Gear Fault Diagnosis Based on Rough Set and Support Vector Machine

By introducing Rough Set Theory and the principle of Support vector machine,a gear fault diagnosis method based on them is proposed.Firstly,diagnostic decision-making is reduced based on rough set theory,and the noise and redundancy in the sample are removed,then,according to the chosen reduction,a support vector machine multi-classifier is designed for gear fault diagnosis.Therefore,SVM’training data can be reduced and running speed can quicken.Test shows its accuracy and effi- ciency of gear fault diagnosis.

Ellipsoidal bounding set-membership identification approach for robust fault diagnosis with application to mobile robots

A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).

Fault Diagnosis of a Rotary Machine Based on Information Entropy and Rough Set

There exists some discord or contradiction of information during the process of fault diagnosis for rotary machine. But the traditional methods used in fault diagnosis can not dispose of the information. A model of fault diagnosis for a rotary machine based on information entropy theory and rough set theory is presented in this paper. The model has clear mathematical definition and can dispose both complete unification information and complete inconsistent information of vibration faults. By using the model, decision rules of six typical vibration faults of a steam turbine and electric generating set are deduced from experiment samples. Finally, the decision rules are validated by selected samples and good identification results are acquired.

FAULT DIAGNOSIS BASED ON INTEGRATION OF CLUSTER ANALYSIS, ROUGH SET METHOD AND FUZZY NEURAL NETWORK

In order to increase the efficiency and decrease the cost of machinerydiagnosis, a hybrid system of computational intelligence methods is presented. Firstly, thecontinuous attributes in diagnosis decision system are discretized with the self-organizing map(SOM) neural network. Then, dynamic reducts are computed based on rough set method, and the keyconditions for diagnosis are found according to the maximum cluster ratio. Lastly, according to theoptimal reduct, the adaptive neuro-fuzzy inference system (ANFIS) is designed for faultidentification. The diagnosis of a diesel verifies the feasibility of engineering applications.

Power transformer fault diagnosis model based on rough set theory with fuzzy representation

Objective Due to the incompleteness and complexity of fault diagnosis for power transformers,a comprehensive rough-fuzzy scheme for solving fault diagnosis problems is presented.Fuzzy set theory is used both for representation of incipient faults' indications and producing a fuzzy granulation of the feature space.Rough set theory is used to obtain dependency rules that model indicative regions in the granulated feature space.The fuzzy membership functions corresponding to the indicative regions,modelled by rules,are stored as cases.Results Diagnostic conclusions are made using a similarity measure based on these membership functions.Each case involves only a reduced number of relevant features making this scheme suitable for fault diagnosis.Conclusion Superiority of this method in terms of classification accuracy and case generation is demonstrated.

A Simple Method to Derive Minimal Cut Sets for a Non-coherent Fault Tree

Minimal cut sets （or prime implicants： minimal combinations of basic event conditions leading to system failure） are important information for reliability/safety analysis and design. To obtain minimal cut sets for general non-coherent fault trees, including negative basic events or multi-valued basic events, a special procedure such as the consensus rule must be applied to the results obtained by logical operations for coherent fault trees, which will require more steps and time. This paper proposes a simple method for a non-coherent fault tree, whose top event is represented as an AND combination of monotonic sub-trees. A ＂monotonic＂ sub-tree means that it does not have both positive and negative representations for each basic event. It is proven that minimal cut sets can be obtained by a conventional method for coherent fault trees. An illustrative example of a simple event tree analysis shows the detail and characteristics of the proposed method.

Gravity-based heuristic for set covering problems and its application in fault diagnosis

A novel algorithm named randomized binary gravita- tional search （RBGS） algorithm is proposed for the set covering problem （SCP）. It differs from previous SCP approaches because it does not work directly on the SCP matrix. In the proposed algo- rithm, the solution of SCP is viewed as multi-dimension position of objects in the binary search space. All objects in the space attract each other by the gravity force, and this force causes a global movement of all objects towards the objects with heavier masses which correspond to good solutions. Computation results show that the proposed algorithm is very competitive. In addition, the proposed aigodthm is extended for SCP to solve the fault diagno- sis problem in graph-based systems.

