Motor Current Signature Analysis for Bearing Fault Detection in Mechanical Systems

Singh, Sukhjeet; Kumar, Amit; Kumar, Navin

doi:10.1016/j.mspro.2014.07.021

Cited by 93 publications

(49 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This method has a drawback that it might be too slow to detect fault. Current monitoring [18][19][20][21]. The method used for diagnosis [22] is accurate and remains one of the most used, but require expensive sensors or specialized tools.…”

Section: Analytical Model Of Cage Induction Machine Dedicated To the mentioning

confidence: 99%

Analytical Model of Cage Induction Machine Dedicated to the Study of the Inner Race Bearing Fault

Hadjami¹,

Razik²,

Oumaamar³

et al. 2018

IJECE

View full text Add to dashboard Cite

This paper presents a new analytical model for inner bearing raceway defect. The model is based on the presentation of different machine inductances as Fourier series without any kind of reference frame transformation. The proposed approach shows that this model is able to give important features on the state of the motor. Simulation based on spectral analysis of stator current signal using Fast Fourier Transform (FFT) and experimental results are given to shed light on the usefulness of the proposed model.

show abstract

Section: Analytical Model Of Cage Induction Machine Dedicated To the mentioning

confidence: 99%

Analytical Model of Cage Induction Machine Dedicated to the Study of the Inner Race Bearing Fault

Hadjami¹,

Razik²,

Oumaamar³

et al. 2018

IJECE

View full text Add to dashboard Cite

show abstract

“…The primary usage is diagnosis of bearings and gearboxes as small damages in either cause small displacements which propagates back to the motor in terms of air gap changes. Singh et al [53] detected outer race faults in a bearing by using frequency data in an MCSA. A two-pole three-phase induction motor was used with an assumed healthy bearing inside, and a test bearing was located on the shaft with a radial load.…”

Section: Motor Current Signature Analysismentioning

confidence: 99%

A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management

Kandukuri

Klausen

Karimi

et al. 2016

Renewable and Sustainable Energy Reviews

170

View full text Add to dashboard Cite

“…However, the irregularities in load were not considered. Motor Current Signature Analysis (MCSA) [14] detected the faults in bearings using 2D wavelet scalogram. Condition monitoring is one of the most efficient mechanisms with which the rate of faults can be reduced to a significant rate.…”

Section: Related Workmentioning

confidence: 99%

Vibration Analysis of Industrial Drive for Broken Bearing Detection Using Probabilistic Wavelet Neural Network

Jayakumar

Thangavel²

2015

IJPEDS

View full text Add to dashboard Cite

A reliable monitoring of industrial drives plays a vital role to prevent from the performance degradation of machinery. Today's fault detection system mechanism uses wavelet transform for proper detection of faults, however it required more attention on detecting higher fault rates with lower execution time. Existence of faults on industrial drives leads to higher current flow rate and the broken bearing detected system determined the number of unhealthy bearings but need to develop a faster system with constant frequency domain. Vibration data acquisition was used in our proposed work to detect broken bearing faults in induction machine. To generate an effective fault detection of industrial drives, Biorthogonal Posterior Vibration Signal-Data Probabilistic Wavelet Neural Network (BPPVS-WNN) system was proposed in this paper. This system was focused to reducing the current flow and to identify faults with lesser execution time with harmonic values obtained through fifth derivative. Initially, the construction of Biorthogonal vibration signal-data based wavelet transform in BPPVS-WNN system localizes the time and frequency domain. The Biorthogonal wavelet approximates the broken bearing using double scaling and factor, identifies the transient disturbance due to fault on induction motor through approximate coefficients and detailed coefficient. Posterior Probabilistic Neural Network detects the final level of faults using the detailed coefficient till fifth derivative and the results obtained through it at a faster rate at constant frequency signal on the industrial drive. Experiment through the Simulink tool detects the healthy and unhealthy motor on measuring parametric factors such as fault detection rate based on time, current flow rate and execution time.

show abstract

Motor Current Signature Analysis for Bearing Fault Detection in Mechanical Systems

Cited by 93 publications

References 20 publications

Analytical Model of Cage Induction Machine Dedicated to the Study of the Inner Race Bearing Fault

Analytical Model of Cage Induction Machine Dedicated to the Study of the Inner Race Bearing Fault

A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management

Vibration Analysis of Industrial Drive for Broken Bearing Detection Using Probabilistic Wavelet Neural Network

Contact Info

Product

Resources

About