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.
Highlights
Vibration and noises normally reduce the operational precision and even shorten the service life of machinery
The waveform correlation coefficients between the separated components and related sources are shown in Fig. 8: the separation performances become better as the number of hidden neurons (HN) increases (HN ≤ 5), and change very litter (HN > 5), the source 2 and source 4 can be well separated as HN is only 2
This paper presents fundamental theory and key principals of nonlinear factor analysis, and validates the effectiveness of NLFA according to numerical case studies and an experimental study on a test bed with shell structures
Summary
Vibration and noises normally reduce the operational precision and even shorten the service life of machinery. The measured acoustical signals are complicated and rough information of mechanical systems, which are caused by a complicated mixing of sources and transmission effects of mechanical structures. NONLINEAR FACTOR ANALYSIS AND ITS APPLICATION TO ACOUSTICAL SOURCE SEPARATION AND IDENTIFICATION. To build a precise acoustical transmission model for complicated mechanical systems is still a challenging task and costs plenty of time and resources Signal processing provides another way to interpret operating conditions of a system through response signals, and has benefited for system analysis, machinery condition monitoring and fault diagnosis. Nonlinear factor analysis was a powerful tool in real physical systems, and it has been applied to trading equity [27], structural health monitoring [28], Aliasing detection of images [29], and integrative data analysis [30].
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