A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants.

The safety and public health during nuclear power plant operation can be enhanced by accurately recognizing and diagnosing potential problems when a malfunction occurs. However, there are still obvious technological gaps in fault diagnosis applications, mainly because adopting a single fault diagnosis method may reduce fault diagnosis accuracy. In addition, some of the proposed solutions rely heavily on fault examples, which cannot fully cover future possible fault modes in nuclear plant operation.

A robust functional-data-analysis method for data recovery in multichannel sensor systems.

Multichannel sensor systems are widely used in condition monitoring for effective failure prevention of critical equipment or processes. However, loss of sensor readings due to malfunctions of sensors and/or communication has long been a hurdle to reliable operations of such integrated systems. Moreover, asynchronous data sampling and/or limited data transmission are usually seen in multiple sensor channels.

Faulty measurement substitution and control reconfiguration by using a multivariate flow control loop.

A two-tank multivariate loop was designed and built to support research related to instrumentation and control, equipment and sensor monitoring. This test bed provides the framework necessary to investigate and test control strategies and fault detection methods applicable to sensors, equipment, and actuators, and was used to experimentally develop and demonstrate a fault-tolerant control strategy using six correlated variables in a single-tank configuration. This work shows the feasibility of using data-based empirical models to perform fault detection and substitute faulty measurements with predictions and to perform control reconfiguration in the presence of actuator failure in a real system.

Model-based monitoring and fault diagnosis of fossil power plant process units using Group Method of Data Handling.

This paper presents an incipient fault diagnosis approach based on the Group Method of Data Handling (GMDH) technique. The GMDH algorithm provides a generic framework for characterizing the interrelationships among a set of process variables of fossil power plant sub-systems and is employed to generate estimates of important variables in a data-driven fashion. In this paper, ridge regression techniques are incorporated into the ordinary least squares (OLS) estimator to solve regression coefficients at each layer of the GMDH network.