Pan Zuozhou, Zhang Zhengyuan, Meng Zong, Wang Yuebing
College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, PR China.
College of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore.
ISA Trans. 2023 Nov;142:427-444. doi: 10.1016/j.isatra.2023.07.015. Epub 2023 Jul 22.
To improve the accuracy of bearing fault diagnosis in a multisensor monitoring environment, it is necessary to obtain more accurate and effective fault classification features for bearings. Accordingly, a bearing fault classification feature extraction method based on multisensor fusion technology and an enhanced binary one-dimensional ternary pattern (EB-1D-TP) algorithm were proposed in this study. First, an optimal equalization weighting algorithm was established to realize high-precision fusion of bearing signals by introducing an optimal equalization factor and determining the theoretical optimal equalization factor value. Second, an enhanced binary encoding method similar to balanced ternary encoding was developed, which increases the difference between the different fault features of the bearing. Finally, the new sequence obtained after encoding was used as the input to a support vector machine to classify and diagnose the faults of the rolling bearing. The experimental results show that the algorithm can significantly improve the accuracy and speed of rolling-bearing fault classification. Combining fusion-encoding features with other intelligent classification methods, the classification results were improved.
为提高多传感器监测环境下轴承故障诊断的准确性，有必要获取更准确有效的轴承故障分类特征。因此，本研究提出了一种基于多传感器融合技术和增强型二进制一维三元模式（EB-1D-TP）算法的轴承故障分类特征提取方法。首先，通过引入最优均衡因子并确定理论最优均衡因子值，建立了最优均衡加权算法，以实现轴承信号的高精度融合。其次，开发了一种类似于平衡三元编码的增强型二进制编码方法，该方法增加了轴承不同故障特征之间的差异。最后，将编码后得到的新序列作为支持向量机的输入，对滚动轴承的故障进行分类诊断。实验结果表明，该算法能显著提高滚动轴承故障分类的准确性和速度。将融合编码特征与其他智能分类方法相结合，提高了分类结果。
