A Diagnosis Method of Multiple Faults of Induction Motors Based on Vibration Signal Analysis

Kabul, Ahmet; Ünsal, Abdurrahman

doi:10.1109/sdemped51010.2021.9605511

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…To improve the early feature extraction ability to rotate machinery faults, Zhu et al proposed a fault detection method based on the combination of parameter adaptive variational mode decomposition and the Teager energy operator (TEO) [17]. Kabul et al analyzed the characteristic harmonic components of stator current and vibration signals based on the Fourier transform (FT) to extract the features under mixed faults [18]. However, the frequency information from sudden temporal changes cannot be used by FT [19].…”

Section: Introductionmentioning

confidence: 99%

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

Zhong

Wang

2023

Meas. Sci. Technol.

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To accurately and sensitively track the stator current of the induction motor (IM) and detect faults, stochastic resonance (SR) and Teager energy operator (TEO) are combined to detect the fault in the residual stator current of sliding mode observer (SMO) under strong noise interference and complex weak fault conditions. Firstly, a new approach law is constructed to establish an SMO for better state tracking. Secondly, SR is used to absorb noise and amplify the detection residual of the SMO, and the output results are estimated by TEO in the time domain to achieve fault detection. Finally, the detection results of IM stator and rotor winding faults and sensor intermittent faults are presented. The experimental results show that the SMO has higher state tracking accuracy and a faster rate of convergence. Moreover, the residual of stator current is processed by SR and TEO, and the effectiveness of fault detection is enhanced.

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Section: Introductionmentioning

confidence: 99%

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

Zhong

Wang

2023

Meas. Sci. Technol.

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“…Similarly, Wang et al [17] used a third-order energy operator for current signal demodulation to enhance the fault features. Kabul and Unsal [18] observed the Hilbert envelope spectrum of a current signal and performed spectral analysis to detect fault-related harmonic components. Aiming to address the problem that the supply frequency component of the current signal is dominant and the fault sideband amplitude is too low to detect, some frequency spectrum preprocessing methods based on the stator current are applied to motor fault diagnosis [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Instantaneous Square Current Signal Analysis for Motors Using Vision Transformer for the Fault Diagnosis of Rolling Bearings

Chen,

Zhou,

et al. 2023

Applied Sciences

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Using vibration signals for bearing fault diagnosis can generally achieve good diagnostic results. However, it is not suitable for practical industrial applications due to the restricted installation and high cost of vibration sensors. Therefore, the easily obtainable motor current signal (MCS) has received widespread attention in recent years. Meanwhile, traditional fault diagnosis methods cannot meet the diagnostic accuracy requirements because of the low signal-to-noise ratio (SNR) of the MCS. Committed to achieving bearing fault diagnosis through MCS, a rolling bearing fault diagnosis method, ISCV-ViT, based on the MCS and the Vision Transformer (ViT) model, is proposed. In particular, a signal processing method based on the instantaneous square current value (ISCV) is proposed to process the MCS directly obtained through a frequency converter into time-domain images. Then, the ViT model is applied for bearing fault diagnosis. Finally, experimental verification is carried out based on the public bearing dataset of Paderborn University (PU) and the bearing dataset of Shenzhen Technology University (SZTU). The analysis of the experimental results demonstrates that the average accuracy of the ISCV-ViT for the two datasets is up to 96.60% and 94.87%, respectively.

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“…The research focused on the challenges that are now being addressed and those that will be addressed in the future in the development of autonomous diagnostic methods. The LabVIEW platform was used to produce cutting-edge capabilities for online control of induction motors [ 20 , 21 , 22 ]. It has been determined that the use of stator current analysis-based demodulation methods is the most suited method for diagnosing bearing faults.…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

et al. 2022

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In modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system make the motor highly reliable, helping to avoid unscheduled downtimes, which cause more revenue loss and disruption of production. This can be achieved only when the irregularities produced because of the faults are sensed at the moment they occur and diagnosed quickly so that suitable actions to protect the equipment can be taken. This requires intelligent control with a high-performance scheme. Hence, a Field Programmable Gate Array (FPGA) based on neuro-genetic implementation with a Back Propagation Neural network (BPN) is suggested in this article to diagnose the fault more efficiently and almost instantly. It is reported that the classification of the neural network will provide the output within 2 µs although the clone procedure with microcontroller requires 7 ms. This intelligent control with a high-performance technique is applied to the IMD fed by a Voltage Source Inverter (VSI) to diagnose the fault. The proposed approach was simulated and experimentally validated.

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A Diagnosis Method of Multiple Faults of Induction Motors Based on Vibration Signal Analysis

Cited by 8 publications

References 21 publications

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

Instantaneous Square Current Signal Analysis for Motors Using Vision Transformer for the Fault Diagnosis of Rolling Bearings

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

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