A novel approach to detect short circuits in low voltage induction motor by stray flux measurement

Frosini, Lucia; Borin, Alessandro; Girometta, Luca; Venchi, G.

doi:10.1109/icelmach.2012.6350083

Cited by 70 publications

(27 citation statements)

References 24 publications

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“…The method pursues the following subsequent steps [38]:  each integer multiple of the fundamental, between 100 and 1000 Hz, is normalized with respect to the 50 Hz fundamental;  for every integer harmonic component taken into account a mean value of ten acquisitions is computed, for both the healthy and the faulty condition of the machine (m h , m f , respectively);  the difference of the mean values mentioned above (d = m h -m f ) is calculated;  in order to evaluate the diagnostic content of the processed data, the absolute value of the difference (Δ=|d|) is compared with the standard deviation (S) of the harmonics in the case of the healthy motor.…”

Section: The Proposed Fault Detection Methodsmentioning

confidence: 99%

Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Frosini

Harlişca

Szabó

2015

IEEE Trans. Ind. Electron.

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230

124

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Rolling bearing faults are generally slowly progressive; therefore the development of an effective diagnostic technique could be worth to detect such faults in their incipient phase and to prevent complete failure of the motor. The methods proposed in the literature for this purpose are mainly based on measuring and analyzing the vibration and current. Here, a novel technique based on the stray flux measurement in different positions around the electrical machine is proposed. The main advantages of this method are due to the simplicity and the flexibility of the custom flux probe with its amplification and filtering stage. The flux probe can be easily positioned on the machines and adapted to a wide range of power levels. The paper also reports an extensive survey on the stray flux based fault detection methods for induction motors, prior to introducing a novel sensor/diagnostic scheme.

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Section: The Proposed Fault Detection Methodsmentioning

confidence: 99%

Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Frosini

Harlişca

Szabó

2015

IEEE Trans. Ind. Electron.

Self Cite

230

124

View full text Add to dashboard Cite

show abstract

“…Nevertheless, as stated [29], other magnitudes, such as stray flux [19], [35], [36], have specific advantages that make them attractive for certain applications.…”

Section: -Inter-turn Short-circuit Fault Diagnosis Via Analysis Of Stmentioning

confidence: 99%

“…Other short-circuit FD methods based on measurement of the stray flux can be found in [36], where the authors use different flux probes placed at different positions around the machine. The FD is based on the analysis of the harmonics multiple of the main frequency through statistical evaluation.…”

Section: -Inter-turn Short-circuit Fault Diagnosis Via Analysis Of Stmentioning

confidence: 99%

Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

Riera-Guasp

Antonino-Daviu

Capolino

2015

IEEE Trans. Ind. Electron.

467

234

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“…In recent years, researchers develop new magnetic flux detection devices to increase sensitivity and accuracy of condition monitoring systems. A "C-shape" flux probe is developed in [7], which can be installed for different machine sizes. Nonetheless, these search coils are based on Faraday's Law of Induction and can not measure static magnetic field [5].…”

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.

show abstract

A novel approach to detect short circuits in low voltage induction motor by stray flux measurement

Cited by 70 publications

References 24 publications

Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

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

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