A Temperature and Magnetic Field-Based Approach for Stator Inter-Turn Fault Detection

Wu, Yihang; Liu, Mingyao; Song, Han; Li, Cong; Xue-li, Yang

doi:10.1109/jsen.2022.3198146

Cited by 14 publications

(7 citation statements)

References 30 publications

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“…The approaches based on infrared thermography strongly depend on the accuracy and optimal camera location or adequate region of interest definition [34][35][36][37].…”

Section: Classicmentioning

confidence: 99%

“…Later, deviations in thermal and magnetic symmetries induced by the faults are assessed for detection. For its part, the work presented in [37] develops a metal-coated fiber Bragg grating (FBG) sensor to monitor the temperature and magnetic field around the end winding of IMs. The sensor measurements are decoupled by a filtering stage, and the separated data is analyzed in the time-frequency domain for detecting the ITSC.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Machine Learning Strategy and Data Fusion for Detecting Incipient ITSC Faults in IM

2023

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The new technological developments have allowed the evolution of the industrial process to this new concept called Industry 4.0, which integrates power machines, robotics, smart sensors, communication systems, and the Internet of Things to have more reliable automation systems. However, electrical rotating machines like the Induction Motor (IM) are still widely used in several industrial applications because of their robust elements, high efficiency, and versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions; hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs, and its appearance is due to electrical stresses leading to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology capable of performing the assessment and automatic detection of incipient electric faults like ITSC in IMs; the proposed method is supported through the processing of different physical magnitudes such as vibration, stator currents and magnetic stray-flux and their fusion of information. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as their fusion following a feature-level fusion approach and their reduction through the Linear discriminant Analysis technique. Furthermore, the fusion and reduction of information from different physical magnitudes lead to performing automatic fault detection and identification by a simple Neural-Network (NN) structure since all considered conditions can be represented in a 2D plane. The proposed method is evaluated under a complete set of experimental data, and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) can lead to achieving a global classification ratio of up to 99.4% during the detection of ITSC in IMs and an improvement higher than 30% in comparison with classical approaches that consider the analysis of a unique physical magnitude. Additionally, the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

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“…The approaches based on infrared thermography strongly depend on the accuracy and optimal camera location or adequate region of interest definition [34][35][36][37].…”

Section: Classicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Machine Learning Strategy and Data Fusion for Detecting Incipient ITSC Faults in IM

2023

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“…In [72], FBG technology and magnetostrictive material (Terfenol-D) were presented at integration which offered a noninvasive sensor for online magnetic field monitoring in large hydro generators to detect rotor winding defects caused by inter-turn short circuits. The application of FBG flux sensing for stray flux monitoring in induction machines and, hence, stator winding fault detection was explored in [73]. The sensor used was a nickelcoated FBG, where nickel-coating geometric deformation with a magnetic field change was used as a medium of flux to strain the conversion.…”

Section: Magnetic Field Sensing Applicationmentioning

confidence: 99%

“…In this study, an FBG array sensor with three FBG heads was encapsulated in the carbon-fiber-reinforced polymer for increased mechanical protection and ease of installation, demonstrating good performance in tests on an operating machine. A simultaneous relative flux and absolute temperature sensing scheme through the application of a single nickel-coated FBG sensing head in the induction machine end windings was explored in [73] and reported an effective performance in in-service tests. [38,39].…”

Section: Multiphysical Sensing Applicationmentioning

confidence: 99%

Fiber Optic Fiber Bragg Grating Sensing for Monitoring and Testing of Electric Machinery: Current State of the Art and Outlook

et al. 2022

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This paper presents a review of the recent trends and the current state of the art in the application of fiber optic fiber Bragg gratings (FBG) sensing technology to condition the monitoring (CM) and testing of practical electric machinery and the associated power equipment. FBG technology has received considerable interest in this field in recent years, with research demonstrating that the flexible, multi-physical, and electromagnetic interference (EMI) immune in situ sensing of a multitude of physical measurands of CM interest is possible and cannot be obtained through conventional sensing means. The unique FBG sensing ability has the potential to unlock many of the electric machine CM and design validation restrictions imposed by the limitations of conventional sensing techniques but needs further research to attain wider adoption. This paper first presents the fundamental principles of FBG sensing. This is followed by a description of recent FBG sensing techniques proposed for electric machinery and associated power equipment and a discussion of their individual benefits and limitations. Finally, an outlook for the further application of this technique is presented. The underlying intention is for the review to provide an up-to-date overview of the state of the art in this area and inform future developments in FBG sensing in electric machinery.

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“…19 Also, Bento et al 20 highlighted its limited promising potential to prevent permanent damage to an IM, particularly for algorithms that cannot detect very-low-severity SITFs and for those with high complexity, requiring a high processing effort and time to identify SITF. In Kumar et al 21 and Wu et al, 22 the thermal and magnetic field sensors were placed preferably at the stator end windings or mounted along the inner stator casing walls for monitoring the temperature and magnetic field. However, their key limitations were the contact-based implementation and manufacturing-stage installation of the sensors.…”

Section: Introductionmentioning

confidence: 99%

Voltage–current locus‐based stator winding inter‐turn fault detection in induction motors

Dongare

Umre

Ballal

2023

Circuit Theory & Apps

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Monitoring the condition of induction motors requires continuous inspection of all motor components. The three-phase stator windings forming the critical components of the motors and handling heavy currents as high as twice the blocked rotor current under inter-turn short circuits necessitate a fault detection system that is efficient, appropriate, and worthwhile. The proposed algorithm takes measurements of the three-phase supply voltages and phase currents and plots their loci for each cycle under various fault levels. The proposed simple, nonintrusive, less expensive, and fault-sensitive voltage-current loci (£(v-i))-based technique accurately identifies the faulty phase and detects stator inter-turn faults (SITFs). In addition, the algorithm corrects supply voltage imbalances, winding asymmetries, and calibration errors of sensing elements. It contributes to examining the insulation condition of the stator windings at the onset and identifying the faulty phase of a motor. The experimental results for a 1 hp, 415 V squirrel-cage induction motor (SCIM) in a laboratory investigation validate the competence of the proposed technique under various fault levels and load conditions.

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A Temperature and Magnetic Field-Based Approach for Stator Inter-Turn Fault Detection

Cited by 14 publications

References 30 publications

Statistical Machine Learning Strategy and Data Fusion for Detecting Incipient ITSC Faults in IM

Statistical Machine Learning Strategy and Data Fusion for Detecting Incipient ITSC Faults in IM

Fiber Optic Fiber Bragg Grating Sensing for Monitoring and Testing of Electric Machinery: Current State of the Art and Outlook

Voltage–current locus‐based stator winding inter‐turn fault detection in induction motors

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