<p class="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/407033609_From_Causal_Auditing_to_Value_Maximization_for_Core_Distribution_Assets-Part_I_Underground_Cable_Management?_sg%5B0%5D=QmJFgDmBsHtmP5ZVUtdWBpqROFMOOYmq8jmFZuimDSpwu9AzPm134GjiR9DYpAd7urotbUv15CgkNJHDOYVX8mWg8eriBtfknBjpmADW.vq68eEqikG4hlAFgT0-nuFX8Szs88hCgPsMIb7ay7kZOu6HwYOJ4byv5gauO5wRzJtNgWrKdb2ajeOiluLNqRg&_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHJvZmlsZSIsInByZXZpb3VzUGFnZSI6InByb2ZpbGUiLCJwb3NpdGlvbiI6InBhZ2VDb250ZW50In19" target="_blank" rel="noreferrer noopener">Read the Full </a><a href="https://www.researchgate.net/publication/407269971_From_Causal_Auditing_to_Value_Maximization_for_Core_Distribution_Assets_-Part_II_Switchgear_Management?_sg%5B0%5D=zmBwtIn5mYT87ql8Cbx8kjES0XHK2Htw8C43VdxYDY3I-ZHiZ0wRC_9PrDmN41tYPCa6QUDofiwKMdVvapAK4CE-NQJJn4CCo_DyL0Ei.XLVGD9DuL0IBSwmQgWaGBdovJV5OhJR116u2-C0Dva9sqkeIRLaSoUSD4RwZIiTNO0qW1yLLECUYCzb2mCl0cA&_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHJvZmlsZSIsInByZXZpb3VzUGFnZSI6InByb2ZpbGUiLCJwb3NpdGlvbiI6InBhZ2VDb250ZW50In19" target="_blank" rel="noreferrer noopener">Paper </a><a href="https://www.researchgate.net/publication/407033609_From_Causal_Auditing_to_Value_Maximization_for_Core_Distribution_Assets-Part_I_Underground_Cable_Management?_sg%5B0%5D=QmJFgDmBsHtmP5ZVUtdWBpqROFMOOYmq8jmFZuimDSpwu9AzPm134GjiR9DYpAd7urotbUv15CgkNJHDOYVX8mWg8eriBtfknBjpmADW.vq68eEqikG4hlAFgT0-nuFX8Szs88hCgPsMIb7ay7kZOu6HwYOJ4byv5gauO5wRzJtNgWrKdb2ajeOiluLNqRg&_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHJvZmlsZSIsInByZXZpb3VzUGFnZSI6InByb2ZpbGUiLCJwb3NpdGlvbiI6InBhZ2VDb250ZW50In19" target="_blank" rel="noreferrer noopener">Here</a>(ResearchGate)<br><strong>DOI:</strong><em> </em><a href="https://doi.org/10.5281/zenodo.20751877" target="_blank" rel="noreferrer noopener">10.5281/zenodo.20751877</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-95bb92d4bb2a8cc282b5e2585462b83c wp-block-paragraph">With the evolution of smart distribution grids, the predictive maintenance of heavy assets is shifting from calendar-based population management to data-driven individualized management. Focusing on the core primary asset in distribution grids—switchgear—this paper proposes a dynamic retirement and precise life-extension framework based on Remaining Useful Life (RUL) auditing. This study establishes an asymmetric resource scheduling boundary anchored on the “High-Voltage Isolation Cost,” rejecting the pseudo-proposition that all components require monitoring. Instead, it proposes a generalized auditing architecture targeting two core heavy assets: Air-Insulated Switchgear (AIS) and Gas-Insulated Switchgear (GIS). Asset owners can independently or flexibly deploy targeted audits for these equipments based on actual grid configuration needs. By introducing the signature “6-Mirror Panel Auditing” mechanism of the Bianque System, data undergoes blind-selection trials within survival analysis models. This process physically and reversely reveals the disparate degradation mechanisms: the “middle-age crisis” (an inverted-U hazard rate) of AIS mechanical operating mechanism fatigue, and the irreversible monotonic degradation of GIS chemical seal aging. Furthermore, this paper proposes a “10-Decile Sequential Soft-Threshold Calibration and Dual Retirement Boundary” mechanism. Combined with Optimal Stopping Theory, it achieves highly reliable interventions where the process relies on economic calculation, while the bottom line is secured by a mandatory scheduling alarm at the 9th decile. This avoids additional losses from unplanned outages caused by unexpected failures. Ultimately, the system translates survival curves into precise commercial decision points, maximizing the Return on Investment (ROI) across the lifecycle of heavy distribution grid assets.</p>
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Project Identity: Bianque-System
Author: Yi Zeng
Manuscript PDF: Read the Full Paper Here(ResearchGate)
DOI: 10.5281/zenodo.20751877
GITHUB:Shinar-of-Clark/Laplace_Paper_Framework
With the evolution of smart distribution grids, the predictive maintenance of heavy assets is shifting from calendar-based population management to data-driven individualized management. Focusing on the core primary asset in distribution grids—switchgear—this paper proposes a dynamic retirement and precise life-extension framework based on Remaining Useful Life (RUL) auditing. This study establishes an asymmetric resource scheduling boundary anchored on the “High-Voltage Isolation Cost,” rejecting the pseudo-proposition that all components require monitoring. Instead, it proposes a generalized auditing architecture targeting two core heavy assets: Air-Insulated Switchgear (AIS) and Gas-Insulated Switchgear (GIS). Asset owners can independently or flexibly deploy targeted audits for these equipments based on actual grid configuration needs. By introducing the signature “6-Mirror Panel Auditing” mechanism of the Bianque System, data undergoes blind-selection trials within survival analysis models. This process physically and reversely reveals the disparate degradation mechanisms: the “middle-age crisis” (an inverted-U hazard rate) of AIS mechanical operating mechanism fatigue, and the irreversible monotonic degradation of GIS chemical seal aging. Furthermore, this paper proposes a “10-Decile Sequential Soft-Threshold Calibration and Dual Retirement Boundary” mechanism. Combined with Optimal Stopping Theory, it achieves highly reliable interventions where the process relies on economic calculation, while the bottom line is secured by a mandatory scheduling alarm at the 9th decile. This avoids additional losses from unplanned outages caused by unexpected failures. Ultimately, the system translates survival curves into precise commercial decision points, maximizing the Return on Investment (ROI) across the lifecycle of heavy distribution grid assets.
