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Found 9 papers, 0 papers with code
On Designing Features for Condition Monitoring of Rotating Machines
The designed input features are given as input to the classifier with end-to-end training in a single framework for machine conditions recognition.
A Novel Vision Transformer with Residual in Self-attention for Biomedical Image Classification
Biomedical image classification requires capturing of bio-informatics based on specific feature distribution.
Guided Sampling-based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis
Also, the best model obtained is transferred to the next generation for quick model evaluation in the NSGA-II evolutionary framework.
Knowledge Transfer based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis
We have used the Case Western Reserve University dataset, Paderborn university dataset, and gearbox fault detection dataset to demonstrate the effectiveness of the proposed framework for the selection of the best suitable architecture capable of excellent diagnostic performance, classification accuracy almost up to 100%
Quick Learning Mechanism with Cross-Domain Adaptation for Intelligent Fault Diagnosis
Therefore, the proposed method is capable of learning the new target data pattern quickly.
A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification
Clustering techniques have been proved highly suc-cessful for Takagi-Sugeno (T-S) fuzzy model identification.
Improved Adaptive Type-2 Fuzzy Filter with Exclusively Two Fuzzy Membership Function for Filtering Salt and Pepper Noise
The performance of the proposed filter is compared with the various state-of-the-art methods in terms of peak signal-to-noise ratio and computation time.
An Entropy-based Variable Feature Weighted Fuzzy k-Means Algorithm for High Dimensional Data
This paper presents a new fuzzy k-means algorithm for the clustering of high dimensional data in various subspaces.
Intelligent Condition Based Monitoring Techniques for Bearing Fault Diagnosis
In recent years, intelligent condition-based monitor-ing of rotary machinery systems has become a major researchfocus of machine fault diagnosis.
