Parameter selection algorithm with self adaptive growing neural network classifier for diagnosis issues

Neural networks have been widely used in the field of intelligent information processing such as classification, clustering, prediction, and recognition. In this paper, a non-parametric supervised classifier based on neural networks is proposed for diagnosis issues. A parameter selection with self adaptive growing neural network (SAGNN) is developed for automatic fault detection and diagnosis in industrial environments. The growing and adaptive skill of SAGNN allows it to change its size and structure according to the training data. An advanced parameter selection criterion is embedded in SAGNN algorithm based on the computed performance rate of training samples. This approach (1) improves classification results in comparison to recent works, (2) achieves more optimization at both stages preprocessing and classification stage, (3) facilitates data visualization and data understanding, (4) reduces the measurement and storage requirements and (5) reduces training and time consumption. In growing stage, neurons are added to hidden subspaces of SAGNN while its competitive learning is an adaptive process in which neurons become more sensitive to different input patterns. The proposed classifier is applied to classify experimental machinery faults of rotary elements and to detect and diagnose disturbances in chemical plant. Classification results are analyzed, explained and compared with various non-parametric supervised neural networks that have been widely investigated for fault diagnosis.