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基于變分模式分解和微積分增強能量算子的滾動軸承故障診斷

Fault diagnosis of rolling bearings based on variational mode decomposition and calculus enhanced energy operator

  • 摘要: 針對滾動軸承故障振動信號的特點,考慮變分模式分解在復雜信號分解及微積分增強能量算子在瞬態成分檢測方面的優勢,提出基于變分模式分解和微積分增強能量算子的滾動軸承故障診斷方法.首先利用變分模式分解將復雜信號分解為多個本質模式函數,以削弱背景噪聲的影響和滿足能量算子對信號單分量的要求;然后根據提出的敏感分量選取原則,從本質模式函數中選出包含主要故障信息的本質模式函數為敏感分量;最后利用微積分增強能量算子強化敏感分量中的瞬態沖擊,并根據敏感分量瞬時能量的時域波形及Fourier頻譜診斷滾動軸承故障.分析結果表明該方法能夠有效診斷滾動軸承故障.

     

    Abstract: Aiming at the characteristics of rolling bearing fault vibration signals and considering the merits of variational mode decomposition in mono-component separation and calculus enhanced energy operator in transient impulse detection, this article introduces a new method termed fault diagnosis of rolling bearings based on variational mode decomposition and calculus enhanced energy opera-tor. Firstly, the vibration signal is decomposed into several intrinsic mode functions by variational mode decomposition to reduce the noise interferences and to satisfy the mono-component requirement by energy operator. Then, the sensitive intrinsic mode function containing the main fault information about the bearing is selected by the proposed criterion. Finally, the impulses are strengthened using calculus enhanced energy operator, and the bearing fault is diagnosed by the time domain waveform and Fourier spectrum of the sensitive mono-component instantaneous energy. The analysis results show that the proposed method can effectively diagnose the rolling bearing faults.

     

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