Assessing electric vehicles behavior in power networks: A non-stationary discrete Markov chain approach

Bahmani, Mohammad Hassan; Esmaeili Shayan, Mostafa; Fioriti, Davide

doi:10.1016/j.epsr.2023.110106

Cited by 5 publications

(1 citation statement)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, considering the manual factors brought by the two-dimensional matrix construction methods above, this article proposes a CNN fault diagnosis method combined with uniform angle sampling. Uniform angle sampling techniques are widely used in many industries [37,38], and thus data for order analysis can be easily sourced. Firstly, uniform angle sampling techniques are used to collect data with equal-angle intervals rather than the commonly used sampling method with equal time intervals.…”

Section: Introductionmentioning

confidence: 99%

Improved Fault Diagnosis of Roller Bearings Using an Equal-Angle Integer-Period Array Convolutional Neural Network

Li,

Yuan,

Zhang

et al. 2024

Electronics

View full text Add to dashboard Cite

This article presents a technique to carry out fault classification using an equal-angle integer-period array convolutional neural network (EAIP-CNN) to process the electrostatic signal of working roller bearings. Firstly, electrostatic signals were collected using uniform angle sampling to ensure the angle intervals between two adjacent data points stayed the same and the signal length was fixed to a pre-determined number of rotation cycles. Then, this one-dimensional signal was transformed into a two-dimensional matrix, where the component of each row was the signal in one period, and the ordinate value of each row represented the corresponding rotation period. Therefore, the row and column indexes of the matrix had a specific meaning instead of simply splitting and stacking the data. Finally, the matrixes were utilized to train the CNN network and test the classification performance. The results show that the classification rate using this technique reaches 95.6%, which is higher than that of 2D CNNs without equal-angle integer-period arrays.

show abstract

Section: Introductionmentioning

confidence: 99%