Fault detection and diagnosis using statistical control charts and artificial neural networks

Abstract

In order to operate a successful plant or process, continuous improvement must be made in the areas of safety, quality and reliability. Central to this continuous improvement is the early or proactive detection and correct diagnosis of process faults. This research examines the feasibility of using cumulative summation (CUSUM) control charts and artificial neural networks together for fault detection and diagnosis (FDD). The proposed FDD strategy was tested on a model of the heat transport system of a CANDU nuclear reactor. The results of the investigation indicate that a FDD system using CUSUM control charts and a radial basis function (RBF) neural network is not only feasible but also of promising potential. The control charts and neural network are linked by using a characteristic fault signature pattern for each fault which is to be detected and diagnosed. When tested, the system was able to eliminate all false alarms at steady state, promptly detect six fault conditions, and correctly diagnose five out of the six faults. The diagnosis for the sixth fault was inconclusive.