Rough Set Theory Based Approach for Fault Diagnosis Rule Extraction of Distribution System

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.

基于故障树反演的复合材料成形生产线故障诊断

复合材料成形装备生产线因其复杂性和高度集成性,故障的产生可能涉及多个环节和因素,因此需要一种系统性的方法来准确地分析和定位故障源。本文梳理了复合材料产品制造过程中可能出现的故障类型和影响因素,构建了一个全面的复合材料成形装备产线故障树,借助故障树反演技术,将实际故障事件映射到故障树上,通过逆向推导,得出可能导致这些故障事件的基本故障事件,该方法在提高生产质量、降低故障风险方面具有重要意义,为复合材料制造领域的技术改进和创新提供了有力支持。

STUDY ON MODULAR FAULT TREE ANALYSIS TECHNIQUE WITH CUT SETS MATRIX METHOD

A new fault tree analysis (FTA) computation method is put forth by using modularization technique in FTA with cut sets matrix, and can reduce NP (Nondeterministic polynomial) difficulty effectively. This software can run in IBM PC and DOS 3.0 and up. The method provides theoretical basis and computation tool for application of FTA technique in the common engineering system

基于卷积神经网络的轴承故障诊断综述

轴承故障的诊断一直以来都是诊断领域的一大挑战,尽早发现轴承故障有助于减少损失并预防潜在危险。本文旨在对卷积神经网络在轴承故障诊断领域的应用进行系统综述,详细分析了卷积神经网络模型的结构和原理,阐述了其在轴承故障诊断领域的发展历程,并深入探讨了常见公共数据集的特征,评述了卷积神经网络在轴承故障诊断中的优势和不足、当前面临的困难以及未来可能的研究方向。

掺氢天然气管道故障树分析及建模软件开发

针对掺氢天然气管道可能存在的风险与隐患,分析国内外各类管道失效数据,建立了掺氢天然气管道故障树,共包含10个子树和148个底事件,在掺氢管道失效库基础上采用C#程序设计语言开发了掺氢天然气管道故障树建模软件;应用建模软件对已建立的掺氢天然气管道故障树进行分析。软件计算通过行列法求解出最小割集共189个,其中一阶最小割集106个,二阶最小割集83个;采用双循环法得到148个底事件结构重要度。分析得出防腐措施是否正常工作以及自然灾害是影响掺氢天然气管道安全运行的重要因素。

基于椭球计算的集员滤波传感器故障检测方法

为了解决未知故障信号诊断问题,提出了一种基于椭球计算的集员滤波传感器故障检测方法。为了防止数据冲突和降低能量消耗,通过加权一次丢弃调度来控制传感器单元和滤波器之间的信息分发。然后有效将分布式集员滤波方法应用于处理WTOD协议下未知但有界的故障检测问题。进一步通过处理具有一定不等式约束的优化问题,对两个最小迹意义下的椭球进行了优化。最后仿真案例结果证明了提出方法能够有效实现传感器网络的未知故障信号检测,且不需要了解所有状态,极大地扩展了适用范围。

基于符号键合图的电静压伺服机构多故障诊断研究

针对电静压伺服机构(EHA)服役时存在多种故障且难以诊断的问题,提出了一种考虑参数贡献度的符号键合图(SBG)故障诊断方法。首先,根据EHA工作原理与键合图理论构建了键合图模型,设定了系统参数数值及分布标准差,再利用键合图模型推导了其解析冗余式(ARR),并构建了故障特征矩阵,将键合图模型转换为符号键合图模型,根据符号键合图元件之间的因果关系和一致性路径,推理出了EHA可能故障集;然后,定义并计算了EHA系统参数贡献度,筛选出了高贡献度系统参数,对可能故障集中难以引起解析冗余式残差数据变化的低贡献度系统参数进行了剔除,缩小了可能的故障参数范围;最后,利用键合图仿真软件20-sim,通过改变EHA中Q轴电阻与电机磁通链的数值,进行了多故障仿真实验,对所提多故障诊断方法进行了验证。研究结果表明:当EHA发生多故障时,将剔除低贡献度系统参数的可能故障集同传感器数据、解析冗余式残差数据进行结合,能准确诊断出预置的两个高贡献度故障参数,相比于常规SBG故障诊断的可能故障集,能更准确地锁定EHA系统故障参数。

