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工業技術研究院

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技術名稱: 設備能耗肇因診斷技術*

技術簡介

感測器收集設備運轉值,決定重要的參數去建立正常運轉狀態下的效率推估模型(M1)。模型(M1)用以監測實際效率的偏差量並以統計方法檢定。若檢定成立,代表實際效率已經變差。以效率不佳的數據建立一組設備的效率推估模型(M2),並且調整模型(M2)的運轉參數用以修正預測效率值。調整量較大的運轉參數為影響較大的因子,系統呈現警報顯示。

Abstract

The sensors collect equipment operating values and determines important parameters to establish an efficiency estimation model (M1) under normal conditions. The model (M1) is used to monitor the deviation of actual efficiency and verify it with statistical methods. If the verification is established, it means that the actual efficiency has deteriorated. Establish a set of equipment efficiency estimation model (M2) with poor efficiency data, and adjust the operating parameters of the model (M2) to correct the predicted efficiency value. Operating parameters with larger adjustments are the factors with greater influence, and the system displays an alarm display.

技術規格

效率模型模組:以極限梯度演算法建立設備能耗(效率)預測模型 效率檢定模組:統計方法Z-SCORE判定設備實際效率值與推估效率值的差異統計量 參數檢測模組:模擬參數的變化對效率的影響,找出影響設備效率的重要參數 參數分析模組: 以決策樹演算技術分析場域設備與同類型(同噸數或同馬力)的設備在運轉效率最差(好)狀況下的共同影響參數。

Technical Specification

The efficiency model module uses the XGBoost algorithm to establish the equipment energy consumption (efficiency) prediction model. The efficiency verification module uses the statistical method Z-SCORE to determine the difference between the real efficiency value and the estimated efficiency value of the equipment. The parameter detection module is used to simulate the impact of parameter changes on efficiency and find out important parameters. The parameter analysis module uses decision tree technology to analyze the operating parameters of the same type of equipments.

技術特色

本研究開發的設備能耗肇因診斷技術是利用機器學習建模技術,建立參數與能耗的相關模型。依此模型推估設備運轉參數變化對能耗的影響,並且可依據同類型設備分析影響效率的參數,當設備運轉處於低效運轉時可找出最關聯的因子給予調變建議或發出警示。

應用範圍

工廠能源管理、設備維護管理

接受技術者具備基礎建議(設備)

伺服器、網路資訊設備、設備維護管理系統

接受技術者具備基礎建議(專業)

伺服器管理、SQL資料庫管理、設備維護管理系統管理

技術分類 02 D工業節能研究

聯絡資訊

聯絡人:林昌民 智慧節能系統技術組

電話:+886-3-5916187 或 Email:linbarry@itri.org.tw

客服專線:+886-800-45-8899

傳真:+886-3-5820050

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