Induction machine drive condition monitoring and diagnostic research—a survey

Singh, Gyanendra Kumar; Kazzaz, Sa’ad Ahmed Saleh Al

doi:10.1016/s0378-7796(02)00172-4

Cited by 298 publications

(133 citation statements)

References 51 publications

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“…These forces are known as Maxwell's forces [5]. The magnitude of motor vibration at any point is determined by the magnitude of the force and the mechanical response of the motor structure and mounting [6].…”

Section: Speed Estimation Techniquementioning

confidence: 99%

A non-intrusive method for estimating motor efficiency using vibration signature analysis

Dlamini¹,

Naidoo²,

Manyage³

2013

International Journal of Electrical Power & Energy Systems

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An accurate method of estimating the efficiency of in-service motors is needed in order to determine the performance of installed motors without disrupting the motor driven process.. In this paper, the motor efficiency is estimated using a non-intrusive implementation of the compensated slip method. The motor speed is accurately estimated using motor vibration signature analysis. A few other efficiency estimation techniques are implemented and their performance is compared to the proposed method. It was found that the non-intrusive compensated slip method produced relatively accurate results without having an adverse impact on the availability of the motor under test. This method provides an attractive alternative to highly intrusive techniques that offer similar accuracy.

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Section: Speed Estimation Techniquementioning

confidence: 99%

A non-intrusive method for estimating motor efficiency using vibration signature analysis

Dlamini¹,

Naidoo²,

Manyage³

2013

International Journal of Electrical Power & Energy Systems

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“…During the past few decades, various studies of fault diagnosis have been conducted for motors and pumps [1][2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Fault Diagnosis for Current and Vibration Signals in Pumps and Motors using a Reconstructed Phase Portrait

Jung

Bae

2015

Int. J. Fuzzy Log. Intell. Syst.

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In this paper, we measure the current and vibration signals of one-dimensional time series that occur in a motor and pump, respectively. These machines are representative rotary and pumping machines. We also eliminate unnecessary components such as noise by preprocessing the current and vibration signals. Then, in order to diagnose fault signals for the pump and motor, we transform from one-dimensional time series to a two-dimensional phase portrait using Takens' embedding method. After this transformation, we review the variation in the pattern according to the fault signals.

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“…As it is not economical to introduce redundant backup machines, online monitoring for induction machines is important for safe operation and production quality. In order to keep machines in good condition, techniques such as fault monitoring, detection, classification, and diagnosis have become increasingly essential [1]- [3].There are invasive and noninvasive methods for machine fault detection [4], [5], [7] The noninvasive methods are more preferable than the invasive methods because they are based on easily accessible and inexpensive measurements to diagnose the machine conditions without disintegrating the machine structure. Recently, artificial intelligence (AI) techniques have been proposed for the noninvasive machine fault detection [4], [6], [8].…”

Section: Introductionmentioning

confidence: 99%

Induction Machine Fault Detection Using Generalized Feed Forward Neural Network

Ghate¹,

Dudul²

2009

Journal of Electrical Engineering and Technology

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-Industrial motors are subject to incipient faults which, if undetected, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based incipient fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. GFFD NN model is designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.

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Induction machine drive condition monitoring and diagnostic research—a survey

Cited by 298 publications

References 51 publications

A non-intrusive method for estimating motor efficiency using vibration signature analysis

A non-intrusive method for estimating motor efficiency using vibration signature analysis

Analysis of Fault Diagnosis for Current and Vibration Signals in Pumps and Motors using a Reconstructed Phase Portrait

Induction Machine Fault Detection Using Generalized Feed Forward Neural Network

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