人工智慧方法於旋轉電機振動故障診斷之研究

Vibration Fault Diagnosis of Rotating Machinery Using Artificial Intelligent Approach

黃燕昌(Yann-Chang Huang) ； 孫火清(Huo-Ching Sun)

《2008資通技術管理與應用會議》 2008 (2008/12) Pp. 129-137

https://doi.org/10.29649/ZTJSGL.200812.0129

摘要

本論文提出自組織映射神經網路模式，利用其與汽渦輪發電機組之大型旋轉電機故障症兆與常見故障種類間之特徵映射關係，發展一故障診斷新方法。此模式利用單一故障樣本對網路進行非監督式學習，經由模式自組織方式，將輸入（振動頻譜特徵）與輸出（故障種類）之對應關係進行特徵映射。最後，根據輸出神經元於輸出層之相對位置，可進行機組振動故障之診斷。所提診斷模式實際測試於機組振動資料並與現有方法比較，測試結果證明本診斷系統具有優異之診斷性能。

關鍵字

旋轉電機 ； 振動故障診斷

並列摘要

This paper presents a self-organizing feature map (SOFM) based neural network approach to handle the multiple faults diagnosis of steam turbine-generator sets. The proposed method is trained by using the single fault sample and thus can diagnose the vibration faults of the rotating machinery according to the feature map of the trained network. The proposed approach has been tested on the practical vibration data records of units and compared with the existing methods. The test results confirm that the proposed model possesses superior diagnosis accuracy.

並列關鍵字

Rotating machinery ； Vibration fault diagnosis