散货港口多电机传动运输系统故障诊断与容错控制
|
摘要
散货港口多电机传动运输系统是一种设备运动控制与运输流程控制相结合的大型系统,该系统目前达到的总体技术水平是:PLC程控器的集散型控制网络;工艺经验的顺序控制;非系统化的故障检测。本文将模型控制、系统诊断、容错控制技术应用对象从工业领域扩展到港口运输领域,从工业过程控制系统扩展到运输流程控制系统,研究和解决散货港口多电机传动运输系统故障诊断与容错控制的理论应用和工程实际问题。本文的主要研究内容:
(1)建立散货港口多电机传动运输系统的控制模型
在分析散货港口多电机传动运输系统组成结构的基础上,建立基于关联协同向量的散货港口运输系统多变量控制模型,研究控制模型线性应用和非线性应用的普适性,论证散货港口运输系统控制模型的完整性、一致性、外延性。
构建散货运输系统流程控制律和输送机多电机驱动系统协调控制律,验证所提出的建模方法在散货码头装卸运输系统工程应用中的可行性,解决散货港口输送机多电机传动系统动力匹配、功率分配等协调控制问题。
(2)散货港口多电机传动运输系统的故障诊断
在散货港口多电机传动运输系统控制模型的基础上,建立基于多变量状态监测的故障诊断模型和基于模式特征的故障诊断模型,确定二种故障诊断模型的应用范围及其局限性,实现系统化的故障检测与诊断(FDD——Fault Detect and Diagnosis)。
以输煤控制系统流程故障诊断为研究对象,应用多变量状态监测的故障诊断模型,构造基本故障函数,实现流程故障的准确识别。
以长距离大运量输送机失速和跑偏故障为研究对象,应用模式特征的故障诊断模型,研究输送机失速和跑偏故障的信号检测、特征提取和诊断方法。
(3)散货装卸运输过程动载干扰故障的自适应检测
散货装卸运输系统由于负荷剧烈波动、重载连续冲击等因素的影响,动载干扰问题十分突出。这是一类非线性、非平稳的随机干扰,它严重影响到检测信号与控制信号的可靠性与准确性,影响到运输流程和设备的正常运行。通过分析散货港口多电机传动运输系统运行过程中随机性动载干扰产生原因,提出抑制散货运输系统动载干扰的自适应滤波基本策略和方法。
建立门座式卸船机起升机构动载干扰的参考模型,进行动载干扰自适应滤波,解决模型参数离线预估计算法和零速空载跟踪起重量零位等技术问题,消除门座式卸船机起升机构起制动过程的速度和加速度动载干扰,实现起重量准确检测。
研究散货运输过程中大块分离物冲击载荷干扰的发生机理,根据动量原理建立大块分离物冲击载荷模型,构造自适应滤波器,通过收敛因子的自调整快速逼近递推,辩识大块分离物瞬时冲击载荷的干扰信号。
(4)散货港口多电机传动运输系统的容错控制
以散货港口多电机传动运输系统故障诊断为基础,融合智能容错控制方法,将主动性容错控制技术和被动性容错控制技术相结合,研究散货港口装卸运输设备和运输流程控制律重组(CRR——Control Rule Reconfiguration),提升系统的控制性能,实现安全控制与可靠控制。
对于装卸运输设备故障,由径向基函数网络构造设备故障分类器(FCD——FaultClassifying Device),构建基于控制参考重置的广义主动性容错控制结构,避免控制律在线重组可能引起的控制系统完整性缺失和不稳定,重点解决输送机跑偏状态下综合纠偏及其减速减载运行的容错控制问题。
对于运输流程故障,在分析流程设备的互通互用性基础上,研究基于控制律在线重组的运输流程被动性容错控制方法,通过既定调度方式和指标调度方式相结合的控制律在线调度,实现散货运输流程的容错控制。
运用参数、部件、通道、设备等多种控制方式,嵌入式单片机、PLC程控器、主控工作站等多种控制层次,设计新型的散货港口多电机传动运输系统容错控制网络结构。同时,开发应用程序与数据接口,将故障诊断与容错处理应用软件作为独立的外挂程序,嵌入到港口输煤控制软件。
散货港口多电机运输系统故障诊断与容错控制技术的研究,取得了十多项专利成果,填补了散货港口运输自动化领域的技术空白。论文主要创新点有:
——基于多变量关联协同模型,解决散货港口多电机传动运输系统数字量与模拟量混合建模、大功率传动设备和运输网络系统控制协调问题。
——通过多变量状态信息组合和变换,提取故障模式特征,解决散货港口运输系统故障诊断、输送机失速和跑偏故障诊断问题。
——建立随机性动载干扰模型,通过自适应滤波,抑制门座式卸船机起升机构速度与加速度动载干扰、输送系统大块分离物冲击干扰。
——构建基于径向基函数网络的故障分类器,通过控制参考变量在线重置、既定调度方式和指标调度方式相结合的控制律在线重组,有效整合现有控制网络的软硬件资源,灵活实现散货港口多电机传动运输系统容错控制。
论文的研究成果对于各类散货港口运输系统具有推广价值。近年来的应用项目有:国家大型港口建设、国家重点工程、现有码头的技术改造、新港口的建设。研究成果的应用取得了可观的经济效益和社会效益,近年来成果推广新增产值每年超亿元;利税效益每年超千万元。论文的相关课题多次获得上海市、国家安监局的科技进步奖。
The multi-motor driving transport system of bulk port is a kind of large system, combining equipment motion control and transport process control. At present, the level of the system technology is: PLC distributed control network, sequencing control by operation experience, non-systematic fault detecting. In this paper, the applying field of model control, system diagnosis, tolerant control is expanded from industry to port transport. Both the engineering problems and the theories application of fault diagnosis and tolerant control are studied for the multi-motor driving transport system of bulk port. The main content of the paper are as the following:
(1) Construct control model of multi-motor driving transport system of bulk port.
By analyzing the structure of the multi-motor driving transport system of bulk port, the multi-variable control model of associate-cooperate vectors is constructed. The universal adaptability of linear and non-linear applications, the models features of the integrality, consistency and extension are demonstrated.
The flow control rules of the transport system and the corresponding control rules of the conveyer driving system are set up. All of these prove the feasibility of the model in the transport system of bulk port. Therefore, the problems of the power matching and power distributing of the conveyer driving system are solved.
(2) Fault diagnosis of multi-motor driving transport system of bulk port.
According to the control model of the multi-motor driving system of bulk port, the fault diagnosis models based on multi-variable state detection and pattern characteristics are built up. The application range and localization of the models are discussed, in order to realize the fault detect and diagnosis (FDD) of the system.
Taking the flow fault diagnosis of the coal transport system as the object, the model of multi-variable state detection is applied to set up basic fault functions, and to recognize the flow faults.
Taking the conveyer faults of the stall and off-tracking as the object, the model of pattern characteristics is applied to study detecting the signal, extracting the characteristics, and the diagnosis method.
(3) Adaptive detection of dynamic disturbing fault in bulk transport.
Affected by the load fierce fluctuation and heavy-load continuous impulsion, the problem of dynamic disturbing is very evident in the transport system of bulk port. It is a non-linear and non-smooth random disturbance, which seriously affects the reliability and accuracy of the detecting and controlling signals, and the normal running of the transport process and equipment. Aiming at the random dynamic interference of the multi-motor driving transport system of bulk port, the basic strategy and method of the self-adaptive filtering are presented.
