摘  要：利用振動信號采集到的齒輪故障信息，依據點蝕的故障機理和頻譜特征，采用小波分解將信號分解在不同頻帶，有效抑制了背景噪聲，從而得到故障特征頻帶，獲得周期性突變的故障信息。選擇故障所處頻帶重構信號，對故障進行診斷。結合倒頻譜方法可以有效地識別故障特征頻率。結果表明小波分析與倒頻譜相結合是齒輪故障檢測中一種有效的診斷方法。

關鍵詞：齒輪；故障；小波分析；倒頻譜；點蝕

中圖分類號：TH132.41， TM930.12        文獻標識碼：A       

文章編號：1672-4984（2008）01-0031-04

Gear diagnosis and applying base on the method of wavelet-cepstrum

JIANG Yu， LI Li， ZHAO Mei-yun

（College of Mechanical and Material Engineering，China Three Gorges University，Yichang 443002，China）

Abstract： Based on the vibration signal to collect the gear failure information， the signal was decomposed in different frequency bands using the wavelet analysis according to the failure mechanism and spectrum signature of the pitting corrosion. It conduced to restrain the noises effectively and get the periodic break information. Selecting the bands where the failure located recomposed the signal and diagnosed the failure. Combining the cepstrum method could identify the characteristic frequency availably. The result proves that the wavelet-cepstrum method has great validity in gear failure diagnosis.

Key words： Gear；Failure；Wavelet analysis；Cepstrum；Pitting corrosion

Editor：liyan