Incipient Fault Detection and Isolation of Field Devices in Nuclear Power Systems Using Principal Component Analysis
- University of Tennessee, Knoxville (United States)
An integrated method for the detection and isolation of incipient faults in common field devices, such as sensors and actuators, using plant operational data is presented. The approach is based on the premise that data for normal operation lie on a surface and abnormal situations lead to deviations from the surface in a particular way. Statistically significant deviations from the surface result in the detection of faults, and the characteristic directions of deviations are used for isolation of one or more faults from the set of typical faults. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data and fit a hyperplane to the data. The fault direction for each of the scenarios is obtained using the singular value decomposition on the state and control function prediction errors, and fault isolation is then accomplished from projections on the fault directions. This approach is demonstrated for a simulated pressurized water reactor steam generator system and for a laboratory process control system under single device fault conditions. Enhanced fault isolation capability is also illustrated by incorporating realistic nonlinear terms in the PCA data matrix.
- OSTI ID:
- 20826709
- Journal Information:
- Nuclear Technology, Journal Name: Nuclear Technology Journal Issue: 2 Vol. 136; ISSN 0029-5450; ISSN NUTYBB
- Country of Publication:
- United States
- Language:
- English
Similar Records
Fault Detection via Occupation Kernel Principal Component Analysis
Fault isolation and fault-tolerant control for Takagi-Sugeno fuzzy time-varying delay stochastic distribution systems