Fault Diagnosis of Vehicle Transmission System Based on Rough Set Theory

Rough set theory is used to treat the data of vehicle transmission system faults. The minimum fault feature vector can be obtained by calculating the importance and dependency of each attribute. Real time diagnosis, as a result, can be actualized. Ultimate decision making can be done by analyzing the consistency of decision information. The result shows that rough set theory is useful and possesses its unique merits in this field.

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.

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.

Dynamic Settings and Interactions between Basin Subsidence and Orogeny in Zhoukou Depression and Dabie Orogenic Belt

This paper presents a study of the geo dynamic setting and the relation between orogenic uplift and basin subsidence in the inland Zhoukou depression and Dabie orogenic belt. Since the Mesozoic the evolution of Zhoukou depression can be divided into three stages: (1) foreland basin, (2) transitional stage, (3) fault depression. Formation and variations of basin were not only related to the orogenesis, but also consistent with the orogenic uplift.

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.

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.

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.

Condition Monitoring and Fault Diagnosis Based on Rough Set Theory

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.

Application of Rough Set Theory in Fault Diagnostic Rules Acquisition

Rough set theory is a new mathematical tool to deal with vagneness and uncertainty. But original rough sets theory only generates deterministic rules and deals with data sets in which there is no noise. The variable precision rough set model （VPRSM） is presented to handle uncertain and noisy information. A method based on VPRSM is proposed to apply to fault diagnosis feature extraction and rules acquisition for industrial applications. An example for fault diagnosis of rotary machinery is given to show that the method is very effective.

A NEW APPROACH OF SIMULTANEOUS FAULTS DIAGNOSIS BASED ON RANDOM SETS AND DSMT

Simultaneous faults often occur in running equipments, in order to solve the problems of the simultaneous faults, a new approach based on random sets and Dezert-Smarandache Theory (DSmT) is proposed in this paper. Firstly, the simultaneous faults' model is built based on the generalized frame of discernment in DSmT. Secondly, according to the unified description of combination rules in evidence reasoning based on random sets, a new combination rule for simultaneous faults diagnosis is proposed. Thirdly, according to the working characteristics and environment of the sensors used to acquire fault characteristic information, a new method to construct basic probability assignment function is pro- posed based on membership. Finally, diagnosis result is obtained by use of the new combination rule combined with decision rules. A case pertaining to the fault diagnosis for a multi-function rotor test-bed is given, and the result shows that the proposed diagnosis approach is feasible and efficient.

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.

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.

Rough set and radial basis function neural network based insulation data mining fault diagnosis for power transformer

Rough set (RS) and radial basis function neural network (RBFNN) based insulation data mining fault diagnosis for power transformer is proposed. On the one hand rough set is used as front of RBFNN to simplify the input of RBFNN and mine the rules. The mined rules whose “confidence” and “support” is higher than requirement are used to offer fault diagnosis service for power transformer directly. On the other hand the mining samples corresponding to the mined rule, whose “confidence and support” is lower than requirement, are used to be training samples set of RBFNN and these samples are clustered by rough set. The center of each clustering set is used to be center of radial basis function, i.e., as the hidden layer neuron. The RBFNN is structured with above base, which is used to diagnose the case that can not be diagnosed by mined simplified valuable rules based on rough set. The advantages and effectiveness of this method are verified by testing.

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

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。

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。

基于SVM的液压机械驱动齿轮组故障诊断研究

针对液压机械驱动齿轮组故障诊断结果精准度不佳、可靠性差等问题,本文提出基于SVM的液压机械驱动齿轮组故障诊断研究。采集了液压机械驱动齿轮组振动信号,构建液压机械驱动齿轮组故障信号分离模型;运用低秩算法分离液压机械驱动齿轮箱振源信号,设计齿轮组故障信号约束条件,完成液压机械驱动齿轮组分类;根据分类结果,采用SDAE模型提取液压机械驱动齿轮组故障特征,并将提取结果输入到支持向量机内训练,其最终输出结果就是最佳诊断结果,实现基于SVM的液压机械驱动齿轮组故障诊断研究。实验结果表明,通过对该方法开展故障检测及故障诊断测试,本文方法下分类错误率不超过3.5%,验证了该方法的可行性高。

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。