A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm

Feng, He; Ye, Qing

doi:10.3390/s22041410

Cited by 86 publications

(44 citation statements)

References 42 publications

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“…The fault diagnosis method needs to process different fault signals of dry reactor in order to obtain an evaluation criterion. Finally, the state of the dry reactor corresponding to the signal can be obtained by judging the characteristics of a certain field signal and the difference between it and the threshold value in the evaluation criterion [11]. There are many fault diagnosis methods, which method is suitable for the application of dry reactor and can obtain accurate criteria which will be the content of research.…”

Section: Multistate Fault Diagnosis Methods For Dry Reactormentioning

confidence: 99%

Online Fault Detection of Dry Reactor Based on Improved Kalman Filter

et al. 2022

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In order to meet the requirements of online fault detection for dry reactor, an online fault detection technology based on improved Kalman filter is proposed. The main content of the technology is based on the dry reactor detection technology, through the study of improved Kalman filter, the use of fault diagnosis and other methods, and finally through the experiments and analysis to build improved Kalman filter dry reactor online fault detection research means. The experimental results show that the maximum relative error of the improved Kalman filter is 6.039%, and the average relative error is 2.388%. The improved algorithm is very effective and greatly improves the prediction accuracy. The research based on improved Kalman filter can meet the demand of online fault detection of reactor.

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Section: Multistate Fault Diagnosis Methods For Dry Reactormentioning

confidence: 99%

Online Fault Detection of Dry Reactor Based on Improved Kalman Filter

et al. 2022

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“…In the design of the research model, this paper takes two steps: the first step is to use the logistic model for regression fitting analysis; the second step is to conduct an intermediary test with KHB. Similar methods are often used in computational sociology ( 12 , 13 ).…”

Section: Methodsmentioning

confidence: 99%

Mediating Effects of Adolescent Physical Activity, Self-Rated Health and Family Income

Wang

Shen²,

Deng³

et al. 2022

Front. Public Health

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ObjectiveTo investigate and analyse the situation and relationship between adolescent physical activity and self-assessment of health to provide a reference for adolescent physical activity research.MethodsA questionnaire was used to investigate the physical activity and self-rated health of 1,804 adolescents aged 14–18 years.ResultsThere was a significant relationship between adolescents' physical activity and self-rated health. The coefficient was 0.109 (P < 0.01) in urban areas and 0.127 (P < 0.01) in rural areas. At the same time, it was found that when family income was used as the intermediary variable between physical activity and self-rated health, the intermediary effect was 0.12 (P < 0.01), and the intermediary effect accounted for 25.97%.ConclusionAdolescent obesity, physical activity, smoking, wellbeing and physical activity can affect adolescents' self-rated health status. At the same time, it is also found that adolescents' family income is an intermediary variable between physical activity and self-rated health.Suggestions(1) Increase the methods of sports venues, sports organizations and sports activities, improve the possibility of teenagers participating in physical activities, and improve teenagers' self-rated health; (2) There is a large gap between the physical activity and self-rated health of urban and rural adolescents. Increasing the guidance of physical activity of adolescents in rural areas promotes the balance of self-rated health of urban and rural adolescents. (3) Family income is the intermediary variable of teenagers' physical activities and self-rated health. Reducing family expenditure through financial transfer payments or reducing taxes and fees can increase the level of teenagers' physical and mental health.

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“…Li [9] proposed an attention mechanism (AM) algorithm weighted long short-term memory (LSTM) neural network for the purpose of getting better fault diagnosis results in Tennessee Eastman process. He et al [10] suggested a fault diagnosis method based on wavelet packet transform and convolutional neural network. Nauyen et al [11] proposed a new DNN-based vibration signal-based bearing fault diagnosis method.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Based Two-Layer Transfer Learning for Bearing Fault Diagnosis

Zhang

et al. 2022

IEEE Access

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Rolling bearing fault diagnosis is one of crucial tasks in mechanical equipment fault diagnosis. Currently, artificial intelligence and machine learning-driven fault diagnosis methods are extensively utilized for rolling bearing. When compared to traditional techniques, the diagnostic accuracy has significantly improved. These methods, however, need a substantial amount of labelled training data, which is difficult to obtain in actual failures. In order to resolve this problem, Transfer Learning (TL) was created to learn in the target domain by accessing knowledge from the pertinent labelled source domain. Inspired by Maximum Mean Discrepancy, this paper puts forward a Convolutional Neural Network (CNN) based Two-layer Transfer Learning (CTTL) method for fault diagnosis. In the first layer, the fault features are automatically extracted by CNN and a term called Feature Weighted Maximum Mean Discrepancy (WMMD) is considered to minimise the difference between source and target domains. In the second layer, the Third Dataset, which is based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method was designed on the basis of the predicted labels from the first layer. The Calinski-Harabaz (CH) index of the Target Dataset controls the iteration times of CTTL. CTTL change the process of Transfer Learning method from learning the distribution of domains to learning the distribution of fault types in more detail, which will get higher accuracy. Proposed CTTL is tested by the bearing datasets of Case Western Reserve University (CWRU) and XJTU-SY. The experimental findings reveal that CTTL is capable of achieving a high diagnosis accuracy across different load domains. In the majority of experiments, CTTL outperformed other algorithms, including Deep Neural Network, Support Vector Machine, and several other methods.

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A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm

Cited by 86 publications

References 42 publications

Online Fault Detection of Dry Reactor Based on Improved Kalman Filter

Online Fault Detection of Dry Reactor Based on Improved Kalman Filter

Mediating Effects of Adolescent Physical Activity, Self-Rated Health and Family Income

Convolutional Neural Network Based Two-Layer Transfer Learning for Bearing Fault Diagnosis

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