混合决策系统的粗集模型及在转台故障诊断中的应用
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摘要
机器学习和知识发现是人工智能最重要的研究方向,而复杂环境下信息的模糊性、不完备性和不确定性是知识发现面临的主要困难。粗糙集理论模拟人类认知推理中粒化和近似的特点,是刻画分类数据不一致性的有效数学工具,已经成功应用于符号数据的知识发现。目前,对于广泛存在的符号、数值、模糊和不完备变量共存混合决策系统的复杂分类问题还没有得到系统的研究。
本文基于粗糙计算方法论中粒化和近似的思想,针对名义型属性、数值型属性、模糊属性、丢失型不完备属性和遗漏型不完备属性共存的混合决策系统,建立了混合数据分类和知识发现的一般模型,研究了基于仿生学理论的多种属性约简算法。将粗糙集理论应用到转台的故障诊断中,分析了转台的主要故障,采用小波变换的方法对故障特征进行提取,设计了粗糙神经网络故障诊断系统。具体对以下几个方面进行了研究:
Pawlak粗糙集建立在严格的等价关系基础之上,只能处理符号变量,但实际应用中普遍存在数值变量、模糊变量,还常常伴随着属性值的缺失,这为粗糙集的应用带来了困难。针对以上几个方面的问题,分析了混合决策系统、模糊决策系统和不完备决策系统的特点,针对广义不完备混合决策系统建立了广义不完备邻域粗糙集模型;针对广义不完备模糊决策系统,建立了广义不完备模糊邻域粗糙集模型,研究了不完备信息的辨识方法,分析了邻域算子对模型分类精度的影响。广义不完备邻域粗糙集模型和广义不完备模糊邻域粗糙集模均是对Pawlak粗糙集推广,有效地解决了实际应用中混合属性、模糊属性和不完备属性的问题。
针对多种属性共存的混合决策系统,研究了决策系统的属性约简问题。首先分析了经典的属性约简算法。然后研究了基于遗传算法、克隆选择算法和小生境微粒群算法的不完备混合决策系统属性约简算法,并给出了实现方法。仿真结果显示这几种属性约简算法都能有效地克服求解全部约简的NP-hard问题,实现对混合决策系统的属性约简。
在分析转台系统的主要故障的基础上,研究了采用小波变换进行故障特征提取的方法。转台的主要故障分为六类:软件故障、测角系统故障、通讯系统故障、接口板故障、执行器故障和机械故障,分析了故障原因及产生机理。介绍了小波分析的基本理论,利用小波分析理论检测信号的奇异性和去除信号中的高频噪声。以转台测角系统激磁信号常见故障为例,采用小波变换的方法对第一、二类间断点、幅值超差、失真度超差、饱和等故障进行了特征提取。
论文最后分析了粗糙集和神经网络的优缺点,设计了转台的粗糙神经网络故障诊断系统。首先利用粗糙集理论建立了转台的故障决策表,然后采用粗糙集的方法对故障决策表进行属性约简。粗糙集方法的应用有效去掉了冗余属性,使训练样本集简化,缩短了神经网络的训练时间。神经网络有良好的容错性和扩展性,所以将其作为后置系统,可以增强故障诊断系统的容错及抗干扰能力。粗糙神经网络分类器和辨识器的设计可以有效地实现对转台故障的识别和定位。最后给出了转台粗糙神经网络故障诊断系统的硬件设计和软件实现方法,实验结果证明了该方法的可行性和有效性。
综上所述,本文对粗糙集方法在混合、不完备、模糊信息系统的知识发现方法和实际故障诊断问题中的应用进行了深入系统的研究,拓宽了粗糙集理论的应用范围,实现了转台的故障诊断和健康管理。
Machine learning and knowledge discovery is one of the most important research directions in artificial intelligence, and the fuzzy, incompleteness and uncertainty of information under complex environment are the key problems in knowledge discovery. Rough set theory, which simulates the capability of granulation and approximation in human cognition, is an effective mathematical tool to characterize inconsistency in classification data. This theory has been applied in knowledge discovery from symbolic data. However, most of data sets in real-world applications are character, numerical, fuzzy, incomplete or their mixture. Not much work has been devoted on such issues.
In this paper, the hybrid decision system including the mixture of character, numerical, fuzzy and incomplete attributes is studied, and proposed the general model of mixed data classification and knowledge discovery based on granulation and approximate of rough set theory. Then studied several attributes reduction algorithm based on bionics. The rough set theory is application to the fault diagnosis of turn table. The faults of turn table are analysed and fault features are extracted by wavelet transform, and finally the rough set-neural network fault diagnosis system is designed. The main contributions of the work are listed as follows.
