摘要
在确保机械系统的可靠性和功能性、生产效率和安全性方面,滚动轴承的状态监测和故障诊断极其重要。然而,故障特征通常总是被背景噪音和其他不稳定的干扰成分所掩盖,这使得这项工作变得非常困难。为了更好地提取轴承故障特征,提出了结合改进的正余弦优化算法(improved sine cosine algorithm, ISCA)的弱选择近似共轭梯度追踪(weak selection approximate conjugate gradient pursuit, WACGP)算法。将惯性权重和非线性参数更新策略引入正余弦优化算法(sine cosine algorithm, SCA)中,提高了信号稀疏表示的效率和精度,以便用字典原子最大限度地逼近原始信号,并且将弱选择策略引入近似共轭梯度追踪(approximate conjugate gradient pursuit, ACGP)中,提高了提取轴承故障特征的速度和能力。通过对轴承的故障仿真信号和实际轴承内、外圈和滚动体振动信号的分析,验证了该方法的有效性。详细说明了与基于正余弦优化的梯度追踪算法的比较,突出了所提出的方法的优点。
Ensuring the dependability,functionality,production effectiveness,and safety of mechanical systems necessitates assessing the conditions and detecting faults in rolling bearings.However,the fault features are usually hidden due to the interference of background noise and other unstable factors.To address this issue,the weak selection approximate conjugate gradient pursuit(WACGP)method and an improved sine cosine algorithm(ISCA)were introduced for more effective extraction of bearing fault features.Sine cosine algorithm(SCA)includes an inertia weight and nonlinear parameter update approach to improve the efficiency and accuracy of sparse signal representation,while the approximate conjugate gradient pursuit(ACGP)was modified to increase the speed and ability of identifying bearing fault characteristics.The validity of the method was confirmed by analyzing some bearing fault simulation signals and a certain actual vibration signals of the bearing’s inner and outer ring.The proposed method outperforms the gradient pursuit algorithm based on sine cosine optimization in terms of efficiency and accuracy.
作者
惠亦聪
张延超
陈润霖
李喆
刘佳鑫
崔亚辉
HUI Yicong;ZHANG Yanchao;CHEN Runlin;LI Zhe;LIU Jiaxin;CUI Yahui(School of Mechanical and Precision Instrument Engineering,Xi’an University of Technology,Xi’an 710048,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2024年第10期292-298,共7页
Journal of Vibration and Shock
基金
国家重点研发计划(2018YFB2000505)。
