This research addresses the diagnosis of broken rotor bar faults in three-phase induction motors,focusing on steady-state conditions under different load levels and fault severity.Although numerous techniques exist,th...This research addresses the diagnosis of broken rotor bar faults in three-phase induction motors,focusing on steady-state conditions under different load levels and fault severity.Although numerous techniques exist,there is still a significant gap in comprehensive comparative evaluations that rigorously assess the interaction between signal processing,feature selection,and pattern classifiers,particularly concerning their robustness to noise and multiple performance criteria.An experimental investigation was carried out with electrical current and mechanical vibration signals,several signal preprocessing techniques,two feature selection strategies,Correlation-Based Feature Selection(CFS)and Wrapper,and a wide range of pattern classifiers,Decision Tree(DT),Naive Bayes(NB),Artificial Neural Network(ANN),and Support Vector Machine(SVM).The performance of the configurations was quantified by a multicriteria indicator,complemented by a dedicated robustness assessment by introducing white noise into the input signals.The most significant results reveal that vibration signals exhibit superior diagnostic robustness compared to electrical current signals,especially under noisy conditions.Furthermore,Wrapper-based feature selection consistently outperforms CFS,and configurations combining Wrapper with DT or NB classifiers emerge as the most suitable for detecting and diagnosing broken bars.Furthermore,the Wrapper-DT configuration efficiently classified defects even with the inclusion of 40%noise.This work provides data-driven insights into robust configurations for broken bar diagnosis,guiding the development of more reliable predictive maintenance systems,emphasizing signal modality,robust feature selection,and real-time applications.展开更多
文摘This research addresses the diagnosis of broken rotor bar faults in three-phase induction motors,focusing on steady-state conditions under different load levels and fault severity.Although numerous techniques exist,there is still a significant gap in comprehensive comparative evaluations that rigorously assess the interaction between signal processing,feature selection,and pattern classifiers,particularly concerning their robustness to noise and multiple performance criteria.An experimental investigation was carried out with electrical current and mechanical vibration signals,several signal preprocessing techniques,two feature selection strategies,Correlation-Based Feature Selection(CFS)and Wrapper,and a wide range of pattern classifiers,Decision Tree(DT),Naive Bayes(NB),Artificial Neural Network(ANN),and Support Vector Machine(SVM).The performance of the configurations was quantified by a multicriteria indicator,complemented by a dedicated robustness assessment by introducing white noise into the input signals.The most significant results reveal that vibration signals exhibit superior diagnostic robustness compared to electrical current signals,especially under noisy conditions.Furthermore,Wrapper-based feature selection consistently outperforms CFS,and configurations combining Wrapper with DT or NB classifiers emerge as the most suitable for detecting and diagnosing broken bars.Furthermore,the Wrapper-DT configuration efficiently classified defects even with the inclusion of 40%noise.This work provides data-driven insights into robust configurations for broken bar diagnosis,guiding the development of more reliable predictive maintenance systems,emphasizing signal modality,robust feature selection,and real-time applications.
