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Development of practical anomaly detection based on robust sparse modeling

Research Project

Project/Area Number 18K13953
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 25010:Social systems engineering-related
Research InstitutionToyo University (2019-2021)
Waseda University (2018)

Principal Investigator

Ohkubo Masato  東洋大学, 経営学部, 講師 (40777976)

Project Period (FY) 2018-04-01 – 2022-03-31
Project Status Completed (Fiscal Year 2021)
Budget Amount *help
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Keywords異常検出 / ロバスト統計 / スパース・モデリング / タグチメソッド / MTシステム / 高次元主成分分析 / 統計的パターン認識 / ガウシアン・グラフィカル・モデリング / マハラノビス・タグチ・システム / 条件付き異常検出 / ガンマ・ダイバージェンス / ベキ密度ダイバージェンス / スパースモデリング
Outline of Final Research Achievements

We consider the problems accompanied with the anomaly detection for sensor data. Since sensor data is automatically acquired and accumulated in real time, there is a possibility that a large amount of anomaly data is mixed in the learning data. Such a learning data can bring significant reduction of the performance of anomaly detection, even if the conventional statistical modeling method is applied. Therefore, we conduct theoretical research so that we apply robust and sparse modeling that can estimate statistical models without being affected by mixed anomaly data to our statistical anomaly detection procedures.

Academic Significance and Societal Importance of the Research Achievements

統計的異常検出法は製造業における設備機器の状態監視保全の中核をなす技術であるだけでなく,様々な製品・サービスに応用され,安心・安全な社会システムの構築に重要な役割を担っている.本研究の成果である統計的異常検出法は,特にセンサーから自動で取得・蓄積されたデータを対象とした場合の異常検出性能を飛躍的に向上させるとともに,その原因の特定に有益な情報を同時に提供するものである.この研究成果により,統計的異常検出法の応用可能性が広がり,設備機器の故障予測や重篤な事故の未然防止等の様々な社会問題の解決につながることが期待される.

Report

(5 results)
  • 2021 Annual Research Report   Final Research Report ( PDF )
  • 2020 Research-status Report
  • 2019 Research-status Report
  • 2018 Research-status Report
  • Research Products

    (16 results)

All 2021 2020 2019 2018

All Journal Article (4 results) (of which Int'l Joint Research: 2 results,  Peer Reviewed: 3 results,  Open Access: 2 results) Presentation (12 results) (of which Int'l Joint Research: 7 results,  Invited: 3 results)

  • [Journal Article] Anomaly detection for noisy data with the Mahalanobis-Taguchi system2020

    • Author(s)
      Masato Okubo and Yasushi Nagata
    • Journal Title

      Quality Innovation Prosperity

      Volume: 24 Issue: 2 Pages: 75-92

    • DOI

      10.12776/qip.v24i2.1441

    • Related Report
      2020 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Development of High-dimensional Data Analysis Procedures in Mahalanobis-Taguchi System2019

    • Author(s)
      大久保豪人
    • Journal Title

      Oukan (Journal of Transdisciplinary Federation of Science and Technology)

      Volume: 13 Issue: 2 Pages: 117-122

    • DOI

      10.11487/trafst.13.2_117

    • NAID

      130007730125

    • ISSN
      1881-7610, 2189-6399
    • Year and Date
      2019-10-15
    • Related Report
      2019 Research-status Report
    • Open Access
  • [Journal Article] Anomaly detection for unlabelled unit space using the Mahalanobis-Taguchi system2019

    • Author(s)
      Masato Ohkubo and Yasushi Nagata
    • Journal Title

      Total Quality Management & Business Excellence

      Volume: - Issue: 5-6 Pages: 1-15

    • DOI

      10.1080/14783363.2019.1616542

    • Related Report
      2019 Research-status Report
    • Peer Reviewed
  • [Journal Article] Conditional anomaly detection based on a latent class model2019

