扇形段拉矫力数据突变特征提取方法  

Catastrophe feature extraction method of sector segment tensile force data

作　　者：代超 容芷君[1] 但斌斌[1] 都胜朝 刘洋 肖浩 DAI Chao;RONG Zhijun;DAN Binbin;DU Shengchao;LIU Yang;XIAO Hao(School of Machinery and Automation,Wuhan University of Science and Technology,Wuhan 430081,China;Department of Equipment Management,Wuhan Iron and Steel Co.,Ltd.,Wuhan 430081,China;Department of Engineering Equipment and Technology,China Communications Second Aviation Engineering Bureau Co.,Ltd.,Wuhan 430081,China)

机构地区：[1]武汉科技大学机械自动化学院,湖北武汉430081 [2]武汉钢铁有限公司设备管理部,湖北武汉430081 [3]中交第二航务工程局有限公司工程装备技术部,湖北武汉430081

出　　处：《计算机集成制造系统》2025年第1期254-263,共10页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(51475340);面向多生产模式的柔性智能制造系统关键技术研究资助项目(2022BAA059)。

摘　　要：多维时序拉矫力的幅值突变表征着扇形段机组的故障,多维时序数据的时间和空间复杂度对突变特征提取方法的准确率和效率提出了更高的要求。利用多维时序数据的时间和空间相似性,提出小波相关滤波结合主成分分析的多维时序突变特征提取方法。首先,将每一维时域含噪拉矫力分别变换到小波域,利用拉矫力的自相似性进行降噪;然后,将去噪后的拉矫力映射到正交空间中,利用多维拉矫力之间的互相似性,通过线性变换将多维拉矫力映射到一维,再根据一维特征曲线的统计特征确定阈值,提取突变特征;最后,应用基于时间的滑动窗口动态更新数据,实现状态的连续在线识别;通过现场实测数据分析表明,小波相关滤波-主成分分析模型对多维时序数据的突变特征提取的准确率和效率分别达到90.39%和97.56%。The amplitude mutation of multidimensional time series tension and straightening force represents the fault of fan-shaped unit.The time and space complexity of multidimensional time series data put forward higher requirements for the accuracy and efficiency of the mutation feature extraction method.Based on the temporal and spatial similarity of multidimensional time series data,a feature extraction method of multidimensional time series mutation based on wavelet correlation filtering combined with Principal Component Analysis(PCA)was proposed.Each one-dimensional time-domain noisy tension correction was transformed into the wavelet domain,and the self-similarity of tension correction was used to reduce noise.Then,the denoised tension and correction force was mapped to the orthogonal space,and the multidimensional tension and correction force were mapped to one dimension by linear transformation based on the mutual similarity between multidimensional tension and correction force.The threshold was determined according to the statistical characteristics of one-dimensional feature curve,and the mutation feature was extracted.Finally,the sliding window based on time was used to dynamically update data to realize continuous online state recognition.The analysis of field measured data showed that the accuracy and efficiency of wavelet correlation filtering-PCA model for the mutation feature extraction of multidimensional time series data were 90.39%and 97.56%respectively.

关 键 词：扇形段 多维时间序列 主成分分析 突变特征 

分 类 号：TG233.6[金属学及工艺—铸造]