Electrical Monitoring of Mechanical Looseness for Induction Motors With Sleeve Bearings

Jung, Junyeong; Lee, Sang Bin; Lim, Chaewoong; Cho, Chang‐Hee; Kim, Kwon-Hee

doi:10.1109/tec.2016.2583473

Cited by 22 publications

(10 citation statements)

References 20 publications

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“…For example, the characteristic frequencies of the fault harmonic components are, for bar breakages in squirrel cage induction machines [ 12 , 13 , 14 ] and rotor asymmetries in wound-rotor induction machines

[ 15 ]

for mixed eccentricity faults

[ 16 , 17 , 18 ]

for inter-turn short-circuits in the stator winding

[ 19 , 20 , 21 , 22 ]

and for mechanical looseness

[ 23 ]

where s is the slip,

is the frequency of the power supply and p is the pole pairs number. Other types of faults, such as bearing faults [ 24 , 25 , 26 ], or gearbox faults [ 27 ] generate components in the stator current with frequency expressions similar to Equations ( 1 )–( 4 ).…”

Section: Introductionmentioning

confidence: 99%

Partial Inductance Model of Induction Machines for Fault Diagnosis

Pineda-Sánchez

Puche-Panadero

Martínez-Román

et al. 2018

Sensors

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The development of advanced fault diagnostic systems for induction machines through the stator current requires accurate and fast models that can simulate the machine under faulty conditions, both in steady-state and in transient regime. These models are far more complex than the models used for healthy machines, because one of the effect of the faults is to change the winding configurations (broken bar faults, rotor asymmetries, and inter-turn short circuits) or the magnetic circuit (eccentricity and bearing faults). This produces a change of the self and mutual phase inductances, which induces in the stator currents the characteristic fault harmonics used to detect and to quantify the fault. The development of a machine model that can reflect these changes is a challenging task, which is addressed in this work with a novel approach, based on the concept of partial inductances. Instead of developing the machine model based on the phases’ coils, it is developed using the partial inductance of a single conductor, obtained through the magnetic vector potential, and combining the partial inductances of all the conductors with a fast Fourier transform for obtaining the phases’ inductances. The proposed method is validated using a commercial induction motor with forced broken bars.

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[ 15 ]

for mixed eccentricity faults

[ 16 , 17 , 18 ]

for inter-turn short-circuits in the stator winding

[ 19 , 20 , 21 , 22 ]

and for mechanical looseness

[ 23 ]

where s is the slip,

Section: Introductionmentioning

confidence: 99%

Partial Inductance Model of Induction Machines for Fault Diagnosis

Pineda-Sánchez

Puche-Panadero

Martínez-Román

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…This rotor-pedestal coupling dynamic solution procedure is shown in Figure 9. Implicit Newmark-b method is applied to obtain the response of finite element rotor, explicit Newmark-b method is applied to get the lumped mass's response, the fit looseness forces between the outer ring and the sleeve are solved by equation 1and the SFD forces are solved by equation (5). All forces are obtained from the response of the rotor, sleeve and outer ring at the last computational step.…”

Section: Solving Methodsmentioning

confidence: 99%

Passive vibration reduction of a squeeze film damper for a rotor system with fit looseness between outer ring and housing

Wang

2020

Journal of Low Frequency Noise, Vibration and Active Control

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Clearances between bearing outer ring and sleeve can generally be maintained to provide a margin for the thermal expansion of the bearings. However, temperature variation, improper assembly and long-term vibration can enlarge the clearances and accelerate mechanical wear, leading to what is known as the fit looseness fault. Therefore, it is important to study a fit looseness fault model and investigate how to control the vibration coming from the fit looseness fault. In this paper, a Jeffcott rotor system with three disks was modeled as a single unit. A fit looseness model was applied in the whole rotor model to study the contact problems and response characteristics using a numerical integration method. Then, a squeeze film damper model was applied to assess the vibration reduction effects on the whole rotor system with the fit looseness fault. By comparing the results of the fit looseness fault without squeeze film damper and with squeeze film damper, it is found that the squeeze film damper can reduce nonlinear vibration responses effectively generated by the fit looseness fault for the nonlinear contact. This research work contributes to understanding the mechanism of fit looseness fault and controlling strong nonlinear vibration responses due to the fit clearances.

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“…The motor current signal reflects the electrical relationship between the stator and the rotor, where helpful information regarding the IM condition can be observed in its frequency spectrum. For instance, the presence of electrical faults such as stator short circuits and broken rotor bar; and mechanical faults such as bearing faults, misalignment, air-gap eccentricity, and mechanical looseness induces additional frequency components in the motor current stator signal [ 22 , 23 , 24 , 25 , 26 ]. Besides, the inherent defect in the IM design or construction and the driven-systems variation may induce multiple harmonics into the motor current signal.…”

Section: Motor Current Signaturementioning

confidence: 99%

A Novel Fault Detection and Identification Framework for Rotating Machinery Using Residual Current Spectrum

Purbowaskito

Lan

Fuh

2021

Sensors

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A novel framework of model-based fault detection and identification (MFDI) for induction motor (IM)-driven rotating machinery (RM) is proposed in this study. A data-driven subspace identification (SID) algorithm is employed to obtain the IM state-space model from the voltage and current signals in a quasi-steady-state condition. This study aims to improve the frequency–domain fault detection and identification (FDI) by replacing the current signal with a residual signal where a thresholding method is applied to the residual signal. Through the residual spectrum and threshold comparison, a binary decision is made to find fault signatures in the spectrum. The statistical Q-function is used to generate the fault frequency band to distinguish between the fault signature and the noise signature. The experiment in this study is performed on a wastewater pump in an existing industrial facility to verify the proposed FDI. Two faulty conditions with mathematically known and mathematically unknown faulty signatures are experimented with and diagnosed. The study results present that the residual spectrum demonstrated to be more sensitive to fault signatures compare to the current spectrum. The proposed FDI has successfully shown to identify the fault signatures even for the mathematically unknown faulty signatures.

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Electrical Monitoring of Mechanical Looseness for Induction Motors With Sleeve Bearings

Cited by 22 publications

References 20 publications

Partial Inductance Model of Induction Machines for Fault Diagnosis

Partial Inductance Model of Induction Machines for Fault Diagnosis

Passive vibration reduction of a squeeze film damper for a rotor system with fit looseness between outer ring and housing

A Novel Fault Detection and Identification Framework for Rotating Machinery Using Residual Current Spectrum

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