    • Author(s)
      Masato Okubo and Yasushi Nagata
    • Journal Title

      Total Quality Management and Business Excellence

      Volume: 30 Issue: sup1 Pages: S227-S239

    • DOI

      10.1080/14783363.2019.1665847

    • Related Report
      2019 Research-status Report
    • Peer Reviewed / Int'l Joint Research
  • [Presentation] Mahalanobis-Taguchi Method for Anomaly Detection and Classification2021

    • Author(s)
      Kentaro Honma, Masato Ohkubo, Yasushi Nagata
    • Organizer
      Asian Network for Quality (ANQ) Congress 2021
    • Related Report
      2021 Annual Research Report
    • Int'l Joint Research
  • [Presentation] A Consideration of the Recognition Taguchi Method Using High-Dimensional Principal Component Analysis2021

    • Author(s)
      Ryo Asano, Masato Ohkubo, Yasushi Nagata
    • Organizer
      Asian Network for Quality (ANQ) Congress 2021
    • Related Report
      2021 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Discriminant T-method and its application2021

    • Author(s)
      Shota Nakayama, Masato Ohkubo, Yasushi Nagata
    • Organizer
      Asian Network for Quality (ANQ) Congress 2021
    • Related Report
      2021 Annual Research Report
    • Int'l Joint Research
  • [Presentation] ロバスト・スパース・グラフィカル・モデリングに基づくマハラノビス・タグチ法2019

    • Author(s)
      大久保豪人・永田靖
    • Organizer
      日本品質管理学会 第119回研究発表会
    • Related Report
      2019 Research-status Report
  • [Presentation] Anomaly detection for noisy data with the Mahalanobis-Taguchi system2019

    • Author(s)
      Masato Ohkubo and Yasushi Nagata
    • Organizer
      22th QMOD conference on Quality and Service Sciences ICQSS
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research
  • [Presentation] THE MAHALANOBIS-TAGUCHI SYSTEM BASED ON STATISTICAL MODELING2019

    • Author(s)
      Masato Ohkubo
    • Organizer
      The Fifth International Conference on the Interface between Statistics and Engineering
    • Related Report
      2019 Research-status Report
    • Int'l Joint Research / Invited
  • [Presentation] 潜在クラスモデルに基づくマハラノビス・タグチ法2018

    • Author(s)
      大久保豪人・永田靖
    • Organizer
      日本品質管理学会 第116回研究発表会
    • Related Report
      2018 Research-status Report
  • [Presentation] Conditional anomaly detection based on a latent class model2018

    • Author(s)
      Masato Ohkubo and Yasushi Nagata
    • Organizer
      21th QMOD conference on Quality and Service Sciences ICQSS
    • Related Report
      2018 Research-status Report
    • Int'l Joint Research
  • [Presentation] グラフィカル・モデリングに基づくマハラノビス・タグチ法2018

    • Author(s)
      大久保豪人
    • Organizer
      2018年度統計関連学会連合大会
    • Related Report
      2018 Research-status Report
    • Invited
  • [Presentation] Anomaly detection with Mahalanobis-Taguchi method based on robust sparse graphical modeling2018

    • Author(s)
      Masato Ohkubo, Hironori Fujisawa and Yasushi Nagata
    • Organizer
      Asian Network for Quality (ANQ) Congress 2018
    • Related Report
      2018 Research-status Report
    • Int'l Joint Research
  • [Presentation] 統計的モデリングに基づくマハラノビス・タグチ・システム2018

    • Author(s)
      大久保豪人
    • Organizer
      第9回横幹連合コンファレンス
    • Related Report
      2018 Research-status Report
    • Invited
  • [Presentation] ベイズ的主成分分析を応用したマハラノビス・タグチ法2018

    • Author(s)
      大久保豪人・永田靖
    • Organizer
      日本品質管理学会 第48回年次大会研究発表会
    • Related Report
      2018 Research-status Report

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Published: 2018-04-23   Modified: 2023-01-30  

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