Congying Deng
Chongqing University of Posts and Telecommunications
26 Papers
11 Citations
Congying Deng is an academic researcher from Chongqing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Machining. The author has an hindex of 5, co-authored 11 publications.
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Papers
Evaluation of machine tools with position-dependent milling stability based on Kriging model
TL;DR: In this paper, a Kriging model is developed to describe the relationship between positions and modal parameters, with which a modal fitting technique is used to reorder the frequency response function (FRF) at the tool tip at any position in the whole working space.
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Reliability analysis of chatter stability for milling process system with uncertainties based on neural network and fourth moment method
TL;DR: A reliability analysis of the milling system with uncertainties is developed in this paper to predict reliable chatter-free machining parameters and a reliable stability lobe diagram (RSLD) can be plotted to obtain more reliable and accurate stable region instead of the conventional SLD.
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Interactive channel attention for rotating component fault detection with strong noise and limited data
TL;DR: Wang et al. as mentioned in this paper proposed an improved intelligent fault detection method for rotating component based on interactive channel attention (ICA), which contains two submodules to help the CNN model pay attention to channel correlation of both global and local channels.
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Fault Diagnosis Method for Imbalanced Data Based on Multi-Signal Fusion and Improved Deep Convolution Generative Adversarial Network
TL;DR: Wang et al. as mentioned in this paper proposed an intelligent fault diagnosis method based on deep learning to address the imbalanced data problem and enhance the diagnosis accuracy, where signals from multiple sensors are processed by the wavelet transform to enhance data features, which are then squeezed and fused through pooling and splicing operations.
Semi-supervised Ensemble Fault Diagnosis Method based on Adversarial Decoupled Auto-encoder with Extremely Limited Labels
Congying Deng,Zihao Deng,Jianguo Miao +2 more
- 01 Oct 2023
TL;DR: A novel semi-supervised ensemble fault diagnosis framework, ADAE-LFDM, is proposed to enhance mechanical equipment reliability with extremely limited labels, achieving over 97% diagnostic accuracy with a single labeled sample per fault type.
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