A Preprocessing and Modeling Approach for Gearbox Pitting Severity Prediction under Unseen Operating Conditions and Fault Severities
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
May 6, 2024
Rik Vaerenberg
Douw Marx
Seyed Ali Hosseinli
Fabrizio De Fabritiis
Hao Wen
Rui Zhu
Konstantinos Gryllias
http://orcid.org/0000-0002-2650-7120
Seyed Ali Hosseinli
Fabrizio De Fabritiis
Hao Wen
Rui Zhu
Konstantinos Gryllias
Abstract
Gear pitting is a common gearbox failure mode that can lead to unplanned machine downtime, inefficient power transmission and a higher risk of sudden catastrophic failure. Consequently, there is strong incentive to create machine learning models that are capable of detecting and quantifying the severity of gearbox pitting faults. The performance of machine learning models is however highly dependent on the availability of training data and since training data for a wide variety of different operating conditions and fault severities is rarely available in practice, machine learning models must be designed to be robust to unseen operating conditions and fault severities. Furthermore, models should be capable of identifying data outside of the training data distribution and adjusting the confidence in a prediction accordingly. This work presents a strategy for pitting severity estimation in gearboxes under unseen operating conditions and fault severities in response to the PHM North America 2023 Conference Data Challenge. The strategy includes the design of dedicated validation sets for quantifying model performance on unseen data, an investigation into the most appropriate preprocessing methods, and a specialized convolutional neural network with an integrated out of distribution detection model for identifying samples from foreign operating conditions and fault severities. The results show that the best models are capable of some generalization to unseen operating conditions, but the generalization to unseen pitting severities is more challenging.
##plugins.themes.bootstrap3.article.details##
Keywords
preprocessing, pitting, unseen conditions, CNN, gearbox
References
Alam, S., Sonbhadra, S. K., Agarwal, S., & Nagabhushan, P. (2020, May). One-class support vector classifiers: A survey. Knowledge-Based Systems, 196, 105754. doi: 10.1016/j.knosys.2020.105754
Antoni, J. (2007). Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions. Journal of Sound and vibration, 304(3-5), 497–529.
Antoni, J., Xin, G., & Hamzaoui, N. (2017). Fast computation of the spectral correlation. Mechanical Systems and Signal Processing, 92, 248–277.
Bishop, C. (1994, August). Novelty detection and neural network validation. IEE Proceedings Vision, Image and Signal Processing, 141, 217-222(5).
Borghesani, P., Pennacchi, P., Randall, R., Sawalhi, N., & Ricci, R. (2013, April). Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions. Mechanical Systems and Signal Processing, 36(2), 370–384. doi: 10.1016/j.ymssp.2012.11.001
Box, G. E. P., & Cox, D. R. (1964, July). An Analysis of Transformations. Journal of the Royal Statistical Society: Series B (Methodological), 26(2), 211–243. doi: 10.1111/j.2517-6161.1964.tb00553.x
Breunig, M. M., Kriegel, H.P., Ng, R. T., & Sander, J. (2000, May). LOF: Identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data (pp. 93–104). New York, NY, USA: Association for Computing Machinery. doi: 10.1145/342009.335388
Cartocci, N., Napolitano, M. R., Costante, G., Valigi, P., & Fravolini, M. L. (2022, May). Aircraft robust data-driven multiple sensor fault diagnosis based on optimality criteria. Mechanical Systems and Signal Processing, 170, 108668. doi: 10.1016/j.ymssp.2021.108668
Chen, Z., Li, C., & Sanchez, R.V. (2015). Gearbox Fault Identification and Classification with Convolutional Neural Networks. Shock and Vibration, 2015, 1–10. doi: 10.1155/2015/390134
Cui, Y., Jia, M., Lin, T.Y., Song, Y., & Belongie, S. (2019). Class-balanced loss based on effective number of samples. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition (pp. 9268–9277).
