Detection of Eccentricity Faults in Induction Machines Based on Nameplate Parameters

Nandi, S.; Ilamparithi, T.; Lee, Sang Bin; Hyun, Doosoo

doi:10.1109/tie.2010.2055772

Cited by 134 publications

(72 citation statements)

References 33 publications

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“…First, a distinction is made between static and dynamic eccentricity, visualized in Figure 1, [15]- [18]. Regarding static eccentricity, the rotor is rotating around its own gravitational center, but deviated with respect to the center of the stator.…”

Section: Eccentricity In Rotating Electric Machinerymentioning

confidence: 99%

Misalignment and unbalance fault severity estimation using stator current measurements

Corne

Knockaert

Desmet

2017

2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)

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Abstract-The feasibility of using stator current measurements in order to detect mechanical problems in electric rotating machinery has been proven by many authors several decades ago. However, this way of detecting problems has never been exploited as a full-covering and reliable prognostic monitoring technology. Although the imposed current-signatures of different evolving mechanical faults in electric machines are well characterized, the relation between the severity of those faults and the corresponding faulty signatures is still not elucidated thoroughly. As the authors built an innovative test-rig on which all kinds of mechanical faults can be emulated with a wide variety of controllable environmental conditions, those specific relations can finally be investigated. The test-rig contains an 11kW induction machine with an integrated active magnetic bearing at drive-end side. The magnetic bearing can easily manipulate the rotor's position corresponding to a real manifesting and evolving mechanical fault with a high accuracy and reproducibility. In this paper, the relation of the current fault-signatures and the severity of misalignment and mechanical unbalance is investigated. After theoretically describing the different eccentricity-related rotorfaults, the faults are induced and emulated on the induction machine under test. The fault-components in the stator currents' spectra are monitored while the severity of the faults is being increased. By fitting corresponding trending-functions on the obtained results, the severity of misalignment and unbalance can be estimated out of stator current measurements.

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Section: Eccentricity In Rotating Electric Machinerymentioning

confidence: 99%

Misalignment and unbalance fault severity estimation using stator current measurements

Corne

Knockaert

Desmet

2017

2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)

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“…Among the most common faults encountered in the area of induction motors are: the opening or shorting of one or more of the stator's phase windings [3], the presence of broken rotor bar(s) or cracked rotor's end-rings [4], air-gap irregularities [5], bearing faults, and eccentricity faults [6].…”

Section: Introductionmentioning

confidence: 99%

The Use of a Multilabel Classification Framework for the Detection of Broken Bars and Mixed Eccentricity Faults Based on the Start-Up Transient

Georgoulas

Climente-Alarcón

Antonino-Daviu

et al. 2017

IEEE Trans. Ind. Inf.

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Abstract-In this article a data driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multi-label classification problem with each label corresponding to one specific fault. The faulty conditions examined, include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three "problem transformation" methods are tested and compared. For the feature extraction stage, the startup current is exploited using two well-known time-frequency (scale) transformations. This is the first time that a multi-label framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time-frequency transformation.

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“…[1], [2], rotor faults (broken bar or cracked end-ring) [3], [4], and inverter faults (short-circuit and open-circuit) [5]; mechanical faults, such as bearing faults [6], [7] and eccentricity faults [8], [9]; and sensor faults. A lot of studies have been published on the diagnostic techniques of electrical and mechanical faults and an overview of these techniques is given in [10].…”

Section: Introductionmentioning

confidence: 99%

Speed and Current Sensor Fault Detection and Isolation Based on Adaptive Observers for IM Drives

Yu¹,

Wang²,

Xü³

et al. 2014

Journal of Power Electronics

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This paper focuses on speed and current sensor fault detection and isolation (FDI) for induction motor (IM) drives. A new, accurate and high-efficiency FDI approach is proposed so that a system can continue operating with good performance even in the presence of speed sensor faults, current sensor faults or both. The proposed three paralleled adaptive observers are capable of current sensor fault detection and localization. By using observers, the rotor flux and rotor speed can be estimated which allows the system to run under the speed sensorless vector control mode when a speed sensor fault occurs. In order to detect speed sensor faults, a threshold-based scheme is proposed. To verify the feasibility and effectiveness of the proposed FDI strategy, experiments are carried out under different conditions based on a dSPACE DS1104 induction motor drive platform.

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Detection of Eccentricity Faults in Induction Machines Based on Nameplate Parameters

Cited by 134 publications

References 33 publications

Misalignment and unbalance fault severity estimation using stator current measurements

Misalignment and unbalance fault severity estimation using stator current measurements

The Use of a Multilabel Classification Framework for the Detection of Broken Bars and Mixed Eccentricity Faults Based on the Start-Up Transient

Speed and Current Sensor Fault Detection and Isolation Based on Adaptive Observers for IM Drives

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