<p class="has-ast-global-color-8-color has-text-color has-link-color wp-elements-1 wp-block-paragraph"><strong>Project Identity:</strong><em> Bianque-System</em><br><strong>Author:</strong><em> Yi Zeng</em><br><strong>Manuscript PDF</strong>:<em> </em><a href="https://www.researchgate.net/publication/407521258_From_Causal_Auditing_to_Value_Maximization_for_Core_Distribution_Assets_-Part_III_Transformer_Management?_sg%5B0%5D=RFgxygrU5I_I8ob6Wrf5HFQ8zUfllWo5rJnbJWGr966yBElrybcaHist_Ftg3I0WR0lKeurmYev9_kRzfEz9g8BTXQOcXpob9Sm2ROLV.CGZ9a0mIhu_CHT4uc48v39omYrCqh1tSwv-n5vHDDROMN9Ds7DlzSTawbtVxUyXI-m41hrzw21ly1wMabBETlA&_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHJvZmlsZSIsInBvc2l0aW9uIjoicGFnZUNvbnRlbnQifX0" target="_blank" rel="noreferrer noopener">Read the Full Paper Here</a>(ResearchGate)<br><strong>DOI:</strong><em> </em><a href="https://doi.org/10.5281/zenodo.20811106" target="_blank" rel="noreferrer noopener">10.5281/zenodo.20811106</a><br><em><strong>GITHUB</strong>:</em><a href="https://github.com/Shinar-of-Clark/Bianque-System" target="_blank" rel="noreferrer noopener">Shinar-of-Clark/Laplace_Paper_Framework</a></p>
<p class="has-ast-global-color-8-color has-text-color has-link-color wp-elements-2 wp-block-paragraph">Power transformers are the most critical and capital-intensive assets in power distribution networks. Conventional equipment condition monitoring heavily relies on static thresholds and invasive sensors, failing to capture the dynamic Remaining Useful Life (RUL) and economic limits of the assets. This paper proposes the third part of the Bianque System, introducing a non-invasive “Algorithmic Alchemy” framework capable of extracting dynamic survival features from existing conventional SCADA, DGA, and underlying Digital Fault Recorder (DFR) data. We propose a “Dual-Track Survival” architecture: for oil-immersed transformers, we introduce the bionic telomere degradation hypothesis, utilizing Partial Integro-Differential Equations (PIDE) with constrained boundaries and Bellman’s optimal stopping theory to deeply evaluate the economic residual value of assets under the “Hayflick Limit”; for dry-type transformers, we construct a physical degradation triad (THD </p>
<p class="has-ast-global-color-8-color has-text-color has-link-color wp-elements-3 wp-block-paragraph"> PD) model to quantify time-varying risks before approaching the “Grade 9” bottom line. To combat dual uncertainties, the system establishes a “Six-Mirror Racing” model evaluation and a Bayesian nested Monte Carlo convergence mechanism. Furthermore, this paper establishes an ecological closed-loop via the “Laplace Platform,” relying on underlying high-speed analog-to-digital conversion mechanisms to extract microsecond-level transient sudden mutation features. These features are fed back as new covariates for diagnostic ecological evolution, achieving continuous cyclical enhancement of predictive capabilities.</p>
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Project Identity: Bianque-System
Author: Yi Zeng
Manuscript PDF: Read the Full Paper Here(ResearchGate)
DOI: 10.5281/zenodo.20811106
GITHUB:Shinar-of-Clark/Laplace_Paper_Framework
Power transformers are the most critical and capital-intensive assets in power distribution networks. Conventional equipment condition monitoring heavily relies on static thresholds and invasive sensors, failing to capture the dynamic Remaining Useful Life (RUL) and economic limits of the assets. This paper proposes the third part of the Bianque System, introducing a non-invasive “Algorithmic Alchemy” framework capable of extracting dynamic survival features from existing conventional SCADA, DGA, and underlying Digital Fault Recorder (DFR) data. We propose a “Dual-Track Survival” architecture: for oil-immersed transformers, we introduce the bionic telomere degradation hypothesis, utilizing Partial Integro-Differential Equations (PIDE) with constrained boundaries and Bellman’s optimal stopping theory to deeply evaluate the economic residual value of assets under the “Hayflick Limit”; for dry-type transformers, we construct a physical degradation triad (THD
PD) model to quantify time-varying risks before approaching the “Grade 9” bottom line. To combat dual uncertainties, the system establishes a “Six-Mirror Racing” model evaluation and a Bayesian nested Monte Carlo convergence mechanism. Furthermore, this paper establishes an ecological closed-loop via the “Laplace Platform,” relying on underlying high-speed analog-to-digital conversion mechanisms to extract microsecond-level transient sudden mutation features. These features are fed back as new covariates for diagnostic ecological evolution, achieving continuous cyclical enhancement of predictive capabilities.