The reference model of the dynamic disturbing of gantry crane hoisting mechanism is set up, and a self-adaptive filter is developed. Founding the off-line pre-estimating algorithm for the model parameters speeds up detecting on-line. When the crane is in the state of zero-speed, and zero-load, the self zero-tracking will act, and the zero drift of the detecting system can be corrected. The adaptive filtering can effectively eliminate the signals of speed and acceleration interference when the crane starts or brakes. The applying results show that the new technique has increased the accuracy of detecting lifted load.
The interference mechanism of the impact load of the large separated material is studied. Based on momentum theory, an impact model of bulk matter is founded and a kind of adaptive filtering method to restrain the instantaneous impact is put forward. A self-adjusting convergence factor speeds up the approaching, and the impact interfering signal is identified.
(4) Tolerant control of multi-motor driving transport system of bulk port.
According to the fault diagnosis for multi-motor driving transport system of bulk port, mixing with the intelligent tolerant control method, combining the active and passive fault tolerant control technology, the control rule reconfiguration (CRR) of the transport equipment and process are studied, which improves the control performance, realizes the safety control and the reliable control of the system.
Based on the radial basis function network (RBFN), an equipment fault classifying device (FCD) is proposed. The structure of generalized active tolerant control is studied by control reference variable replacement, in order to avoid non-integrality and non-stability of the system caused by the control rule reconfiguration online. In the applications, the key problem is to research the synthetic tolerant control of rectifying the deviation, slowing down and deloading in the state of the conveyer running off-tracking.
By the interoperability of the flow equipment, the passive tolerant control method of the transport flow is studied. The control rule is reconfigurated online—scheduled online, in the fixed scheduling way or the index scheduling way.
The new network structure of the tolerant control of the multi-motor driving transport system of bulk port is designed by many ways of control, such as parameter, components, channels, equipments and so on, and by many modes of control, such as embedded single chip, PLC, control workstation and so on. At the same time, the program and data interface of the tolerant control are developed, which are embedded into the transport control system of the coal port as an attachable program.
In the research on fault diagnosis and tolerant control of the multi-motor driving transport system of bulk port, more than ten patents have been applied, which fill the technology blanks in the automatic transport of bulk port. The main contributions include:
——Based on the multi-variable associate-cooperate model, the problems are solved, such as mixed-modeling of digital and analog, and corresponding control for high power multi-motor driving equipment and transport network system of bulk port.
——Through grouping and mapping the multi-variable state information, the fault pattern characteristic are extracted and the fault diagnosis problems are solved, such as the process faults, the stall faults, and the off-tracking faults in the bulk transport.
——The model of random dynamical interference and adaptive filter are founded. The speed and acceleration disturbing of the gantry crane, and impulsion interference of large separated materials of the transport system are cancelled.
——Based on the RBFN, the fault classifying device is constructed. By the control reference variable replacement and the control rule reconfiguration, the available software and hardware resource of the transport system is utilized efficiently, and the tolerant control is realized flexibly.
The research results of the thesis are of practical value to all kinds of transport system of bulk port, and have been applied to many engineering projects, including the technological reform of the existing ports and the construction of new ports. The applications of the research results have brought considerable economic benefits and social benefits—increase production value by more than one hundred million Yuan (RMB), and profits tax value over ten million Yuan (RMB) annually. The relative subjects have won the science and technology awards from the government.
(1)建立散货港口多电机传动运输系统的控制模型
在分析散货港口多电机传动运输系统组成结构的基础上,建立基于关联协同向量的散货港口运输系统多变量控制模型,研究控制模型线性应用和非线性应用的普适性,论证散货港口运输系统控制模型的完整性、一致性、外延性。
构建散货运输系统流程控制律和输送机多电机驱动系统协调控制律,验证所提出的建模方法在散货码头装卸运输系统工程应用中的可行性,解决散货港口输送机多电机传动系统动力匹配、功率分配等协调控制问题。
(2)散货港口多电机传动运输系统的故障诊断
在散货港口多电机传动运输系统控制模型的基础上,建立基于多变量状态监测的故障诊断模型和基于模式特征的故障诊断模型,确定二种故障诊断模型的应用范围及其局限性,实现系统化的故障检测与诊断(FDD——Fault Detect and Diagnosis)。
以输煤控制系统流程故障诊断为研究对象,应用多变量状态监测的故障诊断模型,构造基本故障函数,实现流程故障的准确识别。
以长距离大运量输送机失速和跑偏故障为研究对象,应用模式特征的故障诊断模型,研究输送机失速和跑偏故障的信号检测、特征提取和诊断方法。
(3)散货装卸运输过程动载干扰故障的自适应检测
散货装卸运输系统由于负荷剧烈波动、重载连续冲击等因素的影响,动载干扰问题十分突出。这是一类非线性、非平稳的随机干扰,它严重影响到检测信号与控制信号的可靠性与准确性,影响到运输流程和设备的正常运行。通过分析散货港口多电机传动运输系统运行过程中随机性动载干扰产生原因,提出抑制散货运输系统动载干扰的自适应滤波基本策略和方法。
建立门座式卸船机起升机构动载干扰的参考模型,进行动载干扰自适应滤波,解决模型参数离线预估计算法和零速空载跟踪起重量零位等技术问题,消除门座式卸船机起升机构起制动过程的速度和加速度动载干扰,实现起重量准确检测。
研究散货运输过程中大块分离物冲击载荷干扰的发生机理,根据动量原理建立大块分离物冲击载荷模型,构造自适应滤波器,通过收敛因子的自调整快速逼近递推,辩识大块分离物瞬时冲击载荷的干扰信号。
(4)散货港口多电机传动运输系统的容错控制
以散货港口多电机传动运输系统故障诊断为基础,融合智能容错控制方法,将主动性容错控制技术和被动性容错控制技术相结合,研究散货港口装卸运输设备和运输流程控制律重组(CRR——Control Rule Reconfiguration),提升系统的控制性能,实现安全控制与可靠控制。
对于装卸运输设备故障,由径向基函数网络构造设备故障分类器(FCD——FaultClassifying Device),构建基于控制参考重置的广义主动性容错控制结构,避免控制律在线重组可能引起的控制系统完整性缺失和不稳定,重点解决输送机跑偏状态下综合纠偏及其减速减载运行的容错控制问题。
对于运输流程故障,在分析流程设备的互通互用性基础上,研究基于控制律在线重组的运输流程被动性容错控制方法,通过既定调度方式和指标调度方式相结合的控制律在线调度,实现散货运输流程的容错控制。
运用参数、部件、通道、设备等多种控制方式,嵌入式单片机、PLC程控器、主控工作站等多种控制层次,设计新型的散货港口多电机传动运输系统容错控制网络结构。同时,开发应用程序与数据接口,将故障诊断与容错处理应用软件作为独立的外挂程序,嵌入到港口输煤控制软件。
散货港口多电机运输系统故障诊断与容错控制技术的研究,取得了十多项专利成果,填补了散货港口运输自动化领域的技术空白。论文主要创新点有:
——基于多变量关联协同模型,解决散货港口多电机传动运输系统数字量与模拟量混合建模、大功率传动设备和运输网络系统控制协调问题。
——通过多变量状态信息组合和变换,提取故障模式特征,解决散货港口运输系统故障诊断、输送机失速和跑偏故障诊断问题。
——建立随机性动载干扰模型,通过自适应滤波,抑制门座式卸船机起升机构速度与加速度动载干扰、输送系统大块分离物冲击干扰。
——构建基于径向基函数网络的故障分类器,通过控制参考变量在线重置、既定调度方式和指标调度方式相结合的控制律在线重组,有效整合现有控制网络的软硬件资源,灵活实现散货港口多电机传动运输系统容错控制。
论文的研究成果对于各类散货港口运输系统具有推广价值。近年来的应用项目有:国家大型港口建设、国家重点工程、现有码头的技术改造、新港口的建设。研究成果的应用取得了可观的经济效益和社会效益,近年来成果推广新增产值每年超亿元;利税效益每年超千万元。论文的相关课题多次获得上海市、国家安监局的科技进步奖。
The multi-motor driving transport system of bulk port is a kind of large system, combining equipment motion control and transport process control. At present, the level of the system technology is: PLC distributed control network, sequencing control by operation experience, non-systematic fault detecting. In this paper, the applying field of model control, system diagnosis, tolerant control is expanded from industry to port transport. Both the engineering problems and the theories application of fault diagnosis and tolerant control are studied for the multi-motor driving transport system of bulk port. The main content of the paper are as the following:
(1) Construct control model of multi-motor driving transport system of bulk port.
