Automatic Pattern Identification Based on the Complex Empirical Mode Decomposition of the Startup Current for the Diagnosis of Rotor Asymmetries in Asynchronous Machines

作者：Georgoulas George^*; Tsoumas Ioannis P; Antonino Daviu Jose Alfonso; Climente Alarcon Vicente; Stylios Chrysostomos D; Mitronikas Epaminondas D; Safacas Athanasios N

来源：IEEE Transactions on Industrial Electronics, 2014, 61(9): 4937-4946.

DOI：10.1109/TIE.2013.2284143

• Asynchronous rotating machines
• broken rotor bar detection
• complex empirical mode decomposition (EMD)
• hidden Markov models (HMMs)
• pattern recognition

This paper presents an advanced signal processing method applied to the diagnosis of rotor asymmetries in asynchronous machines. The approach is based on the application of complex empirical mode decomposition to the measured start-up current and on the subsequent extraction of a specific complex intrinsic mode function. Unlike other approaches, the method includes a pattern recognition stage that makes possible the automatic identification of the signature caused by the fault. This automatic detection is achieved by using a reliable methodology based on hidden Markov models. Both experimental data and a hybrid simulation-experimental approach demonstrate the effectiveness of the proposed methodology.

• 出版日期2014-9