提前诊断出机械系统中的异常信息对于防止生产事故的产生非常重要。在各种诊断方法中,符号化时间序列分析(STSA,Symbolic time series analysis)是一种常用的异常诊断方法,然而它的诊断效果和符号化时间序列的形成紧密相关。在对之前方...提前诊断出机械系统中的异常信息对于防止生产事故的产生非常重要。在各种诊断方法中,符号化时间序列分析(STSA,Symbolic time series analysis)是一种常用的异常诊断方法,然而它的诊断效果和符号化时间序列的形成紧密相关。在对之前方法总结分析的基础上,提出了一种高效实用的符号化方法——基于概率密度空间划分的符号化方法。在该方法中,首先对时间序列进行概率密度统计分析,进而确定若干个概率相等的区间,然后对属于特定区间的值赋予一个特定的符号。为了检验该方法的效果,将基于概率密度空间划分的符号化时间序列分析方法用于轴承疲劳实验的异常诊断当中。通过对比实验表明:概率密度符号化方法相比于传统的空间划分方法对异常更加敏感,能够更早诊断出轴承的异常。展开更多
为进一步提高变压器机械故障智能诊断的准确性,文中基于变压器振动信号时间序列符号化的模式表征,提出了一种基于栈式自编码器的变压器机械故障诊断模型。首先对振动信号时间序列进行符号化模式表征和构建复杂网络,提取了基于度分布的...为进一步提高变压器机械故障智能诊断的准确性,文中基于变压器振动信号时间序列符号化的模式表征,提出了一种基于栈式自编码器的变压器机械故障诊断模型。首先对振动信号时间序列进行符号化模式表征和构建复杂网络,提取了基于度分布的变压器振动信号特征向量,据此构建了基于栈式自编码器(stacked auto encoder,SAE)的变压器机械故障诊断模型。对某10 k V干式变压器正常与典型机械故障下振动信号的分析结果表明,变压器振动信号时间序列的符号化模式表征及度分布能较好地表征其动力学特征,所构建的基于SAE变压器机械故障模型具有较高的识别准确率,可达95%,研究结果可为变压器的机械故障诊断提供新思路。展开更多
The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution...The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution of sensors collecting more and more data exacerbates the problem. Representing a time series effectively is an essential task for decision-making activities such as classification, prediction, and knowledge discovery. In this paper, we propose a new symbolic representation method for long time series based on trend features, called trend feature symbolic approximation （TFSA）. The method uses a two-step mechanism to segment long time series rapidly. Unlike some previous symbolic methods, it focuses on retaining most of the trend features and patterns of the original series. A time series is represented by trend symbols, which are also suitable for use in knowledge discovery, such as association rules mining. TFSA provides the lower bounding guarantee. Experimental results show that, compared with some previous methods, it not only has better segmentation efficiency and classification accuracy, but also is applicable for use in knowledge discovery from time series.展开更多
文摘提前诊断出机械系统中的异常信息对于防止生产事故的产生非常重要。在各种诊断方法中,符号化时间序列分析(STSA,Symbolic time series analysis)是一种常用的异常诊断方法,然而它的诊断效果和符号化时间序列的形成紧密相关。在对之前方法总结分析的基础上,提出了一种高效实用的符号化方法——基于概率密度空间划分的符号化方法。在该方法中,首先对时间序列进行概率密度统计分析,进而确定若干个概率相等的区间,然后对属于特定区间的值赋予一个特定的符号。为了检验该方法的效果,将基于概率密度空间划分的符号化时间序列分析方法用于轴承疲劳实验的异常诊断当中。通过对比实验表明:概率密度符号化方法相比于传统的空间划分方法对异常更加敏感,能够更早诊断出轴承的异常。
文摘为进一步提高变压器机械故障智能诊断的准确性,文中基于变压器振动信号时间序列符号化的模式表征,提出了一种基于栈式自编码器的变压器机械故障诊断模型。首先对振动信号时间序列进行符号化模式表征和构建复杂网络,提取了基于度分布的变压器振动信号特征向量,据此构建了基于栈式自编码器(stacked auto encoder,SAE)的变压器机械故障诊断模型。对某10 k V干式变压器正常与典型机械故障下振动信号的分析结果表明,变压器振动信号时间序列的符号化模式表征及度分布能较好地表征其动力学特征,所构建的基于SAE变压器机械故障模型具有较高的识别准确率,可达95%,研究结果可为变压器的机械故障诊断提供新思路。
基金supported by the National High-Tech R&D Program(863)of China(Nos.2012AA012600,2011AA010702,2012AA01A401,and 2012AA01A402)the National Natural Science Foundation of China(No.60933005)the National Science and Technology of China(No.2012BAH38B04)
文摘The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution of sensors collecting more and more data exacerbates the problem. Representing a time series effectively is an essential task for decision-making activities such as classification, prediction, and knowledge discovery. In this paper, we propose a new symbolic representation method for long time series based on trend features, called trend feature symbolic approximation （TFSA）. The method uses a two-step mechanism to segment long time series rapidly. Unlike some previous symbolic methods, it focuses on retaining most of the trend features and patterns of the original series. A time series is represented by trend symbols, which are also suitable for use in knowledge discovery, such as association rules mining. TFSA provides the lower bounding guarantee. Experimental results show that, compared with some previous methods, it not only has better segmentation efficiency and classification accuracy, but also is applicable for use in knowledge discovery from time series.