By analyzing the structure of the multi-motor driving transport system of bulk port, the multi-variable control model of associate-cooperate vectors is constructed. The universal adaptability of linear and non-linear applications, the models features of the integrality, consistency and extension are demonstrated.
The flow control rules of the transport system and the corresponding control rules of the conveyer driving system are set up. All of these prove the feasibility of the model in the transport system of bulk port. Therefore, the problems of the power matching and power distributing of the conveyer driving system are solved.
(2) Fault diagnosis of multi-motor driving transport system of bulk port.
According to the control model of the multi-motor driving system of bulk port, the fault diagnosis models based on multi-variable state detection and pattern characteristics are built up. The application range and localization of the models are discussed, in order to realize the fault detect and diagnosis (FDD) of the system.
Taking the flow fault diagnosis of the coal transport system as the object, the model of multi-variable state detection is applied to set up basic fault functions, and to recognize the flow faults.
Taking the conveyer faults of the stall and off-tracking as the object, the model of pattern characteristics is applied to study detecting the signal, extracting the characteristics, and the diagnosis method.
(3) Adaptive detection of dynamic disturbing fault in bulk transport.
Affected by the load fierce fluctuation and heavy-load continuous impulsion, the problem of dynamic disturbing is very evident in the transport system of bulk port. It is a non-linear and non-smooth random disturbance, which seriously affects the reliability and accuracy of the detecting and controlling signals, and the normal running of the transport process and equipment. Aiming at the random dynamic interference of the multi-motor driving transport system of bulk port, the basic strategy and method of the self-adaptive filtering are presented.
The reference model of the dynamic disturbing of gantry crane hoisting mechanism is set up, and a self-adaptive filter is developed. Founding the off-line pre-estimating algorithm for the model parameters speeds up detecting on-line. When the crane is in the state of zero-speed, and zero-load, the self zero-tracking will act, and the zero drift of the detecting system can be corrected. The adaptive filtering can effectively eliminate the signals of speed and acceleration interference when the crane starts or brakes. The applying results show that the new technique has increased the accuracy of detecting lifted load.
The interference mechanism of the impact load of the large separated material is studied. Based on momentum theory, an impact model of bulk matter is founded and a kind of adaptive filtering method to restrain the instantaneous impact is put forward. A self-adjusting convergence factor speeds up the approaching, and the impact interfering signal is identified.
(4) Tolerant control of multi-motor driving transport system of bulk port.
According to the fault diagnosis for multi-motor driving transport system of bulk port, mixing with the intelligent tolerant control method, combining the active and passive fault tolerant control technology, the control rule reconfiguration (CRR) of the transport equipment and process are studied, which improves the control performance, realizes the safety control and the reliable control of the system.
Based on the radial basis function network (RBFN), an equipment fault classifying device (FCD) is proposed. The structure of generalized active tolerant control is studied by control reference variable replacement, in order to avoid non-integrality and non-stability of the system caused by the control rule reconfiguration online. In the applications, the key problem is to research the synthetic tolerant control of rectifying the deviation, slowing down and deloading in the state of the conveyer running off-tracking.
By the interoperability of the flow equipment, the passive tolerant control method of the transport flow is studied. The control rule is reconfigurated online—scheduled online, in the fixed scheduling way or the index scheduling way.
The new network structure of the tolerant control of the multi-motor driving transport system of bulk port is designed by many ways of control, such as parameter, components, channels, equipments and so on, and by many modes of control, such as embedded single chip, PLC, control workstation and so on. At the same time, the program and data interface of the tolerant control are developed, which are embedded into the transport control system of the coal port as an attachable program.
In the research on fault diagnosis and tolerant control of the multi-motor driving transport system of bulk port, more than ten patents have been applied, which fill the technology blanks in the automatic transport of bulk port. The main contributions include:
——Based on the multi-variable associate-cooperate model, the problems are solved, such as mixed-modeling of digital and analog, and corresponding control for high power multi-motor driving equipment and transport network system of bulk port.
——Through grouping and mapping the multi-variable state information, the fault pattern characteristic are extracted and the fault diagnosis problems are solved, such as the process faults, the stall faults, and the off-tracking faults in the bulk transport.
——The model of random dynamical interference and adaptive filter are founded. The speed and acceleration disturbing of the gantry crane, and impulsion interference of large separated materials of the transport system are cancelled.
——Based on the RBFN, the fault classifying device is constructed. By the control reference variable replacement and the control rule reconfiguration, the available software and hardware resource of the transport system is utilized efficiently, and the tolerant control is realized flexibly.
The research results of the thesis are of practical value to all kinds of transport system of bulk port, and have been applied to many engineering projects, including the technological reform of the existing ports and the construction of new ports. The applications of the research results have brought considerable economic benefits and social benefits—increase production value by more than one hundred million Yuan (RMB), and profits tax value over ten million Yuan (RMB) annually. The relative subjects have won the science and technology awards from the government.
