Abstract
The effectiveness of signal processing plays a critical role in machine condition monitoring and health diagnosis, especially under the presence of noise contamination. This paper presents a new approach to unifying techniques in the time, scale, and frequency domains. Specifically, spectral post-processing is performed on the data set extracted by wavelet transforms to enhance the effectiveness of defect feature extraction. The theoretical framework for such a generalized signal transformation platform is introduced, and boundary conditions for implementing the new technique are discussed. Comparison with enveloping technique based on band-pass filtering and wavelet transform has shown that the new technique is more effective in identifying structural defects in bearings, and computationally more efficient, thus providing a good alternative to envelope analysis for defect signature extraction in machine condition monitoring.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.