基于粗糙集人工神经网络的锅炉故障诊断模型

基于粗糙集的故障诊断特征提取【导言】故障诊断在各个领域中扮演着重要的角色。

精确而及时的故障诊断可以帮助我们解决问题并提高设备和系统的可靠性。

然而，随着技术的不断发展和系统规模的增大，故障诊断的复杂性也在增加。

针对这一问题，基于粗糙集的故障诊断特征提取方法应运而生，为故障诊断研究带来了新的突破。

【正文】1. 粗糙集理论简介粗糙集理论由波兰学者Zdzisław Pawlak于1982年提出，被广泛应用于模糊、不确定和决策论问题。

该理论基于粗糙集、决策规则和等价类的概念，提供了一种处理不完全和不确切信息的方法。

在故障诊断中，粗糙集理论能够帮助我们从数据中提取有用的特征，以用于定位和分析故障。

2. 故障诊断特征提取的挑战在故障诊断中，我们往往需要从大量的数据中提取出包含故障信息的有用特征。

然而，由于数据的复杂性和多样性，特征提取变得非常具有挑战性。

基于粗糙集的故障诊断特征提取方法通过采用近似与精确度的概念，能够从数据中挖掘出具有较高可区分度的特征。

3. 基于粗糙集的故障诊断特征提取方法基于粗糙集的故障诊断特征提取方法主要包括数据预处理、特征选择和模型构建三个步骤。

3.1 数据预处理数据预处理是故障诊断特征提取的第一步。

在这个步骤中，我们需要对原始数据进行质量控制、噪声去除和数据格式转换等操作，以提高数据的可靠性和可用性。

3.2 特征选择特征选择是故障诊断特征提取的核心步骤。

在这个步骤中，我们需要根据数据的性质和目标需求，选择并提取出最具有代表性和判别性的特征。

基于粗糙集的特征选择方法基于精确度和纯度的概念，通过计算每个特征的重要性指标，从而实现特征的筛选和排序。

3.3 模型构建模型构建是故障诊断特征提取的最后一步。

在这个步骤中，我们需要基于已选择的特征构建故障诊断模型。

常用的模型包括决策树、神经网络和支持向量机等。

通过将特征与相应的故障类别进行关联，我们可以实现故障的诊断和定位。

4. 基于粗糙集的故障诊断特征提取方法的优势基于粗糙集的故障诊断特征提取方法具有以下几个优势：4.1 应对不完备和不确定的数据粗糙集理论能够处理不完备和不确定的数据，使得故障诊断在现实环境中更具鲁棒性和适应性。

