基于小波阈值降噪算法的滚动轴承故障诊断OA北大核心CSTPCD
Rolling bearing fault diagnosis based on the wavelet threshold denoising algorithm
滚动轴承因平稳的运行特性广泛用于工业生产领域,其安全稳定运行对工业生产有重要意义.针对滚动轴承的故障诊断问题,提出基于小波阈值降噪(wavelet threshold denoising,简称WTD)算法.研究结果表明:相对于其他3种算法,WTD算法具有较强的故障诊断能力.因此,WTD算法具有有效性.
Rolling bearings are widely used in industrial production due to their smooth operation,and their safe and stable operation is of great significance to industrial production.Aiming at the fault diagnosis of rolling bearings,a wavelet threshold denoising(WTD)algorithm was proposed.The results showed that compared with the other three algorithms,WTD algorithm had strong fault diagnosis ability.Therefore,WTD algorithm had effectiveness.
竺德;李鑫;高清维;卢一相;孙冬
安徽大学电气工程与自动化学院,安徽合肥 230601
机械工程
滚动轴承故障诊断小波阈值降噪算法调幅调频模型北方苍鹰优化算法支持向量机
rolling bearingsfault diagnosiswavelet threshold denoising algorithmamplitude modulated and frequency modulated modelnorthern goshawk optimization algorithmsupport vector machine
《安徽大学学报（自然科学版）》 2024 (004)
50-56 / 7
国家自然科学基金资助项目(62071001);安徽省自然科学基金资助项目(2208085QF206,2308085QF224,2008085MF183);安徽省高校自然科学重点科研项目(KJ2021A0013)
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