Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the mil...Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model.However,due to the complexity of the milling system structure and the uncertainty of the milling failure index,it is often impossible to construct model expert knowledge effectively.Therefore,a milling system fault detection method based on fault tree analysis and hierarchical BRB(FTBRB)is proposed.Firstly,the proposed method uses a fault tree and hierarchical BRB modeling.Through fault tree analysis(FTA),the logical correspondence between FTA and BRB is sorted out.This can effectively embed the FTA mechanism into the BRB expert knowledge base.The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion.Secondly,evidence reasoning(ER)is used to ensure the transparency of the model reasoning process.Thirdly,the projection covariance matrix adaptation evolutionary strategies(P-CMA-ES)is used to optimize the model.Finally,this paper verifies the validity model and the method’s feasibility techniques for milling data sets.展开更多
This paper presents the fault diagnosis of face milling tool based on machine learning approach.While machining,spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are a...This paper presents the fault diagnosis of face milling tool based on machine learning approach.While machining,spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired.A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform(DWT)technique.The decision tree technique is used to select significant features out of all extracted wavelet features.C-support vector classification(C-SVC)andν-support vector classification(ν-SVC)models with different kernel functions of support vector machine(SVM)are used to study and classify the tool condition based on selected features.From the results obtained,C-SVC is the best model thanν-SVC and it can be able to give 94.5%classification accuracy for face milling of special steel alloy 42CrMo4.展开更多
In this paper an expert system for remote fault diagnosis in the ship lift was developed by analysis of the fault tree and combination with VPN. The fault tree was constructed based on the operation condition of the s...In this paper an expert system for remote fault diagnosis in the ship lift was developed by analysis of the fault tree and combination with VPN. The fault tree was constructed based on the operation condition of the ship lift. The diagnosis model was constructed by hierarchical classification of the fault tree structure, and the inference mechanism was given. Logical structure of the fault diagnosis in the ship lift was proposed. The implementation of the expert system for remote fault diagnosis in the ship lift was discussed, and the expert system developed was realized on the VPN virtual network. The system was applied to the Gaobaozhou ship lift project, and it ran successfully.展开更多
针对应急通信车通信网络综合组网复杂性和故障关联关系复杂性越来越高的问题,提出了基于故障树分析(Fault Tree Analysis,FTA)法的应急通信车通信网络故障集中诊断方法。该方法可以实现通信网络故障的快速诊断,并能够将诊断结果反馈到...针对应急通信车通信网络综合组网复杂性和故障关联关系复杂性越来越高的问题,提出了基于故障树分析(Fault Tree Analysis,FTA)法的应急通信车通信网络故障集中诊断方法。该方法可以实现通信网络故障的快速诊断,并能够将诊断结果反馈到应急通信车智能通信网络管控系统,通过故障专家知识库支撑和资源调控智能化辅助,实现人工干预或通信网络自适应快速调整和恢复,从而提升应急通信车可靠性、维修性水平。该设计方法可推广应用到大型复杂通信系统和通信、指挥车辆平台通信网络的运维管理系统中,具有较广阔的设计分析和工程应用前景。展开更多
针对空间有效载荷系统高复杂性和高可靠性需求的特性,设计了一种基于SysML (System Modeling Language)的故障诊断方法.该方法融入MBSE (Model Based System Engineering)思想,提出了基于SysML的空间有效载荷系统故障分析流程.基于SysM...针对空间有效载荷系统高复杂性和高可靠性需求的特性,设计了一种基于SysML (System Modeling Language)的故障诊断方法.该方法融入MBSE (Model Based System Engineering)思想,提出了基于SysML的空间有效载荷系统故障分析流程.基于SysML对空间有效载荷系统建立了故障分析相关的模型,其中,为满足故障分析建模的需求,对SysML元模型进行扩展定义,从而实现对组件间关系和故障表征与直接关联组件间关系的描述;基于所建模型构建故障诊断的整体框架,并提供从SysML数字模型到FTA (Fault Tree Analysis)的转换逻辑,从而实现对所有故障可能性的获取.通过案例分析,对提出方法在实际应用中的具体流程进行分析,并验证了该方法的有效性和实用性.展开更多
大众迈腾发动机不能起动的影响因素错综复杂,需要寻求一种新的方法来提高诊断的有效性。故障树分析法(Fault Tree Analysis,FTA)为科学诊断电控发动机这种复杂的动力机械提供了新思路。以起动控制逻辑作为构建故障树的基础,分析了迈腾B8...大众迈腾发动机不能起动的影响因素错综复杂,需要寻求一种新的方法来提高诊断的有效性。故障树分析法(Fault Tree Analysis,FTA)为科学诊断电控发动机这种复杂的动力机械提供了新思路。以起动控制逻辑作为构建故障树的基础,分析了迈腾B8L2.0T发动机从按下E378到起动机驱动齿轮带动飞轮旋转的整个过程,建立了“起动机不能运转”和“起动机运转但发动机不能起动”故障树,依据各个事件内在的控制规律设计了故障诊断流程。实验结果表明:故障树模型符合大众迈腾发动机的工作机理,根据故障诊断流程能够准确锁定迈腾发动机不能起动的故障部位。展开更多
基金This work was supported in part by the Natural Science Foundation of China under Grant 62203461 and Grant 62203365in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736+3 种基金in part by the Teaching reform project of higher education in Heilongjiang Province under Grant Nos.SJGY20210456 and SJGY20210457in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038in part by the graduate academic innovation project of Harbin Normal University under Grant Nos.HSDSSCX2022-17,HSDSSCX2022-18 andHSDSSCX2022-19in part by the Foreign Expert Project of Heilongjiang Province under Grant No.GZ20220131.
