In order to raise the efficiency,automatization and intelligentization of condition monitoring and fault diagnosis for complex equipment systems,rough set theory is used to the field. A feature reduction algorithm bas...In order to raise the efficiency,automatization and intelligentization of condition monitoring and fault diagnosis for complex equipment systems,rough set theory is used to the field. A feature reduction algorithm based on rough set theory is adopted to extract condition information in monitoring and diagnosis for an engine,so that the technology condition monitoring parameters are optimized. The decision tables for each fault source are built and the diagnosis rules rooting in rough set reduction is applied to carry through intelligent fault diagnosis. The cases studied show that rough set method in condition monitoring and fault diagnosis can lighten the work burden in feature selection and afford advantages for autonomic learning and decision during diagnosis.展开更多
As the first step of service restoration of distribution system,rapid fault diagnosis is a significant task for reducing power outage time,decreasing outage loss,and subsequently improving service reliability and safe...As the first step of service restoration of distribution system,rapid fault diagnosis is a significant task for reducing power outage time,decreasing outage loss,and subsequently improving service reliability and safety.This paper analyzes a fault diagnosis approach by using rough set theory in which how to reduce decision table of data set is a main calculation intensive task.Aiming at this reduction problem,a heuristic reduction algorithm based on attribution length and frequency is proposed.At the same time,the corresponding value reduction method is proposed in order to fulfill the reduction and diagnosis rules extraction.Meanwhile,a Euclid matching method is introduced to solve confliction problems among the extracted rules when some information is lacking.Principal of the whole algorithm is clear and diagnostic rules distilled from the reduction are concise.Moreover,it needs less calculation towards specific discernibility matrix,and thus avoids the corresponding NP hard problem.The whole process is realized by MATLAB programming.A simulation example shows that the method has a fast calculation speed,and the extracted rules can reflect the characteristic of fault with a concise form.The rule database,formed by different reduction of decision table,can diagnose single fault and multi-faults efficiently,and give satisfied results even when the existed information is incomplete.The proposed method has good error-tolerate capability and the potential for on-line fault diagnosis.展开更多
>Transformer faults are quite complicated phenomena and can occur due to a variety of reasons.There have been several methods for transformer fault synthetic diagnosis,but each of them has its own limitations in re...>Transformer faults are quite complicated phenomena and can occur due to a variety of reasons.There have been several methods for transformer fault synthetic diagnosis,but each of them has its own limitations in real fault diagnosis applications.In order to overcome those shortcomings in the existing methods,a new transformer fault diagnosis method based on a wavelet neural network optimized by adaptive genetic algorithm(AGA)and an improved D-S evidence theory fusion technique is proposed in this paper.The proposed method combines the oil chromatogram data and the off-line electrical test data of transformers to carry out fault diagnosis.Based on the fusion mechanism of D-S evidence theory,the comprehensive reliability of evidence is constructed by considering the evidence importance,the outputs of the neural network and the expert experience.The new method increases the objectivity of the basic probability assignment(BPA)and reduces the basic probability assigned for uncertain and unimportant information.The case study results of using the proposed method show that it has a good performance of fault diagnosis for transformers.展开更多
