Rolling Bearing Fault Diagnosis Based on Time-Frequency Feature Extraction and IBA-SVM

Zhang, Mei; Yin, Jie; Chen, Wanli

doi:10.1109/access.2022.3198701

Cited by 21 publications

(7 citation statements)

References 22 publications

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“…In Discrete Fourier Transform (DFT) engineering, FFT is an implementation method [27] in which the calculation process is simplified. It is easier to obtain frequency domain signals by performing bearing time domain FFT transformation.…”

Section: Fast Fourier Transformmentioning

confidence: 99%

Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis

Lee,

Le,

Chen

et al. 2024

Mathematics

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Motor fault diagnosis is an important task in the operational monitoring of electrical machines in manufacturing. This study proposes an effective bearing fault diagnosis model for electrical machinery based on machine learning techniques. The proposed model is a combination of three processes: feature extraction of signals collected from the motor based on multi-resolution analysis, fast Fourier transform, and envelope analysis. Next, redundant or irrelevant features are removed using the feature selection technique. A binary salps swarm algorithm combined with an extended repository is the proposed method to remove unnecessary features. As a result, an optimal feature subset is obtained to improve the performance of the classification model. Finally, two classifiers, k-nearest neighbor and support vector machine, are used to classify the fault of the electric motor. There are four input datasets used to evaluate the model performance, and UCI is the benchmark dataset to verify the effectiveness of the proposed feature selection technique. The remaining three datasets include the bearing dataset collected from experiments, with an average classification accuracy of 99.9%, as well as Case Western Reserve University (CWRU) and Machinery Failure Prevention Technology (MFPT), which are public datasets with average classification accuracies of 99.6% and 98.98%, respectively. The experimental results show that this method is more effective in diagnosing bearing faults than other traditional methods and prove its robustness.

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Section: Fast Fourier Transformmentioning

confidence: 99%

Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis

Lee,

Le,

Chen

et al. 2024

Mathematics

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“…O artigo [11], utilizou redes neurais profundas DNN para classificar falhas em rolamentos com acurácia de 99,7%. O artigo de [12], utilizou o algoritmo k-NN para classificar sinais de corrente elétrica de um motor elétrico cujos rolamentos apresentaram diferentes condic ¸ões de falha. Para a realizac ¸ão dos experimentos, os autores utilizaram o conjunto de dados da SKF [13], que é constituído por sinais de corrente referentes a quatro condic ¸ões de falha em rolamentos: falha na pista externa, falha na pista interna, falha na esfera e falha no rolo.…”

Section: Trabalhos Correlatosunclassified

Diagnóstico de Falhas em Rolamentos de Motores Elétricos com Base na Análise da Assinatura da Corrente do Motor

Oliveira,

Pinto,

Resende

2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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This article presents a comparative analysis of the performance of classification algorithms applied to fault diagnosis in electric motor bearings. To represent the faults, 13 statistical descriptors of the electric current signals available in the Paderborn dataset were extracted. The classification problem consisted of nine failure classes in addition to the normal operating condition class. The models were built both in the time domain and in the time scale domain, using wavelet transform filters, Coiflet 5, Daubechies 4 (Daub 4) and Symlet 8, at predefined resolution levels. The k-NN, SVM and Decision Tree algorithms were used to build the models. The performance of the models was evaluated based on the metrics of accuracy, sensitivity and F1 score. The best accuracy results were obtained using the SVM algorithm, with Symlet and Coiflet 5 filters, reaching a value of 0.9967%. The articles main contribution, in addition to the use of Wavelets and SVM combined, was that it demonstrated results that were comparable when using electric current signals as entry to vibration signals that are already widely used in the industry and that the use of the Wavelet Transform, along which Machine Learning algorithms, when using good descriptors, can be a viable solution to implement diagnostic systems automatic detection of more accurate faults in electric motor bearings.

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“…Unique fault patterns are encapsulated within distinct frequency bands, rendering the frequency domain signal more adept for fault diagnosis compared to the original vibration signal [41]. Zhang et al conducted an FFT transformation on vibration signals and derived features from the resultant spectral signals to facilitate bearing fault diagnosis [42]. On the other hand, Mao et al applied an FFT transformation to the bearing vibration signal, and the ensuing spectral information underwent processing via a generative adversarial network (GAN) [43], which was employed for the early detection of bearing failures.…”

Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

Rolling Bearing Fault Diagnosis across Operating Conditions Based on Unsupervised Domain Adaptation

Zhong,

Liu,

Mao

et al. 2023

Lubricants

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In practical industrial scenarios, mechanical equipment frequently operates within dynamic working conditions. To address the challenge posed by the incongruent data distribution between source and target domains amidst varying operational contexts, particularly in the absence of labels within the target domain, this study presents a solution involving deep feature construction and an unsupervised domain adaptation strategy for rolling bearing fault diagnosis across varying working conditions. The proposed methodology commences by subjecting the original vibration signal of the bearing to a fast Fourier transform (FFT) to extract spectral information. Subsequently, an innovative amalgamation of a one-dimensional convolutional layer and an auto-encoder were introduced to construct a convolutional auto-encoder (CAE) dedicated to acquiring depth features from the spectrum. In a subsequent step, leveraging the depth features gleaned from the convolutional auto-encoder, a balanced distribution adaptation (BDA) mechanism was introduced to facilitate the domain adaptation of features from both the source and target domains. The culminating stage entails the classification of adapted features using the K-nearest neighbor (KNN) algorithm to attain cross-domain diagnosis. Empirical evaluations are conducted on two extensively used datasets. The findings substantiate that the proposed approach is capable of accomplishing the cross-domain fault diagnosis task even without labeled data within the target domain. Furthermore, the diagnostic accuracy and stability of the proposed method surpass those of various other migration and deep learning approaches.

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Rolling Bearing Fault Diagnosis Based on Time-Frequency Feature Extraction and IBA-SVM

Cited by 21 publications

References 22 publications

Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis

Application of Salp Swarm Algorithm and Extended Repository Feature Selection Method in Bearing Fault Diagnosis

Diagnóstico de Falhas em Rolamentos de Motores Elétricos com Base na Análise da Assinatura da Corrente do Motor

Rolling Bearing Fault Diagnosis across Operating Conditions Based on Unsupervised Domain Adaptation

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