本文主旨係依軋鋼廠直流馬達模擬結果及量測數據之分析，期能預知直流馬達可能發生之故障，進而即時予以適當檢修維護，以確保系統之正常運轉，其中本文之方法，乃先依軋鋼廠裝置系統中之各項電機元件，予以建立模式，續依系統單線圖將各項模式予以連接整合，且對於軋鋼廠之負載變動、供電品質不良、軸承偏心及轉子線圈層間短路等，均予以執行模擬測試，據以評估馬達故障發生之機率。此外本文並將所擷取之資料予以特徵值呈現，輔以類神經網路之助，以提昇故障預警判斷效能，又為驗證本文所提之檢視方法，並經由量測信號予以對照模擬運轉結果，以佐證本文於馬達運轉故障監視研究施行之可行性。

In this thesis, simulations and measurements of DC motors of a steel plant have been carried out in order to develop a forewarning system such that the predictive maintenance can be better realized. In the method, each component of the steel devices was first individually formulated. Then, after connecting each model through a one-line diagram, the integrated scheme was validated through different scenarios, including load variations, low power-quality, bearing bias and shorting of a stator phase winding. It was expected that the fault probability can be prudently evaluated through the scenarios investigated. In the mean time, for each acquired data, they were presented by a feature value in order to facilitate the neural network training, hence anticipating the fault forewarning performance can be also significantly upgraded. Furthermore, test results of this proposed approach have been compared with the measurement data so as to better confirm the feasibility of the method applied for the motor-fault monitoring study.

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