2013, 39(2): 178-183.
Abstract:
Based on artificial neural network, a method for fault detection and fault identification was proposed, which can be used during the model-based fault diagnosis (MBFD) process. Firstly, back-propagation (BP) neural network was presented for parameter estimation, combing with the system model, the faults can be detected. Then the adaptive resonance theory (ART2) neural network was used for data clustering, and based on the clustering results, the faults will be identified. Finally, a fault diagnostic system was developed based on BP&ART2 neural network. The presented BP-based parameters estimation method can estimate the parameters under different states accurately, which is important for the fault detection. ART2-based data clustering method can distinguish both the known and unknown fault modes, which is much efficient when diagnosing the system without enough priori information. A brushless direct current motor was selected as the application case, simulation result has proved that the presented method is available to estimate the motor parameters, detect and identify some typical faults, such as brushes position offset and armature open.