無人飛行載具(Unmanned Aerial Vehicle，UAV)近幾年隨著科技的發展，因其 構造簡單、低成本、高機動性、垂直起落等特性，在各領域被廣泛地使用，所以 為了使無人機能穩健地完成任務，提升無人機的安全性，且及時診斷出無人機失 效是相當具有價值的研究。本論文提出了基於時域、頻域分析與人工類神經網路 之螺旋槳異常檢知，使用訊號式分析的方法來檢視故障特徵，利用機體上不同的 振動訊號來檢驗四旋翼螺旋槳損壞情況，並透過時域與頻域的分析比較出機體的 振動情況，再由機器學習作為分類器辨別出系統螺旋槳的故障狀態。本研究探討 三種不同螺旋槳損壞情況對於機體的振動進行比較，並尋找出特徵頻率檢視頻域 的指標，再結合時域的指標統計，選擇出對振動訊號較敏感的時域指標，最後分 析出相異指標之間的差異性與同質性，凸顯出顯著特徵，並將顯著特徵加入機器 學習實現分類螺旋槳損壞的結果。

Unmanned Aerial vehicles (UAV) is widely applied in many fields in recent years. It has the advantages of simple structure, low cost, high maneuverability, and Vertical Take-Off and Landing (VTOL). Hence, it is motivated to study the operation safety for UAVs and the on-line fault diagnosis. As an attempt to tackle this issue, this thesis investigates the damaged propellers through vibration signal in time domain and frequency domain and neural network. Fault features are then identified by utilizing signal-based analysis methods. The first step is to inspect the damage of the quadrotor propeller through different vibration signals on the frame, and then to compare the vibration of the frame through the analysis in both time domain and frequency domain. At last the condition of the system propellers is detected by the machine learning classifier. This research compares the propeller damages in three different conditions from the vibration of the frame. The characteristic frequencies are identified and used to access the condition of the propellers. In addition, the statistics index is used to select and analyze the most sensitive feature in the time domain vibration data. In such a way, the significant features are acquired by comparing with homogeneity. Finally, the selected significant features are fed to the model of machine learning for training and achieved fault detection and diagnosis on the propellers.

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