Magnetic Flux Analysis for the Condition Monitoring of Electric Machines: A Review

Zamudio-Ramírez, Israel; Osornio-Ríos, Roque Alfredo; Antonino-Daviu, José A.; Razik, Hubert; Romero-Troncoso, René de Jesús

doi:10.1109/tii.2021.3070581

Cited by 60 publications

(33 citation statements)

References 105 publications

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“…Particularly, aspects related to the main sensors used to acquire magnetic flux signals as well as the leading signal processing and classification techniques are commented. The discussion is focused on the diagnosis of different types of faults in the most common rotating electric machines used in industry, namely: squirrel cage induction machines (SCIM), wound rotor induction machines (WRIM), permanent magnet machines (PMM) and wound field synchronous machines (WFSM) [2] Performance of electric motors and losses in terms of heat and temperature are reviewed in this paper. Airgap eccentricity, electromagnetic performance, effect of temperature and losses are shown as factors affecting the efficiency.…”

Section: Condition-based Maintenance In Electric Machinesmentioning

confidence: 99%

Industry 4.0 in maintenance: Using condition monitoring in electric machines

Domingues¹

2021

2021 International Conference on Decision Aid Sciences and Application (DASA)

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Industry traditionally considered maintenance as a cost and a necessity to replace equipment and machines, but the path has changed to better focus on maintenance to prevent faults and it was designated as predictive. The ones motivated to take these advantages are faced with two of the biggest barriers: the investment it requires and the difficulty to develop algorithms. The costs of installation are still high, but the avoided costs surpass it. Also, Internet of Things (IoT) has brought a big shift, which is been known as the industry 4.0. One of the potentials in maintenance is the conditioning monitoring. Condition monitoring sensors and devices are now linked to maintenance platforms, providing real-time data. This new connectivity is both more affordable and easier to implement than predictive maintenance. Real-time data allows managers to adjust preventive maintenance plans while providing greater reliability. At the same time, artificial intelligence manages this data to recognize patterns, which is one of the most promising advances in digital reliability. So, regardless of the ability to immediately implement a preventive maintenance plan, condition monitoring is an asset itself.The present paper presents the common faults on electric machines, their effects, their impact on the industry and the main techniques on condition control to prevent them. It is also added the reflection on the use of IoT to enhance the potential of condition control maintenance.The implementation of continuous improvement actions throughout the life of the equipment allows to increase efficiency, either by overcoming weaknesses or by adapting production or operational capacities to processes, production or maintenance, avoiding under maintenance or over maintenance and minimizing operating costs.

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Section: Condition-based Maintenance In Electric Machinesmentioning

confidence: 99%

Industry 4.0 in maintenance: Using condition monitoring in electric machines

Domingues¹

2021

2021 International Conference on Decision Aid Sciences and Application (DASA)

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“…Induction machines for example, are currently one of the most widely used electric machines globally, because of their numerous advantages, including ruggedness, minimal maintenance, compactness, and efficiency. Although there are various techniques, with each having its own merits and limitations [1], given the great usage of these machines globally, there will never be a superfluous number of condition monitoring and fault detection techniques. Rather, a blend of techniques that are application dependent is being realized.…”

Section: Introductionmentioning

confidence: 99%

Short Circuit and Broken Rotor Faults Severity Discrimination in Induction Machines Using Non-invasive Optical Fiber Technology

Alalibo

Cao

2022

Energies

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Multiple techniques continue to be simultaneously utilized in the condition monitoring and fault detection of electric machines, as there is still no single technique that provides an all-round solution to fault finding in these machines. Having various machine fault-detection techniques is useful in allowing the ability to combine two or more in a manner that will provide a more comprehensive application-dependent condition-monitoring solution; especially, given the increasing role these machines are expected to play in man’s transition to a more sustainable environment, where many more electric machines will be required. This paper presents a novel non-invasive optical fiber using a stray flux technique for the condition monitoring and fault detection of induction machines. A giant magnetostrictive transducer, made of terfenol-D, was bonded onto a fiber Bragg grating, to form a composite FBG-T sensor, which utilizes the machines’ stray flux to determine the internal condition of the machine. Three machine conditions were investigated: healthy, broken rotor, and short circuit inter-turn fault. A tri-axial auto-data-logging flux meter was used to obtain stray magnetic flux measurements, and the numerical results obtained with LabView were analyzed in MATLAB. The optimal positioning and sensitivity of the FBG-T sensor were found to be transverse and 19.3810 pm/μT, respectively. The experimental results showed that the FBG-T sensor accurately distinguished each of the three machine conditions using a different order of magnitude of Bragg wavelength shifts, with the most severe fault reaching wavelength shifts of hundreds of picometres (pm) compared to the healthy and broken rotor conditions, which were in the low-to-mid-hundred and high-hundred picometre (pm) range, respectively. A fast Fourier transform (FFT) analysis, performed on the measured stray flux, revealed that the spectral content of the stray flux affected the magnetostrictive behavior of the magnetic dipoles of the terfenol-D transducer, which translated into strain on the fiber gratings.

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“…Moreover, the production of rare earth materials that are used in magnets is influenced by policies, and this situation affects the stable manufacturing of the PMSMs. In contrast to the PMSM, the SCIM, without using rare earth materials, is more reliable, easier to manufacture, and simpler in structure [3]. Thus, the SCIMs have been increasingly used as the driving motors in EVs.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Defect Detection of Die Cast Rotor in Induction Motor Based on Circular Flux Sensing Coils

Zhu

Wang

et al. 2022

IEEE Trans. Ind. Inf.

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The die cast rotor bars in squirrel cage induction motors (SCIMs) are easily subjected to porosity or other defects in production, which considerably affects the motors' reliability and efficiency in operation. Planar flux sensing coils have been investigated for the defect detection of SCIM rotor. However, these types of sensors cannot accurately evaluate the severity of porosity or broken bar. This study develops a novel instrument to inspect and quantitatively analyze the rotor quality of SCIM. The sensor consists of the electromagnetic flux sensing coils directly from a SCIM stator. By injecting a DC voltage at phases A and B of the sensor, the induced voltage signal is generated from phase C. A quantitative fault indicator (QFI) is constructed on the basis of the instrument voltage output. The variation trend of the QFI with respect to fault severity is investigated by establishing a theoretical sensor model. Experimental results indicate that the proposed method can accurately detect the porosity and broken bar and evaluate their severities for the die cast rotor. The developed solution can be easily implemented with low cost and computational complexity, which can achieve real-time inspection of SCIM rotor in the production line.

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Magnetic Flux Analysis for the Condition Monitoring of Electric Machines: A Review

Cited by 60 publications

References 105 publications

Industry 4.0 in maintenance: Using condition monitoring in electric machines

Industry 4.0 in maintenance: Using condition monitoring in electric machines

Short Circuit and Broken Rotor Faults Severity Discrimination in Induction Machines Using Non-invasive Optical Fiber Technology

Real-Time Defect Detection of Die Cast Rotor in Induction Motor Based on Circular Flux Sensing Coils

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