Elasha, F., Ruiz-Cárcel, C., Mba, D., Kiat, G., Nze, I., & Yebra, G. (2014, July). Pitting detection in worm gearboxes with vibration analysis. Engineering Failure Analysis, 42, 366–376. doi: 10.1016/j.engfailanal.2014.04.028
Ganaie, M., Hu, M., Malik, A., Tanveer, M., & Suganthan, P. (2022, October). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151. doi: 10.1016/j.engappai.2022.105151
Hendriks, J., Dumond, P., & Knox, D. (2022, April). Towards better benchmarking using the CWRU bearing fault dataset. Mechanical Systems and Signal Processing, 169, 108732. doi: 10.1016/j.ymssp.2021.108732
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Kumar, A., Parey, A., & Kankar, P. K. (2023, June). Supervised Machine Learning Based Approach for Early Fault Detection in Polymer Gears Using Vibration Signals. MAPAN, 38(2), 383–394. doi: 10.1007/s12647-022-00608-8
Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. In I. Guyon et al. (Eds.), Advances in neural information processing systems (Vol. 30). Curran Associates, Inc. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard,
R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4), 541–551.
Lee, J., Wu, F., Zhao, W., Ghaffari, M., & Liao, L. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 21. doi:
http://dx.doi.org/10.1016/j.ymssp.2013.06.004
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018, May). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834. doi: 10.1016/j.ymssp.2017.11.016
Li, X., Li, J., Qu, Y., & He, D. (2019). Gear pitting fault diagnosis using integrated cnn and gru network with both vibration and acoustic emission signals. Applied Sciences, 9(4), 768.
Li, X., Li, J., Zhao, C., Qu, Y., & He, D. (2020, August). Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection. Mechanical Systems and Signal Processing, 142, 106740. doi: 10.1016/j.ymssp.2020.106740
Loshchilov, I., & Hutter, F. (2017). Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101.
Louizos, C., & Welling, M. (2017, 06–11 Aug). Multiplicative normalizing flows for variational Bayesian neural networks. In D. Precup & Y. W. Teh (Eds.), Proceedings of the 34th international conference on machine learning (Vol. 70, pp. 2218–2227). PMLR. Mauricio, A., Smith, W. A., Randall, R. B., Antoni, J., & Gryllias, K. (2020). Improved envelope spectrum via feature optimisation-gram (iesfogram): A novel tool for rolling element bearing diagnostics under non-stationary operating conditions. Mechanical Systems and Signal Processing, 144, 106891. doi: https://doi.org/10.1016/j.ymssp.2020.106891
McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–127.
Mcfadden, P., & Toozhy, M. (2000, November). Application of synchronous averaging to vibration monitoring of rolling element bearings. Mechanical Systems and Signal Processing, 14(6), 891–906. doi: 10.1006/mssp.2000.1290 Medina, R., Cerrada, M., Cabrera, D., Sanchez, R.V., Li, C., & Oliveira, J. V. D. (2019, May). Deep Learning Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals. In 2019 Prognostics and System Health Management Conference (PHM-Paris) (pp. 210–216). Paris, France: IEEE. doi: 10.1109/PHM-Paris.2019.00042
Öztürk, H., Sabuncu, M., & Yesilyurt, I. (2008, April). Early Detection of Pitting Damage in Gears using Mean Frequency of Scalogram. Journal of Vibration and Control, 14(4), 469–484. doi: 10.1177/1077546307080026 Prognostics and Health Management Society. (2023).
PHM North America 2023 Conference Data Challenge. Online data repository. (Accessed on 2023-10-17. https://data.phmsociety.org/phm2023-conference-data-challenge)
Qin, Y., Wang, Z., & Xi, D. (2022, January). Tree CycleGAN with maximum diversity loss for image augmentation and its application into gear pitting detection. Applied Soft Computing, 114, 108130. doi:
10.1016/j.asoc.2021.108130
Qu, Y., He, M., Deutsch, J., & He, D. (2017, May). Detection of Pitting in Gears Using a Deep Sparse Autoencoder. Applied Sciences, 7(5), 515. doi: 10.3390/app7050515
Rosenthal, E., & Ratna, S. (2022, March). Spacecutter. https://github.com/EthanRosenthal/spacecutter, https://www.ethanrosenthal.com/2018/12/06/spacecutter ordinal regression/. Salameh, J. P., Cauet, S., Etien, E., Sakout, A., & Rambault, L. (2018, October). Gearbox condition monitoring in wind turbines: A review. Mechanical Systems and Signal Processing, 111, 251–264. doi: 10.1016/j.ymssp.2018.03.052
Schmidt, S., & Heyns, P. S. (2019, March). An open set recognition methodology utilising discrepancy analysis for gear diagnostics under varying operating conditions. Mechanical Systems and Signal Processing, 119, 1–22. doi: 10.1016/j.ymssp.2018.09.016
Shimodaira, H. (2000). Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 90(2), 227-244. doi: https://doi.org/10.1016/S03783758(00)00115-4
Su, X., Tomovic, M. M., & Zhu, D. (2019). Diagnosis of gradual faults in high-speed gear pairs using machine learning. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41, 1–11.
