Title:
パイプ漏れを予測する新規な自律的人工知能システム
Document Type and Number:
Japanese Patent JP7043512
Kind Code:
B2
Abstract:
Embodiments of the disclosure are directed towards pipe leak prediction systems configured to predict whether a pipe (e.g., a utility pipe carrying some substance such as waster) is likely to leak. The pipe leak prediction system may include one or more predictive models based on one or more machine learning techniques, and a predictive model can be trained using data for the characteristics of various pipes in order to determine the patterns associated with pipes without leaks and the patterns associated with pipes with leaks. A predictive model can be validated, used to construct a confusion matrix, and used to generate insights and inferences associated with the determinant variables used to make the predictions. The predictive model can be applied to data for various pipes in order to predict which of those pipes will leak. Any pipes that are identified as likely to leak can be assigned for further investigation for potential repair or preventative maintenance.
Inventors:
Abbas, Hussein
Application Number:
JP2019554997A
Publication Date:
March 29, 2022
Filing Date:
April 02, 2018
Export Citation:
Assignee:
Oracle International Corporation
International Classes:
G06Q10/04; G06N20/00; G06N20/20
Domestic Patent References:
| JP2012014619A | ||||
| JP2011198267A |
Other References:
Mohammad Burhan Abdulla et al.,Pipeline leak detection using artificial neuralnetwork: Experimental study,2013 5th InternationalConference on Modelling, Identification and Control (ICMIC),2013年09月02日,pp.328-332
R.B. Santos et al.,Real-Time Monitoring of Gas Pipeline through Artificial Neural Networks,2013 BRICS Congress on ComputationalIntelligence and 11th Brazilian Congress on Computational Intelligence,2013年,pp.329-334
Mohammed S. El Abbasy et al.,Artificial neural network models for predicting condition of offshore oil and gas pipelines,Automation in Construction 45,2014年,pp.50-65
Rui Wang et al.,Pipe Failure Prediction: A Data Mining Method,ICDE Conference 2013,2013年,pp.1208-1218
Brett Lantz,Rによる機械学習,株式会社翔泳社,2017年03月02日,pp.18-21,284-304,311-313,326-334
辺 松 外3名,機械学習による経年劣化タイミング解析手法,情報処理学会 シンポジウム DAシンポジウム 2016,2016年09月07日,pp.44-49
叶賀 卓 外1名,単極脳波信号を用いたトランプゲーム中の子どもの喜怒哀楽予測モデル構築,電気学会研究会資料,2014年09月01日,pp.117-120
下田 倫大 ,詳解 Apache Spark 初版 ,株式会社技術評論社 ,2016年06月01日,pp.206-211,223
R.B. Santos et al.,Real-Time Monitoring of Gas Pipeline through Artificial Neural Networks,2013 BRICS Congress on ComputationalIntelligence and 11th Brazilian Congress on Computational Intelligence,2013年,pp.329-334
Mohammed S. El Abbasy et al.,Artificial neural network models for predicting condition of offshore oil and gas pipelines,Automation in Construction 45,2014年,pp.50-65
Rui Wang et al.,Pipe Failure Prediction: A Data Mining Method,ICDE Conference 2013,2013年,pp.1208-1218
Brett Lantz,Rによる機械学習,株式会社翔泳社,2017年03月02日,pp.18-21,284-304,311-313,326-334
辺 松 外3名,機械学習による経年劣化タイミング解析手法,情報処理学会 シンポジウム DAシンポジウム 2016,2016年09月07日,pp.44-49
叶賀 卓 外1名,単極脳波信号を用いたトランプゲーム中の子どもの喜怒哀楽予測モデル構築,電気学会研究会資料,2014年09月01日,pp.117-120
下田 倫大 ,詳解 Apache Spark 初版 ,株式会社技術評論社 ,2016年06月01日,pp.206-211,223
Attorney, Agent or Firm:
Fukami patent office