An UBSS Method for Signals with Non-Uniform Energy Distribution of Varied Frequency Bins

Naixin Chen; Chunli Zhu; Lei Chen

doi:10.1109/IECON51785.2023.10311827

An UBSS Method for Signals with Non-Uniform Energy Distribution of Varied Frequency Bins

Naixin Chen, Chunli Zhu^*, Lei Chen

^*Corresponding author for this work

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Coupled signals sensing from electromechanical systems have significant impacts on the intelligent fault diagnosis application, which is generally formulated as an undertermined blind source separation (UBSS) issue. However, it is especially challenging when dealing with signals of non-uniform energy distribution of different frequency bins. In this work, we proposed an UBSS framework with an adaptive optimal frequency bin selection approach, for improving the signal sparsity and estimation accuracy of the mixing matrix. Simulation results show that the proposed method achieves average accuracy of 82.40%, 86.91 % and 87.19% with the signal-to-noise ratio (SNR) set as 10, 15 and 30 text{dB} of the tested case, respectively. This work has a good potential on reducing the intelligent state monitoring system's false alarm rate.

Original language	English
Title of host publication	IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350331820
DOIs	https://doi.org/10.1109/IECON51785.2023.10311827
Publication status	Published - 2023
Event	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 - Singapore, Singapore Duration: 16 Oct 2023 → 19 Oct 2023

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
ISSN (Print)	2162-4704
ISSN (Electronic)	2577-1647

Conference

Conference	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
Country/Territory	Singapore
City	Singapore
Period	16/10/23 → 19/10/23

Keywords

Non-Uniform Energy Distribution
Single-Source Points Identifi-cation
Underdetermined Blind Source Seperation

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10.1109/IECON51785.2023.10311827

Cite this

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title = "An UBSS Method for Signals with Non-Uniform Energy Distribution of Varied Frequency Bins",

abstract = "Coupled signals sensing from electromechanical systems have significant impacts on the intelligent fault diagnosis application, which is generally formulated as an undertermined blind source separation (UBSS) issue. However, it is especially challenging when dealing with signals of non-uniform energy distribution of different frequency bins. In this work, we proposed an UBSS framework with an adaptive optimal frequency bin selection approach, for improving the signal sparsity and estimation accuracy of the mixing matrix. Simulation results show that the proposed method achieves average accuracy of 82.40%, 86.91 % and 87.19% with the signal-to-noise ratio (SNR) set as 10, 15 and 30 text{dB} of the tested case, respectively. This work has a good potential on reducing the intelligent state monitoring system's false alarm rate.",

keywords = "Non-Uniform Energy Distribution, Single-Source Points Identifi-cation, Underdetermined Blind Source Seperation",

author = "Naixin Chen and Chunli Zhu and Lei Chen",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 ; Conference date: 16-10-2023 Through 19-10-2023",

year = "2023",

doi = "10.1109/IECON51785.2023.10311827",

language = "English",

series = "IECON Proceedings (Industrial Electronics Conference)",

publisher = "IEEE Computer Society",

booktitle = "IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society",

address = "United States",

}

Chen, N, Zhu, C & Chen, L 2023, An UBSS Method for Signals with Non-Uniform Energy Distribution of Varied Frequency Bins. in IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), IEEE Computer Society, 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023, Singapore, Singapore, 16/10/23. https://doi.org/10.1109/IECON51785.2023.10311827

An UBSS Method for Signals with Non-Uniform Energy Distribution of Varied Frequency Bins. / Chen, Naixin; Zhu, Chunli ; Chen, Lei.
IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2023. (IECON Proceedings (Industrial Electronics Conference)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An UBSS Method for Signals with Non-Uniform Energy Distribution of Varied Frequency Bins

AU - Chen, Naixin

AU - Zhu, Chunli

AU - Chen, Lei

PY - 2023

Y1 - 2023

N2 - Coupled signals sensing from electromechanical systems have significant impacts on the intelligent fault diagnosis application, which is generally formulated as an undertermined blind source separation (UBSS) issue. However, it is especially challenging when dealing with signals of non-uniform energy distribution of different frequency bins. In this work, we proposed an UBSS framework with an adaptive optimal frequency bin selection approach, for improving the signal sparsity and estimation accuracy of the mixing matrix. Simulation results show that the proposed method achieves average accuracy of 82.40%, 86.91 % and 87.19% with the signal-to-noise ratio (SNR) set as 10, 15 and 30 text{dB} of the tested case, respectively. This work has a good potential on reducing the intelligent state monitoring system's false alarm rate.

AB - Coupled signals sensing from electromechanical systems have significant impacts on the intelligent fault diagnosis application, which is generally formulated as an undertermined blind source separation (UBSS) issue. However, it is especially challenging when dealing with signals of non-uniform energy distribution of different frequency bins. In this work, we proposed an UBSS framework with an adaptive optimal frequency bin selection approach, for improving the signal sparsity and estimation accuracy of the mixing matrix. Simulation results show that the proposed method achieves average accuracy of 82.40%, 86.91 % and 87.19% with the signal-to-noise ratio (SNR) set as 10, 15 and 30 text{dB} of the tested case, respectively. This work has a good potential on reducing the intelligent state monitoring system's false alarm rate.

KW - Non-Uniform Energy Distribution

KW - Single-Source Points Identifi-cation

KW - Underdetermined Blind Source Seperation

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DO - 10.1109/IECON51785.2023.10311827

M3 - Conference contribution

AN - SCOPUS:85179508515

T3 - IECON Proceedings (Industrial Electronics Conference)

BT - IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE Computer Society

T2 - 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

Y2 - 16 October 2023 through 19 October 2023

ER -

An UBSS Method for Signals with Non-Uniform Energy Distribution of Varied Frequency Bins

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