Vibration Signal Decomposition using Dilated CNN



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


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).
Abstract 325 | PDF Downloads 112



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

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