A New approach of preprocessing with SVM optimization based on PSO for bearing fault diagnosis

Thelaidjia, Tawfik; Chenikher, Salah

doi:10.1109/his.2013.6920452

Cited by 9 publications

(8 citation statements)

References 27 publications

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“…Nodes Frequency bands (kHz) [3,0] 0.000-0.625 [3,4] 3.750-4.375 [3,1] 0.625-1.250 [3,5] 4.375-5.000 [3,2] 1.875-2.500 [3,6] 3.125-3.750 [3,3] 1.250-1.875 [3,7] 2.500-3.125 Fig. 9.…”

Section: Nodes Frequency Bands (Khz)mentioning

confidence: 99%

“…where the value of E is    j n n E E 2 1 (7) Vibration signals in normal state, in a spring fatigue fault state and in a screw losing state are chosen to conduct the WPT, the transform results are depicted in Fig. 10.…”

Section: Nodes Frequency Bands (Khz)mentioning

confidence: 99%

“…In this paper, the FFT and the WPT are used to capture signal characteristics of vibration signature. A SVM classifier whose parameters are optimized with the PSO method [7] is employed. With PSO algorithm, WPT and SVM, a novel hybrid intelligent fault detection and classification method for HVCBs is presented.…”

Section: Introductionmentioning

confidence: 99%

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Fault diagnosis of high-voltage circuit breakers using wavelet packet technique and support vector machine

Jin

Tang

Qian

et al. 2017

CIRED - Open Access Proceedings Journal

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The subject addressed in the paper is mechanical fault diagnosis of high-voltage circuit breakers (HVCBs) based on vibration signal analysis. In this paper, the vibration signature measured from the SF 6 HVCB is processed using the fast Fourier transform (FFT) and the wavelet packet technique (WPT). With the FFT and the WPT, the vibration signature is decomposed into different frequency bands, and the energy of the signal is extracted from individual frequency bands and calculated as the individual condition eigenvector. Followed by that, parameters captured from the WPT are optimized with the particle swarm optimization (PSO) and the result is used to train a multi-fault classification model based on the kernel method of the support vector machine (SVM). At the end, fitness values are evaluated with the trained SVM to classify conditions and diagnose faults of the SF 6 HVCB.

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“…Nodes Frequency bands (kHz) [3,0] 0.000-0.625 [3,4] 3.750-4.375 [3,1] 0.625-1.250 [3,5] 4.375-5.000 [3,2] 1.875-2.500 [3,6] 3.125-3.750 [3,3] 1.250-1.875 [3,7] 2.500-3.125 Fig. 9.…”

Section: Nodes Frequency Bands (Khz)mentioning

confidence: 99%

Section: Nodes Frequency Bands (Khz)mentioning

confidence: 99%

See 1 more Smart Citation

Fault diagnosis of high-voltage circuit breakers using wavelet packet technique and support vector machine

Jin

Tang

Qian

et al. 2017

CIRED - Open Access Proceedings Journal

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“…Researchers such as Martis and Chakraborty tried to classify and explore the principles for the cause of arrhythmia disease [12]. As the representative of machine learning algorithms, support vector machine (SVM) is one of the most commonly used diagnostic classification models, and has been used by researchers such as Ekiz et al to diagnose heart disease through SVM [13]; by Chen et al to diagnose hepatitis disease [14] and by Zeng et al to diagnose Alzheimer's disease [15]. Pang and Zhang tried to use a naive Bayesian network to reveal the connection between abnormal tongue appearances and diseases in a particular population [16].…”

Section: Introductionmentioning

confidence: 99%

Effective attention-based network for syndrome differentiation of AIDS

Pang

Wei

Zhao

et al. 2020

BMC Med Inform Decis Mak

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Background Syndrome differentiation aims at dividing patients into several types according to their clinical symptoms and signs, which is essential for traditional Chinese medicine (TCM). Several previous works were devoted to employing the classical algorithms to classify the syndrome and achieved delightful results. However, the presence of ambiguous symptoms substantially disturbed the performance of syndrome differentiation, This disturbance is always due to the diversity and complexity of the patients’ symptoms. Methods To alleviate this issue, we proposed an algorithm based on the multilayer perceptron model with an attention mechanism (ATT-MLP). In particular, we first introduced an attention mechanism to assign different weights for different symptoms among the symptomatic features. In this manner, the symptoms of major significance were highlighted and ambiguous symptoms were restrained. Subsequently, those weighted features were further fed into an MLP to predict the syndrome type of AIDS. Results Experimental results for a real-world AIDS dataset show that our framework achieves significant and consistent improvements compared to other methods. Besides, our model can also capture the key symptoms corresponding to each type of syndrome. Conclusion In conclusion, our proposed method can learn these intrinsic correlations between symptoms and types of syndromes. Our model is able to learn the core cluster of symptoms for each type of syndrome from limited data, while assisting medical doctors to diagnose patients efficiently.

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“…As the extra parameters of SVM viz: regularization and kernel parameters, play a crucial role in constructing an efficient classification model, it is an essential step to optimize these parameters. Inspired by the social behavior of fish schooling, bird flocking, and swarm theory, particle swarm optimization (PSO) has been widely used for SVM parameter optimization (Thelaidjia & Chenikher, 2013). Compared with other evolutionary computation techniques, PSO is easy for implementation and effective.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and optimized support vector machine for severity fault diagnosis in ball bearing

Thelaidjia¹,

Moussaoui²,

Chenikher³

2016

10.5267/j.esm

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In this paper, a method for severity fault diagnosis of ball bearings is presented. The method is based on wavelet packet transform (WPT), statistical parameters, principal component analysis (PCA) and support vector machine (SVM). The key to bearing faults diagnosis is features extraction. Hence, the proposed technique consists of preprocessing the bearing fault vibration signal using statistical parameters and energy obtained through the application of Db8-WPT at the third level of decomposition. After feature extraction from vibration signal, PCA is employed for dimensionality reduction. Finally, particle swarm optimization with passive congregation-based support vector machine is used to classify seven kinds of bearing faults. The classification results indicate the effectiveness of the proposed method for severity faults diagnosis in ball bearings.

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A New approach of preprocessing with SVM optimization based on PSO for bearing fault diagnosis

Cited by 9 publications

References 27 publications

Fault diagnosis of high-voltage circuit breakers using wavelet packet technique and support vector machine

Fault diagnosis of high-voltage circuit breakers using wavelet packet technique and support vector machine

Effective attention-based network for syndrome differentiation of AIDS

Feature extraction and optimized support vector machine for severity fault diagnosis in ball bearing

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