Abstract
Detection of an incipient internal defect from a measurement point on the surface of a machine is often made difficult due to the corruption of the source signal by background noise usually through the transmission path. In order to detect or recover the source signal, it is essential to suppress or remove this background noise. With the advancement of digital signal processing techniques, this removal can be achieved using fixed or adaptive filters. The main problem with the application of filtering techniques is that one has to know the characteristics of the noise in advance or may require more than two channels of data if using an adaptive filter. This paper reports on the application of Blind Deconvolution method to recover fault information from the measured (observed) signals of a damaged bearing through the transmission channel and which was also corrupted by noise.. The Blind Deconvolution method manages to recover the fault signal by estimating the inverse of the transmission (channel transfer function) path using the eigenvector algorithm (EVA). Modified crest factor (MCF) and Arithmetic Mean (AM) algorithms were used to optimise the equalizer parameters. Computer simulation and experimental studies were used to verify the applicability of the technique.
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Tan, A.C.C., Karimi, M., Mathew, J. (2006). Blind Deconvolution using the Eigenvector Algorithm (EVA) Method for the Enhancement of Bearing Signal Through the Transmission Channel. In: Mathew, J., Kennedy, J., Ma, L., Tan, A., Anderson, D. (eds) Engineering Asset Management. Springer, London. https://doi.org/10.1007/978-1-84628-814-2_23
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DOI: https://doi.org/10.1007/978-1-84628-814-2_23
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