引文
[1]王杰,陆春峰.国际干散货航运市场回顾与展望.世界海运,2006,29(2):1-3
[2]交通部.2005年公路水路交通行业发展统计公报.http://www.moc.gov.cn
[3]上海国际港务(集团)公司课题组.上海港装卸工艺现状与发展趋势研究总报告.2006
[4]Verma Anand,Agarwal et al..Bulk materials handling for port applications-a review.Bulk Solids Handling,2005,25(1):34-37
[5]李浩涛.我国港口大宗干散货装卸工艺和设备的技术发展.中国港口,1999,(6):13-15
[6]Wadhwa,Lal C.Optimizing deployment of shiploaders at bulk export terminal.Journal of Waterway,Port,Coastal and Ocean Engineering,2000,126(6):297-304
[7]肖曙光,陈琦等.九十年代先进水平的港口散货装卸系统.港口装卸,2000,(6):1-5
[8]孙国正.面向21世纪的物流工程及物料搬运技术.中国机械工程学会物料搬运分会第六届年会论文集,2000:1-5
[9]宋伟刚.带式输送机的发展与评述.物料搬运与升离技术,1998,(2):5-8
[10]刘雪平.大型带式输送机及其控制驱动系统的发展趋势.煤矿机械,2000,(11):1-2
[11]张家锟.大型港口机械电控技术的新发展.起重运输机械,1993,(10):3-9
[12]Li Ze-Yi,Li Mei-Zhen,Cai Peng.Loading and unloading technology design for bulk cargo wharf in TGP reservoir area.People's Changjiang River,2003,34(1):17-18
[13]李士瀛,由清育.现代港口机械技术.北京:人民交通出版社,1995
[14]一兵译.港口起重机的控制技术及其发展动向(上).港口科技动态,1992,(8):18-23
[15]一兵译.港口起重机的控制技术及其发展动向(下).港口科技动态,1992,(9):24-31
[16]李德华.交流调速控制系统.北京:电子工业出版社,2003
[17]祝龙记,王汝琳.直接转矩控制在带式输送机电气传动中的应用研究.煤炭学报,2004,29(3):376-380
[18]尹慧民.CST软启动装置在大型带式输送机中的应用.煤炭科技,2002,(3):42-43
[19]上海国际港务(集团)公司.现代散货港口重大装备自动化技术开发.上海市引进技术吸收与创新计划项目申报书,2007
[20]Sawodny O,Aschemann H,Kumpel J et al..Anti-sway control for boom cranes.Proceedings of the American Control Conference,2002:244-249
[21]Osamu Itoh,Hirohisa Migita,Jun Itch.Application of fuzzy control to automatic crane operation.Proceedings of IECO'93 International Conference on Industrial Electronics,Control,and Instrumentation,1993:161-164
[22]钱学清.起重机电子控摇系统的研究.博士学位论文,上海:上海交通大学,1999
[23]肖鹏,王冰.模糊神经网络技术在防摇控制系统中的研究与应用.起重运输机械,2005, (11):21-23
[24]何衍庆.集散控制系统原理及应用.北京:化学工业出版社,2002
[25]张斌.分散控制系统在深圳妈湾发电总厂的应用.电力行业信息化2005年会论文集,大连,2005:428-431
[26]陈宇.秦皇岛港务集团公司煤五期装船机控制系统.河北省人工智能学会2005年会论文集,天津,2005:203-205
[27]上海国际港务(集团)公司.大型集装箱装卸设备远程监测系统项目申报书,2006
[28]刘斌.工业控制网在煤炭装卸港口的应用.起重运输机械,2006,(7):74-5
[29]黄有方,史建民,赵婉莹.大型港口设备的现代电控系统的远程诊断技术.上海海运学院学报,2000,21(4):44-49
[30]钱华明.故障诊断与容错技术及其在组合导航系统中的应用研究.博士学位论文,哈尔滨:哈尔滨工程大学,2004
[31]周东华,叶银忠.现代故障诊断与容错控制.北京:清华大学出版社,2000
[32]Jiang Jin.Fault-tolerant control systems-an introductory overview.Acta Automatica Sinica,2005,31(1):161-174
[33]Wu N Eva,Zhou Kemin,Salomon Gregory.Control reconfigurability of linear time-invariant systems.Automatica,2000,36(11):1767-1771
[34]Frank P M.Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-a survey and some new results.Automatica,1990,26(3):459-474
[35]郑君里.信号与系统.第二版.北京:高等教育出版社,2004
[36]Gross A,Morlet J.Decomposition of hardy functions into square integrable wavelets of constant shape.ALAM Journal of Math.,1984,15(2):723-736
[37]Beard R V.Failure accommodation in linear systems through self-reorganization:Dissertation.Man Vehicle Lab.MIT,Cambridge,Massachusetts,1971
[38]Mehra R K,Peschon J.An innovation approach to fault detection and diagnosis in dynamics.Automation,1971,7(6):637-640
[39]胡寿松.自动控制原理.北京:国防工业出版社,1998
[40]Isermann R.Process fault detection based on modeling and estimation methods-a survey.Automation,1984,20(3):387-404
[41]吴今培,肖健华.智能故障诊断与专家系统.北京:科学出版社,1997
[42]Doherty N F.Knowledge-based approaches to fault diagnosis:a practical evaluation of the relative merits of deep and shallow knowledge.Journal of Engineering Manufacture,1994,208(B1):39-45
[43]边肇祺等.模式识别.北京:清华大学出版社,2000
[44]Sunil Elanayar V T,Yung C Shin.State estimation of continuous-time radial basis function networks.Automatica,2000,36(3):399-407
[45]吴今培.模糊诊断理论及其应用.北京:科学出版社,1995
[46]Astrom K J.Intelligent control.Proceedings of the 1st European Control Conference,Grenoble,1991:2328-2329
[47]胡昌华.控制系统故障诊断与容错控制的分析和设计.北京:国防工业出版社,2000
[48]王仲生.智能故障诊断与容错控制.西安:西北工业大学出版社,2005
[49]王珍熙.可靠性、冗余及容错技术.北京:航空工业出版社,1991
[50]孙金生.动态大系统的分散容错控制.南京理工大学学报,1998,22(2):117-120
[51]Niederlinski A.A heuristic approach to the design of interacting multivariable systems.Automatic,1971,7(6):691-701
[52]Salijak D D.Reliable control using multiple control systems.International Journal of Control,1980,31(2):323-329
[53]藤召胜.智能检测系统与数据融合.北京:机械工业出版社,2000
[54]Monlin B.A multi-agent system supporting cooperating work done by persons and machines.IEEE International Conference on SMC,1991:1889-1993
[55]Jaege C A.Smart materials.Structures and Mathematical Issues,U.S.Army Research Office Workshop,1988:15-17
[56]Colins M.A universal framework for managed built-in test.International Test Conference,1993:21-29
[57]Hiroyuki.Fault tolerance for distributed process control system.Proceedings of 2002 SICE Annual Conference,2002:631-636.
