基于优化分簇贝叶斯网的转子振动故障诊断

针对柴油发动机的充电发电机结构及振动的复杂性导致其转子振动故障具有多层次性、耦合性和随机性,以及故障信息不完整性等特点,提出了一种基于振动频谱分析和贝叶斯网络的转子振动故障诊断方法。该方法将故障源和故障现象根据专家经验数值化表示并离散化,运用改进的优化分簇算法,构建特定振动故障类型的贝叶斯诊断网络,利用贝叶斯网络推理算法诊断出故障概率分布,并利用具体的故障证据、设定值对该方法进行验证。仿真及实验结果表明,该方法能在故障信息不完整情况下,依据不完整证据信息更新各网络节点的概率状态,实现对不确定信息的推理和估计,得到较好的诊断结果,提高了转子振动故障的诊断准确度。     

贝叶斯网络数据细化的柴油发电机故障诊断

针对传统故障诊断方法在不确定问题诊断方面的不足,提出了基于贝叶斯网络的数据细化的柴油发电机故障诊断法。对柴油发电机转子的某些特定故障,结合专家知识确定转子特定状态下故障与振动频率、幅值及相关描述的依存关系,将获取的观测数据细化处理,利用结构学习,构建了基于贝叶斯网络的柴油发电机故障诊断模型,通过参数学习确定各节点的条件概率。实验结果表明,在已知信息具有模糊性和不完备性时,基于贝叶斯网络数据细化的故障诊断技术可明显提高诊断正确率。     

基于贝叶斯网络的电子设备故障诊断技术研究

针对电子设备故障诊断的难点,通过分析贝叶斯网络的特点,设计了一种基于贝叶斯网络的故障诊断系统。首先介绍了故障及故障诊断的相关概念;然后在理解贝叶斯网络的基础上分析其优点,研究了贝叶斯的推理;进而研究贝叶斯故障诊断的关键技术;最后介绍了基于贝叶斯网络故障诊断系统的整体框架。     

基于模糊贝叶斯网络的多态系统可靠性分析及在液压系统中的应用

为使贝叶斯网络能够对模糊信息和不确定信息进行处理,提出一种新的基于模糊贝叶斯网络的多态系统可靠性分析方法。该方法将模糊集合理论引入到贝叶斯网络可靠性分析中,考虑部件故障状态、部件故障率的模糊性以及部件间故障逻辑关系的不确定性,使贝叶斯网络具有处理模糊信息的能力。该方法采用模糊数描述系统和部件的故障状态,利用模糊子集描述部件的故障率,运用贝叶斯网络的条件概率表描述部件间的不确定联系。该方法应用到载重车液压悬架系统的可靠性分析实例中,分析结果表明该方法在进行系统可靠性分析时能够充分利用系统的模糊信息和不确定信息,从而提高系统可靠性分析的效率。     

Application of Bayesian networks for diagnostics in the assembly process by considering small measurement data sets

The ability of variation source diagnosis in the auto body assembly process plays an essential role in the success of the manufacturing enterprises. However, it is more challenging to identify the process faults associated with the compliant sheet metal parts based on small measurement data sets. A new Bayesian networks (BN) modeling approach under the condition of small data sets is proposed. The main causal links are identified based on mapping of the variation sensitivity matrix. The interaction effects are detected according to the conditional mutual information tests. After the network structure is determined, the Bayesian approach is used to obtain the conditional probability tables by incorporating prior probability distributions. The evaluation of diagnostic performance concerning evidence number and log-odds noise levels is also presented. A real bracket assembly case was used to illustrate the whole procedures for fixture fault diagnosis. The examined test cases demonstrate the proposed BN approach is practical and effective, even when incomplete evidences are observed and a medium-level noise is present.     

基于本体的机械故障诊断贝叶斯网络

针对机械设备维护与故障诊断过程中的不确定性,提出了一种将本体语义表示与贝叶斯网络相结合的故障概率推理模型。从异构多源的维护诊断信息和非结构化的专家经验知识出发,建立语义知识模型并进行概率扩展。利用贝叶斯分类器实现异常工况识别,给出了基于最大可能解释(MPE)的故障概率推理算法,从而根据运行工况、故障征兆和证据信息推理获得故障诊断解释。将本体语义描述的精确性和贝叶斯网络的概率推理能力相结合,既实现了诊断领域知识的形式化描述与共享,又能在一定程度上消除诊断过程的不确定性。某凉水塔风机转子典型故障诊断实例表明,该模型具有较好的故障识别效果。     

