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摘要: 基于稀疏表示理论，提出了一种采用可调品质因子小波变换(TQWT)的滚动轴承故障诊断新方法，分析了包含早期故障成分的原始采集振动信号的特点和早期故障信号的特性，研究了稀疏表示模型在解决故障特征提取问题和故障类型识别问题的应用；运用TQWT将原始信号转换为一组子带小波系数集，研究了利用迭代收缩阈值算法提取出稀疏小波系数的有效性和谱峭度对故障冲击信号敏感的特性，通过计算各子带信号分量的谱峭度，选取包含故障信息明显的子带小波系数，建立了包含稀疏故障信号分量的故障特征提取方法；利用提取出的故障信号稀疏表示分类模型，实现了基于稀疏表示的滚动轴承故障诊断方法。试验结果表明：在凯斯西储数据集上，提出的故障特征提取方法在剔除干扰成分方面有显著效果，提出方法对于4种类型数据的平均诊断准确率为99.83%，对于10种类型数据的平均诊断准确率为97.73%；与只运用TQWT和迭代收缩阈值算法进行故障特征提取的方法相比，故障诊断精度提高了11.60%，算法运行时间减小8%；在QPZZ-Ⅱ旋转机械平台采集到的振动数据集上，提出的方法对于4种类型数据的平均诊断准确率为100%；与传统小波去噪方法相比，准确率提高了35.67%，算法运行时间减小了7.25%。可见，本文提出的方法可以有效解决滚动轴承故障诊断问题。Abstract: Based on the sparse representation theory, a new method of rolling bearing fault diagnosis was proposed using the tunable-Q wavelet transform (TQWT). The characteristics of the original vibration signals and early fault signals containing early fault components were analyzed, and the applications of the sparse representation model to solve the problem of fault feature extraction and fault type recognition were studied. The original signal was transformed into a set of sub-band wavelet coefficients using the TQWT. The effectiveness of extracting sparse wavelet coefficients using an iterative threshold shrinkage algorithm and the sensitivity of spectral kurtosis to fault impact signals were studied. By calculating the spectral kurtosis of each sub-band signal component and selecting the sub-band wavelet coefficient that contains obvious fault information, a fault feature extraction method for the sparse fault signal component was established. Using the sparse representation classification model of extracted fault signals, the method of rolling bearing fault-type recognition based on sparse representation was realized. Experimental results indicate that the proposed fault feature extraction method has a significant effect in eliminating interference components in the Case Western Reserve University dataset. The average diagnostic accuracy for the four types of data is 99.83%. The average diagnostic accuracy for the 10 types of data is 97.73%. Compared with the TQWT and iterative threshold shrinkage algorithm for fault feature extraction, the fault diagnosis accuracy of the proposed method improves by 11.60%, and the running time reduces by 8%. For the vibration dataset collected by the QPZZ-Ⅱ rotating machinery platform, the average diagnostic accuracy of the proposed method for the four types of data is 100%. Compared with the traditional wavelet denoising method, the accuracy of the proposed method improves by 35.67%, and the running time reduces by 7.25%. Therefore, the proposed method can effectively solve the problem of rolling-bearing fault diagnosis. 7 tabs, 7 figs, 30 refs.
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图 1 2层TQWT分解与重构过程
Figure 1. Decomposition and reconstruction process of two-layer TQWT
表 1 第1组试验数据类型
Table 1. First group experimental data types
故障类型 故障尺寸/mm 电机转速/(r·min-1) 标签 无故障 0 1 797 1 滚动体故障 0.177 8 2 内圈故障 3 外圈故障 4 
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表 2 第2组试验数据类型
Table 2. Second group experimental data types
故障类型 故障尺寸/mm 电机转速/(r·min-1) 标签 无故障 0 1 797 1 内圈故障 0.177 8 1 797 2 内圈故障 1 772 3 内圈故障 1 750 4 外圈故障 1 797 5 外圈故障 1 772 6 外圈故障 1 750 7 滚动体故障 1 797 8 滚动体故障 1 772 9 滚动体故障 1 750 10
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表 3 故障信号经TQWT后各子带谱峭度
Table 3. Kurtosis value of each subband of fault vibration signal after TQWT transformation
子带 1 2 3 4 5 6 7 8 9 10 11 正常信号谱峭度 8.41 22.34 18.77 3.65 6.77 64.74 5.92 2.98 4.33 14.52 3.88 滚动体故障信号谱峭度 12.24 3.31 7.89 48.06 26.97 8.67 9.69 22.89 23.37 8.75 5.06 内圈故障信号普峭度 20.71 7.95 10.05 19.06 30.02 13.02 6.85 12.95 58.92 6.49 5.10 外圈故障信号普峭度 38.48 7.07 4.28 10.13 58.20 13.26 12.40 21.65 30.39 12.72 6.61
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表 4 故障识别结果
Table 4. Fault identification results
试验次数 1 2 3 4 5 6 7 8 9 10 10次试验平均值 平均分类准确率/% 100.00 100.00 99.72 99.44 100.00 99.72 99.72 100.00 100.00 99.72 99.83 正常数据分类准确率/% 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 滚动体分类准确率/% 100.00 100.00 98.89 97.78 100.00 98.89 98.89 100.00 100.00 98.89 99.33 内圈分类准确率/% 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 外圈分类准确率/% 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00
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表 5 五种方法故障识别结果对比
Table 5. Comparison of fault identification results for five kinds of methods
方法 准确率/% 运行时间/s 准确率标准偏差/% 1 95.30 0.81 1.02 2 95.75 0.75 1.36 3 97.47 0.70 0.84 4 98.28 0.62 0.79 5 99.83 0.68 0.18
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表 6 三种方法故障识别结果对比
Table 6. Comparison of fault identification results for three kinds of methods
方法 准确率/% 运行时间/s 准确率标准偏差/% 1 86.13 4.03 1.48 4 86.38 3.86 0.77 5 97.73 3.68 0.73
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表 7 两种方法故障识别结果对比
Table 7. Comparison of fault identification results for two kinds of methods
方法 准确率/% 运行时间/s 准确率标准偏差/% 4 64.33 0.69 2.57 5 100.00 0.64 0.00
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