{
  "abstract": "Component degradation in aero-engines can cause local model mismatch. Insufficient coordination between component-response updates and engine-level prediction correction may limit improvements in gas-path prediction accuracy. Therefore, a data-enhanced component degradation digital-twin framework is proposed for aero-engine gas-path parameter prediction. It connects degraded component-response updates with engine-level prediction correction through selective component replacement, gas-path and shaft interface coupling, and engine-level prediction coordination. Under prescribed low-pressure compressor (LPC) efficiency degradation, a multi-self-attention long short-term memory (MA-LSTM) network replaces the LPC input–output mapping. The physics-based representations of the remaining components are retained, forming a data-driven model integrated-mechanism model (DMIM). The learned component response is connected to downstream gas-path modules and shaft calculations through mass flow rate, total temperature, total pressure, total specific enthalpy and output torque. DMIM predictions are then coordinated with predictions from a parallel engine-level data-driven model using an altered Kalman filter strategy. The framework is evaluated using six simulated LPC-efficiency degradation scenarios comprising two degradation magnitudes and three degradation durations. The proposed framework achieves a mean coefficient of determination (R2) of 0.94, a mean absolute percentage error (MAPE) of 2.3%, and a maximum MAPE across predicted parameters of 3.9%. Compared with the original physics-based model and the standalone MA-LSTM model, the mean MAPE decreases by approximately 61.0% and 36.1%, respectively. These results indicate that coordinated component-level updates and engine-level prediction can improve overall gas-path parameter prediction under the specified degradation scenarios.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Information Technology Construction and Management, Jilin Engineering Vocational College, Siping 136001, China",
    "School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Yunchao Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Yuzeng Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Changyi Xu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/31x09g0gekmrp.bib",
  "cited_by": 0,
  "doi": "10.3390/math14214072",
  "fulltext_url": null,
  "id": "31x09g0gekmrp",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/4072",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/31x09g0gekmrp.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T12:20:57Z",
      "native_id": "10.3390/math14214072",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/4072"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Data-Enhanced Component Degradation Digital Twin for Aero-Engine Gas-Path Parameter Prediction",
  "title_text": "Data-Enhanced Component Degradation Digital Twin for Aero-Engine Gas-Path Parameter Prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T00:21:40Z",
  "url": "https://research.draconitic.com/paper/31x09g0gekmrp",
  "venue": "Mathematics",
  "versions": []
}