Bearing fault detection of induction motor using SWPT and DAG support vector machines

Abid, Firas Ben; Zgarni, Slaheddine; Braham, Ahmed

doi:10.1109/iecon.2016.7793237

Cited by 18 publications

(3 citation statements)

References 20 publications

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“…The collection part of eigenvalue is as follows: It can be seen that the peak and transient power eigenvalues of the electrical abnormity are different from the eigenvalues of the electrical opening and closing, which is the main feature of the difference. Fifty abnormal switching eigenvalues were extracted by each electrical appliance, and the eigenvalues were sent to DAG-SVM for training [6]. It is verified by experiment that the accuracy of single electrical fault detection can reach 100%.…”

Section: Abnormal Signal Detectionmentioning

confidence: 97%

Abnormal Detection of Household Appliances Based on Wavelet Denoising and DAG-SVM

Wang¹,

Yin²,

Zuo³

et al. 2018

dtetr

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In order to solve the problem of abnormal detection of household appliances and realize the extraction of abnormal signal features of household appliances and the positioning of the time, this paper proposes an abnormal processing method based on wavelet denoising and DAG-SVM. The noise is eliminated by the wavelet threshold denoising method, which reduces the interference of the noise to the fault signal. Thus, the real signal can be reduced from the signal of strong noise. The abnormal signal of household electrical appliances was extracted after noise reduction. These characteristics are input into DAG-SVM for training and modeling. The validity of this method is verified by simulation and experiment.

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Section: Abnormal Signal Detectionmentioning

confidence: 97%

Abnormal Detection of Household Appliances Based on Wavelet Denoising and DAG-SVM

Wang¹,

Yin²,

Zuo³

et al. 2018

dtetr

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show abstract

“…SVMs were used to detect BRBs [70] and stator short circuits [71] on IMs, with kernels calculated using an artificial immune system approach. SVMs were also used to detect bearing failures using the FFT [72] and the stationary WPT (SWPT) [73]. The later was able to detect four different types of bearing failures with high accuracy.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Review of Fault Diagnosis Methods for Induction Machines in Railway Traction Applications

Issa,

Clerc,

Hologne-Carpentier

et al. 2024

Energies

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Induction motors make up approximately 80% of the electric motors in the railway sector due to their robustness, high efficiency, and low maintenance cost. Nevertheless, these motors are subject to failures which can lead to costly downtime and service interruptions. In recent years, there has been a growing interest in developing fault diagnosis systems for railway traction motors using advanced non-invasive detection and data analysis techniques. Implementing these methods in railway applications can prove challenging due to variable speed and low-load operating conditions, as well as the use of inverter-fed motor drives. This comprehensive review paper summarizes general methods of fault diagnosis for induction machines. It details the faults seen in induction motors, the most relevant signals measured for fault detection, the signal processing techniques for fault extraction as well as some classification algorithms for diagnosis purposes. By giving the advantages and drawbacks of each technique, it helps select the appropriate method that could address the challenges of railway applications.

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“…On the other hand, Abid et al [18] used the SVM method of the directed acyclic graph (DAG) to classify faulty bearings and improved the performance of the SVM by combining the stationary wavelet packet transform (SWPT) and the DAG. The current signal was used, and the SWPT was applied to extract features and to overcome the shortcomings of other wavelet transforms.…”

Section: Related Workmentioning

confidence: 99%

CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis

Chung

Liang

HongJin

2023

Sensors

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This paper introduces a one-dimensional convolutional neural network (CNN) hardware accelerator. It is crafted to conduct real-time assessments of bearing conditions using economical hardware components, implemented on a field-programmable gate array evaluation platform, negating the necessity to transfer data to a cloud-based server. The adoption of the down-sampling technique augments the visible time span of the signal in an image, thereby enhancing the accuracy of the bearing condition diagnosis. Furthermore, the proposed method of quaternary quantization enhances precision and shrinks the memory demand for the neural network model by an impressive 89%. Provided that the current signal data sampling rate stands at 64 K samples/s, the proposed design can accomplish real-time fault diagnosis at a clock frequency of 100 MHz. Impressively, the response duration of the proposed CNN hardware system is a mere 0.28 s, with the fault diagnosis precision reaching a remarkable 96.37%.

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Bearing fault detection of induction motor using SWPT and DAG support vector machines

Cited by 18 publications

References 20 publications

Abnormal Detection of Household Appliances Based on Wavelet Denoising and DAG-SVM

Abnormal Detection of Household Appliances Based on Wavelet Denoising and DAG-SVM

Review of Fault Diagnosis Methods for Induction Machines in Railway Traction Applications

CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis

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