A novel extension diagnosis method was proposed for enhancing the diagnosis ability of the conventional dissolved gas analysis. Based on the extension theory a matter-element model was established for qualitatively an...A novel extension diagnosis method was proposed for enhancing the diagnosis ability of the conventional dissolved gas analysis. Based on the extension theory a matter-element model was established for qualitatively and quantitatively describing the fault diagnosis problem of power transformers. The degree of relation based on the dependent functions was employed to determine the nature and the grade of the faults in a transformer system. And the proposed method was verified with the experimental data. The results show that accuracy rate of the diagnosis method exceeds 90% and two kinds of faults can be detected at the same time.展开更多
当前高压电容式电压互感器(capacitor voltage transformer,CVT)缺少有效的在线监测数据,辨识不足。利用在线监测多数据源存在线性相关的数据特性,提出了基于分析数据相关系数进行有效数据辨识的方法。针对目前高压CVT故障诊断普遍存在...当前高压电容式电压互感器(capacitor voltage transformer,CVT)缺少有效的在线监测数据,辨识不足。利用在线监测多数据源存在线性相关的数据特性,提出了基于分析数据相关系数进行有效数据辨识的方法。针对目前高压CVT故障诊断普遍存在信息单一、精度不高、局部放电在线监测装置故障信号检测受干扰因素影响较大、准确性差等问题,提出了基于多维信息融合的故障诊断方法。首先,利用因子分析对CVT的诊断指标进行数据层信息融合,提取各故障类型对应的公共因子方差贡献值,作为反映故障类型差异的特征值;然后,利用模糊理论进行特征层信息融合,将公共因子方差贡献值作为隶属函数的输入参数,识别CVT的故障类型,准确诊断高压CVT故障。案例验证了所提方法的有效性,为CVT故障诊断提供了理论参考和实践经验。展开更多
辛周期模态分解(symplectic period mode decomposition, SPMD)方法可以准确地提取周期脉冲分量,是一种有效的滚动轴承单一故障诊断方法。但在滚动轴承出现复合故障时,尤其是强背景噪声下,周期脉冲信号往往较微弱,使得SPMD难以提取出不...辛周期模态分解(symplectic period mode decomposition, SPMD)方法可以准确地提取周期脉冲分量,是一种有效的滚动轴承单一故障诊断方法。但在滚动轴承出现复合故障时,尤其是强背景噪声下,周期脉冲信号往往较微弱,使得SPMD难以提取出不同周期的脉冲分量,进而限制了其在复合故障诊断中的应用。对此,提出了改进的辛周期模态分解(improved symplectic period mode decomposition, ISPMD)方法。该方法首先采用求差增强技术和最小噪声幅值反卷积相结合的方法对信号进行降噪,增强周期脉冲,以准确估计故障周期;然后构造对应的周期截断矩阵,并通过辛几何相似变换和周期冲击强度获得辛几何周期分量;最后对残差信号采用迭代分解,进而得到不同周期的辛几何周期分量。试验结果表明,ISPMD能准确提取出周期脉冲分量,是一种有效的滚动轴承复合故障诊断方法。展开更多
文摘In order to raise the efficiency,automatization and intelligentization of condition monitoring and fault diagnosis for complex equipment systems,rough set theory is used to the field. A feature reduction algorithm based on rough set theory is adopted to extract condition information in monitoring and diagnosis for an engine,so that the technology condition monitoring parameters are optimized. The decision tables for each fault source are built and the diagnosis rules rooting in rough set reduction is applied to carry through intelligent fault diagnosis. The cases studied show that rough set method in condition monitoring and fault diagnosis can lighten the work burden in feature selection and afford advantages for autonomic learning and decision during diagnosis.
基金Project Supported by National Natural Science Foundation of China （50607023）, Natural Science Femdation of CQ CSTC （2006BB2189）
文摘As the first step of service restoration of distribution system,rapid fault diagnosis is a significant task for reducing power outage time,decreasing outage loss,and subsequently improving service reliability and safety.This paper analyzes a fault diagnosis approach by using rough set theory in which how to reduce decision table of data set is a main calculation intensive task.Aiming at this reduction problem,a heuristic reduction algorithm based on attribution length and frequency is proposed.At the same time,the corresponding value reduction method is proposed in order to fulfill the reduction and diagnosis rules extraction.Meanwhile,a Euclid matching method is introduced to solve confliction problems among the extracted rules when some information is lacking.Principal of the whole algorithm is clear and diagnostic rules distilled from the reduction are concise.Moreover,it needs less calculation towards specific discernibility matrix,and thus avoids the corresponding NP hard problem.The whole process is realized by MATLAB programming.A simulation example shows that the method has a fast calculation speed,and the extracted rules can reflect the characteristic of fault with a concise form.The rule database,formed by different reduction of decision table,can diagnose single fault and multi-faults efficiently,and give satisfied results even when the existed information is incomplete.The proposed method has good error-tolerate capability and the potential for on-line fault diagnosis.
