Vibration Signal Decomposition using Dilated CNN

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Oct 28, 2023
Eli Gildish Michael Grebshtein Yehudit Aperstein Igor Makienko

Abstract

Vibration sensors have gained increasing popularity as valuable tools for Prognostics and Health Management (PHM) applications, enabling early detection of mechanical failures in industrial machines. Vibration signals comprise two main sources of information: periodic vibrations from components, phase-locked to the rotating speed (e.g., gears), and non-deterministic broadband vibrations associated with bearings, structure, and background noise. In PHM applications, it is important to decompose vibrations into these two sources to optimize the use of different diagnostic methods for each signal component. In practice, the decomposition should be cost-effective by working without supplementary information about system operating conditions and kinematics.

Existing methods of vibration source separation commonly rely on an auto-regression (AR) model of vibrations and employ adaptive filtering techniques to estimate its parameters. However, these methods suffer from degraded accuracy in complex geared vibrations containing numerous periodic components and requiring large filter length to promise high frequency resolution in component separation.

To address these challenges, we propose a new method that utilizes dilated Convolutional Neural Networks (CNNs) instead of adaptive filtering to improve the accuracy of decomposing complex vibration signals, all without the need for any supplementary information.

To evaluate the performance of the new method, we conducted experiments using both simulated signals and real-world vibrations. The simulation results demonstrate improved accuracy in signal decomposition when our method is used instead of adaptive filtering. Additionally, the new method applied to real vibrations, showcases significant enhancement in bearing failure detection through accurate isolation of bearing-related vibrations.

This study reveals the potential of our new method in various PHM applications requiring highly accurate diagnostics and prognostics in complex geared vibrations, particularly when supplementary information about operating conditions and system kinematics is unavailable.

How to Cite

Gildish, E., Grebshtein, M., Aperstein, Y., & Makienko, I. (2023). Vibration Signal Decomposition using Dilated CNN. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3502
Abstract 927 | PDF Downloads 291

##plugins.themes.bootstrap3.article.details##

Keywords

Autonomous PHM, Predictive Maintenance, Condition Monitoring, Vibration-based Monitoring, Industrial IoT, Machine Learning, Deep Learning

References
Abboud, D., Antoni, J., Sieg-Zieba, S., & Eltabach, M. (2016). Deterministic-random separation in nonstationary regime. Journal of Sound and Vibration, 362, 305-326.

Abboud, D., Assoumane, A., Marnissi, Y., & El Badaoui, M. (2019, July). Synchronous fitting for deterministic signal extraction in non-stationary regimes: application to helicopter vibrations. In Surveillance, Vishno and AVE conferences.

Antoni, J., & Randall, R. (2004). Unsupervised noise cancellation for vibration signals: part II—a novel frequency-domain algorithm. Mechanical Systems and Signal Processing, vol. 18, no. 1, pp. 103-117.

Araujo, A., Norris, W., & Sim, J. (2019). Computing receptive fields of convolutional neural networks. Distill, 4(11), e21.
Borovykh, A., Bohte, S., & Oosterlee, C. W. (2018). Dilated convolutional neural networks for time series forecasting. Journal of Computational Finance, Forthcoming.

Braun, S. (2011). The Synchronous (Time Domain) Average revisited. Mechanical Systems and Signal Processing, vol 25(4), pp. 1087-1102.

Dixit, S., & Nagaria, D. (2017). LMS adaptive filters for noise cancellation: A review. International Journal of Electrical and Computer Engineering (IJECE), 7(5), 2520-2529.

Feng, K., Borghesani, P., Smith, W.A., Randall, R.B., Chin, Z.Y., Ren, J., & Peng, Z. (2018) (2019). Vibration-based updating of wear prediction for spur gears, Wear 426–427, pp. 1410–1415.

Fujieda, S., Takayama, K., & Hachisuka, T. (2018). Wavelet convolutional neural networks. arXiv preprint arXiv:1805.08620.

Fyfe, K.R, and Munck, E.D.S. (1997). Analysis of computed order tracking. Mechanical Systems and Signal Processing, vol 11 no. 2, pp.187 – 205.

Gildish, E., Grebshtein, M., Aperstein, Y., Kushnirski, A., & Makienko, I. (2022, June). Helicopter bolt looseningmonitoring using vibrations and machine learning, PHME CONF, vol. 7, no. 1, pp. 146–155.

Gildish, E., Grebshtein, M., Aperstein, Y., & Makienko, I. (2022). Vibration-Based Estimation of Gearbox Operating Conditions: Machine Learning Approach, To be published in 2023 International Conference on Control, Automation and Diagnosis (ICCAD) IEEE.

Groover, C., Trethewey, M., Maynard, K. & Lebold, M. (2005). Removal of order domain content in rotating equipment signals by double resampling. Mechanical Systems and Signal Processing, vol. 19, no. 3, pp. 483-500.

Jackson, K. (1996). Vibration Analysis Level 2 - Understanding the Basics. Integrated Maintenance Solutions Inc. LeCun, Y.,

Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. Li, Y., Li, K., Chen, C., Zhou, X., Zeng, Z., & Li, K. (2021). Modeling temporal patterns with dilated convolutions for time-series forecasting. ACM Transactions on Knowledge
Discovery from Data (TKDD), 16(1), 1-22. Mishra, K., Basu, S., & Maulik, U. (2021). A Dilated Convolutional Based Model for
Time Series Forecasting. SN Computer Science, 2, 1-11.

Peeters, B., Cornelis, B., Janssens, K. & Van der Auweraer H. (2007). Removing Disturbing Harmonics in Operational Modal Analysis. Proceedings of the 2nd International Operational Modal Analysis Conference. April 30 - May 2, Copenhagen, Denmark

Peeters, B. & Van der Auweraer H. (2005). PolyMax: a revolution in operational modal analysis. Proceedings of the 1st International Operational Modal Analysis Conference. April 26-27, Copenhagen, Denmark.

Priestley, M. B. (1981). Spectral analysis and time series: probability and mathematical statistics (No. 04; QA280, P7.

Randall, R (2004). Unsupervised noise cancellation for vibration signals: Part I - Evaluation of adaptive algorithms. Mechanical Systems and Signal Processing, vol. 18, pp. 89-101.

Randall, R., & Sawalhi, N. (2011). A New Method for Separating Discrete Components from a Signal. Sound and Vibration, vol. 45, pp. 6-9.

Tiboni, M., Remino, C., Bussola R., & Amici, C. (2022). A review on vibration-based condition monitoring of rotating machinery, Applied Sciences, 12(3), p.972.

Zimroz, R., Bartelmus, W., Barszcz, T., & Urbanek, J. (2014). Diagnostics of bearings in presence of strong operating conditions non-stationarity – a procedure of load dependent features processing with application to wind turbine bearings. Mech. Syst. Signal Process. 46 (1), pp. 16–27.
Section
Technical Research Papers