Title: A fault diagnosis method of intelligent electronic equipment based on dynamic fusion

Authors: Jun Qian; Kaijun Nie

Addresses: Suzhou College of Information Technology, College of Communication and Electronics, Suzhou 215200, China ' Suzhou College of Information Technology, College of Communication and Electronics, Suzhou 215200, China

Abstract: In order to overcome the problems of low accuracy of fault data extraction and long diagnosis time, a fault diagnosis method of intelligent electronic equipment based on dynamic fusion is proposed. Convolution neural network and long-term and short-term memory neural network are dynamically fused to extract the characteristic parameters of intelligent electronic equipment based on neural network. Combined with the principle of feature collection, the fault information of intelligent electronic equipment is collected, the covariance matrix of data noise data is constructed, the key noise is extracted, the noise reduction of fault data is completed, and the fault diagnosis of intelligent electronic equipment is carried out according to the results of fault information fusion and fuzzy kernel c-means clustering. The experimental results show that the highest accuracy of fault data extraction is about 95%, and the shortest time of sample fault data extraction is about 0.5 s.

Keywords: dynamic fusion; intelligent electronic equipment; noise interference degree; fuzzy kernel c-means clustering; fault diagnosis.

DOI: 10.1504/IJPD.2023.135741

International Journal of Product Development, 2023 Vol.27 No.4, pp.318 - 332

Received: 03 Aug 2021
Accepted: 18 Feb 2022
Published online: 04 Jan 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article