[58]Kabore P,Wang H.Design of fault diagnosis filters and fault-tolerant control for a class of nonlinear systems.IEEE Transactions on Automatic Control,2001,46(11):1805-1810
[59]刘伟.基于专家系统的容错控制及在合成反应过程中的应用.化工自动化及仪表,1996,23(5):13-16
[60]宋国宁,蒋兴松.大型过程工业自动化全过程体系结构.信息与控制,1994,23(2):65-70
[61]何凤有,袁兴.矿井提升机容错控制技术的研究及其实现.煤矿机械,2006,27(10):75-76
[62]刘湘崇,梁彦.导弹控制系统的容错控制研究.控制与决策,2006,21(10):1185-1189
[63]张建华,史运涛.燃煤电厂协调控制系统故障诊断与容错控制应用研究.中国电机工程学报,2004,24(8):175-179
[64]上海市科技情报研究所.散货港口自动化运输系统容错控制水平检索报告.2003
[65]运输机械设计选用手册.北京:化学工业出版社,1999
[66]王玉兴.斗轮堆取料机变幅装置运动学、动力学建模和仿真研究.博士学位论文,吉林:吉林工业大学,1998
[67]赵丁选.轮式装载机倾翻稳定性及其仿真与监控.博士学位论文,吉林:吉林工业大学,1999
[68]Harrison A.Belt conveyor research 1980-2000.Bulk Solids Handling,2001,21(2):159-164
[69]宋伟刚,柳洪义.带式输送机动力学及其计算机仿真的研究.机械工程学报,2003,39(9):133-138
[70]宋伟刚,赵琛.带式输送机阻力随带速变化规律性的实验研究.煤矿机械,2001,(3):16-18
[71]交通部水运司.港口起重运输机械设计手册.北京:人民交通出版社,2001
[72]刘勤国.抓斗卸船机工作机构数学建模及仿真研究.硕士学位论文,吉林:吉林大学,2001
[73]Munz R E.Considerations for remotely monitoring and controlling conveyor belts in underground coal mines.Conference Record of the 1988 IEEE,Industry Applications Society Annual Meeting,1988:1243-1249
[74]任廷志,刘才.带式输送机多卷筒驱动最佳负荷分配.机械设计,2004,21(2):58-59
[75]郭永存.多电机传动带式输送机功率配比的研究.煤炭科学技术,2003,31(1):46-48
[76]张雪雁.含液力耦合器的带式输送机起动特性优化设计.河北理工学院学报,2003,25(3):36-39
[77]Ma Xian-Min.Genetic taboo hybrid strategy for PID regulator parameter adaptation in belt conveyor.Proceedings of Machine Learning and Cybernetics 2006 International Conference,2006:607-612
[78]Yusong Pang,Lodewijks G.Large-scale conveyor belt system maintenance decision-making by using fuzzy causal modeling.Proceedings of IEEE Conference on Intelligent Transportation Systems,2005:63-67
[79]汤蕴璎.电机学--机电能量转换.北京:机械工业出版社,1982
[80]蒋慰孙,叶银忠.多变量控制系统分析与设计.北京:中国石化出版社,1997
[81]王祥忠.开放型集散控制系统中的过程信息模型.中国科学技术大学学报,1998,28(5):562-566
[82]Jeinsch T.A robust model-based information system for monitoring and fault detection of large scale belt conveyor systems.Proceedings of the 4th World Congress on Intelligent Control and Automation,2002:3283-3287
[83]褚建新,顾伟.集散控制系统控制律的系统化建模方法.自动化仪表,2002,23(10):52-54
[84]王桂增.高等过程控制.北京:清华大学出版社,2002
[85]上海市科学技术奖励委员会.上海市科学技术进步奖授奖名册.2003
[86]上海市科技情报研究所.长距离输送系统动态监测与故障诊断技术水平检索报告.2007
[87]Harrison A.New technique for monitoring defects in underground steel cord belts.Proceedings of 21 st International Conference on Safety in Mine,1985:21-25
[88]黄民,魏任之.矿用钢绳芯带式输送机实时工况监测与故障诊断技术.煤炭学报,2005,30(2):246-251
[89]胡成.钢绳芯带式输送机实时工况监测与故障诊断系统.煤炭科学技术,2003,30(10):4-7
[90]李恩.钢绳芯带式输送机计算机实时工况监测与故障诊断系统.博士学位论文,徐州:中国矿业大学,2001
[91]陈岚,周晓谋.煤矿堆取料机自动控制系统的故障诊断设计.煤矿机械,2005,(2):119-121
[92]孟凡芹,王耀才.带式输送机打滑的模糊测量方法.煤矿机电,2000,(6):16-18
[93]孙新梅,孟凡芹.带式输送机运行故障智能诊断方法.煤炭科技,2004,(1):27-29
[94]Zheng Zhang,Xinyu Shao,Daoyuan Yu.Fault diagnosis of a wheel loader by artificial neural networks and fuzzy logic.Proceedings of 2006 IEEE Conference on Robotics,Automation and Mechatronics,2006:1-5
[95]温熙森,胡茑庆,邱静.模式识别与状态监控.长沙:国防科大出版社,1997
[96]沈爱弟等.差值运算的输送带失速检测装置.中国实用新型专利,200720067394.8,2007
[97]沈爱弟等.托辊式输送带带速检测传感器.中国实用新型专利,200720067393.3,2007
[98]胡慧萍.带式输送机胶带跑偏原因及纠编分析.矿山机械,2000,(9):35-37
[99]顾伟等.输送带跑偏信号检测装置.中国实用新型专利,200720067396.7,2007
[100]张贤达.现代信号处理.第2版.北京:清华大学出版社,2002
[101]Hughes P,Ip S F A,Cook J.Adaptive filters-a review of techniques.British Telecom Technology Journal,1992,10(1):28-48
[102]Hojjati M H,Lukasiewicz S A.Application of the adaptive matrix filter to inverse heat conduction problems.Journal of Thermal Stresses,2003,26(11):1171-1184
[103]Howell M N,Gordon T J.Continuous action reinforcement learning automata and their application to adaptive digital filter design.Engineering Applications of Artificial Intelligence,2001,14(5):549-561
[104]Sristi P,Lu W-S,Antoniou A.A new variable-step-size LMS algorithm and its application in subband adaptive filtering for echo cancellation.Proceedings of IEEE International Symposium on Circuits and Systems,2001:1721-1724
[105]Longhua,Mu Jiangzi.Application of adaptive filtering in harmonic analysis and detection.Proceedings of 2005 IEEE/PES Transmission and Distribution Conference,2005:1-4
[106]李富才,訾艳阳,何正嘉.自适应滤波与相关滤波在冲击响应信号特征提取中的应用.振动与冲击,2004,23(3):24-28
[107]李保安,李行善,罗先和.动态秤重系统计量误差的动态校正.仪器仪表学报,2001,22(3):251-254
[108]Tzes Anthony,Le Ke.Application of frequency domain adaptive infinite impulse response filtering for identification of flexible structure dynamics.Mechanical Systems & Signal Processing,1996,10(1):65-91
[109]陈章位,赵玉刚.基于参考模型的自适应环境噪声处理.噪声与振动控制,2003,(5):30-32
[110]Kazemi,Ahad,Sarlak et al.An adaptive noise canceling method for single-phase unified power quality conditioner.Proceedings of 2006 1st IEEE Conference on Industrial Electronics and Applications,2006:961-965
[111]Nakanishi,Isao,Itoh et al..Accelerated frequency domain adaptive filter using modified DFT pair and its application to noise canceling.Proceedings of IEEE International Symposium on Circuits and Systems,2000:361-354
[112]Mastorocostas Paris,Varsamis et al.A recurrent fuzzy filter for adaptive noise cancellation.Proceedings of International Conference on Computational Intelligence for Modeling,Control and Automation,2005:408-413
[113]褚建新,顾伟.具有自适应动载滤波与校零功能的起重机超载限制器原理分析.仪器仪表学报,1998,19(5):449-453
[114]褚建新,顾伟.一种新型多功能超载限制器的设计.自动化仪表,2000,21(1):22-24
[115]褚建新,顾伟.抑制大块分离物冲击力的一种自适应滤波方法.仪器仪表学报,2004,25(4):744-745
