https://hal-supelec.archives-ouvertes.fr/hal-00790421/document

Nuclear Power Plant Components Condition Monitoring by Probabilistic Support Vector Machine

Fault diagnosis and classification based on wavelet transform and neural network

A survey of the state of condition-based maintenance (CBM) in the nuclear power industry

Framework for fault diagnosis with multi-source sensor nodes in nuclear power plants based on a Bayesian network

Tools of sensor-data-driven anomaly detection facilitate condition monitoring of dynamical systems especially if the physics-based models are either inadequate or unavailable. Along this line, symbolic dynamic filtering (SDF) has been reported in literature as a real-time data-driven tool of feature extraction for pattern identification from sensor time series. However, an inherent difficulty for a data-driven tool is that the quality of detection may drastically suffer in the event of sensor degradation. This paper proposes an anomaly detection algorithm for condition monitoring of nuclear power plants, where symbolic feature extraction and the associated pattern classification are optimized by appropriate partitioning of (possibly noise-contaminated) sensor time series. In this process, the system anomaly signatures are identified by masking the sensor degradation signatures. The proposed anomaly detection methodology is validated on the International Reactor Innovative & Secure (IRIS) simulator of nuclear power plants, and its performance is evaluated by comparison with that of principal component analysis (PCA).

/pdf/anomaly-detection-in-nuclear-power-plants-via-symbolic-4p5jg7y9g8.pdf

B. Lu

Papers

Monitoring and fault diagnosis of the steam generator system of a nuclear power plant using data-driven modeling and residual space analysis