Software debugging accounts for a vast majority of the financial and time costs in software developing and maintenance. Thus, approaches of software fault localization that can help automate the debugging process have...Software debugging accounts for a vast majority of the financial and time costs in software developing and maintenance. Thus, approaches of software fault localization that can help automate the debugging process have become a hot topic in the field of software engineering. Given the great demand for software fault localization, an approach based on the artificial bee colony （ABC） algorithm is proposed to be integrated with other related techniques. In this process, the source program is initially instrumented after analyzing the dependence information. The test case sets are then compiled and run on the instrumented program, and execution results are input to the ABC algorithm. The algorithm can determine the largest fitness value and best food source by calculating the average fitness of the employed bees in the iteralive process. The program unit with the highest suspicion score corresponding to the best test case set is regarded as the final fault localization. Experiments are conducted with the TCAS program in the Siemens suite. Results demonstrate that the proposed fault localization method is effective and efficient. The ABC algorithm can efficiently avoid the local optimum, and ensure the validity of the fault location to a larger extent.展开更多
To diagnosethe reciprocating mechanical fault.We utilizedlocal waveti me-frequency approach.Firstly,we gave the principle.Secondly,the application of local wave ti me-frequency was given.Finally,we discusseditsvirtue ...To diagnosethe reciprocating mechanical fault.We utilizedlocal waveti me-frequency approach.Firstly,we gave the principle.Secondly,the application of local wave ti me-frequency was given.Finally,we discusseditsvirtue in reciprocating mechanical fault diagnosis.展开更多
针对全并联自耦变压器(auto-transformer,AT)牵引网故障点反射波波头微弱难辨识,且线路并联结构导致故障行波折反射复杂而造成定位困难的问题,提出基于变分模态分解(variational mode decomposition,VMD)和改进能量算子的牵引网故障行...针对全并联自耦变压器(auto-transformer,AT)牵引网故障点反射波波头微弱难辨识,且线路并联结构导致故障行波折反射复杂而造成定位困难的问题,提出基于变分模态分解(variational mode decomposition,VMD)和改进能量算子的牵引网故障行波单端定位方法。首先,深入分析牵引网故障行波的传输特性,研究全并联结构对行波折反射的影响,确定不同故障类型和潮流特征,以此为依据提取故障特征量,将波头辨识转化为能量突变值提取;随后,利用VMD去噪提取电压行波真实分量,再针对第二反射波波头微弱难标定问题,在滑动时间窗口(sliding time window,STW)下,结合对称差分能量算子(symmetrical differencing energy operator,SDEO)构造故障信号的二次瞬时能量谱,效果良好。仿真结果表明:所提方法抗过渡电阻能力强,能够反应不同工况下故障牵引网电磁能量的变化,具有较高的定位精度。展开更多
局部线性嵌入算法采用欧氏距离选择邻域点,这通常会损失数据集本身的非线性特征,造成邻域点选取错误,且仅使用欧氏距离构造权重会导致信息挖掘不充分。针对以上问题,提出基于概率模型与信息熵的局部线性嵌入算法(Probability informatio...局部线性嵌入算法采用欧氏距离选择邻域点,这通常会损失数据集本身的非线性特征,造成邻域点选取错误,且仅使用欧氏距离构造权重会导致信息挖掘不充分。针对以上问题,提出基于概率模型与信息熵的局部线性嵌入算法(Probability information entropy-LLE,PIE-LLE)。首先,为了使邻域点选择更加合理,从数据集的概率分布角度出发,考虑样本点及其邻域的概率分布,为样本点构造符合局部分布的邻域集合。其次,为了充分提取样本的局部结构信息,在权重构造阶段,分别计算样本所属邻域概率以及每个样本的信息熵,融合二者信息重构低维样本。最后,在两个轴承故障数据集上的实验表明,所提方法故障识别准确度最高达到了100%,高于其他对比算法;在邻域点个数5~15范围内,PIE-LLE算法展现出良好的低维可视化效果;在参数敏感性实验中,该算法可以保持Fisher指标较大,有效提高了算法的分类准确度和稳定性。展开更多
