工程科学学报，第40卷，第8期：989-995,2018年8月 Chinese Journal of Engineering,Vol.40,No.8:989-995,August 2018 DOI:10.13374/j.issn2095-9389.2018.08.013;http://journals.ustb.edu.cn 基于周期势系统随机共振的轴承故障诊断 张景玲12)，杨建华12)四，唐超权”，黄大文)，刘后广) 1)中国矿业大学机电工程学院，徐州2211162)中国矿业大学江苏省矿山机电装备高校重点实验室，徐州221116 ☒通信作者，E-mail:jianhuayang(@cumt.cdu.cn 摘要提出基于普通变尺度和周期势自适应随机共振理论，检测噪声背景下轴承滚动体的故障特征.在具体实施过程中， 首先用普通变尺度的方法满足随机共振中小参数的条件，然后用随机权重粒子群优化算法作为自适应随机共振参数寻优的 优化算法，同时用改进的信噪比作为评价指标.噪声背景下含轴承滚动体故障的实验信号经过普通变尺度下的自适应随机共 振处理和优化后，微弱的故障特征可以有效的提取出来.将普通变尺度下的双稳态自适应随机共振和周期势自适应随机共振 进行了对比，结果表明周期势自适应随机共振比双稳态自适应随机共振能进一步提高信噪比，并且比双稳态自适应随机共振 迭代次数少，用时短.这说明提出的基于普通变尺度和周期势系统自适应随机共振的轴承滚动体故障诊断方法具有优越性， 尤其是在工程实际中，故障监测所需的数据量大，计算时间长，如能较早的预警，可以提高诊断效率并减少不必要的损失.因 此，这种轴承滚动体故障诊断方法对提高机械设备故障诊断效率具有参考价值 关键词轴承故障：自适应随机共振：周期势系统：改进的信噪比：强噪声 分类号TH165.3:TN911.6 Bearing fault diagnosis by stochastic resonance method in periodical potential system ZHANG Jing-ling'2),YANG Jian-hua),TANG Chao-quan!,HUANG Da-wen,LIU Hou-guang 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 Corresponding author,E-mail:jianhuayang@cumt.edu.cn 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,gen- eral 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.Mean- while,an improved signal-to-noise ratio (ISNR)was set as the evaluation index in the adaptive stochastic resonance.After being pro- cessed and optimized by the adaptive stochastic resonance based on the general scale transformation method,the experimental weak sig- nal 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.More- over,the adaptive stochastic resonance in the periodical potential system involves fewer iterations,and the computation time is shorter 收稿日期：2017-08-31 基金项目：国家自然科学基金资助项目(11672325,61603394)：江苏省自然科学基金资助项目(BK20150185):江苏高校优势学科建设工程和 江苏高校品牌建设工程资助项目工程科学学报,第 40 卷,第 8 期:989鄄鄄995,2018 年 8 月 Chinese Journal of Engineering, Vol. 40, No. 8: 989鄄鄄995, August 2018 DOI: 10. 13374 / j. issn2095鄄鄄9389. 2018. 08. 013; http: / / journals. ustb. edu. cn 基于周期势系统随机共振的轴承故障诊断 张景玲1,2) , 杨建华1,2) 苣 , 唐超权1) , 黄大文1) , 刘后广1) 1) 中国矿业大学机电工程学院, 徐州 221116 2) 中国矿业大学江苏省矿山机电装备高校重点实验室, 徐州 221116 苣 通信作者, E鄄mail: jianhuayang@ cumt. edu. cn 摘 要 提出基于普通变尺度和周期势自适应随机共振理论,检测噪声背景下轴承滚动体的故障特征. 在具体实施过程中, 首先用普通变尺度的方法满足随机共振中小参数的条件,然后用随机权重粒子群优化算法作为自适应随机共振参数寻优的 优化算法,同时用改进的信噪比作为评价指标. 噪声背景下含轴承滚动体故障的实验信号经过普通变尺度下的自适应随机共 振处理和优化后,微弱的故障特征可以有效的提取出来. 将普通变尺度下的双稳态自适应随机共振和周期势自适应随机共振 进行了对比,结果表明周期势自适应随机共振比双稳态自适应随机共振能进一步提高信噪比,并且比双稳态自适应随机共振 迭代次数少,用时短. 这说明提出的基于普通变尺度和周期势系统自适应随机共振的轴承滚动体故障诊断方法具有优越性, 尤其是在工程实际中,故障监测所需的数据量大,计算时间长,如能较早的预警,可以提高诊断效率并减少不必要的损失. 因 此,这种轴承滚动体故障诊断方法对提高机械设备故障诊断效率具有参考价值. 关键词 轴承故障; 自适应随机共振; 周期势系统; 改进的信噪比; 强噪声 分类号 TH165郾 3; TN911郾 6 收稿日期: 2017鄄鄄08鄄鄄31 基金项目: 国家自然科学基金资助项目(11672325, 61603394);江苏省自然科学基金资助项目(BK20150185);江苏高校优势学科建设工程和 江苏高校品牌建设工程资助项目 Bearing fault diagnosis by stochastic resonance method in periodical potential system ZHANG Jing鄄ling 1,2) , YANG Jian鄄hua 1,2) 苣 , TANG Chao鄄quan 1) , HUANG Da鄄wen 1) , LIU Hou鄄guang 1) 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 苣 Corresponding author, E鄄mail: jianhuayang@ cumt. edu. cn 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, gen鄄 eral 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. Mean鄄 while, an improved signal鄄to鄄noise ratio (ISNR) was set as the evaluation index in the adaptive stochastic resonance. After being pro鄄 cessed and optimized by the adaptive stochastic resonance based on the general scale transformation method, the experimental weak sig鄄 nal 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鄄to鄄noise ratio better than the adaptive stochastic resonance in the bistable system. More鄄 over, the adaptive stochastic resonance in the periodical potential system involves fewer iterations, and the computation time is shorter