly detection is usually based on mechanistic analysis methods, which are widely used in the process monitoring systems of large power plants nowadays. These methods model the process mechanism related to core temperature and determine the abnormal condition by empirical knowledge [9][10][11][12]. However, these methods have limitations due to high labor costs, low detection efficiency and poor realtime performance.…”

Section: Introductionmentioning

confidence: 99%

Feature Disentangling Autoencoder for Anomaly Detection of Reactor Core Temperature with Feature Increment Strategy

Mao

et al. 2023

Processes

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Anomaly detection for core temperature has great significance in maintaining the safety of nuclear power plants. However, traditional auto-encoder-based anomaly detection methods might extract the latent space features with redundancy, which may lead to missing and false alarms. To address this problem, the idea of feature disentangling is introduced under the auto-encoder framework in this paper. First, a feature disentangling auto-encoder (DAE) is proposed where a latent space disentangling loss is designed to disentangle the features. We further propose an incrementally feature disentangling auto-encoder (IDAE), which is the improved version of DAE. In the IDAE model, an incremental feature generation strategy is developed, which enables the model to evaluate the disentangling degree to adaptively determine the feature dimension. Furthermore, an iterative training framework is designed, which focuses on the parameter training of the newly incremented feature, overcoming the difficulty of model training. Finally, we illustrate the effectiveness and superiority of the proposed method on a real nuclear reactor core temperature dataset. IDAE achieves average false alarm rates of 4.745% and 6.315%, respectively, using two monitoring statistics, and achieves average missing alarm rates of 6.4% and 2.9%, respectively, using two monitoring statistics, outperforming the other methods.

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Section: Introductionmentioning

confidence: 99%