Research on pipeline blocking state recognition algorithm based on mixed domain feature and KPCA-ELM
by Jingzong Yang; Zao Feng; Xiaodong Wang; Guoyong Huang
International Journal of Computing Science and Mathematics (IJCSM), Vol. 9, No. 5, 2018

Abstract: Aiming at the problem of recognition on pipeline blockage, a method based on mixed domain feature and KPCA-ELM is proposed. Firstly, the original acoustic impulse response signals are analysed by statistical analysis and local mean decomposition (LMD), in order to construct the mixed domain features, which are made up of time, frequency and time-frequency domain features. Then the kernel principal component analysis (KPCA) is adopted to reduce the high-dimensional features of mixed domain and extract the main features which reflect the operation state of main components. Finally, the main features are input to extreme learning machine (ELM) for state recognition. After the feature extraction by KPCA, the redundancy of input features is eliminated. The simulation results show that KPCA is more sensitive to the nonlinear characteristics of the pipeline blockage signal when compared with PCA. Meanwhile, ELM is superior to BP in terms of classification accuracy and time consuming.

Online publication date: Fri, 28-Sep-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computing Science and Mathematics (IJCSM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email