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Tuesday, July 9 • 15:30 - 15:50

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Vibration-based condition monitoring represents the most efficient technology for early prediction and detection of failures in rotating machines. Faults can be detected by extracting typical features of vibration signature and comparing them to known thresholds of acceptable behaviour. Defining appropriate limit thresholds independently of the speed regimes, in order to perform a real time monitoring of any fault condition in system operation, is often a task not easy to achieve. The paper aims at presenting in this sense an effective condition monitoring technique for rotating machines, relying on a black box modelling approach of system dynamic behaviour. The powerful capabilities of the methodology are highlighted by implementing the model for a typical problem of rotating machines fault diagnosis. The proposed approach involves first identifying a Nonlinear ARX model, trained using the data from the healthy (nominal) operation of the machine. The model is then used for simulation of system known dynamics, to compute residuals by subtracting the model-produced outputs from the corresponding measured signals. Through an accurate monitoring of the properties of residuals, such as their mean, variance and root mean square, the method is able to successfully distinguish normal and faulty operations as well as to properly rank fault severity. The high mode-discrimination power of each considered residuals feature demonstrates the robustness of the technique and its attractiveness to face with rotating machines health monitoring problems.


Sébastien Laurier Chapleau

Senior Technical Specialist - Innovation - Dynamics & Acoustics, Bell (Flight)


Tuesday July 9, 2019 15:30 - 15:50 EDT
Westmount 3
  T10 Sig. Proc. & nonlin. mthds., RS03 Machinery health monit