Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines

作者：

Highlights：

• We perform broken rotor bar fault detection and diagnosis in asynchronous machines.

• PCA is applied for the extraction of the faulty component during start-up.

• HMMs are applied for the classification of three conditions.

• An HMM is trained for anomaly detection.

• The proposed schemes have been experimentally validated.

摘要

•We perform broken rotor bar fault detection and diagnosis in asynchronous machines.•PCA is applied for the extraction of the faulty component during start-up.•HMMs are applied for the classification of three conditions.•An HMM is trained for anomaly detection.•The proposed schemes have been experimentally validated.

论文关键词：Broken rotor bar fault diagnosis,Principal Component Analysis,Hidden Markov Modeling

论文评审过程：Available online 21 June 2013.

论文官网地址：https://doi.org/10.1016/j.eswa.2013.06.006