基于多字典-共振稀疏分解的脉冲故障特征提取

针对信号共振稀疏分解(RBSSD)方法中因字典单一导致其在处理低信噪比信号时存在分解不完全，以及因参数繁多选取困难而使其在实际工程中存在应用局限的问题，提出了多字典-共振稀疏分解(MD-RBSSD)方法。该方法在RBSSD调Q字典的基础上添加了Symlet8字典和正弦字典，通过对RBSSD分解后的低共振分量进行再次分离来实现对故障脉冲的增强提取。同时，引入相关峭度指标对提取结果进行量化评价，以验证分解结果的可靠性。算法仿真、实验分析和工程实例结果均表明，与传统RBSSD方法相比，所提出的MD-RBSSD方法能够更加准确有效地提取故障冲击成分，降低了RBSSD参数选择的难度，从而增加了RBSSD方法在工程领域的适用性。

Impulse Fault Signature Extraction Based on Multi-dictionary Resonance-based Sparse Signal Decomposition

RBSSD method often suffered from incomplete decompositions when dealing with low signal-to-ratio signals due to the single dictionary property, and has some limitations especially in practical applications due to the difficulty in parameter selection. To address this issue, a new multi-dictionary RBSSD (MD-RBSSD) method was proposed herein. Instead of using single dictionary, the proposed MD-RBSSD method introduced symlet8 wavelet dictionary and sine dictionary on the basis of the tunable-Q dictionary used in RBSSD. Thus, the low-resonance components obtained using RBSSD is further decomposed to extract fault impulse signatures. Furthermore, the correlated kurtosis was introduced to provide a quantitative evaluation for the decomposition results. Simulations, experiments and engineering case study were used to verify the effectiveness of the proposed method. The results show that compared with the traditional RBSSD method, the proposed MD-RBSSD method may extract fault impulse components more accurately and efficiently, reduce the parameter selection difficulty of RBSSD, and improve the applicability of RBSSD in engineering applications.