Novel fault diagnosis scheme utilizing deep learning networks

Abstract Information availability and processing advances have allowed automated fault diagnosis methods to gain popularity in nuclear industry. However, stringent safety requirements coupled with nuclear industry requires that such methods avoid any kind of misdiagnosis. In manuscript, a novel fault diagnosis model is being proposed which can not only diagnose faults in an early time frame but also has the ability to identify fault scenarios that cannot be confidently classified. Moreover, for each type of fault identified, severity of the fault can also be calculated. The proposed method utilizes Principle Component Analysis & Deep Learning networks of Long Short Term Memory and Convolution Neural Network. The study was conducted on simulation model of IP-200 nuclear power plant using RELAP5/MOD 4.0 thermal-hydraulic code. Proposed fault diagnosis model was verified using different operational states along-with 4 different fault types and 1 blind case. Simulation data suggests that proposed model is a feasible scheme for fault diagnosis of nuclear power plant.