Modeling and detecting the stator winding inter turn fault of permanent magnet synchronous motors using stator current signature analysis

Yassa, Nacera; Rachek, M.

doi:10.1016/j.matcom.2018.04.012

Cited by 31 publications

(14 citation statements)

References 16 publications

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“…The three‐phase model of the PMSM has been simulated with respect to the relationships that have been mentioned in the previous sections about this motor. Initially, we short circuit 30% and 40% of the phase “ a ” and then, we design the motor model and selected the parameters according to Reference 13 , 15 , 28 , and 31 and tested them, under normal and faulty conditions, according to the parameters in Table 1. Then, the data are extracted for hybrid intelligence training by performing simulation in the fault percentage.…”

Section: Simulation Of Faulty Winding Using Hybrid Intelligence Algormentioning

confidence: 99%

“…According to the articles in the References 13,15,28, and 31 and mentioned below, a test can be answered and validated in a simulation situation with Matlab R2016a and with windows 10‐64 bit. Our citations to prove our techniques are the following articles and PMSM's parameters are shown as below:…”

Section: Peer Reviewmentioning

confidence: 99%

“…Additionally, which will model the fault, the dynamics of the device parameters should be investigated and formulated. Model‐primarily based fault detection techniques have proposed the usage of current, voltage, axial current, evaluation of d ‐ q axes, and motor current signature analysis (MCSA) 12,13 …”

Section: Introductionmentioning

confidence: 99%

“…Inter‐turn fault detection studies have led to the development of a selection of strategies. Stator stream analysis is utilized in Reference 28. Moon et al 29 compared the outcomes of the residual voltage and contemporary evaluation approach and finite element analysis (FEA).…”

Section: Introductionmentioning

confidence: 99%

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ANFIS model based on fuzzy C‐mean , grid partitioning and subtractive clustering to detection of stator winding inter‐turn fault for PM synchronous motor

Mola

Amiri‐Ahouee

2021

Int Trans Electr Energ Syst

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Summary In this paper, one‐of‐a‐kind hybrid intelligence models based on the adaptive neuro‐fuzzy inference system (ANFIS) the use of C‐mean fuzzy clustering (FCM), grid partitioning (GP), and subtractive clustering (SC) fashions are used. Three extraordinary gaining knowledge of algorithms that were incorporated with the ANFIS version are used to locate fault prevalence within the permanent magnet synchronous motor (PMSM). Due to the fact it may both stumble on any inconvenience with any force and is flexible enough, and it can be used in offline and online identification of this method. In the beginning, the dynamic model of the PMSM along with its positive fault could be delivered. On the grounds that fault detection in these engines could be very critical, one‐of‐a‐kind strategies have been proposed for detecting stator deflection in electric machines. On the way to determine the fault percentage in the PMSM, hybrid intelligence fashions are used to identify the fault. The advantages of the proposed algorithm are the potential to stumble on faults with one‐of‐a‐kind domain names. The inputs of the proposed set of rules are PMSM current and torque alerts in normal and defective conditions. Inside the proposed set of rules, the club function structure turned into created with the FCM, SC, and GP methods. The outcomes show that the proposed approach can follow the fault in a brief time.

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Section: Simulation Of Faulty Winding Using Hybrid Intelligence Algormentioning

confidence: 99%

Section: Peer Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ANFIS model based on fuzzy C‐mean , grid partitioning and subtractive clustering to detection of stator winding inter‐turn fault for PM synchronous motor

Mola

Amiri‐Ahouee

2021

Int Trans Electr Energ Syst

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“…The regularly used signals are vibration signals, 13 acoustic emission (AE) signals, 14 or electrical motor currents. 15…”

Section: Fault Diagnosis In Hydraulic Equipmentmentioning

confidence: 99%

Fault diagnosis of various rotating equipment using machine learning approaches – A review

Manikandan

Duraivelu

2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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Fault diagnosis of various rotating equipment plays a significant role in industries as it guarantees safety, reliability and prevents breakdown and loss of any source of energy. Early identification is a fundamental aspect for diagnosing the faults which saves both time and costs and in fact it avoids perilous conditions. Investigations are being carried out for intelligent fault diagnosis using machine learning approaches. This article analyses various machine learning approaches used for fault diagnosis of rotating equipment. In addition to this, a detailed study of different machine learning strategies which are incorporated on various rotating equipment in the context of fault diagnosis is also carried out. Mainly, the benefits and advance patterns of deep neural network which are applied to multiple components for fault diagnosis are inspected in this study. Finally, different algorithms are proposed to propagate the quality of fault diagnosis and the conceivable research ideas of applying machine learning approaches on various rotating equipment are condensed in this article.

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A Review of Fault Features for Inter-turn Short Circuit Fault of PM Motor

Shen

Zhang

Han

2020

Lecture Notes in Electrical Engineering

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Modeling and detecting the stator winding inter turn fault of permanent magnet synchronous motors using stator current signature analysis

Cited by 31 publications

References 16 publications

ANFIS model based on fuzzy C‐mean , grid partitioning and subtractive clustering to detection of stator winding inter‐turn fault for PM synchronous motor

ANFIS model based on fuzzy C‐mean , grid partitioning and subtractive clustering to detection of stator winding inter‐turn fault for PM synchronous motor

Fault diagnosis of various rotating equipment using machine learning approaches – A review

A Review of Fault Features for Inter-turn Short Circuit Fault of PM Motor

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