Analysis of Progressively Unbalanced Induction Motor Current Signals Based on Information Entropy
The article presents the capabilities of a new fault identification method used for different fault levels. The method allows identifying unbalanced induction motor by only using stator current signals. The signal analysis was done by using wavelet packet decomposition and reconstruction (WPDR) and information entropy methods. The validation of proposed method was carried out by comparing unbalanced fault progressive simulation and experimentally obtained results. The experimental results were also analysed to identify the most band of frequencies (node) for the proposed method. Signals were divided into five overlapped time intervals in order to investigate which interval is the most informative for fault diagnosis.