基于声发射技术的滚动轴承初期缺陷监测试验分析

The Incipient Defect Detection in Rolling Bearings Based on the Acoustic Emission Technique

轴承是旋转机械的关键部件,对其状态进行监测,可避免故障和减少停机时间,具有重要意义。轴承状态监测最成熟技术为振动分析,但其存在低速旋转机械故障检测灵敏度低、初期缺陷检测识别能力有限等不足,采用声发射(AE)技术实现初期缺陷故障的监测。并结合小波包去噪、希尔伯特变换(HT)包络提取和自相关函数,对AE信号的模式进行分析处理,以实现在信噪比极低的初始阶段在线识别轴承局部缺陷。实验结果表明,此方法相比传统的包络分析方法,能在低9分贝信噪比的情况下检测出初始缺陷。

Bearings are the critical components of rotating machinery and monitoring their condition is important to avoid the catastrophic failures and reduce the machinery down-time. The most established technique for bearing condition monitoring is vibration analysis. However,it has some drawbacks such as(i) the low sensitivity to fault detection in low speed rotating machines and(ii) limited capability of defect detection in early stage. The acoustic emission(AE) is gaining ground as a complementary condition monitoring technique and it offers earlier fault detection in this paper. This paper proposes a novel envelope analysis method for bearing incipient defect detection;this method is able to identify the localized defects in an incipient stage,in which the signal-to-noise ratio(SNR) is extremely low. This method combines the wavelet packet,for AE signal denoising,the Hilbert Transform(HT) for envelope extraction and autocorrelation function,so as to find patterns in the AE signal. An extensive experimental investigation is carried out in order to evaluate the performance of the proposed method under extremely low SNR,adding high level of noise to the signals. The results indicate that the proposed enhanced envelope method is able to detect incipient defects with 9 d B lower SNR than the traditional envelope analysis.