[116]顾伟,褚建新.基于动态模型自适应辨识原理的起重质量检测技术.机械工程学报,2006,42(1):187-190
[117]顾伟,褚建新等.料斗大块分离重力式自动监测装置.中国实用新型专利,02260942.3,2002
[118]顾伟,褚建新等.散货港口大块分离物堵塞报警信号处理电路.中国实用新型专利,03232272.0,2003
[119]Yang In-Young,Im Kwang-Hee,Park Je-Woung.On impact characteristic evaluation in CF/Epoxy composites under falling-weight impact loadings.Proceedings of the 3rd International Symposium on Designing,Processing and Properties of Advanced Engineering Materials,2004:749-752
[120]向红,汤伯森.L-P摄动法在跌落冲击问题中的应用.振动与冲击,2002,21(1):39-43
[121]杨其新,关宝树.落石冲击力计算方法的试验研究.铁道学报,1996,18(1):101-106
[122]Nakatsutsumi.Experimental study of impact force due to free-fall sand.Transactions of the Japan Society of Civil Engineers,1984,31(14):288-290
[123]Jianxin Chu,Wei Gu.Adaptive dynamic detection of separated bulk matter in conveyer system.IEEE Trans.Instrument & Measurement,2006,55(3):809-813
[124]Jianxin Chu,Wei Gu.Self-adaptive dynamic detection of bulk matter based on reference model.Proceedings of ICEMI'05,2005:316-319
[125]Ramakrishna G,Malik O P.Radial basis function identifier and pole-shifting controller for power system stabilizer application.IEEE Transactions on Energy Conversion,2004,19(4):663-670
[126]Moravej Z,Vishwakarrna D N,Singh S P.Application of radial basis function neural network for differential relaying of a power transformer.Computers and Electrical Engineering,2003,29(3):421-434
[127]Zayed Ali S,Hussain Amir,Abdullah Rudwan A.A novel multiple-controller incorporating a radial basis function neural network based generalized learning model.Neurocomputing,2006,69(16):1868-1881
[128]Yamauchi Koichiro,Hayami Jiro.Incremental leaning and model selection for radial basis function network through sleep.IEICE Transactions on Information and Systems,2007,E90-D(4):722-735
[129]Yang Zheng Rong.A novel radial basis function neural network for discriminant analysis.IEEE Transactions on Neural Networks,2006,17(3):604-612
[130]Roussos George,Baxter Brad J C.Rapid evaluation of radial basis functions.Journal of Computational and Applied Mathematics,2005,180(1):51-70
[131]Samantaray S R,Dash P K,Panda G.Fault classification and location using HS-transform and radial basis function neural network.Electric Power Systems Research,2006,76(9):897-905
[132]Sarimveis Haralambos,Doganis Philip,Alexandridis Alex.A classification technique based on radial basis function neural networks.Advances in Engineering Software,2006,37(4):218-221
[133]Mali Kalyani,Mitra Sushmita.Symbolic classification,clustering and fuzzy radial basis function network.Fuzzy Sets and Systems,2005,152(3):553-564
[134]Karayiannis Nicolaos B,Xiong Yaohua.Training reformulated radial basis function neural networks capable of identifying uncertainty in data classification.IEEE Transactions on Neural Networks,2006,17(5):1222-1233
[135]Babu G P.Clustering with evolution strategies.Pattern Recognition,1994,27(2):321-329
[136]Konhonen T.Self-organization maps.Springer Series in Information Sciences,1995,(30):174-188
[137]褚建新,顾伟等.输送带自动纠偏装置和方法.中国发明专利,200710037579.9,2007
[2]交通部.2005年公路水路交通行业发展统计公报.http://www.moc.gov.cn
[3]上海国际港务(集团)公司课题组.上海港装卸工艺现状与发展趋势研究总报告.2006
[4]Verma Anand,Agarwal et al..Bulk materials handling for port applications-a review.Bulk Solids Handling,2005,25(1):34-37
[5]李浩涛.我国港口大宗干散货装卸工艺和设备的技术发展.中国港口,1999,(6):13-15
[6]Wadhwa,Lal C.Optimizing deployment of shiploaders at bulk export terminal.Journal of Waterway,Port,Coastal and Ocean Engineering,2000,126(6):297-304
[7]肖曙光,陈琦等.九十年代先进水平的港口散货装卸系统.港口装卸,2000,(6):1-5
[8]孙国正.面向21世纪的物流工程及物料搬运技术.中国机械工程学会物料搬运分会第六届年会论文集,2000:1-5
[9]宋伟刚.带式输送机的发展与评述.物料搬运与升离技术,1998,(2):5-8
[10]刘雪平.大型带式输送机及其控制驱动系统的发展趋势.煤矿机械,2000,(11):1-2
[11]张家锟.大型港口机械电控技术的新发展.起重运输机械,1993,(10):3-9
[12]Li Ze-Yi,Li Mei-Zhen,Cai Peng.Loading and unloading technology design for bulk cargo wharf in TGP reservoir area.People's Changjiang River,2003,34(1):17-18
[13]李士瀛,由清育.现代港口机械技术.北京:人民交通出版社,1995
[14]一兵译.港口起重机的控制技术及其发展动向(上).港口科技动态,1992,(8):18-23
[15]一兵译.港口起重机的控制技术及其发展动向(下).港口科技动态,1992,(9):24-31
[16]李德华.交流调速控制系统.北京:电子工业出版社,2003
[17]祝龙记,王汝琳.直接转矩控制在带式输送机电气传动中的应用研究.煤炭学报,2004,29(3):376-380
[18]尹慧民.CST软启动装置在大型带式输送机中的应用.煤炭科技,2002,(3):42-43
[19]上海国际港务(集团)公司.现代散货港口重大装备自动化技术开发.上海市引进技术吸收与创新计划项目申报书,2007
[20]Sawodny O,Aschemann H,Kumpel J et al..Anti-sway control for boom cranes.Proceedings of the American Control Conference,2002:244-249
[21]Osamu Itoh,Hirohisa Migita,Jun Itch.Application of fuzzy control to automatic crane operation.Proceedings of IECO'93 International Conference on Industrial Electronics,Control,and Instrumentation,1993:161-164
[22]钱学清.起重机电子控摇系统的研究.博士学位论文,上海:上海交通大学,1999
[23]肖鹏,王冰.模糊神经网络技术在防摇控制系统中的研究与应用.起重运输机械,2005, (11):21-23
[24]何衍庆.集散控制系统原理及应用.北京:化学工业出版社,2002
[25]张斌.分散控制系统在深圳妈湾发电总厂的应用.电力行业信息化2005年会论文集,大连,2005:428-431
[26]陈宇.秦皇岛港务集团公司煤五期装船机控制系统.河北省人工智能学会2005年会论文集,天津,2005:203-205
[27]上海国际港务(集团)公司.大型集装箱装卸设备远程监测系统项目申报书,2006
[28]刘斌.工业控制网在煤炭装卸港口的应用.起重运输机械,2006,(7):74-5
[29]黄有方,史建民,赵婉莹.大型港口设备的现代电控系统的远程诊断技术.上海海运学院学报,2000,21(4):44-49
[30]钱华明.故障诊断与容错技术及其在组合导航系统中的应用研究.博士学位论文,哈尔滨:哈尔滨工程大学,2004
[31]周东华,叶银忠.现代故障诊断与容错控制.北京:清华大学出版社,2000
[32]Jiang Jin.Fault-tolerant control systems-an introductory overview.Acta Automatica Sinica,2005,31(1):161-174
[33]Wu N Eva,Zhou Kemin,Salomon Gregory.Control reconfigurability of linear time-invariant systems.Automatica,2000,36(11):1767-1771
