基于DTCWPT和相关峭度图的滚动轴承故障诊断

Fault Diagnosis of Rolling Bearings Based on DTCWPT and Correlation Kurtogram

摘要: 本文提出一种基于双树复小波包变换和相关峭度图的滚动轴承故障诊断新方法，解决了在故障诊断领域广泛应用的以峭度为指标的传统快速峭度图和使用矩形窗对峭度作平均化处理的子带平均峭度图易受偶发性冲击干扰、特征提取准确性降低的问题。所提方法利用了DTCWPT的近似平移不变性、频谱泄露程度低、频带排列有序优点的同时在高频段和低频段细分频带并逐层分解分解信号，然后对滤波信号进行上采样和卷积操作重构为与原信号长度一致的重构信号，结合对周期性特征敏感、抑制偶发性冲击、抗噪能力强的相关峭度为子带选择指标，构造相关峭度图，从中选择最优滤波信号，最后对其进行平方包络谱验证滤波信号的故障成分提取情况，实现滚动轴承的故障诊断。与快速峭度图、子带平均峭度图、基于小波包变换的相关峭度图对比，所提方法在信号分解和指标选择两方面进行改进。通过仿真信号和实验数据进行比较，所提方法可有效避免偶发性脉冲的影响，在表现复杂背景下提取滚动轴承的周期性故障特征成分更加准确，所提方法的鲁棒性更好，工程应用价值更高。

Abstract: This paper proposes a new rolling bearing fault diagnosis method based on Dual-Tree Complex Wavelet Packet Transform (DTCWPT) and correlation kurtogram. It solves the problems that traditional fast kurtogram (using kurtosis as indicator) and subband average kurtogram (using rectangular window for kurtosis averaging)—both widely used in fault diagnosis—are easily disturbed by occasional shocks and have reduced feature extraction accuracy. The proposed method uses DTCWPT’s advantages (approximate shift invariance, low spectral leakage, ordered frequency bands) to subdivide and decompose signals in both high and low frequency ranges. It then upsamples and convolves the filtered signal to reconstruct one with the same length as the original. Using correlation kurtogram (sensitive to periodic features, suppresses occasional shocks, strong anti-noise ability) to select the optimal filtered signal, it finally verifies fault component extraction via squared envelope spectrum to achieve diagnosis. Compared with fast, subband averaged kurtogram, and wavelet packet decomposition correlated kurtogram, the method improves signal decomposition and indicator selection. Tests with simulated and experimental data show that it effectively avoids occasional pulses, extracts periodic fault features more accurately under complex backgrounds, and has better robustness and higher engineering value.