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類神經網路使用多重振動信號於馬達故障診斷

Neural Network in Fault Diagnosis of Motor by Using Multiple Vibrational Signal

摘要


當馬達發生故障時,可藉由振動分析,判別故障發生原因,每一種振動分析方法有一定的鑑別特性,使用單一分析方法,常不能正確診斷出故障原因,本文提出以綜合振動信號診斷馬達故障方法,利用頻譜、頻瀑(Waterfall)及軸心軌跡分析結果相互推理,提高故障鑑別率,並以三個實例,說明綜合多重信號之診斷方法。

並列摘要


When the motor breaks down, we can use vibrations analysis method to diagnose faults. Each vibrations analysis method has different characteristics. Sometimes using the sole analysis method, we can't correctly diagnose the fault. In order to raise the rate of diagnosis ability, this article proposed multiple vibration signal diagnosis motor fault method which includes frequency spectrum analysis, waterfall analysis, and orbital analysis. We use three examples to explain multiple vibration signals for motor fault diagnosis.

被引用紀錄


朱昌勇(2007)。應用倒傳遞類神經網路於TFT-LCD G4.5代Cell廠不良問題與解決方法之研究〔碩士論文,國立中央大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0031-0207200917343626

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