Mechanical fault detection in permanent magnet synchronous motors using equal width discretization-based probability distribution and a neural network model

Akar, Mehmet; Hekim, Mahmut; Orhan, Umut

doi:10.3906/elk-1210-58

Cited by 19 publications

(11 citation statements)

References 21 publications

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“…Only a portion of the selected papers ( [8,9,[11][12][13][14][15][16][17][18]) clearly show statistics on the fault detection rates and mostly are based on the use of AI.…”

Section: Best Detection Resultsmentioning

confidence: 99%

Failure Detection by Signal Similarity Measurement of Brushless DC Motors

Fico¹,

Vázquez²,

Prats

et al. 2019

Energies

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In recent years, Brushless DC (BLDC) motors have been gaining popularity as a solution for providing mechanical power, starting from low cost mobility solutions like the electric bikes, to high performance and high reliability aeronautical Electro-Mechanical Actuator (EMA). In this framework, the availability of fault detection tools suited to these types of machines appears necessary. There is already a vast literature on this topic, but only a small percentage of the proposed techniques have been developed to a sufficiently high Technology Readiness Level (TRL) to be implementable in industrial applications. The investigation on the state of the art carried out during the first phase of the present work, tried to collect the techniques which are closest to possible implementation. To fill a gap identified in the current techniques, a partial demagnetisation detection method is proposed in this paper. This technique takes advantage of the asymmetries generated in the current by the missing magnetic flux to detect the failure. Simulations and laboratory experiments have been carried out to validate the idea, showing the potential and the easy implementation of the method. The results have been examined in detail and satisfactory conclusions have been drawn.

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“…Only a portion of the selected papers ( [8,9,[11][12][13][14][15][16][17][18]) clearly show statistics on the fault detection rates and mostly are based on the use of AI.…”

Section: Best Detection Resultsmentioning

confidence: 99%

Failure Detection by Signal Similarity Measurement of Brushless DC Motors

Fico¹,

Vázquez²,

Prats

et al. 2019

Energies

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“…Akar et al [15] carried out static eccentricity and bearing fault detection for PMSMs. In their study, they used the equal width discretization-based probability distributions of the data set obtained from the PMSM as well as the multilayered artificial neural networks method.…”

Section: Eccentricity Faults In Pmsmsmentioning

confidence: 99%

Eccentricity fault diagnosis in a permanent magnet synchronous motor under nonstationary speed conditions

Eker¹,

Akar²

2017

Turk J Elec Eng & Comp Sci

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Electric motor faults decrease production capacity and increase maintenance costs. Today, predictive detection based on real-time monitoring and fault detection is taking the place of periodic applications. In this study, a new method is presented for the detection of eccentricity faults in permanent magnet synchronous motors. The motor current and the rotor speed were monitored under stationary and nonstationary speed and different load conditions, after which the fault detection was carried out via angular domain-order tracking method. The obtained results were compared with traditional fast Fourier transform results. It was observed that the suggested method can successfully detect the fault for stationary and nonstationary signals. In addition, the position of the fault-related components are constant independent of the motor current frequency, speed, and load levels.

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“…where k = 4Rr z 2 + (R + r) 2 K and E -complete elliptic integrals of 1 st and 2 nd kind, respectively.…”

Section: Estimation Of the Air Coil Inductancementioning

confidence: 99%

“…Simultaneously, synchronous motors like all rotating machines are prone to damage, with permanent magnet machines being especially vulnerable. Permanent magnet motor damage can be broken down into: electric damage -associated with failures in field windings and field connections, mechanical damage of bearings, mechanical damage of magnets and the rotor itself and damage to motor control devices [1][2][3][4][5]. Apart from the listed problems, there is a possibility of electromagnetic and magneto-mechanic damage [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A way of neodymium-iron-boron magnets regeneration in surface-mounted PMSM used in electric vehicles

Łebkowski¹

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. The paper describes an efficient method of magnetization of permanent magnets, mounted on synchronous and BLDC motor rotors, which employs an air coil. A developed mathematical model of an electromagnetic circuit is presented, which was simulated in an ANSYS-MAX-WELL environment. The performed simulations and experimental tests allowed optimization of the physical process of magnetization of permanent magnets mounted on an electrical machine rotor. The adopted method allowed achieving more favourable conditions of magnetization -less financial expenditure allowed to achieve the same results.

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Mechanical fault detection in permanent magnet synchronous motors using equal width discretization-based probability distribution and a neural network model

Cited by 19 publications

References 21 publications

Failure Detection by Signal Similarity Measurement of Brushless DC Motors

Failure Detection by Signal Similarity Measurement of Brushless DC Motors

Eccentricity fault diagnosis in a permanent magnet synchronous motor under nonstationary speed conditions

A way of neodymium-iron-boron magnets regeneration in surface-mounted PMSM used in electric vehicles

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