Pawlak rough set is based on strict equivalence relation and the treatment objects are character attributes. But in practice, many information systems are hybrid systems which including numerical attributes, fuzzy attributes and often accompanying the lost of attributes. So the practice application of rough set is difficult. In order to solve the above problems, the characteristics of the hybrid decision system, fuzzy decision system and incomplete decision system are analysed. The rough set models for general incomplete hybrid decision system and general incomplete fuzzy hybrid decision system are developed, and studied the identification method for incomplete information. The influence on classification by neighborhood operators is studied also. The general incomplete hybrid rough set model and general incomplete fuzzy hybrid rough set model are the expanded of Pawlak rough set model, which can solve the practical application of mixed attributes including fuzzy attributes and incomplete attributes effectively.
The attributes reduct algorithms are discussed for incomplete hybrid decision system. Fist, the classical reduct algorithms are analysed. Then, genetic algorithm, clonal selection algorithm and niche POS algorithm are used in the attributes reduction of incomplete hybrid decision system, and the realization method is given respectively. The simulation results indicate that the proposed algorithm can overcome the NP-hard problem of finding all reductions of hybrid decision system.
The fault feature extraction methods using the wavelet are studied based on the analysis of the faults of turn table. The faults of turn table are classified as follows, soft faults, angular measuring system faults, communication faults, interface board faults, actuator faults and mechanical faults. The causes of the faults are analysed, and the wavelet theory is introduced to detect the signal singularity and remove the high frequency nosie of the signals. For instance, the wavelet theory is used to extract features of discontinuities, amplitude overproof, distortion overproof and saturation of angular measuring system of turn table.
The advantages and disadvantages of rough sets and neural networks are analysed in the last part of the paper, and rough set-neural networks fault diagnosis system is designed for turn table. Fist, the faults decision table is found based on rough set theory. Then the faults decision table is reduced by rough set reduct algorithm. The use of rough set theory can delete redundant attributes effectively and simplify the training sample to shorten the training time of networks. Neural network has good fault-tolerance and scalability, as the post system, can enhance fault-tolerance and anti-jamming capability of the fault diagnosis system. The classifier and recognizer of rough set-neural networks can identify and locate the turn table faults effectively. Finally, the hardware design and software implementation method of turn table rough set-neural networks are proposed, experimental results demonstrate the validity and feasibility.
In conclusion, this paper studied the rough set knowledge discovery method in hybrid, incomplete and fuzzy information system, and the practice application in fault diagnosis. The application scopes of rough set are expended, and the fault diagnosis and health management system of turn table are found finally.
本文基于粗糙计算方法论中粒化和近似的思想,针对名义型属性、数值型属性、模糊属性、丢失型不完备属性和遗漏型不完备属性共存的混合决策系统,建立了混合数据分类和知识发现的一般模型,研究了基于仿生学理论的多种属性约简算法。将粗糙集理论应用到转台的故障诊断中,分析了转台的主要故障,采用小波变换的方法对故障特征进行提取,设计了粗糙神经网络故障诊断系统。具体对以下几个方面进行了研究:
Pawlak粗糙集建立在严格的等价关系基础之上,只能处理符号变量,但实际应用中普遍存在数值变量、模糊变量,还常常伴随着属性值的缺失,这为粗糙集的应用带来了困难。针对以上几个方面的问题,分析了混合决策系统、模糊决策系统和不完备决策系统的特点,针对广义不完备混合决策系统建立了广义不完备邻域粗糙集模型;针对广义不完备模糊决策系统,建立了广义不完备模糊邻域粗糙集模型,研究了不完备信息的辨识方法,分析了邻域算子对模型分类精度的影响。广义不完备邻域粗糙集模型和广义不完备模糊邻域粗糙集模均是对Pawlak粗糙集推广,有效地解决了实际应用中混合属性、模糊属性和不完备属性的问题。
针对多种属性共存的混合决策系统,研究了决策系统的属性约简问题。首先分析了经典的属性约简算法。然后研究了基于遗传算法、克隆选择算法和小生境微粒群算法的不完备混合决策系统属性约简算法,并给出了实现方法。仿真结果显示这几种属性约简算法都能有效地克服求解全部约简的NP-hard问题,实现对混合决策系统的属性约简。
在分析转台系统的主要故障的基础上,研究了采用小波变换进行故障特征提取的方法。转台的主要故障分为六类:软件故障、测角系统故障、通讯系统故障、接口板故障、执行器故障和机械故障,分析了故障原因及产生机理。介绍了小波分析的基本理论,利用小波分析理论检测信号的奇异性和去除信号中的高频噪声。以转台测角系统激磁信号常见故障为例,采用小波变换的方法对第一、二类间断点、幅值超差、失真度超差、饱和等故障进行了特征提取。
论文最后分析了粗糙集和神经网络的优缺点,设计了转台的粗糙神经网络故障诊断系统。首先利用粗糙集理论建立了转台的故障决策表,然后采用粗糙集的方法对故障决策表进行属性约简。粗糙集方法的应用有效去掉了冗余属性,使训练样本集简化,缩短了神经网络的训练时间。神经网络有良好的容错性和扩展性,所以将其作为后置系统,可以增强故障诊断系统的容错及抗干扰能力。粗糙神经网络分类器和辨识器的设计可以有效地实现对转台故障的识别和定位。最后给出了转台粗糙神经网络故障诊断系统的硬件设计和软件实现方法,实验结果证明了该方法的可行性和有效性。
综上所述,本文对粗糙集方法在混合、不完备、模糊信息系统的知识发现方法和实际故障诊断问题中的应用进行了深入系统的研究,拓宽了粗糙集理论的应用范围,实现了转台的故障诊断和健康管理。
