Fault diagnosis method for scintillation detector based on BP neural network

Xie, Yuxi; Yan, Yongjun; Li, X.; Ding, Tiansong; Ma, Chen

doi:10.1088/1748-0221/16/07/t07006

Cited by 11 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A BP neural network is a multi‐layer feedback forward network that is often employed for fault diagnostic [38]. Furthermore, a neural network previously predicted ice accumulation on a NACA 0012 airfoil accurately and rapidly [39].…”

Section: Experiments and Methodsmentioning

confidence: 99%

Ice accretion thickness prediction using flash infrared thermal imaging and BP neural networks

Lü

Qingying

Pan

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

The present study investigated ice accretion thickness under non-incoming flow icing conditions on the ground using an infrared thermography system that converts infrared radiation temperature. Two back propagation (BP) neural network models were developed to measure ice thickness. Both theoretical model and polynomials were employed to fit the icing surface temperature elevation sequence to extract the pixel-level temperature attenuation characteristics, which were served as the input to the BP neural networks. The prediction method of ice thickness by the BP neural network was analysed from three perspectives of sensitivity, dimension, and precision. In addition, K-nearest neighbour (KNN) and support vector regression (SVR) algorithms were compared with BP neural network. In terms of prediction effect, the BP neural network performed the best. The verification of the BP neural network based on the characteristics of the theoretical model proved that the method can effectively predict the thickness of ice accretion, and its prediction error does not exceed 10%.

show abstract

Section: Experiments and Methodsmentioning

confidence: 99%

Ice accretion thickness prediction using flash infrared thermal imaging and BP neural networks

Lü

Qingying

Pan

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…e BP neural network sound conversion model based on MGC parameters is constructed in this paper, as shown in Figure 2. e neuron number of input and output is 60, the hidden layers are 2, and the neurons of hidden layers is 59 [15][16][17][18]. Parameters select the target sound signal value and then place the generated parameters into the WORLD system to synthesize a new sound signal.…”

Section: Bp Neural Network Sound Conversion Modelmentioning

confidence: 99%

Research on the Sound Quality Evaluation Method Based on Artificial Neural Network

Song

Yang

2022

Scientific Programming

View full text Add to dashboard Cite

For the improvement of the traditional evaluation effect of the automobile sound quality, an evaluation model of automobile sound quality is constructed based on BP neural network. The first is to introduce the basic principle of the BP neural network in detail. The second is to use the MGC parameters to establish the vehicle interior sound conversion model. The converted sound characteristic parameters are taken into the WORLD model to synthesize the new sound signals. Furthermore, the wavelet decomposition method is used to remove noise from the synthesized sound signals. Finally, a sound evaluation model based on BP neural network is established. The sound quality of automobiles can be better evaluated by carrying out the ABX test and MOS test in the field of sound conversion. For the newly synthesized sound signal and the target sound signal, it can be seen that the newly synthesized sound signal is more inclined to the target sound signal, and the sound quality is better. In addition, the sound quality is tested through loudness, roughness, sharpness, and level A in the field of sound quality evaluation. The final results show that the quality of newly synthesized sound is better, and the average errors of sound signals meet the sound standard. Therefore, the constructed sound conversion model and the sound evaluation model are feasible and effective.

show abstract

“…In recent years, BP neural network has also been developed in the field of harmonic detection because of its strong adaptive ability and good dynamic performance [7][8] . BP neural network can be divided into three layers, namely input layer.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Harmonic detection technology based on ant colony optimization BP neural network

Zou

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

At present, ip-iq algorithm based on instantaneous reactive power theory is the most widely used in the field of harmonic detection.However, the ip-iq algorithm based on instantaneous reactive power has coordinate transformation and inverse transformation, which leads to cumbersome calculation.the existence of low-pass filter leads to delay and poor dynamic performance of the algorithm.The harmonic detection based on BP neural network has the advantages of simple calculation, fast detection speed, strong adaptive ability and good dynamic performance.However, the initial weights and thresholds are random, so it is easy for the network to fall into local optimal solution during training, resulting in low detection accuracy. This paper presents a harmonic detection method based on ant colony algorithm optimized BP neural network. ACO-BP, BP and ip-iq algorithms are compared by Simulink simulation. The results show that ACO-BP harmonic detection algorithm is feasible and superior

show abstract

Fault diagnosis method for scintillation detector based on BP neural network

Cited by 11 publications

References 17 publications

Ice accretion thickness prediction using flash infrared thermal imaging and BP neural networks

Ice accretion thickness prediction using flash infrared thermal imaging and BP neural networks

Research on the Sound Quality Evaluation Method Based on Artificial Neural Network

Harmonic detection technology based on ant colony optimization BP neural network

Contact Info

Product

Resources

About