基于周期勢系統隨機共振的軸承故障診斷

張景玲; 楊建華; 唐超權; 黃大文; 劉后廣

doi:10.13374/j.issn2095-9389.2018.08.013

基于周期勢系統隨機共振的軸承故障診斷

doi: 10.13374/j.issn2095-9389.2018.08.013

1) 中國礦業大學機電工程學院, 徐州 221116;
2) 中國礦業大學江蘇省礦山機電裝備高校重點實驗室, 徐州 221116

基金項目:

國家自然科學基金資助項目（11672325，61603394）；江蘇省自然科學基金資助項目（BK20150185）；江蘇高校優勢學科建設工程和江蘇高校品牌建設工程資助項目

詳細信息

中圖分類號: TH165.3;TN911.6
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出版歷程
- 收稿日期: 2017-08-31

Bearing fault diagnosis by stochastic resonance method in periodical potential system

1) School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
2) Jiangsu Province Key Lab of Electromechanical Equipment, China University of Mining and Technology, Xuzhou 221116, China

摘要

摘要: 提出基于普通變尺度和周期勢自適應隨機共振理論，檢測噪聲背景下軸承滾動體的故障特征.在具體實施過程中，首先用普通變尺度的方法滿足隨機共振中小參數的條件，然后用隨機權重粒子群優化算法作為自適應隨機共振參數尋優的優化算法，同時用改進的信噪比作為評價指標.噪聲背景下含軸承滾動體故障的實驗信號經過普通變尺度下的自適應隨機共振處理和優化后，微弱的故障特征可以有效的提取出來.將普通變尺度下的雙穩態自適應隨機共振和周期勢自適應隨機共振進行了對比，結果表明周期勢自適應隨機共振比雙穩態自適應隨機共振能進一步提高信噪比，并且比雙穩態自適應隨機共振迭代次數少，用時短.這說明提出的基于普通變尺度和周期勢系統自適應隨機共振的軸承滾動體故障診斷方法具有優越性，尤其是在工程實際中，故障監測所需的數據量大，計算時間長，如能較早的預警，可以提高診斷效率并減少不必要的損失.因此，這種軸承滾動體故障診斷方法對提高機械設備故障診斷效率具有參考價值.
- 軸承故障 /
- 自適應隨機共振 /
- 周期勢系統 /
- 改進的信噪比 /
- 強噪聲
Abstract: In industrial production, bearings are widely used in rotating machinery. Bearing fault diagnosis plays an important role in preventing disasters and protecting lives and properties. Because weak bearing fault characteristics are often submerged in a noise background, the difficulty of extracting the bearing fault feature information is increased. Therefore, this paper proposed a method which combined the general scale transformation theory with the adaptive stochastic resonance in a periodical potential system. This method was used to detect the fault characteristics of the bearing rolling element in the noise background. In the proposed method, general scale transformation was first used to satisfy the condition of small parameters in the stochastic resonance. Then the random particle swarm optimization algorithm was applied to choose the optimal system parameters to affect the adaptive stochastic resonance. Meanwhile, an improved signal-to-noise ratio (ISNR) was set as the evaluation index in the adaptive stochastic resonance. After being processed and optimized by the adaptive stochastic resonance based on the general scale transformation method, the experimental weak signal with a rolling element bearing failure under the noise background could be effectively extracted. In addition, the effect of processing fault signals by the adaptive stochastic resonance in the periodical potential system was compared with the adaptive stochastic resonance method in a bistable system based on the general scale transformation. The results show that the adaptive stochastic resonance in the periodical potential system increases the signal-to-noise ratio better than the adaptive stochastic resonance in the bistable system. Moreover, the adaptive stochastic resonance in the periodical potential system involves fewer iterations, and the computation time is shorter than that of the adaptive stochastic resonance in the bistable system. This indicates that the proposed method of diagnosing bearing element fault based on the general scale transformation and the adaptive stochastic resonance in a periodical potential system is superior. Especially in engineering systems, a large amount of data and extensive computation time is required for fault diagnosis. Because of the early fault warning system achieved by the proposed method, fault diagnosis is more efficient and unnecessary losses are reduced. Therefore, the proposed method can serve as a reference in improving the efficiency of mechanical equipment fault diagnosis in engineering systems.
- bearing fault /
- adaptive stochastic resonance /
- periodical potential system /
- improved signal-to-noise ratio (ISNR) /
- strong noise

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