基于机器学习的云平台故障排查方法

随着云计算的发展,越来越多的企业将系统部署在云环境中,大大提高了企业应用服务的灵活性、弹性、扩展性和效率,浙江电网容器云平台是云计算在电力系统的典型应用。然而,云计算的弹性架构也导致企业应用的运维变得更复杂和难以监控,当前运维手段大多缺乏清晰的云上应用访问可见性,给云环境下的故障排查带来了困难。针对这一问题,提出一种基于机器学习的故障排查方法。首先,通过层次聚类方法动态生成节点的网络拓扑结构,实时监测浙江电网容器云平台的各节点性能指标,以此作为特征向量;然后,采用支持向量机和随机搜索方法对其进行故障分类,达到实时排查故障的目的,有效提高了该云平台的性能和可靠性,验证了机器学习方法在电力系统中的应用前景。     

六缸全自动顶盖机在焦炭塔上的工业应用

九江分公司1Mt／a延迟焦化装置首次在焦炭塔上采用六缸金自动顶盖机替代人工拆装顶盖，该顶盖机设计新颖，操作简单。经过长达一年的连续运行，表明该顶盖机运行平稳、安全可靠，大幅度改善了除焦工的劳动环境，降低了劳动强度，提高了除焦效率，同时也获得了可观的经济效益。     

基于改进小波阈值-CEEMDAN算法的ECG信号去噪研究

Electrocardiogram (ECG) signal denoising has always been a hot research issue. In order to eliminate the noises in ECG signal, a denoising method based on adaptive complete set empirical mode decomposition (CEEMDAN) and wavelet improved threshold function is proposed. Firstly, this method firstly decomposes the ECG signal by CEEMDAN to obtain a set of intrinsic modal functions (IMFs) from high frequency to low frequency. CEEMDAN decomposition is performed on ECG signal to yield several modal components (IMF). Secondly, the correlation coefficient method is used to perform wavelet denoising with improved threshold on the high frequency IMFs. For the low frequency IMFs, by setting a fixed threshold, the IMFs below the threshold is considered to be the baseline drift signal and removed. Finally, the denoised IMFs and the retained IMFs are reconstructed. The experimental results show that the proposed method is more effective than the empirical mode decomposition (EMD) wavelet denoising, and the global average empirical mode decomposition (EEMD) wavelet denoising method.