Abstract
To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can improve the efficiency and accuracy in identifying SSPs. Then, the mixing matrix is obtained by hierarchical clustering, and source signals can also be recovered by the least square method. Second, the optimal combination coefficients between source signals and mixed signals can be calculated based on minimum redundant error energy. Therefore, mixed signals can be optimally linearly combined by source signals via the coefficients. Third, the energy elimination method is used to quantitatively estimate source contributions. Finally, the effectiveness of the proposed method is verified via numerical case studies and experiments with a cylindrical structure, and the results show that source signals can be effectively recovered, and source contributions can be quantitatively estimated by the proposed method.
Highlights
Vibration and radiation noise have a significant effect on the safety and stability of some mechanical systems [1,2], for example, excessive noise of underwater vehicle will interfere with its own detection accuracy
After obtaining the mixing matrix, source signals are recovered by the subspace method under the assumption that the number of active source signals must be smaller than sensors at any TF point
If no or very few single source points (SSPs) corresponding to a source are included in selected TF vectors, this source will be estimated with large error
Summary
Vibration and radiation noise have a significant effect on the safety and stability of some mechanical systems [1,2], for example, excessive noise of underwater vehicle will interfere with its own detection accuracy. After obtaining the mixing matrix, source signals are recovered by the subspace method under the assumption that the number of active source signals must be smaller than sensors at any TF point. To accurately recover source signals and identify the major sources, an effective UBSS method and a source contribution quantitative estimation method are proposed, in which SSPs can be identified by directly searching the identical normalized TF vectors of mixed signals instead of the sparse coding in.
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