基于层次多尺度散布熵的滚动轴承智能故障诊断

针对全寿命周期内滚动轴承振动信号的特征提取与智能诊断问题,该研究提出一种基于层次多尺度散布熵的滚动轴承智能故障诊断方法。首先,在散布熵的基础上,结合层次分解和多尺度分析的理论思想,提出一种信号复杂性度量方法——层次多尺度散布熵(Hierarchical Multiscale Dispersion Entropy, HMDE);其次,为了避免HMDE按经验性选取参数的缺陷,借助鸟群优化算法(Bird Swarm Algorithm, BSA)自适应地确定其重要参数,并采用参数优化的HMDE提取原轴承振动信号中的多层次、多尺度故障特征;最后,将构建的多维度故障特征矩阵输入到支持矩阵机(Support Matrix Machine,SMM)中进行模型训练并完成轴承故障模式及程度的自动判别。通过2组轴承加速寿命试验对所提方法进行了有效性验证。通过与精细复合多尺度散布熵(RefinedCompositeMultiscaleDispersionEntropy,RCMDE)、广义复合多尺度排列熵(GeneralizedCompositeMultiscalePermutationEntropy,GCMPE)、广义精细复合多尺度样本熵(GeneralizedRefined Composite Multiscale Sample Entropy, GRCMSE)、层次模糊熵(Hierarchical Fuzzy Entropy, HFE)、层次样本熵(Hierarchical Sample Entropy, HSE)、修改的层次多尺度散布熵(Modified Hierarchical Multiscale Dispersion Entropy, MHMDE)和层次多尺度排列熵(Hierarchical Multiscale Permutation Entropy, HMPE)方法的识别精度对比,对于XJTU-SY轴承加速寿命试验,本文方法的平均识别精度分别提高了3.89、12.34、6.63、9.15、7.09、0.81和2.63个百分点。对于ABLT-1A轴承加速寿命试验,本文方法的平均识别精度分别提高了2.17、3.51、6.17、9.51、11.51、1.17和3.01个百分点。本文方法实现了全寿命周期内滚动轴承不同故障模式及程度的识别,与传统的基于多尺度熵或层次熵的故障诊断方法相比,能够获取更全面、更丰富的轴承故障特征信息,识别精度得到了较大的提升。本文研究可为全寿命周期内滚动轴承故障诊断提供参考。

Intelligent fault diagnosis of rolling element bearing using hierarchical multiscale dispersion entropy

Fault diagnosis has normally been utilized to effectively identify bearing fault patterns and severities in the whole life cycle in modern intelligent agriculture. In this study, an intelligent fault diagnosis was proposed for the rolling element bearing using hierarchical multiscale dispersion entropy. Firstly, a signal complexity assessment called the hierarchical multiscale dispersion entropy (HMDE) was proposed to integrate the theories and ideas of hierarchical decomposition and multiscale analysis using dispersion entropy theory. Secondly, a swarm intelligence optimization named bird swarm (BSA) was employed to determine the important parameters. The HMDE with the optimized parameter was utilized to extract multilevel and multiscale fault features hidden in the raw bearing vibration signal and avoid empiric selection of HMDE parameters. Finally, the multi-dimensional fault feature matrix was constructed and then fed into the support matrix machine (SMM) for the training of the SMM model. The well-trained SMM model was adopted to automatically identify different fault patterns and severities of rolling bearing. A two-group test of bearing accelerated life was carried out to verify the model. Experimental results showed that the diagnostic accuracy reached 99.66% in the first group, whereas, the diagnostic accuracy of seven (i.e., refined composite multiscale dispersion entropy (RCMDE), generalized composite multiscale permutation entropy (GCMPE), generalized refined composite multiscale sample entropy (GRCMSE), hierarchical fuzzy entropy (HFE), hierarchical sample entropy (HSE), modified hierarchical multiscale dispersion entropy (MHMDE) and hierarchical multiscale permutation entropy (HMPE)) were 95.77%, 87.32%, 93.03%, 90.51%, 92.57%, 98.85%, and 97.03%, respectively. In the second group, the diagnostic accuracy reached 99.34%, whereas, the diagnostic accuracy of seven (i.e., RCMDE, GCMPE, GRCMSE, HFE, HSE, MHMDE and HMPE) were 97.17%, 95.83%, 93.17%, 89.83%, 87.83%, 98.17%, and 96.33%, respectively. It was clearly found that the average accuracy in the first group was improved by the percent point of 3.89, 12.34, 6.63, 9.15, 7.09, 0.81 and 2.63, respectively, where that in the second group increased by the percent point of 2.17, 3.51, 6.17, 9.51, 11.51, 1.17, and 3.01, respectively, compared with the seven (RCMDE, GCMPE, GRCMSE, HFE, HSE, MHMDE, and HMPE). The diagnostic accuracy demonstrated that the different fault patterns and severities of rolling bearings were identified in the whole life cycle. In addition, the broader and richer feature information of bearing faults was achieved with a greatly better identification performance, compared with the traditional fault diagnosis using multiscale or hierarchical entropy. The finding can provide a new idea to improve the fault diagnosis accuracy of rolling bearings in the whole life cycle.