The Developmental Research on Fault Diagnosis System of Mine Fan Based on Grey Theory

Li, Dong Chen

doi:10.4028/www.scientific.net/amm.105-107.656

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…In the case of incomplete reliability data, Dong et al [11] combined fuzzy number theory with Bayesian network to solve the problem that traditional Bayesian network could not complete reliability research in the case of missing and incomplete data, and calculated the reliability of automobile soft close system. Li et al [12] proposed a fault diagnosis method of hoist brake system based on Bayesian network and A-star algorithm, and completed the fault diagnosis of brake system according to the data collected by sensors. The experimental results show that the correctness and accuracy of this diagnosis method are very stable, which is helpful to improve the system reliability.…”

Section: Introductionmentioning

confidence: 99%

Reliability evaluation of electromechanical braking system of mine hoist based on fault tree analysis and Bayesian network

Jin

Wang

et al. 2023

Mechanics & Industry

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Electromechanical braking system is the key way to improve the braking response ability of mine hoist. At present, the reliability research of electromechanical braking system is less. In order to further analyze and improve the reliability of electro-mechanical braking system, this paper adopts the reliability analysis method of electro-mechanical braking system based on fault tree and Bayesian network. Firstly, the fault tree of the electro-mechanical braking system is established, and then the fault tree is transformed into a Bayesian network, and the posterior probability, probability importance and key importance of each root node are inversely deduced. The diagnosis results show that the ball screw is the weakest link of the electro-mechanical braking system. Then the static simulation and fatigue life simulation of the ball screw are carried out for optimization, and the optimal model of the ball screw is determined. Finally, the electro-mechanical brake installed with the optimized ball screw is tested and analyzed. After the reliable performance test of the electro-mechanical brake, it is finally determined that the braking effect of the optimized electro-mechanical brake is stable.

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Section: Introductionmentioning

confidence: 99%

Reliability evaluation of electromechanical braking system of mine hoist based on fault tree analysis and Bayesian network

Jin

Wang

et al. 2023

Mechanics & Industry

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“…A primary mine fan provides sufficient fresh air and regulates the temperature and humidity of the underground air at each operating location. Simultaneously, it dilutes and discharges toxic and harmful underground gases and dust and has the features of high levels of power and long operation times [ 3 , 4 , 5 ]. The normal operation of a primary mine fan significantly affects the safety of personnel, production safety, production efficiency, and many other aspects of coal mine production.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings in Primary Mine Fans under Sample Imbalance Conditions

Cui,

Ding,

Meng

et al. 2023

Entropy

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Rolling bearings are crucial parts of primary mine fans. In order to guarantee the safety of coal mine production, primary mine fans commonly work during regular operation and are immediately shut down for repair in case of failure. This causes the sample imbalance phenomenon in fault diagnosis (FD), i.e., there are many more normal state samples than faulty ones, seriously affecting the precision of FD. Therefore, the current study presents an FD approach for the rolling bearings of primary mine fans under sample imbalance conditions via symmetrized dot pattern (SDP) images, denoising diffusion probabilistic models (DDPMs), the image generation method, and a convolutional neural network (CNN). First, the 1D bearing vibration signal was transformed into an SDP image with significant characteristics, and the DDPM was employed to create a generated image with similar feature distributions to the real fault image of the minority class. Then, the generated images were supplemented into the imbalanced dataset for data augmentation to balance the minority class samples with the majority ones. Finally, a CNN was utilized as a fault diagnosis model to identify and detect the rolling bearings’ operating conditions. In order to assess the efficiency of the presented method, experiments were performed using the regular rolling bearing dataset and primary mine fan rolling bearing data under actual operating situations. The experimental results indicate that the presented method can more efficiently fit the real image samples’ feature distribution and generate image samples with higher similarity than other commonly used methods. Moreover, the diagnostic precision of the FD model can be effectively enhanced by gradually expanding and enhancing the unbalanced dataset.

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Fault Diagnosis of Mine Fan Bearing Based on Beetle Antennae Search

Che

Sun

et al. 2019

IOP Conf. Ser.: Earth Environ. Sci.

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Mine fan is the lifeblood of coal mine safety production, which plays an important role in ensuring the safety of the lives of mine workers. Once it fails to operate, it will cause irreparable serious consequences. Therefore, in order to ensure the safe operation of mine fan, this paper takes the common motor bearing faults in fan faults as the object of study, proposes a method of using rough set attribute reduction combined with Beetle antennae search to optimize BP neural network to establish diagnosis model, and compares it with genetic algorithm, particle swarm optimization, imperial competition algorithm and ant lion optimization algorithm. The experimental results show that the method achieves 90% accuracy in fault diagnosis of mine fan bearing, and has faster convergence speed and shorter operation time. Compared with other optimization algorithms, the method has greater advantages. After many tests, the diagnosis results are stable, which proves that the method is feasible and effective in fault diagnosis of mine fan.

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The Developmental Research on Fault Diagnosis System of Mine Fan Based on Grey Theory

Cited by 3 publications

References 3 publications

Reliability evaluation of electromechanical braking system of mine hoist based on fault tree analysis and Bayesian network

Reliability evaluation of electromechanical braking system of mine hoist based on fault tree analysis and Bayesian network

Fault Diagnosis of Rolling Bearings in Primary Mine Fans under Sample Imbalance Conditions

Fault Diagnosis of Mine Fan Bearing Based on Beetle Antennae Search

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