FPGA Implementation of a Bearing Fault Classification System Based on an Envelope Analysis and Artificial Neural Network

Toumi, Yassine; Bengherbia, Billel; Lachenani, Sidahmed; Zmirli, Mohamed Ould

doi:10.1007/s13369-022-06599-7

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…The traditional bearing condition identification mainly relies on human experience to judge, which makes it difficult to diagnose the operation condition of the bearing quickly and accurately, and now the emergence of automated and intelligent algorithms has brought a new way of thinking for fault diagnosis and condition identification. Toumi et al [19] designed an embedded intelligent system to identify the type and severity of bearing faults using envelope analysis to process the raw vibration signals obtained from the CWRU dataset to obtain the peak amplitudes corresponding to the bearing fault frequencies used as input to the MLP network classifier. Yan et al [20] proposed a fault classification algorithm based on multi-domain feature optimization SVM, which consists of three main stages: multi-domain feature extraction, feature selection and fault identification.…”

Section: Status Of Research On Fault Classification and Identificationmentioning

confidence: 99%

Research on SVM-Based Bearing Fault Diagnosis Modeling and Multiple Swarm Genetic Algorithm Parameter Identification Method

Han

Liu

et al. 2023

Mathematics

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The bearing fault diagnosis of petrochemical rotating machinery faces the problems of large data volume, weak fault feature signal strength and susceptibility to noise interference. To solve these problems, current research presents a combined ICEEMDAN-wavelet threshold joint noise reduction, mutual dimensionless metrics and MPGA-SVM approach for rotating machinery bearing fault diagnosis. Firstly, we propose an improved joint noise-reduction method of an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet thresholding. Moreover, the noise-reduced data are processed by mutual dimensionless processing to construct a mutual dimensionless index sensitive to bearing fault features and complete the fault feature extraction of the bearing signals. Furthermore, we design experiments on faulty bearings of multistage centrifugal fans in petrochemical rotating machinery and processed the input data set according to ICEEMDAN-wavelet threshold joint noise reduction and mutual dimensionless indexes for later validation of the model and algorithm. Finally, a support vector machine model used to effectively identify the bearing failures, and a multi-population genetic algorithm, is studied to optimize the relevant parameters of the support vector machine. The powerful global parallel search capability of the multigroup genetic algorithm is used to search for the penalty factor c and kernel parameter r that affect the classification performance of the support vector machine. The global optimal solutions of c and r are found in a short time to construct a multigroup genetic algorithm-support vector machine bearing fault diagnosis and identification model. The proposed model is verified to have 95.3% accuracy for the bearing fault diagnosis, and the training time is 11.1608 s, while the traditional GA-SVM has only 89.875% accuracy and the training time is 17.4612 s. Meanwhile, to exclude the influence of experimental data on the specificity of our method, the experimental validation of the Western Reserve University bearing failure open-source dataset was added, and the results showed that the accuracy could reach 97.1% with a training time of 14.2735 s, thus proving that the method proposed in our paper can achieve good results in practical applications.

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Section: Status Of Research On Fault Classification and Identificationmentioning

confidence: 99%

Research on SVM-Based Bearing Fault Diagnosis Modeling and Multiple Swarm Genetic Algorithm Parameter Identification Method

Han

Liu

et al. 2023

Mathematics

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“…[7] [10] The wavelet transform effectively converts a one-dimensional vibration signal into a twodimensional image signal. Through this process, the characteristics of the one-dimensional vibration signal are mapped onto the two-dimensional plane, generating a time-frequency map of the signal.Based on the above theory, the wavelet transform is carried out on the vibration signals of normal state, rotor misalignment, rotor unbalance, impeller wear, and rolling bearing failure, and the wavelet transform time-frequency diagram is obtained, and the sample diagram is shown in Figure3.…”

mentioning

confidence: 99%

Research and analysis on fault diagnosis of multistage centrifugal pump for mining

Bao,

Wang

2024

J. Phys.: Conf. Ser.

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The vibration data collected by the fault experiment are analyzed in the time domain and frequency domain respectively, and the conclusion shows that the frequency characteristics of some vibration data cannot be fully characterized due to noise interference. In order to solve the problem of noise interference, this paper uses the VMD algorithm based on the kurtosis peak to denoise the collected data, constructs wavelet time-frequency map samples through the wavelet transform method to provide input sources for the subsequent convolutional neural network, and uses the image samples for fault diagnosis and identification. [1] Through the convolutional neural network built to diagnose and identify the different states of the multistage centrifugal pump, it can be seen that the recognition accuracy, training time, and recognition time of a single sample of various image samples in the three cases of no noise reduction, noise reduction and a small number of samples can be found through the method of comparative analysis, and compared with the traditional SVM method, the superiority of the convolutional neural network using image recognition can be found.

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Design and Hardware Implementation of an Intelligent Industrial IoT Edge Device for Bearing Monitoring and Fault Diagnosis

Bengherbia,

Tobbal,

Chadli

et al. 2023

Arab J Sci Eng

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FPGA Implementation of a Bearing Fault Classification System Based on an Envelope Analysis and Artificial Neural Network

Cited by 5 publications

References 44 publications

Research on SVM-Based Bearing Fault Diagnosis Modeling and Multiple Swarm Genetic Algorithm Parameter Identification Method

Research on SVM-Based Bearing Fault Diagnosis Modeling and Multiple Swarm Genetic Algorithm Parameter Identification Method

Research and analysis on fault diagnosis of multistage centrifugal pump for mining

Design and Hardware Implementation of an Intelligent Industrial IoT Edge Device for Bearing Monitoring and Fault Diagnosis

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