Causes and failures classification of industrial electric motor

Bazurto, Alvaro Jose; Quispe, Enrique C.; Mendoza, Rosaura Castrillon

doi:10.1109/andescon.2016.7836190

Cited by 48 publications

(23 citation statements)

References 10 publications

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“…The study of failures in electric motors is an interest subject to the academic and industrial community, the trend research focuses on approaches, which are used to measure and identify faults by on-line or off-line tests. Among these researchers some authors have classified and grouped motor failures by the nature of the failure (electrical, mechanical), fault location (stator, rotor) [16,17]. It is important to define the term of failure in an electrical machine.…”

Section: Motor Faultsmentioning

confidence: 99%

“…The defects connected with the bars has its origin mainly on thermal stress or fatigue product operating transients such as start-ups, this problem is usually related to high power motors. The second case is frequent mechanical problems such as misalignment of the motor or load variation [16,20,21].…”

Section: Motor Faultsmentioning

confidence: 99%

“…It can be static, dynamic or mixed in nature [9]. Some reasons of this fault can be; bent shafts, wear on the bearings, excessive mechanical vibrations or resonances [16,22,23].…”

Section: Eccentricitymentioning

confidence: 99%

“…According to many publications [9,16,20], 35-40 % of induction motor failures are result of stator winding insulation. Additionally, it is understood that a large portion of stator winding-related failures are initiated by insulation failures.…”

Section: Short Turnmentioning

confidence: 99%

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Condition monitoring of Electrical Submersible Pumps (Faults review)

Al-Hashmi¹

2021

JOPAS

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This paper is a part of condition monitoring of electrical submersible pumps (ESP). It assigned for reviewing and discussing faults or malfunctions of this kind of pumps. Therefore, it reviews all-important information related to this machine such as construction and operation. It focuses specifically on faults and details of these types of fault. Although there are slight differences between building submersible pumps in terms of the different technologies that different manufacturers use in making this type of pump, all kinds of these types of pumps are almost the same. In this context, pumps faults were reviewed based on major and common failures of this type of pumps.

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Section: Motor Faultsmentioning

confidence: 99%

Section: Motor Faultsmentioning

confidence: 99%

“…It can be static, dynamic or mixed in nature [9]. Some reasons of this fault can be; bent shafts, wear on the bearings, excessive mechanical vibrations or resonances [16,22,23].…”

Section: Eccentricitymentioning

confidence: 99%

Section: Short Turnmentioning

confidence: 99%

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Condition monitoring of Electrical Submersible Pumps (Faults review)

Al-Hashmi¹

2021

JOPAS

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“…Therefore, this study will discuss the more common types of faults in induction motors. The measurement includes four conditions: healthy, bearing damage (45%), stator (35%), and rotor (10%) [5], [6]. The signal usually used for measurement is mainly the current signal, that is, the electric signal.…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Fault Classification Based on ROC Curve and t-SNE

Lee

Lin

2021

IEEE Access

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This paper proposes a novel fault classification method with application to induction motors, which is based on integrating and combining with receiver operating characteristic (ROC) curve and tdistribution stochastic neighbor embedding (t-SNE). According to the feature selection methods of ReliefF, symmetrical uncertainty (SU), and fast correlation-based filter (FCBF), the significant features were verified. Additionally, support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT) are also considered as classifiers to identify the simulation results. To begin with, the current signals obtained from distinctive four topologies of working conditions of the motor, which includes healthy, bearing damage, broken rotor bar, and short circuit in stator windings, respectively. The potential feature set is extracted by using Hilbert-Huang transform (HHT) technique. Then, three feature selection methods are adopted to select three optimal feature subsets from the original feature set. Finally, the classification accuracy (ACC) and ROC curve are used to demonstrate the capability of classifiers' recognition. The results showed that the optimal feature subsets significantly reduce the number of selected features and improve the classification ACC and area under the curve (AUC) compared with the original feature set. In conclusion, the proposed method can downgrade the data, demonstrate the scatter plot more intuitively, and identify various types of faults, unlike with other fault diagnosis literature.

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