The frequency response function (FRF), defined as the ratio between the Fourier transform of the time-domain output and the Fourier transform of the time-domain input, is a common tool to analyze the relationships between inputs and outputs of a mechanical system. Learning the FRF for mechanical systems can facilitate system identification, condition-based health monitoring, and improve performance metrics, by providing an input-output model that describes the system dynamics. Existing FRF identification assumes there is a one-to-one mapping between each input frequency component and output frequency component. However, during dynamic operations, the FRF can present complex dependencies with frequency cross-correlations due to modulation effects, nonlinearities, and mechanical noise. Furthermore, existing FRFs assume linearity between input-output spectrums with varying mechanical loads, while in practice FRFs can depend on the operating conditions and show high nonlinearities. Outputs of existing neural networks are typically low-dimensional labels rather than real-time high-dimensional measurements. This paper proposes a vector regression method based on deep neural networks for the learning of runtime FRFs from measurement data under different operating conditions. More specifically, a neural network based on an encoder-decoder with a symmetric compression structure is proposed. The deep encoder-decoder network features simultaneous learning of the regression relationship between input and output embeddings, as well as a discriminative model for output spectrum classification under different operating conditions. The learning model is validated using experimental data from a high-pressure hydraulic test rig. The results show that the proposed model can learn the FRF between sensor measurements under different operating conditions with high accuracy and denoising capability. The learned FRF model provides an estimation for sensor measurements when a physical sensor is not feasible and can be used for operating condition recognition.
|Original language||English (US)|
|Title of host publication||Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability|
|Publisher||American Society of Mechanical Engineers|
|State||Published - Jun 21 2021|
|Event||ASME 2021 16th International Manufacturing Science and Engineering Conference, MSEC 2021 - Virtual, Online|
Duration: Jun 21 2021 → Jun 25 2021
|Name||Proceedings of the ASME 2021 16th International Manufacturing Science and Engineering Conference, MSEC 2021|
|Conference||ASME 2021 16th International Manufacturing Science and Engineering Conference, MSEC 2021|
|Period||6/21/21 → 6/25/21|
Bibliographical noteFunding Information:
Yongzhi Qu acknowledges the Department of Commerce of the United States for partial support of this research under contract No. 70NANB20H175, and partial support from the University of Minnesota in Grant-in-Aid of Research, Artistry and Scholarship (GIA).
Copyright © 2021 by ASME
- Deep learning
- Frequency response function
- Neural network