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Low-complexity storage-reduced digital spectrum-based soft-failure management with Welch's method.

Liang Shu, Zhenming Yu, Zhiquan Wan

    Optics Express
    |May 15, 2020
    PubMed
    Summary

    This study introduces an efficient digital spectrum method for managing soft failures in systems. The solution significantly reduces computational complexity and storage needs while accurately detecting, identifying, and estimating failure magnitudes.

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    Area of Science:

    • Digital signal processing
    • System reliability engineering
    • Fault detection and diagnosis

    Background:

    • Soft failures in digital systems pose challenges to reliability.
    • Existing methods for soft failure management can be computationally intensive and require substantial storage.
    • Effective management requires accurate detection, identification, and magnitude estimation.

    Purpose of the Study:

    • To propose a low-complexity, storage-reduced digital spectrum-based solution for soft failure management.
    • To develop methods for soft failure detection (SFD), identification (SFI), and failure magnitude estimation (FME).
    • To evaluate the robustness and performance of the proposed solution under simulated parameter fluctuations.

    Main Methods:

    • Utilizing Welch's method for digital spectrum calculation to reduce complexity and storage.
    • Implementing soft failure detection (SFD), identification (SFI), and failure magnitude estimation (FME) algorithms.
    • Conducting extensive numerical simulations with random fluctuations in key link parameters.

    Main Results:

    • Achieved a false positive rate below 1% and a false negative rate below 3% for SFD.
    • Realized an SFI accuracy of 97.4%.
    • Maintained mean square errors for FME below 0.4 for various soft failures.
    • Reduced computational complexity by 46.2% and storage by 99.6% using a segment length of 2^5.

    Conclusions:

    • The proposed digital spectrum-based solution offers an efficient approach to soft failure management.
    • The method demonstrates high accuracy in detection, identification, and magnitude estimation.
    • Significant reductions in computational complexity and storage requirements are achievable, making it practical for real-world applications.