In recent years, with the wide application of hydraulic system, the performance assessment for hydraulic system has gained significant attention. However, a few studies focus on health assessment and traditional methods such as distance metric functions have limitation because different metric functions are only suitable for specific requirements. A scheme of performance degradation assessment based on health baseline and metric learning is proposed in this study. First, General regression neural network (GRNN) based observer is employed as health baseline to generate the estimated output of hydraulic system. The residual is obtained by calculating difference between the actual and estimated output. Then, time domain features are extracted from residual error. After that, apply the Mahalanobis metric learning (MML) to find a suitable metric adaptively for the training data set regarding distance. Finally, the distance between current status and normal status is normalized into confidence value (CV) to quantize the performance. A simulation model of hydraulic system is established based on HyPneu and Simulink, then gradual fault are injected to validate the proposed method. The results of experimental analysis demonstrate the effectiveness and adaptability of the performance degradation assessment method.
How to Cite
Hydraulic System, Performance Assessment, Health Baseline, Metric Learning
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.