小波包-最大熵谱估计及其在水轮机故障诊断中的应用

WAVELET PACKET-MAXIMUM ENTROPY SPECTRUM ESTIMATION AND ITS APPLICATION IN TURBINE'S FAULT DIAGNOSIS

充分利用小波包(WP)的时频局部化分析能力和最大熵谱估计(MESE)的频谱细化优点,提出了一种新的谱分析方法——小波包-最大熵谱估计(WP-MESE)。该方法能对振动信号进行多分辨分解,提取故障特征信息,进而进行精确的频谱分析;并运用此法有效地提取了水轮机轴系动态特性信息。实践证明该法是提取微弱故障信息并进行早期诊断的有效方法。

The paper takes advantage of the time-frequency localization of wavelet packet (WP) and the frequency spectrum subdivision of maximum entropy spectrum estimation (MESE), puts forward a new method of the wavelet packet-maximum entropy spectrum estimation (WP-MESE). This method can carry out multi-resolution signal decomposition and extract feature information of different machine parts as monitoring and fault diagnosis of machinery, then analyses exactly its spectrum. As an example, the dynamic specific property information of the shaft system of the turbine can be extracted effectively by using WP-MESE method. It is demonstrated that this novel method is very effective for extracting weak fault feature information and early diagnosing latent mechanical faults.