[34]Frank P M.Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-a survey and some new results.Automatica,1990,26(3):459-474
[35]郑君里.信号与系统.第二版.北京:高等教育出版社,2004
[36]Gross A,Morlet J.Decomposition of hardy functions into square integrable wavelets of constant shape.ALAM Journal of Math.,1984,15(2):723-736
[37]Beard R V.Failure accommodation in linear systems through self-reorganization:Dissertation.Man Vehicle Lab.MIT,Cambridge,Massachusetts,1971
[38]Mehra R K,Peschon J.An innovation approach to fault detection and diagnosis in dynamics.Automation,1971,7(6):637-640
[39]胡寿松.自动控制原理.北京:国防工业出版社,1998
[40]Isermann R.Process fault detection based on modeling and estimation methods-a survey.Automation,1984,20(3):387-404
[41]吴今培,肖健华.智能故障诊断与专家系统.北京:科学出版社,1997
[42]Doherty N F.Knowledge-based approaches to fault diagnosis:a practical evaluation of the relative merits of deep and shallow knowledge.Journal of Engineering Manufacture,1994,208(B1):39-45
[43]边肇祺等.模式识别.北京:清华大学出版社,2000
[44]Sunil Elanayar V T,Yung C Shin.State estimation of continuous-time radial basis function networks.Automatica,2000,36(3):399-407
[45]吴今培.模糊诊断理论及其应用.北京:科学出版社,1995
[46]Astrom K J.Intelligent control.Proceedings of the 1st European Control Conference,Grenoble,1991:2328-2329
[47]胡昌华.控制系统故障诊断与容错控制的分析和设计.北京:国防工业出版社,2000
[48]王仲生.智能故障诊断与容错控制.西安:西北工业大学出版社,2005
[49]王珍熙.可靠性、冗余及容错技术.北京:航空工业出版社,1991
[50]孙金生.动态大系统的分散容错控制.南京理工大学学报,1998,22(2):117-120
[51]Niederlinski A.A heuristic approach to the design of interacting multivariable systems.Automatic,1971,7(6):691-701
[52]Salijak D D.Reliable control using multiple control systems.International Journal of Control,1980,31(2):323-329
[53]藤召胜.智能检测系统与数据融合.北京:机械工业出版社,2000
[54]Monlin B.A multi-agent system supporting cooperating work done by persons and machines.IEEE International Conference on SMC,1991:1889-1993
[55]Jaege C A.Smart materials.Structures and Mathematical Issues,U.S.Army Research Office Workshop,1988:15-17
[56]Colins M.A universal framework for managed built-in test.International Test Conference,1993:21-29
[57]Hiroyuki.Fault tolerance for distributed process control system.Proceedings of 2002 SICE Annual Conference,2002:631-636.
[58]Kabore P,Wang H.Design of fault diagnosis filters and fault-tolerant control for a class of nonlinear systems.IEEE Transactions on Automatic Control,2001,46(11):1805-1810
[59]刘伟.基于专家系统的容错控制及在合成反应过程中的应用.化工自动化及仪表,1996,23(5):13-16
[60]宋国宁,蒋兴松.大型过程工业自动化全过程体系结构.信息与控制,1994,23(2):65-70
[61]何凤有,袁兴.矿井提升机容错控制技术的研究及其实现.煤矿机械,2006,27(10):75-76
[62]刘湘崇,梁彦.导弹控制系统的容错控制研究.控制与决策,2006,21(10):1185-1189
[63]张建华,史运涛.燃煤电厂协调控制系统故障诊断与容错控制应用研究.中国电机工程学报,2004,24(8):175-179
[64]上海市科技情报研究所.散货港口自动化运输系统容错控制水平检索报告.2003
[65]运输机械设计选用手册.北京:化学工业出版社,1999
[66]王玉兴.斗轮堆取料机变幅装置运动学、动力学建模和仿真研究.博士学位论文,吉林:吉林工业大学,1998
[67]赵丁选.轮式装载机倾翻稳定性及其仿真与监控.博士学位论文,吉林:吉林工业大学,1999
[68]Harrison A.Belt conveyor research 1980-2000.Bulk Solids Handling,2001,21(2):159-164
[69]宋伟刚,柳洪义.带式输送机动力学及其计算机仿真的研究.机械工程学报,2003,39(9):133-138
[70]宋伟刚,赵琛.带式输送机阻力随带速变化规律性的实验研究.煤矿机械,2001,(3):16-18
[71]交通部水运司.港口起重运输机械设计手册.北京:人民交通出版社,2001
[72]刘勤国.抓斗卸船机工作机构数学建模及仿真研究.硕士学位论文,吉林:吉林大学,2001
[73]Munz R E.Considerations for remotely monitoring and controlling conveyor belts in underground coal mines.Conference Record of the 1988 IEEE,Industry Applications Society Annual Meeting,1988:1243-1249
[74]任廷志,刘才.带式输送机多卷筒驱动最佳负荷分配.机械设计,2004,21(2):58-59
[75]郭永存.多电机传动带式输送机功率配比的研究.煤炭科学技术,2003,31(1):46-48
[76]张雪雁.含液力耦合器的带式输送机起动特性优化设计.河北理工学院学报,2003,25(3):36-39
[77]Ma Xian-Min.Genetic taboo hybrid strategy for PID regulator parameter adaptation in belt conveyor.Proceedings of Machine Learning and Cybernetics 2006 International Conference,2006:607-612
[78]Yusong Pang,Lodewijks G.Large-scale conveyor belt system maintenance decision-making by using fuzzy causal modeling.Proceedings of IEEE Conference on Intelligent Transportation Systems,2005:63-67
[79]汤蕴璎.电机学--机电能量转换.北京:机械工业出版社,1982
[80]蒋慰孙,叶银忠.多变量控制系统分析与设计.北京:中国石化出版社,1997
[81]王祥忠.开放型集散控制系统中的过程信息模型.中国科学技术大学学报,1998,28(5):562-566
[82]Jeinsch T.A robust model-based information system for monitoring and fault detection of large scale belt conveyor systems.Proceedings of the 4th World Congress on Intelligent Control and Automation,2002:3283-3287
[83]褚建新,顾伟.集散控制系统控制律的系统化建模方法.自动化仪表,2002,23(10):52-54
[84]王桂增.高等过程控制.北京:清华大学出版社,2002
[85]上海市科学技术奖励委员会.上海市科学技术进步奖授奖名册.2003
[86]上海市科技情报研究所.长距离输送系统动态监测与故障诊断技术水平检索报告.2007
[87]Harrison A.New technique for monitoring defects in underground steel cord belts.Proceedings of 21 st International Conference on Safety in Mine,1985:21-25
[88]黄民,魏任之.矿用钢绳芯带式输送机实时工况监测与故障诊断技术.煤炭学报,2005,30(2):246-251
[89]胡成.钢绳芯带式输送机实时工况监测与故障诊断系统.煤炭科学技术,2003,30(10):4-7
[90]李恩.钢绳芯带式输送机计算机实时工况监测与故障诊断系统.博士学位论文,徐州:中国矿业大学,2001
[91]陈岚,周晓谋.煤矿堆取料机自动控制系统的故障诊断设计.煤矿机械,2005,(2):119-121
[92]孟凡芹,王耀才.带式输送机打滑的模糊测量方法.煤矿机电,2000,(6):16-18
[93]孙新梅,孟凡芹.带式输送机运行故障智能诊断方法.煤炭科技,2004,(1):27-29
[94]Zheng Zhang,Xinyu Shao,Daoyuan Yu.Fault diagnosis of a wheel loader by artificial neural networks and fuzzy logic.Proceedings of 2006 IEEE Conference on Robotics,Automation and Mechatronics,2006:1-5
[95]温熙森,胡茑庆,邱静.模式识别与状态监控.长沙:国防科大出版社,1997
[96]沈爱弟等.差值运算的输送带失速检测装置.中国实用新型专利,200720067394.8,2007
[97]沈爱弟等.托辊式输送带带速检测传感器.中国实用新型专利,200720067393.3,2007
