Electromagnetic flux-based condition monitoring for electrical machines

Negrea, Marian; Jover, P.; Arkkio, Antero

doi:10.1109/demped.2005.4662506

Cited by 30 publications

(40 citation statements)

References 8 publications

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“…For the medium and great power machines, the core is compressed before the steel sheets emplacement to minimize the rolling sheets vibrations and to maximize the thermal conduction. The core problems are very low, only 1% if compared to winding problems [4].…”

Section: Performances Of the Pca Methods In Electrical Machines Diagnomentioning

confidence: 96%

“…Lots of technologies of these machines are old and well controlled. However, they remain the seat of several electrical and mechanical faults [1][2][3][4]. Thus, this article deals with faults detection of a wound rotor synchronous machine (WRIM) by the principal component analysis (PCA) method.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, this article deals with faults detection of a wound rotor synchronous machine (WRIM) by the principal component analysis (PCA) method. [1][2][3][4]. The PCA method, which showed his effectiveness in the fault detection and isolation (FDI), was implemented recently for the system diagnosis [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performances of the PCA Method in Electrical Machines Diagnosis Using Matlab

Ramahaleomiarantsoa¹,

Sambatra²,

Héraud³

et al. 2012

MATLAB - A Fundamental Tool for Scientific Computing and Engineering Applications - Volume 1

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Section: Performances Of the Pca Methods In Electrical Machines Diagnomentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performances of the PCA Method in Electrical Machines Diagnosis Using Matlab

Ramahaleomiarantsoa¹,

Sambatra²,

Héraud³

et al. 2012

MATLAB - A Fundamental Tool for Scientific Computing and Engineering Applications - Volume 1

View full text Add to dashboard Cite

“…More specifically, by analyzing these flux signals in frequency domain, different kinds of defects can be found. [15], [16] show results for radial magnetic field with a search coil positioned in the vicinity of an induction machine. In recent years, researchers develop new magnetic flux detection devices to increase sensitivity and accuracy of condition monitoring systems.…”

Section: B Stray Flux Detection Technologiesmentioning

confidence: 99%

Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors

Liu

Cao

Huang

et al. 2016

2016 IEEE Power and Energy Society General Meeting (PESGM)

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Abstract-Non-intrusive monitoring of health state of induction machines within industrial process and harsh environments poses a technical challenge. In the field, winding failures are a major fault accounting for over 45% of total machine failures. In the literature, many condition monitoring techniques based on different failure mechanisms and fault indicators have been developed where the machine current signature analysis (MCSA) is a very popular and effective method at this stage. However, it is extremely difficult to distinguish different types of failures and hard to obtain local information if a non-intrusive method is adopted. Typically, some sensors need to be installed inside the machines for collecting key information, which leads to disruption to the machine operation and additional costs. This paper presents a new non-invasive monitoring method based on GMRs to measure stray flux leaked from the machines. It is focused on the influence of potential winding failures on the stray magnetic flux in induction machines. Finite element analysis and experimental tests on a 1.5-kW machine are presented to validate the proposed method. With time-frequency spectrogram analysis, it is proven to be effective to detect several winding faults by referencing stray flux information. The novelty lies in the implement of GMR sensing and analysis of machine faults.

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“…3). If the number of broken bars and the number of motor poles increase, the dissymmetry becomes higher [33][34][35][36][37][38][39].…”

Section: Lines Of the Near-magnetic Fieldmentioning

confidence: 99%

Finite Element Analysis and Experimental Study of the Near-Magnetic Field for Detection of Rotor Faults in Induction Motors

Pusca¹,

Romary²,

Fireteanu³

et al. 2013

PIER B

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Abstract-This paper deals with the finite element analysis and experimental study concerning the influence of the broken bar and rotor dynamic eccentricity faults on the magnetic field outside a squirrel-cage induction motor. The spatial distribution of the magnetic field, the time variation of the magnetic flux density at a point outside the machine and the time variation of the electromotive force delivered by a coil sensor are evaluated based on the finite element models of the healthy and faulty states of the motor. The increase of amplitude from the healthy to the faulty states of some low frequency harmonics measured in the near-magnetic field is emphasized. For broken bar fault, the increase of the amplitudes of specific harmonics of the coil sensor electromotive force, with frequency lower than 25 Hz, is experimentally confirmed.

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Electromagnetic flux-based condition monitoring for electrical machines

Cited by 30 publications

References 8 publications

Performances of the PCA Method in Electrical Machines Diagnosis Using Matlab

Performances of the PCA Method in Electrical Machines Diagnosis Using Matlab

Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors

Finite Element Analysis and Experimental Study of the Near-Magnetic Field for Detection of Rotor Faults in Induction Motors

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