基金Project Supported by National Natural Science Foundation of China （ 50777069 ）.
文摘>Transformer faults are quite complicated phenomena and can occur due to a variety of reasons.There have been several methods for transformer fault synthetic diagnosis,but each of them has its own limitations in real fault diagnosis applications.In order to overcome those shortcomings in the existing methods,a new transformer fault diagnosis method based on a wavelet neural network optimized by adaptive genetic algorithm(AGA)and an improved D-S evidence theory fusion technique is proposed in this paper.The proposed method combines the oil chromatogram data and the off-line electrical test data of transformers to carry out fault diagnosis.Based on the fusion mechanism of D-S evidence theory,the comprehensive reliability of evidence is constructed by considering the evidence importance,the outputs of the neural network and the expert experience.The new method increases the objectivity of the basic probability assignment(BPA)and reduces the basic probability assigned for uncertain and unimportant information.The case study results of using the proposed method show that it has a good performance of fault diagnosis for transformers.
基金Supported by National Basic Research Program of China （973 Program） （2009CB320600）, National Natural Science Foundation of China （60828007, 60534010, 60821063）, the Leverhulme Trust （F/00. 120/BC） in the United Kingdom, and the 111 Project （B08015）
文摘A novel extension diagnosis method was proposed for enhancing the diagnosis ability of the conventional dissolved gas analysis. Based on the extension theory a matter-element model was established for qualitatively and quantitatively describing the fault diagnosis problem of power transformers. The degree of relation based on the dependent functions was employed to determine the nature and the grade of the faults in a transformer system. And the proposed method was verified with the experimental data. The results show that accuracy rate of the diagnosis method exceeds 90% and two kinds of faults can be detected at the same time.
文摘当前高压电容式电压互感器(capacitor voltage transformer,CVT)缺少有效的在线监测数据,辨识不足。利用在线监测多数据源存在线性相关的数据特性,提出了基于分析数据相关系数进行有效数据辨识的方法。针对目前高压CVT故障诊断普遍存在信息单一、精度不高、局部放电在线监测装置故障信号检测受干扰因素影响较大、准确性差等问题,提出了基于多维信息融合的故障诊断方法。首先,利用因子分析对CVT的诊断指标进行数据层信息融合,提取各故障类型对应的公共因子方差贡献值,作为反映故障类型差异的特征值;然后,利用模糊理论进行特征层信息融合,将公共因子方差贡献值作为隶属函数的输入参数,识别CVT的故障类型,准确诊断高压CVT故障。案例验证了所提方法的有效性,为CVT故障诊断提供了理论参考和实践经验。
文摘辛周期模态分解(symplectic period mode decomposition, SPMD)方法可以准确地提取周期脉冲分量,是一种有效的滚动轴承单一故障诊断方法。但在滚动轴承出现复合故障时,尤其是强背景噪声下,周期脉冲信号往往较微弱,使得SPMD难以提取出不同周期的脉冲分量,进而限制了其在复合故障诊断中的应用。对此,提出了改进的辛周期模态分解(improved symplectic period mode decomposition, ISPMD)方法。该方法首先采用求差增强技术和最小噪声幅值反卷积相结合的方法对信号进行降噪,增强周期脉冲,以准确估计故障周期;然后构造对应的周期截断矩阵,并通过辛几何相似变换和周期冲击强度获得辛几何周期分量;最后对残差信号采用迭代分解,进而得到不同周期的辛几何周期分量。试验结果表明,ISPMD能准确提取出周期脉冲分量,是一种有效的滚动轴承复合故障诊断方法。
