Field winding fault diagnosis in DC motors during manufacturing using thermal monitoring

Canteli, Mario Mañana; Arroyo, Alberto; Ortiz, Alfredo; Estébanez, Carlos Javier Renedo; Pérez, S.; Delgado, F.

doi:10.1016/j.applthermaleng.2010.11.023

Cited by 45 publications

(15 citation statements)

References 16 publications

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“…[3] In the automotive industry the reason why there is a growing need for the development of the procedures for fault detection and diagnosis, in order to increase reliability and the availability of the electric motors part's of the automobile with the purpose of reducing the maintenance costs for the car. [4], [5] Also, an early detection and diagnosis can help avoid car's system breakdown and material damage, [6], [7] II. VIBRATION AND CURRENT ELECTRICAL EQUIPMENT DIAGNOSTICS Basically, vibration is oscillating motion of a particle or body about a fixed reference point.…”

Section: Introductionmentioning

confidence: 99%

Study of DC motor diagnosis based on the vibration spectrum and current analysis

Iorgulescu

Beloiu

2012

2012 International Conference on Applied and Theoretical Electricity (ICATE)

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In this paper fault detection of permanent magnet dc motor is described. In past, Current Signature Analysis (CSA) has become an established tool for online fault analysis of AC induction motors. Presently, very little research has been performed using current signature analysis on DC motors. This paper is a brief introduction to fault diagnosis of DC motors using vibration and current signature analysis. In order to do the diagnostics there are analyzed vibration from the piezoelectric accelerometer. The accelerometer is mounted on the frame's motor. Also, for the current measurement it's used ampere sensor. Initially the measurements were realized by using "healthy" DC motor. Then we made successive measurements for the same type of motor with provoked faults. The many faults of DC motors are regarding collectors motor, the commutator short circuit and displaced permanent magnet out of poles in polar axe. We observe significant vibration and also current spectrum differences between "healthy" motors and faulty electrical motor. The spectral analysis of current and vibration provides a method to detect DC motors faults. Using these methods, diagnostic of commutator short circuit and displaced permanent magnet out of poles in polar axe, would be detected even if the motor operated unload.

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Section: Introductionmentioning

confidence: 99%

Study of DC motor diagnosis based on the vibration spectrum and current analysis

Iorgulescu

Beloiu

2012

2012 International Conference on Applied and Theoretical Electricity (ICATE)

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“…The use of infrared thermography in the area of condition monitoring of electric motors is much more limited. In D.C. machines, infrared thermography is a useful tool to detect possible defects in the commutator as well as in the brushes system [13], which are weak points from a maintenance point of view. Furthermore, some works propose the technique for detecting faults in the field winding of such machines [13].…”

Section: Introductionmentioning

confidence: 99%

“…In D.C. machines, infrared thermography is a useful tool to detect possible defects in the commutator as well as in the brushes system [13], which are weak points from a maintenance point of view. Furthermore, some works propose the technique for detecting faults in the field winding of such machines [13]. In wound rotor induction machines and in synchronous motors and generators, infrared data analysis can provide very interesting information about the condition of the slip ringsbrushes system, indicating possible asymmetries or defective contacts [14].…”

Section: Introductionmentioning

confidence: 99%

Application of Infrared Thermography to Failure Detection in Industrial Induction Motors: Case Stories

López-Pérez¹,

Antonino-Daviu

2017

IEEE Trans. on Ind. Applicat.

142

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“…Recently DC motor faults can be found out by model based analysis with the help of fuzzy logic [1&2]. Before that various methods such as observer based fault identification by current monitoring [3], fault identification by analyzing various parameters of DC motor with the help of fuzzy logic and neural network [4] and fault identification by thermal monitoring [5] are reported in the literature. Soft computing techniques for classification of faults are reported in [6].…”

Section: Introductionmentioning

confidence: 99%

Single Parameter Fault Identification Technique for DC Motor through Wavelet Analysis and Fuzzy Logic

Winston¹,

Saravanan²

2013

Journal of Electrical Engineering and Technology

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-DC motors are widely used in industries like cement, paper manufacturing, etc., even today. Early fault identification in dc motors significantly improves its life time and reduces power consumption. Many conventional and soft computing techniques for fault identification in DC motors including a recent work using model based analysis with the help of fuzzy logic are available in literature. In this paper fuzzy logic and norm based wavelet analysis of startup transient current are proposed to identify and quantify the armature winding fault and bearing fault in DC motors, respectively. Results obtained by simulation using Matlab and Simulink are presented in this paper to validate the proposed work.

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Field winding fault diagnosis in DC motors during manufacturing using thermal monitoring

Cited by 45 publications

References 16 publications

Study of DC motor diagnosis based on the vibration spectrum and current analysis

Study of DC motor diagnosis based on the vibration spectrum and current analysis

Application of Infrared Thermography to Failure Detection in Industrial Induction Motors: Case Stories

Single Parameter Fault Identification Technique for DC Motor through Wavelet Analysis and Fuzzy Logic

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