Machine learning and knowledge discovery is one of the most important research directions in artificial intelligence, and the fuzzy, incompleteness and uncertainty of information under complex environment are the key problems in knowledge discovery. Rough set theory, which simulates the capability of granulation and approximation in human cognition, is an effective mathematical tool to characterize inconsistency in classification data. This theory has been applied in knowledge discovery from symbolic data. However, most of data sets in real-world applications are character, numerical, fuzzy, incomplete or their mixture. Not much work has been devoted on such issues.
In this paper, the hybrid decision system including the mixture of character, numerical, fuzzy and incomplete attributes is studied, and proposed the general model of mixed data classification and knowledge discovery based on granulation and approximate of rough set theory. Then studied several attributes reduction algorithm based on bionics. The rough set theory is application to the fault diagnosis of turn table. The faults of turn table are analysed and fault features are extracted by wavelet transform, and finally the rough set-neural network fault diagnosis system is designed. The main contributions of the work are listed as follows.
Pawlak rough set is based on strict equivalence relation and the treatment objects are character attributes. But in practice, many information systems are hybrid systems which including numerical attributes, fuzzy attributes and often accompanying the lost of attributes. So the practice application of rough set is difficult. In order to solve the above problems, the characteristics of the hybrid decision system, fuzzy decision system and incomplete decision system are analysed. The rough set models for general incomplete hybrid decision system and general incomplete fuzzy hybrid decision system are developed, and studied the identification method for incomplete information. The influence on classification by neighborhood operators is studied also. The general incomplete hybrid rough set model and general incomplete fuzzy hybrid rough set model are the expanded of Pawlak rough set model, which can solve the practical application of mixed attributes including fuzzy attributes and incomplete attributes effectively.
The attributes reduct algorithms are discussed for incomplete hybrid decision system. Fist, the classical reduct algorithms are analysed. Then, genetic algorithm, clonal selection algorithm and niche POS algorithm are used in the attributes reduction of incomplete hybrid decision system, and the realization method is given respectively. The simulation results indicate that the proposed algorithm can overcome the NP-hard problem of finding all reductions of hybrid decision system.
The fault feature extraction methods using the wavelet are studied based on the analysis of the faults of turn table. The faults of turn table are classified as follows, soft faults, angular measuring system faults, communication faults, interface board faults, actuator faults and mechanical faults. The causes of the faults are analysed, and the wavelet theory is introduced to detect the signal singularity and remove the high frequency nosie of the signals. For instance, the wavelet theory is used to extract features of discontinuities, amplitude overproof, distortion overproof and saturation of angular measuring system of turn table.
The advantages and disadvantages of rough sets and neural networks are analysed in the last part of the paper, and rough set-neural networks fault diagnosis system is designed for turn table. Fist, the faults decision table is found based on rough set theory. Then the faults decision table is reduced by rough set reduct algorithm. The use of rough set theory can delete redundant attributes effectively and simplify the training sample to shorten the training time of networks. Neural network has good fault-tolerance and scalability, as the post system, can enhance fault-tolerance and anti-jamming capability of the fault diagnosis system. The classifier and recognizer of rough set-neural networks can identify and locate the turn table faults effectively. Finally, the hardware design and software implementation method of turn table rough set-neural networks are proposed, experimental results demonstrate the validity and feasibility.
In conclusion, this paper studied the rough set knowledge discovery method in hybrid, incomplete and fuzzy information system, and the practice application in fault diagnosis. The application scopes of rough set are expended, and the fault diagnosis and health management system of turn table are found finally.
引文
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