A Multiresolution Taylor-Kalman Approach for Broken Rotor Bar Detection in Cage Induction Motors

作者：Alonso Trujillo Guajardo Luis; Rodriguez Maldonado Johnny; Moonem M A; Angel Platas Garza Miguel

来源：IEEE Transactions on Instrumentation and Measurement, 2018, 67(6): 1317-1328.

DOI：10.1109/TIM.2018.2795895

• Broken rotor bar (BRB) fault signature
• induction motors
• Kalman filter (KF)
• motor current signature analysis (MCSA)
• polynomial fit
• Taylor-Kalman (TK) filter

Broken rotor bar (BRB) fault is common in cage rotor induction motors. Motor current signature analysis (MCSA) has been a popular method to detect BRB faults. In the MCSA, the fault signature frequencies for BRB are close to the fundamental frequency. This spectral nearness leads to the requirement of larger observation windows, which inherently increases BRB fault detection time. This paper presents an alternative algorithm to detect BRB faults in induction motors from MCSA using two Taylor-Kalman (TK) filters in cascade with a subsampling scheme. The proposed BRB fault detection approach allows to use the TK filter to estimate lower frequencies with less computational burden in comparison to conventional TK analysis. Experimental analysis shows that nearly accurate estimates and competitive detection time can be achieved by the proposed BRB detection method. The performance of the proposed algorithm has been compared with classical spectral techniques using numerical simulations and records acquired from induction motors under real operating conditions.

• 出版日期2018-6