Teng, W., Wang, F., Zhang, K., Liu, Y., & Ding, X. (2014, January). Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition. Strojniški vestnik – Journal of Mechanical Engineering, 60(1), 12–20. doi: 10.5545/sv-jme.2013.1295
Welch, P. (1967). The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on audio and electroacoustics, 15(2), 70–73.
Antoni, J. (2007). Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions. Journal of Sound and vibration, 304(3-5), 497–529.
Antoni, J., Xin, G., & Hamzaoui, N. (2017). Fast computation of the spectral correlation. Mechanical Systems and Signal Processing, 92, 248–277.
Bishop, C. (1994, August). Novelty detection and neural network validation. IEE Proceedings Vision, Image and Signal Processing, 141, 217-222(5).
Borghesani, P., Pennacchi, P., Randall, R., Sawalhi, N., & Ricci, R. (2013, April). Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions. Mechanical Systems and Signal Processing, 36(2), 370–384. doi: 10.1016/j.ymssp.2012.11.001
Box, G. E. P., & Cox, D. R. (1964, July). An Analysis of Transformations. Journal of the Royal Statistical Society: Series B (Methodological), 26(2), 211–243. doi: 10.1111/j.2517-6161.1964.tb00553.x
Breunig, M. M., Kriegel, H.P., Ng, R. T., & Sander, J. (2000, May). LOF: Identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data (pp. 93–104). New York, NY, USA: Association for Computing Machinery. doi: 10.1145/342009.335388
Cartocci, N., Napolitano, M. R., Costante, G., Valigi, P., & Fravolini, M. L. (2022, May). Aircraft robust data-driven multiple sensor fault diagnosis based on optimality criteria. Mechanical Systems and Signal Processing, 170, 108668. doi: 10.1016/j.ymssp.2021.108668
Chen, Z., Li, C., & Sanchez, R.V. (2015). Gearbox Fault Identification and Classification with Convolutional Neural Networks. Shock and Vibration, 2015, 1–10. doi: 10.1155/2015/390134
Cui, Y., Jia, M., Lin, T.Y., Song, Y., & Belongie, S. (2019). Class-balanced loss based on effective number of samples. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition (pp. 9268–9277).
Elasha, F., Ruiz-Cárcel, C., Mba, D., Kiat, G., Nze, I., & Yebra, G. (2014, July). Pitting detection in worm gearboxes with vibration analysis. Engineering Failure Analysis, 42, 366–376. doi: 10.1016/j.engfailanal.2014.04.028
Ganaie, M., Hu, M., Malik, A., Tanveer, M., & Suganthan, P. (2022, October). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151. doi: 10.1016/j.engappai.2022.105151
Hendriks, J., Dumond, P., & Knox, D. (2022, April). Towards better benchmarking using the CWRU bearing fault dataset. Mechanical Systems and Signal Processing, 169, 108732. doi: 10.1016/j.ymssp.2021.108732
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Kumar, A., Parey, A., & Kankar, P. K. (2023, June). Supervised Machine Learning Based Approach for Early Fault Detection in Polymer Gears Using Vibration Signals. MAPAN, 38(2), 383–394. doi: 10.1007/s12647-022-00608-8
Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. In I. Guyon et al. (Eds.), Advances in neural information processing systems (Vol. 30). Curran Associates, Inc. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard,
R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4), 541–551.
