计算机科学
断层(地质)
降级(电信)
状态监测
故障检测与隔离
模式识别(心理学)
作者
Salim Aouabdi,Mahmoud Taibi,Slimane Bouras,Nadir Boutasseta
标识
DOI:10.1016/j.ymssp.2016.12.027
摘要
Abstract This paper describes an approach for identifying localized gear tooth defects, such as pitting, using phase currents measured from an induction machine driving the gearbox. A new tool of anomaly detection based on multi-scale entropy (MSE) algorithm SampEn which allows correlations in signals to be identified over multiple time scales. The motor current signature analysis (MCSA) in conjunction with principal component analysis (PCA) and the comparison of observed values with those predicted from a model built using nominally healthy data. The Simulation results show that the proposed method is able to detect gear tooth pitting in current signals.
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