Bearing Fault Diagnosis Using Synthetic Quantitative Index-Based Adaptive Underdamped Stochastic Resonance

Li, Baochen; Tong, Rui; Kang, Jianshe; Chi, Kuo

doi:10.1155/2021/8888079

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…After preprocessing, the text is a collection of words, which is still a natural language and cannot be mathematically calculated, it is necessary to convert the text into a mathematical language that can be applied to clustering algorithms [15].…”

Section: Finished Product Failure Record Text Vectorizationmentioning

confidence: 99%

Data Mining Technology for Equipment Machinery and Information Network Data Resources

Zhang¹

2022

Security and Communication Networks

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In order to solve the problem of aviation equipment system maintenance, it is very difficult to judge the faulty finished product according to the fault phenomenon, the author proposes a data mining-based prediction model for aviation equipment failure finished products. The model takes historical fault record data as input, clusters a large number of fault descriptions through text clustering to obtain fault phenomenon clusters, and establishes a many-to-many relationship between “fault phenomenon” and “fault finished product.” A probability distribution algorithm for faulty finished products is proposed, and by matching new fault phenomena and fault phenomenon clusters, the probability distribution of faulty finished products is calculated. The experimental results show that after calling the model to complete the clustering of the fault information database, 18966 fault phenomenon clusters are obtained, and each fault phenomenon cluster contains 2.9 fault records on average, the many-to-many relationship between the fault phenomenon and the faulty finished product of the fault information database is successfully constructed. The model can effectively predict the probability distribution of products that may fail according to the fault description, and the prediction accuracy can be improved with the increase of the amount of data to meet the actual security needs.

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Section: Finished Product Failure Record Text Vectorizationmentioning

confidence: 99%

Data Mining Technology for Equipment Machinery and Information Network Data Resources

Zhang¹

2022

Security and Communication Networks

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“…e basic idea of the DET method is to select characteristics with small intraclass variation and large interclass variation using effective factors. Features corresponding to effective factors can better distinguish different categories [30,31]. Set p i,j,k as the jth statistical parameter of the kth sample in the ith category.…”

Section: Crest Factormentioning

confidence: 99%

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Guo

Yang

Cheng

et al. 2022

Shock and Vibration

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As the key bearing part of the crane, the low-speed hub bearing of the crane exists in special working conditions of low-speed and alternating heavy load. It is difficult to extract its fault characteristics accurately by existing analysis methods. The main idea of the broadband mode decomposition (BMD) method previously proposed is to search in the association dictionary library containing broadband and narrowband signals. However, when it is applied to the broadband signals interfered by strong noise, the decomposition is easy to produce modal confusion, so the modulated broadband mode decomposition (MBMD) method is proposed. The fault signal just can be analyzed by MBMD, so it is applied to the fault diagnosis of low-speed hub bearing of the crane. To realize the fault identification of low-speed hub bearing of the crane, firstly, the original signal is decomposed by MBMD. Secondly, the eigenvalues of the first three-component signals are calculated, the eigenvalue matrix is constructed, and the marked features are selected by the distance evaluation technique (DET). Finally, the marked features are input into BP neural network for training and testing to identify the types of bearing fault. Compared with EEMD, VMD, and BMD, the MBMD method combined with BP neural network has good performance in feature extraction and fault identification.

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“…Urazghildiiev et al completed the research on the life evaluation of wind turbine by combining artificial neural network and Bayesian algorithm and proved the effectiveness of the model by experiments [ 10 ]. Li et al used hidden semi-Markov models (HSMM) to realize fault early warning of wind turbine and proposed a new method for training HSMM [ 11 ]. Jiang et al estimate the model parameters through the improved forward-backward training algorithm, and its accuracy reaches 96% [ 12 ], which provides a theoretical and practical basis for Li et al's subsequent research [ 13 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…e wavelet switch can adjust the window size according to the frequency and can bring out a variety of solutions. e definition of continuous wavelet transform is shown in equation (11), where equation ( 10) is wavelet basis function, and s and τ are scale factor and translation factor respectively, which respectively control the center frequency of wavelet transform and its translation along the signal on the time axis. Both s and τ take continuous variation values, which is a set of function series obtained from the same generating function φ(t) through expansion and translation.…”

Section: Data Preprocessingmentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Based on Convolution Neural Network Algorithm

Xiao

Wang

2022

Computational Intelligence and Neuroscience

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Relying on expert diagnosis, it solves the problem of fan failure efficiency and meets the needs of automatic inspection and intelligent operation monitoring of fans. In order to make up for the deficiency of intelligent diagnosis of bearing fault based on vibration signal detection, signal transformation, and convolution neural network identification and improve the ability of intelligent diagnosis, this study designs a deep convolution neural network model and diagnosis algorithm with three pairs of convolution pooling layers and two full connection layers. The experimental verification of the proposed method is carried out based on the public data set, and the effects of three different signal transformation methods based on vibration signal through vibration gray map, short-time Fourier transform time-frequency map, and continuous wavelet transform time-frequency map on the accuracy of diagnosis model are compared and analyzed. A very accurate guarantee is received, close to 100%. The final experimental results demonstrate the effectiveness of the information on the accuracy of diagnostic testing and provide new ideas for the verification and testing of wind turbine wind energy. Compared with other machine learning algorithms, the real-time recognition of machine learning based on time-domain statistical features is lower than that of convolutional neural network methods. The effect of the scale of the trained model on the accuracy of the algorithm is discussed. A sample ratio of 50% and 70% was found to be appropriate.

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Bearing Fault Diagnosis Using Synthetic Quantitative Index-Based Adaptive Underdamped Stochastic Resonance

Cited by 7 publications

References 20 publications

Data Mining Technology for Equipment Machinery and Information Network Data Resources

Data Mining Technology for Equipment Machinery and Information Network Data Resources

Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network

Fault Diagnosis of Wind Turbine Based on Convolution Neural Network Algorithm

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