Bearing fault diagnosis based on wavelet packet energy spectrum and SVM

He, Cheng; Wu, Tao; Gu, Runwei; Qu, Huaying

doi:10.1088/1742-6596/1684/1/012135

Cited by 4 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is one of the common kernel learning methods. More and more scholars are using SVM for fatigue damage detection in bearings [19][20][21][22][23][24]. It is proved by the above scholars that the SVM algorithm can have an exciting effect on the detection of fatigue damage in bearings.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Fatigue Damage Monitoring and Early Warning Model for Bearings

Deng

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Since the manual extraction of features is not sufficient to accurately characterize the health status of rolling bearings, machine learning algorithms are gradually being used for fault diagnosis of bearings, which can adaptively learn the required features from the input data. In this paper, k -nearest neighbor, support vector machines, and convolutional neural networks are successfully applied to the fault diagnosis of bearings, for the benefit of achieving the detection and early warning of bearing fatigue damage. The original samples are segmented into semioverlapping samples. When using k -nearest neighbor and support vector machines as early warning models, we searched their hyperparameters with random search and grid search, and the results showed that support vector machines could achieve 87.1% of bearing detection accuracy and k -nearest neighbor could achieve 100% of detection accuracy. When convolutional neural networks are used as the early warning model, the accuracy can reach 99.75%.

show abstract

Section: Introductionmentioning

confidence: 99%

Data-Driven Fatigue Damage Monitoring and Early Warning Model for Bearings

Deng

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Signal feature extraction is the process of extracting information from signals and serves as a fundamental and crucial step in various fields such as pattern recognition, intelligent systems, and mechanical fault diagnosis. The wide applicability of feature extraction has led to its extensive use in speech analysis, image recognition, geological surveys, weather forecasting, mechanical fault diagnosis, and almost all branches of science and engineering ( Liu, Xiao & Zhu, 2020 ; He, Wu & Runwei, 2020 ; Qiao, Khishe & Ravakhah, 2021 ). Frequency domain-based feature extraction is commonly employed as it provides clear physical interpretations and offers more intuitive feature information compared to time domain waveforms.…”

Section: Signal Feature Extraction and Pattern Recognitionmentioning

confidence: 99%

Feature extraction and pattern recognition of gas pipeline flow noise signals in a strong noisy background

Liu,

Lu,

Wen

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

The purpose of this study is to put forward a feature extraction and pattern recognition method for the flow noise signal of natural gas pipelines in view of the complex situation brought by the rapid development and expansion of urban natural gas infrastructure in China, especially in the case that there are active and abandoned pipelines, metal and nonmetal pipelines, and natural gas, water and power pipelines coexist in the underground of the city. Because the underground situation is unknown, gas leakage incidents caused by natural gas pipeline rupture occur from time to time, posing a threat to personal safety. Therefore, the motivation of this study is to provide a feasible method to accelerate the aging, renewal and transformation of urban natural gas pipelines to ensure the safe operation of urban natural gas pipeline network and promote the high-quality development of urban economy. Through the combination of experimental test and numerical simulation, this study establishes a database of urban natural gas pipeline flow noise signals, and uses principal component analysis (PCA) to extract the characteristics of flow noise signals, and develops a mathematical model for feature extraction. Then, a classification and recognition model based on backpropagation neural network (BPNN) is constructed, which realizes the detection and recognition of convective noise signals. The research results show that the theoretical method based on acoustic feature analysis provides guidance for the orderly and safe construction of urban natural gas pipeline network and ensures its safe operation. The research conclusion shows that through the simulation analysis of 75 groups of gas pipeline flow noise under different working conditions. Combined with the experimental verification of ground flow noise signals, the feature extraction and pattern recognition method proposed in this study has a recognition accuracy of up to 97% under strong noise background, which confirms the accuracy of numerical simulation and provides theoretical basis and technical support for the detection and recognition of urban gas pipeline flow noise.

show abstract

“…The next stage of lapping quartz glass is carried out, and so on until the grinding time reaches 180 min. The lapping test processing equipment and processing principle are The principle of wavelet packet energy is to solve the signal energy on different decomposition scales and arrange these energy values into eigenvectors according to the scale order for recognition [22,23]. Wavelet packet energy contains rich signal characteristics, and its wavelet packet decomposition result is d i,j (k) energy E in different frequency bands E i,j .…”

Section: Experimental Designmentioning

confidence: 99%

“…The principle of wavelet packet energy is to solve the signal energy on different decomposition scales and arrange these energy values into eigenvectors according to the scale order for recognition [ 22 , 23 ]. Wavelet packet energy contains rich signal characteristics, and its wavelet packet decomposition result is

energy E in different frequency bands

.…”

Section: Basic Principlesmentioning

confidence: 99%

Study on Wavelet Packet Energy Characteristics on Friction Signal of Lapping with the Fixed Abrasive Pad

et al. 2022

View full text Add to dashboard Cite

The surface condition of the fixed abrasive pad (FAP) has a significant impact on its machining performance, workpiece material removal rate (MRR), and surface roughness. To clarify the wavelet packet energy characteristics of friction signal under different surface conditions of FAP and its mapping relationship with MRR and workpiece surface quality, FAP samples in different processing stages were obtained through a consolidated abrasive grinding quartz glass experiment. Then, the friction signals in different stages were received by the friction and wear experiment between the FAP and quartz glass workpiece, and the wavelet packet analysis was carried out. The experimental results show that with the increase of lapping time, the surface wear degree of the FAP increased gradually, and the MRR of the workpiece, the surface roughness of the FAP, and the surface roughness of the workpiece decreased slowly. In the wavelet packet energy of friction signal during machining, the energy proportion of frequency band 7 showed an upward trend with the increase of lapping time. The energy proportion of frequency band 8 showed a downward trend with the increase of lapping time. The change characteristics of the two are significantly correlated with the surface condition of the FAP.

show abstract

Bearing fault diagnosis based on wavelet packet energy spectrum and SVM

Cited by 4 publications

References 0 publications

Data-Driven Fatigue Damage Monitoring and Early Warning Model for Bearings

Data-Driven Fatigue Damage Monitoring and Early Warning Model for Bearings

Feature extraction and pattern recognition of gas pipeline flow noise signals in a strong noisy background

Study on Wavelet Packet Energy Characteristics on Friction Signal of Lapping with the Fixed Abrasive Pad

Contact Info

Product

Resources

About