欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
采用经验模式分解与基于统计的Mahalanobis—Taguchi system相结合的方法进行机械设备状态识别。利用经验模式分解自适应提取设备状态监测信号的特征，针对机械设备实际运行中存在的大量正常样本和少量异常状态样本的情况，运用统计方法实现对机械设备状态的识别诊断。实验表明，运用经验模式分解与Mahalanobis-Taguchi system方法能有效地识别设备运行状态，提高状态识别的准确性。
英文摘要：
A state recognition method for mechanical equipment was proposed, combining empirical mode decomposition and Mahalanobis-Taguchi system which was based on statistics. First, empirical mode decomposition performed adaptive analysis on equipment monitoring signal to obtain its feature. Focused on mass normal signal samples and few abnormal ones, statistics method implemented recognition and diagnosis of mechanical equipment. Experimental results verify that equipment states are extracted and recognized efficiently by empirical mode decomposition and Mahalanobis-Taguchi system, and the recognition accuracy is enhanced.
同期刊论文项目
同项目期刊论文
Parallel energy-efficient coverage optimization with maximum entropy clustering in wireless sensor n
Multi-agent negotiation mechanisms of statistical classification in wireless multimedia sensor netwo
An improved co-evolutionary particle swarm optimization for wireless sensor networks with dynamic de
期刊信息
位置：