Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Visualization and Aggregation of Nearest Neighbor Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Using diversity of errors for selecting members of a committee classifier
Pattern Recognition
Expert Systems with Applications: An International Journal
Confidence based multiple classifier fusion in speaker verification
Pattern Recognition Letters
A new approach to intelligent fault diagnosis of rotating machinery
Expert Systems with Applications: An International Journal
Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery
Applied Soft Computing
The combination of multiple classifiers using an evidential reasoning approach
Artificial Intelligence
Genetic algorithms for generation of class boundaries
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An LSSVR-based algorithm for online system condition prognostics
Expert Systems with Applications: An International Journal
An intelligent fault diagnosis system for newly assembled transmission
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Identifying gear damage categories, especially for early faults and combined faults, is a challenging task in gear fault diagnosis. This paper proposes a new multidimensional hybrid intelligent diagnosis method to identify different categories and levels of gear damage automatically. In this method, Hilbert transform, wavelet packet transform (WPT) and empirical mode decomposition (EMD) are performed on gear vibration signals to extract additional fault characteristic information. Then, multidimensional feature sets including time-domain, frequency-domain and time-frequency-domain features are generated to reveal gear health conditions. Multiple classifiers based on several classification algorithms and input features are combined with genetic algorithm (GA). Because of the use of multidimensional features and the combination of multiple classifiers, more accurate diagnosis results are expected with the proposed method. Experiments with different gear damage categories and damage levels were conducted, and the vibration signals were captured under different loads and motor speeds. The proposed method is applied to the collected signals to identify the gear damage categories and damage levels. The diagnosis results show it can reliably recognize single damage modes, combined damage modes, and damage levels.