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Machine Learning Based Condition Monitoring for Gear Transmission Systems Using Data Generated by Optimal Multibody Dynamics Models by Josef Koutsoupakis; Panagiotis Seventekidis; Dimitrios Giagopoulos is a Engineering article available to read on EtoBox.

What is Machine Learning Based Condition Monitoring for Gear Transmission Systems Using Data Generated by Optimal Multibody Dynamics Models about?

Condition monitoring (CM) of mechanical systems such as gear transmissions can be performed with vibration measurements and processing of the recorded signals for identification of possible faults. The accuracy and robustness of a CM application depends among others on the availability of data for different health states which typically requires complete experimental measurements. The presented work contains a novel CM scheme using a Convolutional Neural Network (CNN) trained by data generated through numerical simulations of a Multibody Dynamics (MBD) system. The goal is to perform damage identification on different health states by exploiting simulated instead or experimental responses for supervised health state classification. First, a MBD model corresponding to the real system is developed and optimized using experimental measurements of the healthy mechanical structure. Then, data is generated by the MBD model using an uncertainty simulation repetitive load case algorithm to account for various model parameters inaccuracies. The simulated data is used after to train a supervised CNN classifier which is finally validated on vibration measurements of the physical system. The cl

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Author

Josef Koutsoupakis; Panagiotis Seventekidis; Dimitrios Giagopoulos

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)