Fast and Accurate Classification of Time Series Data Using Extended ELM: Application in Fault Diagnosis of Air Handling Units

Abstract

The extreme learning machine (ELM) is famous for its single hidden-layer feed-forward neural network which results in much faster learning speed comparing with traditional machine learning techniques. Moreover, extensions of ELM achieve stable classification performances for imbalanced data. In this paper, we introduce a hybrid method combining the extended Kalman filter (EKF) with cost-sensitive dissimilar ELM (CS-D-ELM). The raw data are preprocessed by EKF to produce inputs for the CS-D-ELM classifier. Experimental results show that the proposed method is more suitable for real-time fault diagnosis of air handling units than traditional approaches.