摘要
提出了一种自组织映射网络训练结果的可视化方法——距离映射祛,该方法通过计算出竞争层神经元权矢量与输入模式的相似度,并综合考虑神经元的网格分布,把输入矢量降维映射到二维平面。结合该方法研究了自组织映射网络在齿轮箱故障识别和状态监测中的应用。与U—矩阵法相比,该方法能更加清楚地将齿轮正常、裂纹和断齿状态的特征数据映射到二维平面的不同区域,将齿轮箱状态聚类分开,特征数据在平面上的映像点轨迹变化趋势直观反映了齿轮箱工作状态的变化,便于及时监测识别出齿轮的早期故障及其变化趋势。
A new method, distance mapping, is presented in order to visualize the trained results by self-organizing maps (SOM) apparently. By means of similarities evaluated based on Euclidean distances between input vectors and output neurons weights combining with the distribution of fixed lattices in the network, high-dimensional input vectors are projected into a two-dimensional space. SOM is employed in fault recognition and condition monitoring of gearbox combining with the proposed visualizing technique. It is proved that feature points under gear normal, tooth cracked and tooth broken conditions are mapped into different areas on two-dimensional space more clearly by distance mapping than U-matrix method, which helps distinguish gearbox conditions correctly. With the trace of the image points for gear feature data on the plane, the variation of gearbox conditions is observed visually, and furthermore, early gear failures occurrence and its varying trend is monitored in time.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2003年第12期99-102,共4页
Journal of Mechanical Engineering
基金
国家自然科学基金(50205009)
湖北省自然科学基金(2000J125)