Lee, J., Wu, F., Zhao, W., Ghaffari, M., & Liao, L. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 21. doi:
http://dx.doi.org/10.1016/j.ymssp.2013.06.004
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018, May). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834. doi: 10.1016/j.ymssp.2017.11.016
Li, X., Li, J., Qu, Y., & He, D. (2019). Gear pitting fault diagnosis using integrated cnn and gru network with both vibration and acoustic emission signals. Applied Sciences, 9(4), 768.
Li, X., Li, J., Zhao, C., Qu, Y., & He, D. (2020, August). Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection. Mechanical Systems and Signal Processing, 142, 106740. doi: 10.1016/j.ymssp.2020.106740
Loshchilov, I., & Hutter, F. (2017). Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101.
Louizos, C., & Welling, M. (2017, 06–11 Aug). Multiplicative normalizing flows for variational Bayesian neural networks. In D. Precup & Y. W. Teh (Eds.), Proceedings of the 34th international conference on machine learning (Vol. 70, pp. 2218–2227). PMLR. Mauricio, A., Smith, W. A., Randall, R. B., Antoni, J., & Gryllias, K. (2020). Improved envelope spectrum via feature optimisation-gram (iesfogram): A novel tool for rolling element bearing diagnostics under non-stationary operating conditions. Mechanical Systems and Signal Processing, 144, 106891. doi: https://doi.org/10.1016/j.ymssp.2020.106891
McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–127.
Mcfadden, P., & Toozhy, M. (2000, November). Application of synchronous averaging to vibration monitoring of rolling element bearings. Mechanical Systems and Signal Processing, 14(6), 891–906. doi: 10.1006/mssp.2000.1290 Medina, R., Cerrada, M., Cabrera, D., Sanchez, R.V., Li, C., & Oliveira, J. V. D. (2019, May). Deep Learning Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals. In 2019 Prognostics and System Health Management Conference (PHM-Paris) (pp. 210–216). Paris, France: IEEE. doi: 10.1109/PHM-Paris.2019.00042
Öztürk, H., Sabuncu, M., & Yesilyurt, I. (2008, April). Early Detection of Pitting Damage in Gears using Mean Frequency of Scalogram. Journal of Vibration and Control, 14(4), 469–484. doi: 10.1177/1077546307080026 Prognostics and Health Management Society. (2023).
PHM North America 2023 Conference Data Challenge. Online data repository. (Accessed on 2023-10-17. https://data.phmsociety.org/phm2023-conference-data-challenge)
Qin, Y., Wang, Z., & Xi, D. (2022, January). Tree CycleGAN with maximum diversity loss for image augmentation and its application into gear pitting detection. Applied Soft Computing, 114, 108130. doi:
10.1016/j.asoc.2021.108130
Qu, Y., He, M., Deutsch, J., & He, D. (2017, May). Detection of Pitting in Gears Using a Deep Sparse Autoencoder. Applied Sciences, 7(5), 515. doi: 10.3390/app7050515
Rosenthal, E., & Ratna, S. (2022, March). Spacecutter. https://github.com/EthanRosenthal/spacecutter, https://www.ethanrosenthal.com/2018/12/06/spacecutter ordinal regression/. Salameh, J. P., Cauet, S., Etien, E., Sakout, A., & Rambault, L. (2018, October). Gearbox condition monitoring in wind turbines: A review. Mechanical Systems and Signal Processing, 111, 251–264. doi: 10.1016/j.ymssp.2018.03.052
Schmidt, S., & Heyns, P. S. (2019, March). An open set recognition methodology utilising discrepancy analysis for gear diagnostics under varying operating conditions. Mechanical Systems and Signal Processing, 119, 1–22. doi: 10.1016/j.ymssp.2018.09.016
Shimodaira, H. (2000). Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 90(2), 227-244. doi: https://doi.org/10.1016/S03783758(00)00115-4
Su, X., Tomovic, M. M., & Zhu, D. (2019). Diagnosis of gradual faults in high-speed gear pairs using machine learning. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41, 1–11.
Teng, W., Wang, F., Zhang, K., Liu, Y., & Ding, X. (2014, January). Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition. Strojniški vestnik – Journal of Mechanical Engineering, 60(1), 12–20. doi: 10.5545/sv-jme.2013.1295
Welch, P. (1967). The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on audio and electroacoustics, 15(2), 70–73.
Section
Technical Papers