Session: 05-05: Vibration Monitoring Analysis

Paper Number: 153005

Instantaneous Angular Speed Tracking of Rotating Machines Using a Bayesian Framework 

Accurate estimation of instantaneous angular speed is crucial for the condition monitoring and diagnostics of rotating machinery. Traditional methods often struggle in noisy environments, especially under fluctuating operational conditions. This paper proposes a Bayesian framework for tracking instantaneous frequency from vibration measurements, offering enhanced precision, robustness, and reliability. Our approach models the instantaneous angular speed as a hidden Markov process. The likelihood is derived from the observed spectrogram and the kinematics of the rotating machine, taking into acount harmonics. A prior distribution is introduced to leverage the frequency's continuity and incorporate domain-specific knowledge. By combining the prior and likelihood using Bayes' theorem, we obtain the posterior distribution, which represents the probability of the instantaneous angular speed given the observed data. The Bayesian estimator is determined through Viterbi dynamic programming algorithm, yielding the most probable sequence of speed states even in the presence of significant noise. This approach effectively handles non-stationary signals and can adapt to varying operating conditions. Simulation results demonstrate the superior accuracy and robustness of our approach compared to state-of-the-art methods, especially under high noise conditions. This innovative methodology has significant potential for applications in predictive maintenance, fault detection, and improving the overall operational efficiency of rotating machinery in various industrial settings.

Presenting Author: Maxime Leiber Safran Tech

Presenting Author Biography: Maxime Leiber is a researcher in signal processing and machine learning at Safran Tech, working on vibration diagnostics

Authors:

Maxime Leiber Safran Tech
Yosra Marnissi Safran Tech
Nacer Yousfi Safran Tech
Jean-Frédéric Diebold Safran Tech
Mohammed El Badaoui Safran Tech