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.
Keywords
- Machine health monitoring (MHM)
- Adaptive feature selection
- Features reduction
- Artificial neural networks (ANNs)
- Fault diagnosis
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Li, B., Chow, M.Y., Tipsuwan, Y., Hung, J.C.: Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Ind. Electron. 47(5), 1060–1069 (2000)
Bellini, A., Immovilli, F., Rubini, R., Tassoni, C.: Diagnosis of bearing faults of induction machines by vibration or current signals: a critical comparison. In: Industry Applications Society Annual Meeting, pp. 1–8 (2008)
Yiakopoulos, C.T., Gryllias, K.C., Antoniadis, I.A.: Rolling element bearing fault detection in industrial environments based on a K-means clustering approach. Expert Syst. Appl. 38(3), 2888–2911 (2011)
Bin, Z., Sconyers, C., Byington, C., Patrick, R., Orchard, M., Vachtsevanos, G.: A probabilistic fault detection approach: application to bearing fault detection. IEEE Trans. Ind. Electron. 58(5), 2011–2018 (2011)
Amar, M., Gondal, I., Willson, C.: Vibration spectrum imaging: a bearing fault classification approach. IEEE Trans. Ind. Electron. 62(1), 494–502 (2015)
Amar, M., Gondal, I., Willson, C.: Multi-size-window spectral augmentation: neural network bearing fault classifier. In: The 8th IEEE Conference on Industrial Electronics and Applications, pp. 261–266 (2013)
Yaqub, M.F., Gondal, I., Kamruzzaman, J.: Inchoate fault detection framework: adaptive selection of wavelet nodes and cumulant orders. IEEE Trans. Instrum. Meas. 61(3), 685–695 (2012)
Bouzida, A., Touhami, O., Ibtiouen, R., Belouchrani, A., Fadel, M., Rezzoug, A.: Fault diagnosis in industrial induction machines through discrete wavelet transform. IEEE Trans. Ind. Electron. 58(9), 4385–4395 (2011)
Su, H., Chong, K.T.: Induction machine condition monitoring using neural network modeling. IEEE Trans. Ind. Electron. 54(1), 241–249 (2007)
Bearing Data Center, http://www.eecs.case.edu/laboratory/bearing/welcome_overview.htm
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Amar, M., Gondal, I., Wilson, C. (2015). Weighted ANN Input Layer for Adaptive Features Selection for Robust Fault Classification. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_5
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DOI: https://doi.org/10.1007/978-3-319-26535-3_5
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