基于贝叶斯网络的复杂系统故障诊断

系统结构和部件关系复杂、试验费用昂贵是小样本下基于不确定性信息的决策问题。针对其特点，建立了基于贝叶斯网络的复杂系统故障诊断模型，并提出采用Leaky Noisy-OR模型来降低数据需求量和计算复杂度。经研究表明，这种方法能综合利用各种来源信息，具有知识表达明确、样本需求量小、故障诊断准确度高等特点，可为复杂系统故障诊断提供决策支持。     

Multi-thresholds for fault isolation in the presence of uncertainties

Monitoring of the faults is an important task in mechatronics. It involves the detection and isolation of faults which are performed by using the residuals. These residuals represent numerical values that define certain intervals called thresholds. In fact, the fault is detected if the residuals exceed the thresholds. In addition, each considered fault must activate a unique set of residuals to be isolated. However, in the presence of uncertainties, false decisions can occur due to the low sensitivity of certain residuals towards faults. In this paper, an efficient approach to make decision on fault isolation in the presence of uncertainties is proposed. Based on the bond graph tool, the approach is developed in order to generate systematically the relations between residuals and faults. The generated relations allow the estimation of the minimum detectable and isolable fault values. The latter is used to calculate the thresholds of isolation for each residual.     

基于小波包分解和支持向量机的机械故障诊断方法

提出应用小波包分解和支持向量机进行机械故障诊断的方法。该方法将振动信号小波包分解后的频带能量作为特征向量，输入到由多个支持向量机构成的多故障分类器中进行故障识别和分类。试验结果表明，与神经网络相比，采用支持向量机进行故障诊断可以获得更高的诊断精度，表明该方法是有效的、可行的。     

基于故障分析模型的贝叶斯网络构建及应用

为快速、准确地构建具有较完备知识的贝叶斯网络诊断模型,提出了基于故障模式、影响(及危害性)分析知识模型的贝叶斯网络自动构建方法.在该方法中,借助影响(及危害性)知识模型中的产品结构层次关系,将产品各层零部件的故障模式加以关联,形成贝叶斯网络结构;并以影响(及危害性)知识模型中的概率知识为依据,确定贝叶斯网络中节点的先验概率和条件概率.以列车自动门为应用对象,实现贝叶斯网络诊断模型的自动构建,并开发了相应的诊断系统,解决了列车自动门的诊断系统开发、应用跟不上维护的难题.最后,通过列车自动门的故障诊断实例,证明了所构建的贝叶斯网络的有效性.     

Fault detection and classification in chemical processes based on neural networks with feature extraction

Feed forward neural networks are investigated here for fault diagnosis in chemical processes, especially batch processes. The use of the neural model prediction error as the residual for fault diagnosis of sensor and component is analyzed. To reduce the training time required for the neural process model, an input feature extraction process for the neural model is implemented. An additional radial basis function neural classifier is developed to isolate faults from the residual generated, and results are presented to demonstrate the satisfactory detection and isolation of faults using this approach.     

分层顺序测试优化方法研究

针对复杂层次系统的故障隔离问题.提出了一种分层顺序测试优化方法.首先基于层次系统故障与测试之间的关联关系,以及故障与不同层级可更换单元的隶属关系,结合故障的先验概率与可用测试成本,构造了层次系统故障隔离问题的数学描述模型;然后以信息增益为启发策略,提出了一种准多步前向搜索算法,实现了不同隔离级的测试顺序优化.经某型卫星电源系统试验验证结果表明,该方可显著提高层次系统的故障隔离效率.     

基于概率粗集理论的故障诊断知识提取方法

结合工程设备系统故障发生具有随机性的特点 ,基于概率粗集 (PRS)理论模型实现了故障诊断知识的提取。通过改进的模糊 C-均值聚类算法对原始故障数据加以量化 ,采用正则条件熵进行诊断知识系统的统计约简 ,对不协调的诊断规则利用最小风险 Bayes决策理论加以分析处理。仿真实验表明 ,该方法弥补了现有方法的不足 ,克服了基本 RS模型方法无法处理不协调规则的缺点 ,可得到有效的简化诊断知识     

基于贝叶斯理论的汽轮发电机密封油系统故障诊断方法

在分析汽轮发电机密封油系统故障特点的基础上,提出了用贝叶斯理论进行故障诊断的方法,给出了求解故障诊断的数学表达式,充分利用样本的先验信息,建立了贝叶斯网络的推理方法,结合Leaky Noisy-OR模型来降低数据需求量,提高了诊断速度和效率。验证了该方法的有效性,实现了故障的前期预报和故障原因的快速诊断。     

