Real-time fault diagnosis of nonlinear systems

This paper concerns the development of a real-time fault detection and diagnosis system for a class of electrical machines. Changes in the system dynamics due to a fault are detected using nonlinear models, namely, nonlinear functions of the measurable variables. At the core of the fault detection and diagnosis system are artificial neural networks and a new neural network structure designed to capture temporal information in the input data. Difficulties such as voltage unbalance, measurement noise, and variable loads, commonly found in practice, are overcome by the system addressed in this paper. Because false alarms are significantly reduced and the system is robust to parameter variations, high detection and diagnosis performance are achieved during both, learning and testing phases. Experimental results using actual data are included to show the effectiveness of the real-time fault detection system developed.