模拟电路是工业设备中最重要的元器件,其故障可能造成重大的人员伤亡,甚至造成巨大的经济损失。针对这一问题,提出一种基于核局部线性判别分析(Kernel Local Linear Discriminant Analysis,KLLDA)的故障诊断方案。利用小波分析和统计分...模拟电路是工业设备中最重要的元器件,其故障可能造成重大的人员伤亡,甚至造成巨大的经济损失。针对这一问题,提出一种基于核局部线性判别分析(Kernel Local Linear Discriminant Analysis,KLLDA)的故障诊断方案。利用小波分析和统计分析对原始信号进行预处理,得到原始特征集;利用KLLDA方法进行降维,并与核主成分分析(Kernel Principal Component Analysis,KPCA)和核线性判别分析(Kernel Linear Discriminant Analysis,KLDA)方法进行比较;采用极限学习机(Extreme Learning Machine,ELM)对测试电路的故障进行定位。对两个故障诊断案例的实验结果表明了该方法的有效性,并表明KLLDA在降维方面总体上优于KPCA和KLDA。展开更多
针对现有风电机组功率曲线建模存在非线性拟合能力不足,且不能很好的捕捉风速与风功率之间的复杂关系,提出了一种基于数据驱动的风电机组功率曲线建模的方法(mELM-CA-LSTM)。该方法利用多个极限机器学习机(Extreme Learning Machine,sho...针对现有风电机组功率曲线建模存在非线性拟合能力不足,且不能很好的捕捉风速与风功率之间的复杂关系,提出了一种基于数据驱动的风电机组功率曲线建模的方法(mELM-CA-LSTM)。该方法利用多个极限机器学习机(Extreme Learning Machine,short for ELM)将单个的风速变量映射到多维特征空间中,组成多个特征图,通过通道注意力机制(Channel Attention,short for CA)减少高维空间特征图的冗余性,最后将长短时记忆网络(Long short-term memory network,short for LSTM)拟合风速与相应风功率之间非线性关系。对比分析了其他功率曲线建模的方法,所提的mELM-CA-LSTM方法在三个数据集上获得的最高的精度,验证了所提方法的有效性。展开更多
To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A mac...To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A machine learning algorithm that uses timeliness managing extreme learning machine is utilized in this article to achieve the above prediction.Compared with traditional learning algorithms,extreme learning machine(ELM) exhibits high performance because of its unique feature of a high generalization capability at a fast learning speed.Timeliness managing ELM is proposed by incorporating timeliness management scheme into ELM.When using the timeliness managing ELM scheme to predict hidden dangers,newly incremental data could be added prior to the historical data to maximize the contribution of the newly incremental training data,because the incremental data may be able to contribute reasonable weights to represent the current production situation according to practical analysis of accidents in some industrial productions.Experimental results from a coal mine show that the use of timeliness managing ELM can improve the prediction accuracy of hidden dangers with better stability compared with other similar machine learning methods.展开更多
文摘模拟电路是工业设备中最重要的元器件,其故障可能造成重大的人员伤亡,甚至造成巨大的经济损失。针对这一问题,提出一种基于核局部线性判别分析(Kernel Local Linear Discriminant Analysis,KLLDA)的故障诊断方案。利用小波分析和统计分析对原始信号进行预处理,得到原始特征集;利用KLLDA方法进行降维,并与核主成分分析(Kernel Principal Component Analysis,KPCA)和核线性判别分析(Kernel Linear Discriminant Analysis,KLDA)方法进行比较;采用极限学习机(Extreme Learning Machine,ELM)对测试电路的故障进行定位。对两个故障诊断案例的实验结果表明了该方法的有效性,并表明KLLDA在降维方面总体上优于KPCA和KLDA。
文摘针对现有风电机组功率曲线建模存在非线性拟合能力不足,且不能很好的捕捉风速与风功率之间的复杂关系,提出了一种基于数据驱动的风电机组功率曲线建模的方法(mELM-CA-LSTM)。该方法利用多个极限机器学习机(Extreme Learning Machine,short for ELM)将单个的风速变量映射到多维特征空间中,组成多个特征图,通过通道注意力机制(Channel Attention,short for CA)减少高维空间特征图的冗余性,最后将长短时记忆网络(Long short-term memory network,short for LSTM)拟合风速与相应风功率之间非线性关系。对比分析了其他功率曲线建模的方法,所提的mELM-CA-LSTM方法在三个数据集上获得的最高的精度,验证了所提方法的有效性。
基金partially supported by the National Key Technologies R&D Program of China under Grant No.2015BAK38B01the National Natural Science Foundation of China under Grant Nos.61174103 and 61272357the Fundamental Research Funds for the Central Universities under Grant No.06500025
文摘To prevent possible accidents,the study of data-driven analytics to predict hidden dangers in cloud service-based intelligent industrial production management has been the subject of increasing interest recently.A machine learning algorithm that uses timeliness managing extreme learning machine is utilized in this article to achieve the above prediction.Compared with traditional learning algorithms,extreme learning machine(ELM) exhibits high performance because of its unique feature of a high generalization capability at a fast learning speed.Timeliness managing ELM is proposed by incorporating timeliness management scheme into ELM.When using the timeliness managing ELM scheme to predict hidden dangers,newly incremental data could be added prior to the historical data to maximize the contribution of the newly incremental training data,because the incremental data may be able to contribute reasonable weights to represent the current production situation according to practical analysis of accidents in some industrial productions.Experimental results from a coal mine show that the use of timeliness managing ELM can improve the prediction accuracy of hidden dangers with better stability compared with other similar machine learning methods.