区间三角模糊多态贝叶斯网络可靠性分析方法研究

针对传统系统可靠性分析方法在处理模糊信息方面的不足,建立了基于区间三角模糊贝叶斯网络的多态系统可靠性分析方法。通过区间三角模糊子集来描述根节点故障率;基于区间三角模糊子集,构建了区间三角模糊多态贝叶斯网络模型;研究了区间三角模糊多态贝叶斯网络可靠性分析算法,给出了求解叶节点故障模糊可能性与去模糊根节点后验概率的计算方法。最后,对塔机倾覆事故进行了可靠性分析,验证了该方法的有效性。     

Machine fault diagnosis based on Gaussian mixture model and its application

This paper presents a simple and efficient machine fault diagnosis approach based on Gaussian mixture model (GMM). After feature vectors that represent different machine conditions are extracted, a GMM for each of the machine conditions is built based on the corresponding extracted feature vectors, machine fault diagnosis can be accomplished through finding out the GMM whose posteriori probability for a given testing feature vector is the maximum of all. Experimental results based on the application on bearing fault diagnosis have shown that GMM can reliably diagnose not only the type of bearing faults, but also the degree of fault severity that are associated with incipient faults, moderate faults, and severe faults. Meanwhile, GMM has better diagnostic performance as compared to the multilayer perceptron neural networks.     

隐性函数S-压缩贝叶斯故障识别方法

支持矢量机(Support vector machines,SVM)已经在小样本故障识别中得到了广泛应用。与之对比,由于先验知识获取和非线性识别较难实现,基于贝叶斯概率的故障识别方法应用较少。针对上述问题,提出隐性函数S-压缩贝叶斯故障识别方法(LS-BFR)。LS-BFR以高斯随机过程和贝叶斯概率为基础,以高斯回归作为隐性函数,通过S-压缩对回归输出进行变换使其具有概率意义,利用贝叶斯概率实现故障识别。为提高LS-BFR非线性故障识别效果,引入核函数方法在高维空间进行隐性高斯过程回归,并给出基于贝叶斯参数估计的核函数参数选择方法。在转子试验台上模拟了不对中和不平衡故障,并利用LS-BFR进行故障识别。试验结果表明,基于隐性函数和S-压缩的LS-BFR方法能有效地进行小样本故障识别,且识别效果优于SVM。     

A machine learning approach for the condition monitoring of rotating machinery

Rotating machinery breakdowns are most commonly caused by failures in bearing subsystems. Consequently, condition monitoring of such subsystems could increase reliability of machines that are carrying out field operations. Recently, research has focused on the implementation of vibration signals analysis for health status diagnosis in bearings systems considering the use of acceleration measurements. Informative features sensitive to specific bearing faults and fault locations were constructed by using advanced signal processing techniques which enable the accurate discrimination of faults based on their location. In this paper, the architecture of a diagnostic system for extended faults in bearings based on neural networks is presented. The multilayer perceptron (MLP) with Bayesian automatic relevance determination has been applied in the classification of accelerometer data. New features like the line integral and feature based sensor fusion are introduced which enhance the fault identification performance. Vibration feature selection based on Bayesian automatic relevance determination is introduced for finding better feature combinations.     

基于贝叶斯改进结构算法的回转窑故障诊断模型研究

针对现有改进互信息爬山(MI&HC)算法精度低、耗时长及简化爬山(SHC)算法产生大量冗余边的问题,提出一种新的结构学习算法,即改进爬山(IHC)算法。通过计算互信息链得到贝叶斯初始结构,利用条件独立性测试以及对孤立节点进行处理来加边补充贝叶斯初始结构得到完全结构,利用改进的爬山搜索算子对完全结构进行搜索直到得出最优结构。将该算法与爬山(HC)算法、MI&HC算法、SHC算法进行比较,仿真结果表明,IHC算法能够得到较高准确率的模型,时间开销最小而且产生的冗余边数远远少于SHC算法产生的冗余边数。最后基于IHC算法,结合某回转窑数据进行训练,得到了回转窑工艺参数的故障诊断模型,对回转窑的烧成带温度实现了较为准确的故障诊断。     

[轨道交通运维技术]基于离散时间贝叶斯网络的列控中心可靠性分析

针对动车组列控中心在实际工作环境中的故障同时具有多态性和动态性的问题,提出一种依据列控中心各单元的功能逻辑关系来建立离散时间贝叶斯网络的分析方法。归纳部件的多种故障模式并描述列控中心故障的多态特性,采用EM算法优化更新条件概率表;针对列控中心动态失效问题,建立动态贝叶斯网络模型,将一次任务划分为启动、运行、制动三个阶段,在各个阶段通过重要度和敏感性对该模型进行可靠性分析。最后,以CTCS-2级列控系统的列控中心为例,对该离散时间贝叶斯网络模型进行验证和分析,结果表明该方法能够很好地表征列控中心的多态性和动态性。