ABSTRACTWireless Sensor Networks (WSN) is a current emerging and hot technique, its emergence changes the way of human beings interact with nature. WSN has high research value and broad application prospects in the military and civil ,and many other areas. However, as the increased of degree of automation in WSN, its structure has become more complex, and the WSN mainly work on the complex conditions and harsh environments, the nodes of WSN have to bear the wind, sun, rain and many other negative factors, it is prone to failure, so the original design features can not be complete. Moreover, the enviromental conditions in the monitored region in which the nodes of WSN were deployed are similar, most likely the majority of nodes simultaneously fail, and resulting in paralysis of the entire network of WSN. Therefore, it is extremly necessary to monitor the working status of the nodes in WSN in the real-time, the timely and accurate fault diagnosis of nodes in WSN can effectively improve the WSN operation reliability and safety, and ensure that WSN complete the scheduled tasks.In this paper, firstly, studied the characteristics, types, levels of nodes’fault in WSN, and then researched the basis of individual characteristics of the Rough Sets theory and neural network algorithms in depth, study the possibility and the way of integrating the Rough Sets theory and Neural Network algorithms. Based on the characteristics of node in WSN, select the BP neural network to integrated with Rough Sets, because BP has the inherent defects as easy to fall into partial minimal and slow convergence, this paper proposed a new improved AMSABP algorithm, for the condition of the input attribute value of fault monitor system is contineous, this paper proposed the integrated RS-AMSABP fault diagnosis method. Firstly, this method get the most simple decision-making table of the fault diagnosis by the improved discriminate matrix, then established diagnosis rules by the table. Finally, constructed the AMSABP network model by the diagnosis rules, and trainning the network through the sample data. The expriment results of fault diagnosis of the node in WSN show that RS-AMSABP algrithom made the high diagnostic accurate to 99.74% and low calculateIIItime compared with other diagnosis method.Because WSN mainly work in the complex and bad enviroment, when the failure occurred in WSN, the input attribute value of fault monitor system is likely contineous, this paper proposed constructed the rough neuron by the two endpoints of the interval numbers of the input attribute of the fault monitor system, and applied rough decision-making analysis method constructed a decision information system of WSN fault diagnosis with the interval numbers, so the problem of the fault diagnosis of nodes in WSN with the interval numbers can be resolved by the the three-layers feed-forward rough neural network with the interval numbers. The simulation results show the diagnosis algrithom based on the Interval-Numbers Rough Neural Network improved the diagnostic accurate to 99.57% when the computing time was greatly reduced.This paper proposed a whole solution scheme for the fault diagnosi of nodes in WSN, effectively meet the actual needs which the developing of WSN technology and application. It has high practical value.Keywords: Wireless Sensor Networks, Rough Sets Theory, Neural Network, Fault DiagnosisIV目录第一章绪论 (1)1.1WSN概述 (1)1.1.1 WSN的系统架构 (3)1.1.2 WSN节点的基本结构 (3)1.2论文的研究意义 (4)1.3WSN节点的故障诊断 (5)1.3.1 WSN的故障划分 (5)1.3.2 WSN的故障诊断特点 (6)1.3.3 WSN节点的传感模块故障 (7)1.3.4 WSN的节点故障的诊断方法 (8)1.4论文的结构 (9)1.5论文的主要创新点 (10)第二章 ROUGH SETS理论与神经网络算法的集成研究 (11)2.1R OUGH S ETS 与神经网络集成的可能性分析 (11)2.2R OUGH S ETS 与神经网络的集成方式研究 (13)2.2.1 Rough Sets理论和神经网络算法的松耦合 (13)2.2.2 粗糙元神经网络 (13)2.2.3 Rough Sets和神经网络算法的强耦合 (14)2.2.4 其它集成方法 (15)2.3本章小节 (16)第三章基于ROUGH SETS理论的决策表属性约简算法 (17)3.1R OUGH S ETS的知识约简 (17)3.1.1 Rough Sets的约简与核 (17)3.1.2 相对约简 (18)3.2决策表的概述 (20)3.3基于R OUGH S ETS的决策表属性约简算法 (23)3.3.1 决策表的删除属性约简算法 (23)V3.3.2 基于差别矩阵的决策表属性约简算法 (24)3.3.3改进的差别矩阵属性约简算法 (27)3.4本章小节 (29)第四章 RS-AMSABP故障诊断算法 (30)4.1新的改进的BP算法-AMSABP算法 (30)4.1.1 AMSABP算法 (30)4.1.2 基于AMSABP算法的函数逼近实验 (32)4.1.4 基于AMSABP网络算法的WSN节点故障诊断实验 (37)4.2RS-AMSABP算法的提出 (42)4.3基于RS-AMSABP算法的WSN节点故障诊断 (42)4.4本章小节 (47)第五章 INRNN故障诊断算法 (48)5.1含有区间数的粗糙元神经网络 (48)5.1.1 INRNN网络模型 (48)5.1.2 INRNN网络的学习算法 (49)5.2基于INRNN算法的WSN节点故障诊断 (51)5.2.1 WSN节点故障模型的建立与仿真 (51)5.2.2 基于RS-INRNN算法的WSN节点故障诊断实验 (55)5.3本章小节 (59)第六章结论与展望 (60)致谢 (61)参考文献 (62)攻读硕士期间的研究成果 (67)VI第一章绪论第一章绪论1.1 WSN概述二十一世纪是信息时代，人类对信息的需求日益增长，当然也就对信息采集系统的自动化程度以及环境适应性的要求越来越高。