Stator current fault diagnosis of induction motor bearings based on the fast Fourier transform

Safin, Nail; Prakht, Vladimir; Dmitrievskii, Vladimir; Dmitrievskii, A. A.

doi:10.3103/s1068371216120063

Cited by 15 publications

(3 citation statements)

References 3 publications

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“…SVD + 1DCNN and SVD-1DCNN have different classification effects on the same dataset. In the two network (351, 80)- [3,3] 100% 0 87.5% 1.25 × 10 − 4 (351, 80)- [4,4] 97.5% 0 85% 0 (351, 80)- [6,6] 90% 1.25 × 10 − 4 82.5% 1.25 × 10 − 4 (351, 80)- [8,8] 90% 0 82.5% 0 (351, 80)- [10,10] 87.5% 0 82.5% 0…”

Section: Analysis Of Two Network' Classification Effectsmentioning

confidence: 99%

“…e collected data include vibration signal, acoustic signal, and temperature signal, and since the vibration signal can directly characterize the state of the mechanical equipment, the vibration signal is most commonly collected in fault diagnosis [3]. In the fault diagnosis of bearing, commonly used signal processing methods include Fourier transform (FT) [4], short-time Fourier transform (STFT) [5], wavelet transform (WT) [6], Wigner-Ville distribution (WVD) [7], and empirical mode decomposition (EMD) [8].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network

Wang

Huang

Dai

et al. 2020

Shock and Vibration

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This paper constructs a novel network structure (SVD-1DCNN) based on singular value decomposition (SVD) and one-dimensional convolutional neural network (1DCNN), which takes the original signal as input to realize intelligent diagnosis of bearing faults. The output of the first convolution layer was also analyzed from the perspectives of time domain and time-frequency domain in the simulation experiment. Through qualitative analysis and quantitative analysis, it was found that the convolution kernel not only extracted the classification features of signals but also gradually highlighted the learned features in the network training process. Moreover, applying this network in fault diagnosis of bearing date provided by the Case Western Reserve University (CWRU) Bearing Data Center, it was found that the convolution kernel could also achieve the above operation. The novel network of this paper achieved a good classification effect on both the simulated signals and the measured signals.

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Section: Analysis Of Two Network' Classification Effectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network

Wang

Huang

Dai

et al. 2020

Shock and Vibration

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“…In other words, the current signal contains bearing fault information. Safin et al (2016) represented the characteristic harmonic component of stator current in an induction motor with faulty bearings during operation. Allal and Khechekhouche (2022) discovered performance in that the residual harmonic is often displayed at a fixed frequency when monitoring the rotor faults.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Motor Bearing Based on Current Bi-Spectrum and Convolutional Neural Network

Jiang

et al. 2023

Lat. Am. j. solids struct.

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Motor bearings are prone to different degrees of performance degradation, fatigue damage and failure undergoing complex and harsh environments. Vibration signal analysis is a mature method for diagnosing motor bearing faults, while it is not applicable for installing additional vibration sensors on many occasions. Practically, the fault of motor bearings changes the air gap flux between the rotor and stator, which leads to harmonic fluctuations in the stator current. The current signals can be used to diagnose the motor bearing faults without additional sensors. Inevitably the harmonics caused by the motor bearing faults will be coupled with the original signals. This paper combines bi-spectrum and Convolution Neural Network (CNN) to analyze the current signals of motor bearing faults. The CNN diagnosis model is trained based on the local bi-spectrum of current, and the CNN parameters are optimized. Diagnose and analyze motor bearing faults with different fault implantation methods, working conditions, fault degrees and fault locations. The diagnostic accuracy reaches more than 80%.

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Fault Diagnosis of the Bearing Outer Ring of an Induction Motor Under DTC Control by Using Hilbert Filter

Idrissi

Derouich

Mahfoud

2022

Lecture Notes in Networks and Systems

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Stator current fault diagnosis of induction motor bearings based on the fast Fourier transform

Cited by 15 publications

References 3 publications

A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network

A Novel Bearing Fault Diagnosis Methodology Based on SVD and One-Dimensional Convolutional Neural Network

Fault Diagnosis of Motor Bearing Based on Current Bi-Spectrum and Convolutional Neural Network

Fault Diagnosis of the Bearing Outer Ring of an Induction Motor Under DTC Control by Using Hilbert Filter

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