计算机联锁接发列车故障设置盘的设计与实现

针对车务接发列车人员使用计算机联锁仿真培训系统开展培训存在的共性问题,采取在既有计算机联锁实训设备上增设接发列车故障设置盘方法实现故障设置,满足接发列车人员实训需要。通过梳理车站固定信号联锁设备故障实训项目,基于TYJL-ADX计算机联锁设备工作原理,提出接发列车故障设置盘的设计和电路实现。经试验表明,该方法不仅满足“故障-安全”的设计原则,而且能有效解决仿真培训系统存在的共性问题,有较强的通用性和推广价值。

船用堆全船断电事故的故障树分析研究

船用堆供电系统的可靠性对核动力装置的安全性有重要影响。参考某典型船用堆供电系统设计,提出了船用堆供电系统故障树建模基本原则,以全船断电事故为顶事件建立故障树,确定了系统的邻接矩阵,并基于此设计了一种故障树辅助生成及最小割集求解程序。通过定性和定量分析得出全船断电事故的发生概率及各电源设备的重要度,并提出相应的改善措施,为船用堆供电系统的设计、使用和维护保养提供技术支撑。

基于Roughset知识获取的故障数据表聚类离散化方法研究

为了从故障诊断实例的数据资源中知识获取,对具有连续属性值的故障实例数据表转化为Rough set(RS)理论离散数据类型的决策表的正确映射进行了研究。将改进的k-means聚类算法用于故障实例数据表的离散映射方案设计。在设置故障实例的导师决策类别数为聚类数k对论域划分的基础上,提出了根据均值聚类中心排序序号构造离散映射符号集、相对均值聚类中心由相似测度确定连续属性值映射编码的离散化方案。实例表明,该方法反映了转子振动故障特征的一般规律,断点设置具有动态自适应和抗干扰特性。获得的决策规则可用于构造和扩充故障诊断知识库。

基于Rough Set的油液故障诊断系统的知识发现

结合RoughSet理论和摩擦学系统的特点,讨论了油液故障诊断系统的不协调性.在包含度方法的基础上,将普通二元关系进行推广,提出了一种不协调油液故障诊断系统知识发现模型,给出具体的运算方法,并通过试验实例验证了该模型的有效性.结果表明,该模型在最大分布约简的基础上进行油液诊断知识获取,能够很好地完成不确定性问题的推理,并且可以推导出具有最大可信度的油液诊断知识规则.

汽车线路故障对汽车损坏后果的试验研究——以迈腾汽车点火控制电路对地短路故障为例

从汽车维修教学中对汽车设置线路故障的风险问题出发,从经济性和安全性的角度,提出一种基于试验研究的预判汽车线路故障对汽车损坏后果的方法。试验的策略可归纳为(:1)为简单、低值电器部件单独搭建其必要的工作电路,并对这些工作电路设置目标故障,再验证目标故障对这些电器设备的损坏情况,进而预测出该故障对整车可能的损坏后果(;2)对工作条件比较复杂的电器设备,可借助原车的部分电路开展试验(;3)对于较为贵重的电器设备,采用旧车拆车件进行试验。以大众迈腾汽车1.8升CEAA发动机点火控制线路对地短路故障为例对该方法进行了进一步的阐述。该试验方法可以为汽车电路故障诊断教学中预判电路故障设置后果提供有益参考。

基于EMD-DRSN和ILSO-SVM的水电机组故障诊断

水电机组的振动信号中蕴藏着丰富的机组状态信息,如果能充分地提取并有效地利用其所含的故障特征,将对识别机组状态、诊断机组故障带来极大的便利。为充分地提取振动信号所蕴含的故障特征,将深度残差收缩网络(DRSN)与经验模态分解(EMD)相结合,前者挖掘数据隐藏信息,后者提取时频特征,进而形成融合特征。随后,为有效利用这些故障特征,采用改进光谱优化算法(ILSO)对支持向量机(SVM)的核函数参数G和惩罚系数C进行寻优,以提高SVM的分类精确度。经分析表明该方法能在一定程度上加深对水电机组故障特征的挖掘,提高故障诊断的效率及准确率。