Name
Title | ||
|---|---|---|
Weighted ANN Input Layer for Adaptive Features Selection for Robust Fault Classification. |
Abstract | ||
|---|---|---|
Model based feature selection for identification of diverse faults in rotary machines can significantly cost time and money and it is nearly impossible to model all faults under different operating environments. In this paper, feedforward ANN input-layer-weights have been used for the adaptive selection of the least number of features, without fault model information, reducing the computations significantly but assuring the required accuracy by mitigating the noise. In the proposed approach, under the assumption that presented features should be translation invariant, ANN uses entire set of spectral features from raw input vibration signal for training. Dominant features are then selected using input-layer-weights relative to a threshold value vector. Different instances of ANN are then trained and tested to calculate F1_score with the reduced dominant features at different SNRs for each threshold value. Trained ANN with best average classification accuracy among all ANN instances gives us required number of dominant features. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26535-3_5 | ICONIP |
Keywords | Field | DocType |
Machine health monitoring (MHM),Adaptive feature selection,Features reduction,Artificial neural networks (ANNs),Fault diagnosis | Pattern recognition,Feature selection,Computer science,Adaptive selection,Threshold limit value,Artificial intelligence,Invariant (mathematics),Vibration,Machine learning,Fault model,Computation,Feed forward | Conference |
Volume | ISSN | Citations |
9490 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Amar | 1 | 6 | 1.14 |
| Iqbal Gondal | 2 | 316 | 48.05 |
| Campbell Wilson | 3 | 23 | 6.62 |