Application of the Case-Based Reasoning Approach for Identification of Acoustic-Emission Control Signals of Complex Technical Objects

Alekhin, Roman; Varshavsky, Pavel; Eremeev, Alexander; Kozhevnikov, Anton

doi:10.1109/rpc.2018.8482124

Cited by 5 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Pretreated dissolved gas analysis (DGA) data is used in the CBR system. Reference [9] developed a case-based reasoning approach for identifying and filtering acoustic emission (AE) noise signals. The paper proposed a parametric case representation method for the AE signal process.…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Data Matching Method for GIS Case-Based Reasoning

et al. 2019

View full text Add to dashboard Cite

With the accumulation of partial discharge (PD) detection data from substation, case-based reasoning (CBR), which computes the match degree between detected PD data and historical case data provides new ideas for the interpretation and evaluation of partial discharge data. Aiming at the problem of partial discharge data matching, this paper proposes a data matching method based on a variational autoencoder (VAE). A VAE network model for partial discharge data is constructed to extract the deep eigenvalues. Cosine distance is then used to calculate the match degree between different partial discharge data. To verify the advantages of the proposed method, a partial discharge dataset was established through a partial discharge experiment and live detections on substation site. The proposed method was compared with other feature extraction methods and matching methods including statistical features, deep belief networks (DBN), deep convolutional neural networks (CNN), Euclidean distances, and correlation coefficients. The experimental results show that the cosine distance match degree based on the VAE feature vector can effectively detect similar partial discharge data compared with other data matching methods.

show abstract