Affiliation:
1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
2. Sichuan Sunny Seal Co. Ltd, Chengdu 610041, China
Abstract
Monitoring the contact state of seal end faces would help the early warning of the seal failure. In the acoustic emission (AE) detection for mechanical seal, the main difficulty is to reduce the background noise and to classify the dispersed features. To solve these problems and achieve higher detection rates, a new approach based on genetic particle filter with autoregression (AR-GPF) and hypersphere support vector machine (HSSVM) is presented. First, AR model is used to build the dynamic state space (DSS) of the AE signal, and GPF is used for signal filtering. Then, multiple features are extracted, and a classification model based on HSSVM is constructed for state recognition. In this approach, AR-GPF is an excellent time-domain method for noise reduction, and HSSVM has advantage on those dispersed features. Finally experimental data shows that the proposed method can effectively detect the contact state of the seal end faces and has higher accuracy rates than some other existing methods.
Funder
Central University Special Funding for Basic Scientific Research Business Expenses
Subject
Mechanical Engineering,Mechanics of Materials,Geotechnical Engineering and Engineering Geology,Condensed Matter Physics,Civil and Structural Engineering
Cited by
27 articles.
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