文摘Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model.However,due to the complexity of the milling system structure and the uncertainty of the milling failure index,it is often impossible to construct model expert knowledge effectively.Therefore,a milling system fault detection method based on fault tree analysis and hierarchical BRB(FTBRB)is proposed.Firstly,the proposed method uses a fault tree and hierarchical BRB modeling.Through fault tree analysis(FTA),the logical correspondence between FTA and BRB is sorted out.This can effectively embed the FTA mechanism into the BRB expert knowledge base.The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion.Secondly,evidence reasoning(ER)is used to ensure the transparency of the model reasoning process.Thirdly,the projection covariance matrix adaptation evolutionary strategies(P-CMA-ES)is used to optimize the model.Finally,this paper verifies the validity model and the method’s feasibility techniques for milling data sets.
文摘This paper presents the fault diagnosis of face milling tool based on machine learning approach.While machining,spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired.A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform(DWT)technique.The decision tree technique is used to select significant features out of all extracted wavelet features.C-support vector classification(C-SVC)andν-support vector classification(ν-SVC)models with different kernel functions of support vector machine(SVM)are used to study and classify the tool condition based on selected features.From the results obtained,C-SVC is the best model thanν-SVC and it can be able to give 94.5%classification accuracy for face milling of special steel alloy 42CrMo4.
文摘In this paper an expert system for remote fault diagnosis in the ship lift was developed by analysis of the fault tree and combination with VPN. The fault tree was constructed based on the operation condition of the ship lift. The diagnosis model was constructed by hierarchical classification of the fault tree structure, and the inference mechanism was given. Logical structure of the fault diagnosis in the ship lift was proposed. The implementation of the expert system for remote fault diagnosis in the ship lift was discussed, and the expert system developed was realized on the VPN virtual network. The system was applied to the Gaobaozhou ship lift project, and it ran successfully.
文摘针对应急通信车通信网络综合组网复杂性和故障关联关系复杂性越来越高的问题,提出了基于故障树分析(Fault Tree Analysis,FTA)法的应急通信车通信网络故障集中诊断方法。该方法可以实现通信网络故障的快速诊断,并能够将诊断结果反馈到应急通信车智能通信网络管控系统,通过故障专家知识库支撑和资源调控智能化辅助,实现人工干预或通信网络自适应快速调整和恢复,从而提升应急通信车可靠性、维修性水平。该设计方法可推广应用到大型复杂通信系统和通信、指挥车辆平台通信网络的运维管理系统中,具有较广阔的设计分析和工程应用前景。
文摘针对空间有效载荷系统高复杂性和高可靠性需求的特性,设计了一种基于SysML (System Modeling Language)的故障诊断方法.该方法融入MBSE (Model Based System Engineering)思想,提出了基于SysML的空间有效载荷系统故障分析流程.基于SysML对空间有效载荷系统建立了故障分析相关的模型,其中,为满足故障分析建模的需求,对SysML元模型进行扩展定义,从而实现对组件间关系和故障表征与直接关联组件间关系的描述;基于所建模型构建故障诊断的整体框架,并提供从SysML数字模型到FTA (Fault Tree Analysis)的转换逻辑,从而实现对所有故障可能性的获取.通过案例分析,对提出方法在实际应用中的具体流程进行分析,并验证了该方法的有效性和实用性.
文摘大众迈腾发动机不能起动的影响因素错综复杂,需要寻求一种新的方法来提高诊断的有效性。故障树分析法(Fault Tree Analysis,FTA)为科学诊断电控发动机这种复杂的动力机械提供了新思路。以起动控制逻辑作为构建故障树的基础,分析了迈腾B8L2.0T发动机从按下E378到起动机驱动齿轮带动飞轮旋转的整个过程,建立了“起动机不能运转”和“起动机运转但发动机不能起动”故障树,依据各个事件内在的控制规律设计了故障诊断流程。实验结果表明:故障树模型符合大众迈腾发动机的工作机理,根据故障诊断流程能够准确锁定迈腾发动机不能起动的故障部位。
