Reciprocating machines such as piston pumps, compressors and internal combustion engines are widely used in several manufacturing industries including automobile, aircraft, paper, oil and gas, etc. Reciprocating seal located directly on the rod/piston of a reciprocating equipment is used for preventing leakage and reducing wear between two parts that are in relative motion. Seals failure is one of the foremost causes of breakdown of reciprocating machinery and such a failure can be catastrophic, resulting in costly downtime and large expenses. Assessment of reciprocating seal is extremely important in the manufacturing industry to avoid fatal breakdown of reciprocating equipment and machines. Prediction of time series using predictive maintenance practices and tools to estimate the evolution of the future conditions of the system is of great interest to the operators for taking timely and appropriate maintenance decisions. In this paper, we have built and trained a hybrid PSO-SVM model to predict the reciprocating seal degradation. Particle swarm optimization is used to optimize the penalty factor and kernel parameter of SVM model. Controlled experiments are designed and performed, and data collected from a dedicated experimental set-up is used to validate the proposed approach.
How to Cite
SVM, PSO, SEALS, PROGNOSIS, FORECASTING
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.