2131. Nonlinear factor analysis and its application to acoustical source separation and identification

Wei Cheng1, Lin Gao2, Jie Zhang3, Jiantao Lu4

1, 3, 4State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University,
Xi’an 710049, Shaanxi, China

2Institute of Biomedical Engineering, Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China

2Corresponding author

E-mail: 1chengw@xjtu.edu.cn, 2gaolin2013@xjtu.edu.cn, 3epicureans@163.com, 4lujiantao1990@stu.xjtu.edu.cn

Received 20 February 2016; received in revised form 15 July 2016; accepted 19 July 2016

DOI https://doi.org/10.21595/jve.2016.17432

Abstract. Acoustical signals of mechanical systems can provide original information of operating conditions, and thus benefit for machinery condition monitoring and fault diagnosis. However, acoustical signals measured by sensors are mixed signals of all the sources, and normally it is impossible to be directly used for acoustical source identification or feature extraction. Therefore, this paper presents nonlinear factor analysis (NLFA) and applies it to acoustical source separation and identification of mechanical systems. The effects by numbers of hidden neurons and mixed signals on separation performances of NLFA are comparatively studied. Furthermore, acoustical signals from a test bed with shell structures are separated and identified by NLFA and correlation analysis, and the effectiveness of NLFA on acoustical signals is validated by both numerical case studies and an experimental case study. This work can benefit for machinery noise monitoring, reduction and control, and also provide pure source information for machinery condition monitoring or fault diagnosis.

Keywords: nonlinear factor analysis, source separation and identification, feature extraction, correlation analysis, noise monitoring and control.


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Cite this article

Cheng Wei, Gao Lin, Zhang Jie, Lu Jiantao Nonlinear factor analysis and its application to acoustical source separation and identification. Journal of Vibroengineering, Vol. 18, Issue 5, 2016, p. 3397‑3411.


© JVE International Ltd. Journal of Vibroengineering. Aug 2016, Vol. 18, Issue 5. ISSN 1392-8716