[98]胡慧萍.带式输送机胶带跑偏原因及纠编分析.矿山机械,2000,(9):35-37
[99]顾伟等.输送带跑偏信号检测装置.中国实用新型专利,200720067396.7,2007
[100]张贤达.现代信号处理.第2版.北京:清华大学出版社,2002
[101]Hughes P,Ip S F A,Cook J.Adaptive filters-a review of techniques.British Telecom Technology Journal,1992,10(1):28-48
[102]Hojjati M H,Lukasiewicz S A.Application of the adaptive matrix filter to inverse heat conduction problems.Journal of Thermal Stresses,2003,26(11):1171-1184
[103]Howell M N,Gordon T J.Continuous action reinforcement learning automata and their application to adaptive digital filter design.Engineering Applications of Artificial Intelligence,2001,14(5):549-561
[104]Sristi P,Lu W-S,Antoniou A.A new variable-step-size LMS algorithm and its application in subband adaptive filtering for echo cancellation.Proceedings of IEEE International Symposium on Circuits and Systems,2001:1721-1724
[105]Longhua,Mu Jiangzi.Application of adaptive filtering in harmonic analysis and detection.Proceedings of 2005 IEEE/PES Transmission and Distribution Conference,2005:1-4
[106]李富才,訾艳阳,何正嘉.自适应滤波与相关滤波在冲击响应信号特征提取中的应用.振动与冲击,2004,23(3):24-28
[107]李保安,李行善,罗先和.动态秤重系统计量误差的动态校正.仪器仪表学报,2001,22(3):251-254
[108]Tzes Anthony,Le Ke.Application of frequency domain adaptive infinite impulse response filtering for identification of flexible structure dynamics.Mechanical Systems & Signal Processing,1996,10(1):65-91
[109]陈章位,赵玉刚.基于参考模型的自适应环境噪声处理.噪声与振动控制,2003,(5):30-32
[110]Kazemi,Ahad,Sarlak et al.An adaptive noise canceling method for single-phase unified power quality conditioner.Proceedings of 2006 1st IEEE Conference on Industrial Electronics and Applications,2006:961-965
[111]Nakanishi,Isao,Itoh et al..Accelerated frequency domain adaptive filter using modified DFT pair and its application to noise canceling.Proceedings of IEEE International Symposium on Circuits and Systems,2000:361-354
[112]Mastorocostas Paris,Varsamis et al.A recurrent fuzzy filter for adaptive noise cancellation.Proceedings of International Conference on Computational Intelligence for Modeling,Control and Automation,2005:408-413
[113]褚建新,顾伟.具有自适应动载滤波与校零功能的起重机超载限制器原理分析.仪器仪表学报,1998,19(5):449-453
[114]褚建新,顾伟.一种新型多功能超载限制器的设计.自动化仪表,2000,21(1):22-24
[115]褚建新,顾伟.抑制大块分离物冲击力的一种自适应滤波方法.仪器仪表学报,2004,25(4):744-745
[116]顾伟,褚建新.基于动态模型自适应辨识原理的起重质量检测技术.机械工程学报,2006,42(1):187-190
[117]顾伟,褚建新等.料斗大块分离重力式自动监测装置.中国实用新型专利,02260942.3,2002
[118]顾伟,褚建新等.散货港口大块分离物堵塞报警信号处理电路.中国实用新型专利,03232272.0,2003
[119]Yang In-Young,Im Kwang-Hee,Park Je-Woung.On impact characteristic evaluation in CF/Epoxy composites under falling-weight impact loadings.Proceedings of the 3rd International Symposium on Designing,Processing and Properties of Advanced Engineering Materials,2004:749-752
[120]向红,汤伯森.L-P摄动法在跌落冲击问题中的应用.振动与冲击,2002,21(1):39-43
[121]杨其新,关宝树.落石冲击力计算方法的试验研究.铁道学报,1996,18(1):101-106
[122]Nakatsutsumi.Experimental study of impact force due to free-fall sand.Transactions of the Japan Society of Civil Engineers,1984,31(14):288-290
[123]Jianxin Chu,Wei Gu.Adaptive dynamic detection of separated bulk matter in conveyer system.IEEE Trans.Instrument & Measurement,2006,55(3):809-813
[124]Jianxin Chu,Wei Gu.Self-adaptive dynamic detection of bulk matter based on reference model.Proceedings of ICEMI'05,2005:316-319
[125]Ramakrishna G,Malik O P.Radial basis function identifier and pole-shifting controller for power system stabilizer application.IEEE Transactions on Energy Conversion,2004,19(4):663-670
[126]Moravej Z,Vishwakarrna D N,Singh S P.Application of radial basis function neural network for differential relaying of a power transformer.Computers and Electrical Engineering,2003,29(3):421-434
[127]Zayed Ali S,Hussain Amir,Abdullah Rudwan A.A novel multiple-controller incorporating a radial basis function neural network based generalized learning model.Neurocomputing,2006,69(16):1868-1881
[128]Yamauchi Koichiro,Hayami Jiro.Incremental leaning and model selection for radial basis function network through sleep.IEICE Transactions on Information and Systems,2007,E90-D(4):722-735
[129]Yang Zheng Rong.A novel radial basis function neural network for discriminant analysis.IEEE Transactions on Neural Networks,2006,17(3):604-612
[130]Roussos George,Baxter Brad J C.Rapid evaluation of radial basis functions.Journal of Computational and Applied Mathematics,2005,180(1):51-70
[131]Samantaray S R,Dash P K,Panda G.Fault classification and location using HS-transform and radial basis function neural network.Electric Power Systems Research,2006,76(9):897-905
[132]Sarimveis Haralambos,Doganis Philip,Alexandridis Alex.A classification technique based on radial basis function neural networks.Advances in Engineering Software,2006,37(4):218-221
[133]Mali Kalyani,Mitra Sushmita.Symbolic classification,clustering and fuzzy radial basis function network.Fuzzy Sets and Systems,2005,152(3):553-564
[134]Karayiannis Nicolaos B,Xiong Yaohua.Training reformulated radial basis function neural networks capable of identifying uncertainty in data classification.IEEE Transactions on Neural Networks,2006,17(5):1222-1233
[135]Babu G P.Clustering with evolution strategies.Pattern Recognition,1994,27(2):321-329
[136]Konhonen T.Self-organization maps.Springer Series in Information Sciences,1995,(30):174-188
[137]褚建新,顾伟等.输送带自动纠偏装置和方法.中国发明专利,200710037579.9,2007