局部保持投影算法的性能主要依赖于构造的最近邻图,而构造最近邻图时容易受到原始数据冗余信息的干扰,以及没有良好的依据选择合适的热核参数带来的影响,导致不能充分挖掘高维数据的局部结构信息,在低维嵌入过程中也易对噪声和异常值较...局部保持投影算法的性能主要依赖于构造的最近邻图,而构造最近邻图时容易受到原始数据冗余信息的干扰,以及没有良好的依据选择合适的热核参数带来的影响,导致不能充分挖掘高维数据的局部结构信息,在低维嵌入过程中也易对噪声和异常值较为敏感,影响其在故障诊断应用中的特征提取能力。针对以上问题,提出基于收缩自编码器和流形排序的局部保持投影算法(Locality Preserving Projections algorithm based on Contractive Auto-Encoder and Manifold Ranking,CAE-MRLPP),并用于机械设备故障诊断。首先,将样本标签信息和斯皮尔曼相关系数结合,预调整样本间距;其次,引入流形排序思想,根据样本点与邻域点在彼此邻域集中的排序位置信息以及二者的互邻个数信息来构造权重;最后,将收缩自编码器与基于流形排序的局部保持投影相融合,通过梯度下降法迭代优化求解出最优的投影矩阵,进而得到故障数据的低维表示。分别在滚动轴承数据集和抽油机数据集上进行了多项验证,故障识别准确度均在98%以上,表明该算法具有良好的特征提取能力,能够有效提高故障识别准确度,同时具有较好的鲁棒性和泛化能力。展开更多
文摘Software debugging accounts for a vast majority of the financial and time costs in software developing and maintenance. Thus, approaches of software fault localization that can help automate the debugging process have become a hot topic in the field of software engineering. Given the great demand for software fault localization, an approach based on the artificial bee colony （ABC） algorithm is proposed to be integrated with other related techniques. In this process, the source program is initially instrumented after analyzing the dependence information. The test case sets are then compiled and run on the instrumented program, and execution results are input to the ABC algorithm. The algorithm can determine the largest fitness value and best food source by calculating the average fitness of the employed bees in the iteralive process. The program unit with the highest suspicion score corresponding to the best test case set is regarded as the final fault localization. Experiments are conducted with the TCAS program in the Siemens suite. Results demonstrate that the proposed fault localization method is effective and efficient. The ABC algorithm can efficiently avoid the local optimum, and ensure the validity of the fault location to a larger extent.
文摘To diagnosethe reciprocating mechanical fault.We utilizedlocal waveti me-frequency approach.Firstly,we gave the principle.Secondly,the application of local wave ti me-frequency was given.Finally,we discusseditsvirtue in reciprocating mechanical fault diagnosis.
文摘针对全并联自耦变压器(auto-transformer,AT)牵引网故障点反射波波头微弱难辨识,且线路并联结构导致故障行波折反射复杂而造成定位困难的问题,提出基于变分模态分解(variational mode decomposition,VMD)和改进能量算子的牵引网故障行波单端定位方法。首先,深入分析牵引网故障行波的传输特性,研究全并联结构对行波折反射的影响,确定不同故障类型和潮流特征,以此为依据提取故障特征量,将波头辨识转化为能量突变值提取;随后,利用VMD去噪提取电压行波真实分量,再针对第二反射波波头微弱难标定问题,在滑动时间窗口(sliding time window,STW)下,结合对称差分能量算子(symmetrical differencing energy operator,SDEO)构造故障信号的二次瞬时能量谱,效果良好。仿真结果表明:所提方法抗过渡电阻能力强,能够反应不同工况下故障牵引网电磁能量的变化,具有较高的定位精度。
文摘局部线性嵌入算法采用欧氏距离选择邻域点,这通常会损失数据集本身的非线性特征,造成邻域点选取错误,且仅使用欧氏距离构造权重会导致信息挖掘不充分。针对以上问题,提出基于概率模型与信息熵的局部线性嵌入算法(Probability information entropy-LLE,PIE-LLE)。首先,为了使邻域点选择更加合理,从数据集的概率分布角度出发,考虑样本点及其邻域的概率分布,为样本点构造符合局部分布的邻域集合。其次,为了充分提取样本的局部结构信息,在权重构造阶段,分别计算样本所属邻域概率以及每个样本的信息熵,融合二者信息重构低维样本。最后,在两个轴承故障数据集上的实验表明,所提方法故障识别准确度最高达到了100%,高于其他对比算法;在邻域点个数5~15范围内,PIE-LLE算法展现出良好的低维可视化效果;在参数敏感性实验中,该算法可以保持Fisher指标较大,有效提高了算法的分类准确度和稳定性。
文摘局部保持投影算法的性能主要依赖于构造的最近邻图,而构造最近邻图时容易受到原始数据冗余信息的干扰,以及没有良好的依据选择合适的热核参数带来的影响,导致不能充分挖掘高维数据的局部结构信息,在低维嵌入过程中也易对噪声和异常值较为敏感,影响其在故障诊断应用中的特征提取能力。针对以上问题,提出基于收缩自编码器和流形排序的局部保持投影算法(Locality Preserving Projections algorithm based on Contractive Auto-Encoder and Manifold Ranking,CAE-MRLPP),并用于机械设备故障诊断。首先,将样本标签信息和斯皮尔曼相关系数结合,预调整样本间距;其次,引入流形排序思想,根据样本点与邻域点在彼此邻域集中的排序位置信息以及二者的互邻个数信息来构造权重;最后,将收缩自编码器与基于流形排序的局部保持投影相融合,通过梯度下降法迭代优化求解出最优的投影矩阵,进而得到故障数据的低维表示。分别在滚动轴承数据集和抽油机数据集上进行了多项验证,故障识别准确度均在98%以上,表明该算法具有良好的特征提取能力,能够有效提高故障识别准确度,同时具有较好的鲁棒性和泛化能力。
