Induction Machine Faults Detection based on a Constant False Alarm Rate Detector

被引：0
作者：
Trachi, Youness [1 ]
Elbouchikhi, Elhoussin [2 ]
Choqueuse, Vincent [1 ]
Wang, Tianzhen [3 ]
Benbouzid, Mohamed [1 ,3 ]
机构：
[1] Univ Brest, FRE 3744, CNRS, IRDL, Brest, France
[2] ISEN Brest, FRE 3744, CNRS, IRDL, Brest, France
[3] Shanghai Maritime Univ, Shanghai 201306, Peoples R China
来源：
PROCEEDINGS OF THE IECON 2016 - 42ND ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2016年
基金：
上海市自然科学基金;
关键词：
Condition monitoring; induction machine; bearing faults; broken rotor bars; MLE; subspace techniques; TLS-ESPRIT; CFAR detector; GLRT criterion; MOTOR; DIAGNOSIS; TRANSFORM; SYSTEM;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper presents a novel approach for induction machine condition monitoring using stator current measurements. The proposed method, based on hypothesis testing, specifically investigates a binary detection problem: the machine is healthy or faulty. The Generalized Likelihood Ratio Test (GLRT) is used to address this statistical detection problem with unknown signal and noise parameters. It is indeed a Constant False Alarm Rate (CFAR) detector. Decision is obtained according to a threshold, which is set to reach a desired false alarm probability. The proposed detector implementation needs estimations that are based on the Maximum Likelihood Estimator (MLE). In particular, Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Techniques (TLS-ESPRIT) estimates frequencies. The proposed CFAR detector is tested on experimental data of bearings faults and broken rotor bars that clearly show it effectiveness.
引用
收藏
页码：6359 / 6363
页数：5
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