傳統包絡譜分析須檢測所有結構共振頻帶於軸承缺陷故障診斷，分析過程中帶通濾波頻率選取範圍易受主觀影響。為了改善上述問題，本研究提出一新概念基於經驗模態分解法(Empirical Mode Decomposition, EMD)選擇合適的本質模態函數(Intrinsic Mode Function, IMF)應用於後續包絡譜分析(Envelope Analysis)及倒頻譜分析(Cepstrum Analysis)，以凸顯軸承缺陷特徵頻率。經由EMD方法的帶通濾波特性，結構共振頻帶位於特定IMF分量內。當滾珠通過缺陷所引發脈衝訊號會與結構系統共振產生幅值調變，選擇合適IMFs分量能夠有效偵測軸承缺陷特徵，代替過去學術研究中所見總是選用第一個IMF分量於診斷。實驗方面，探討雙面轉子平台的滾珠軸承於單缺陷、雙缺陷及三缺陷不同故障型式，以放電加工製作不同軸承缺陷，診斷結果與傳統包絡譜方法結果相互比較。實驗及分析結果顯示，本研究提出方法能有效及正確地診斷出軸承缺陷型式。

Traditional envelope analysis must examine all the resonant frequency bands during the process of bearing fault detection. To eliminate the above deficiency, this research presents an insight concept based on the empirical mode decomposition (EMD) to choose an appropriate resonant frequency band for characterizing feature frequencies of bearing faults by using the envelope analysis and cepstrum analysis subsequently. By the band-pass filtering nature of the empirical mode decomposition, the resonant frequency bands are allocated in a specific intrinsic mode function (IMF). As impulses arising from rolling elements striking bearing faults modulate with structure resonance, appropriate IMFs are potentially able to characterize fault signatures, instead of always using the first IMF. In the study, the single, dual- and triple-fault bearings are used to justify the proposed method and comparisons with the traditional envelope analysis are made. The experimental results show that the proposed insight concept can efficiently and correctly diagnose the bearing fault types.

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