Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural
Network

Yuan, Yuan; Meng, Weina; Sun, Xiaoxia

doi:10.2174/1874155x01408010916

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…Fuzzy neural network combines the advantages of fuzzy logic and neural networks well, and this method is widely used in fault diagnosis [30][31][32]. The fuzzy set represented by the membership function value in the fuzzy theory transforms the information with certain similarities.…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network

Zhou

Wang

et al. 2023

Applied Sciences

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Rotor fault diagnosis has attracted much attention due to its difficulties such as non-stationarity of fault signals, difficulty in fault feature extraction and low diagnostic accuracy of small samples. In order to extract fault feature information of rotors more effectively and to improve fault diagnosis precision, this paper proposed a fault diagnosis method based on variational mode decomposition (VMD) symmetrical polar image and fuzzy neural network. Firstly, the original rotor vibration signal is decomposed by using the VMD method and the relevant parameter selection algorithm of the VMD method is also proposed. Secondly, the intrinsic mode functions (IMF), which are sensitive to the signal characteristics, are selected for signal reconstruction based on a comprehensive evaluation factor method. As well, the reconstructed signal is transformed into a two-dimensional snowflake image through using the symmetrical polar coordinate method. Finally, the image features are extracted by the gray level co-occurrence matrix to form the state feature vector, which is input into the fuzzy neural network to realize the rotor fault diagnosis. Through the analysis of measured signals, the experimental results show that the proposed method can reach a higher recognition rate of 98% and the k-cross-validation experiment is used to demonstrate the robustness of the fuzzy neural network, and the average recognition accuracy of this experiment is 99.2%. Compared with some similar methods, the proposed method still has the highest fault recognition precision 98.4%, and the smallest standard deviation 0.5477.

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Section: Fuzzy Neural Networkmentioning

confidence: 99%

Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network

Zhou

Wang

et al. 2023

Applied Sciences

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“…1 However, various accidents occur during belt conveyor operations due to the combined effects of the transport environment, quality of transport equipment, and impact of material loading, among which belt deviation is the most common accident, 2 particularly for long-distance transportation. The presence of deviations inevitably lead to material spillage, 3 causing environmental pollution, 4,5 increasing transportation and maintenance costs, and even causing belt tear, 6 which are detrimental to clean transport and efficient enterprise development.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning-based multistate monitoring method of belt conveyor turning section

Zhang,

Jiang,

Zhao

et al. 2023

Structural Health Monitoring

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During transportation, bulk materials are susceptible to spillage due to equipment instability and environmental factors, resulting in increased maintenance costs and environmental pollution. Thus, intelligent and efficient condition monitoring is crucial for maintaining operational efficiency of transfer equipment. It facilitates the timely identification of potential safety hazards, preventing accidents from occurring or their impact from spreading, thereby minimizing production and maintenance costs. This study presents a deep-learning-based multioperation synchronous monitoring method suitable for belt conveyors that integrate target segmentation and detection networks to simultaneously diagnose belt deviation, measure conveying load, identify idlers, and do other tasks on a self-made dataset. This method effectively reduces the complexity of multistate simultaneous monitoring and monitoring costs, thereby avoiding environmental pollution caused by transportation accidents. Experimental results show that the segmentation accuracy of the proposed method can be up to 88.72%, with a detection accuracy of 91.3% and an overall inference speed of 90.9 frames per second. Furthermore, by extending the dataset, the proposed method can incorporate additional tasks, such as belt damage, scattered material, and foreign object identifications. This study has practical significance in ensuring the normal and eco-friendly operation of bulk material transportation. Our source dataset is available at https://github.com/zhangzhangzhang1618/dataset-for-turnning-section

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“…Therefore, with a large number M of individual sections, it is advisable to build aggregated models of transport systems. One of the approaches to designing aggregated models of conveyor transport systems is the use of neural networks [32][33][34][35][36][37]. To describe the functioning of a separate conveyor section of the transport system, we use dimensionless variables ( 4) -( 7) of the model ( 1), (2), which allow us to determine the state of the flow parameters of the individual conveyor section at a time…”

mentioning

confidence: 99%

Neural model of conveyor type transport system

Pihnastyi¹,

Khodusov²

2020

Computer Modeling and Intelligent Systems

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In this paper, a model of a transport conveyor system using a neural network is demonstrated. The analysis of the main parameters of modern conveyor systems is presented. The main models of the conveyor section, which are used for the design of control systems for flow parameters, are considered. The necessity of using neural networks in the design of conveyor transport control systems is substantiated. A review of conveyor models using a neural network is performed. The conditions of applicability of models using neural networks to describe conveyor systems are determined. A comparative analysis of the analytical model of the conveyor section and the model using the neural network is performed. The technique of forming a set of test data for the process of training a neural network is presented. The foundation for the formation of test data for learning neural network is an analytical model of the conveyor section. Using an analytical model allowed us to form a set of test data for transient dynamic modes of functioning of the transport system. The transport system is presented in the form of a directed graph without cycles. Analysis of the model using a neural network showed a high-quality relationship between the output flow for different conveyor sections of the transport system.

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Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

Cited by 9 publications

References 3 publications

Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network

Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network

Deep-learning-based multistate monitoring method of belt conveyor turning section

Neural model of conveyor type transport system

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