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<p>The use of machine learning for machine monitoring and fault detection is already quite common to solve some problems within the context of Industry 4.0. However, there are still areas where the use of machine learning techniques is very incipient or almost nonexistent. As an example, one can mention the problem encountered in identifying the lift-off state of shafts in relation to air foil bearings. The main justification for the study’s relevance lies in the absence of a suitable approach for defining the lift-off state, without the need for specially designed test rigs. The state-of–the-art approach needs these test rigs to measure the friction torque of the bearing. The present study employs a convolutional neural network to monitor the lift-off state of a gas foil journal bearing by utilizing experimentally obtained acceleration data, offering a more versatile alternative. The acceleration data from the test rig are analyzed in both the time and frequency domains. The technique used involved converting the signal into vibration images. In all configurations analyzed, the network’s accuracy in identifying the lift-off phenomenon exceeds 90%, demonstrating the potential of the proposed methodology. </p>

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Lift-off identification in gas foil bearings through vibration signal analysis using a convolutional neural network

  • 期刊论文

  • 发表日期：

    2025年6月19日

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  • Leonardo L. Gusmão,
  • Marian Sarrazin,
  • Majid Ahmadzadeh,
  • Robert Liebich,
  • Martin L. Kliemank,
  • Clemens Gühmann,
  • Tiago H. Machado

摘要

The use of machine learning for machine monitoring and fault detection is already quite common to solve some problems within the context of Industry 4.0. However, there are still areas where the use of machine learning techniques is very incipient or almost nonexistent. As an example, one can mention the problem encountered in identifying the lift-off state of shafts in relation to air foil bearings. The main justification for the study’s relevance lies in the absence of a suitable approach for defining the lift-off state, without the need for specially designed test rigs. The state-of–the-art approach needs these test rigs to measure the friction torque of the bearing. The present study employs a convolutional neural network to monitor the lift-off state of a gas foil journal bearing by utilizing experimentally obtained acceleration data, offering a more versatile alternative. The acceleration data from the test rig are analyzed in both the time and frequency domains. The technique used involved converting the signal into vibration images. In all configurations analyzed, the network’s accuracy in identifying the lift-off phenomenon exceeds 90%, demonstrating the potential of the proposed methodology.