Gear fault diagnosis based on CS-improved variational mode decomposition and probabilistic neural network

Lin, Ying; Xiao, Maohua; Liu, Huijia; Li, Zhuolong; Zhou, Shuang; Xu, Xiaomei; Wang, Dicheng

doi:10.1016/j.measurement.2022.110913

Cited by 40 publications

(22 citation statements)

References 19 publications

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“…141 A probabilistic neural network (PNN) is a feedforward neural network based on the Bayesian classification rule that uses parzen window probability density for prediction. 144 The structure of a PNN consists of four layers: the input layer, pattern layer, summation layer, and output layer, as shown in Figure 12. 102 Na 102 employed a PNN to classify looseness in bolted joints based on impedance signatures in three similar specimens.…”

Section: Mechanical Properties Ofmentioning

confidence: 99%

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Chelimilla

Chinthapenta

Kali

et al. 2023

Structural Health Monitoring

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The integrity of bolted joints is still a challenging problem owing to the gross or localized slip at the interfacial surfaces of the joints when subjected to external disturbances such as vibrations. This slip escalates the interfacial movement and, thus, leads to a decrease in preload levels, that is, looseness of the bolted assemblies. In the last decade, modal analysis, wave propagation, and percussion methods were traditionally used to detect looseness in bolted connections. With an increase in computational power, machine learning algorithms such as neural networks, random forests, decision trees, and support vector machines complemented the traditional methods in accurate looseness estimation. Subsequently, this integration paved the path for real-time health monitoring of bolted joints. This paper summarizes recent investigations on looseness detection in bolted assemblies based on traditional methods and machine learning algorithms. The working principle, advantages, challenges, and applications of the aforementioned methods are also detailed in this paper. Apart from these investigations, the latest studies on the Internet of Things-based health monitoring of structures are also reviewed to explore their adaptability in remote monitoring of bolted connections for damage detection in the future.

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Section: Mechanical Properties Ofmentioning

confidence: 99%

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Chelimilla

Chinthapenta

Kali

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…The inertia weight is adjusted in Equations (7) and (8), and a constriction factor is introduced [ 32 , 33 , 34 , 35 ]. All particles in PSO iteratively update their speed and location according to Equations (7) and (8).…”

Section: Model Solutionmentioning

confidence: 99%

Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization

Zhang

Hu²,

Zhang

et al. 2022

IJERPH

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Western China is a sparsely populated area with dispersed transportation infrastructure, making it challenging to meet people’s accessibility and mobility requirements. Rescue efficiency in sparse networks is severely hampered by the difficulty rescue teams have in getting to the scene of abrupt traffic accidents. This paper develops a layout optimization model for multiple rescue points to address this issue, suggests an improved particle swarm algorithm by including a variation that can reduce optimization time and avoid the disadvantage of precocity, and designs a MATLAB program using an adaptive variation algorithm. The proposed approach increases the effectiveness of rescue in a sparse network and optimizes the distribution of emergency resources.

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“…Specifically, a combination of particle swarm optimization and small batch gradient descent algorithm was used to improve the convergence speed and accuracy during network training. h. Li et al [4] used techniques such as wavelet packet decomposition and singular value decomposition to preprocess data for gear fault diagnosis problem and proposed an improved BP neural network model for diagnosis. Better results were achieved when dealing with actual industrial data.Y.Yang [5] studied the stock price prediction problem and proposed a feature selection method based on particle swarm optimization algorithm for selecting the most representative features, making the BP neural network more effective for prediction.C.Lin [6] applied BP neural network to traffic flow prediction, using historical data and real-time data to build prediction models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of deformation and damage of foundation support system based on BP neural network

Fan

2023

J. Phys.: Conf. Ser.

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The pit in the ground that needs to be excavated during the construction of a building is known as a foundation pit. In this paper, a MATLAB neural network toolbox is proposed to establish a three-layer BP neural network to predict building settlement. The relative error of the predicted values obtained after training is between -2% and 1%, and the maximum absolute error is 0.049 mm. this error is acceptable for rock-on-rock engineering problems. It shows that the building settlement prediction using artificial neural network method is feasible and the prediction results are credible. It automatically identifies the complex nonlinear relationships between the dependent and independent variables through self-learning of the optimal operation strategy, and considers the interactions among the factors in a comprehensive and integrated manner, which has good application prospects for solving nonlinear problems in the field of geotechnical engineering.

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Gear fault diagnosis based on CS-improved variational mode decomposition and probabilistic neural network

Cited by 40 publications

References 19 publications

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization

Prediction of deformation and damage of foundation support system based on BP neural network

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