CN1260626C - 受控过程中的偏差的根本原因诊断 - Google Patents
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Abstract
一种工业过程诊断设备(100),可以确定工业过程偏差的根源或者“根本原因”。其中,设置了多个配置模型(112),每个配置模型都表示一个工业过程的物理(实际)实现。选择多个模型之一并且使用选择的模型和至少一个与该过程相关的过程信号来进行诊断。在上述诊断的基础上确定偏差的根本原因。
Description
技术领域
本发明涉及工业过程控制和过程控制环,尤其是涉及这种过程控制环的诊断。
背景技术
过程控制环用于加工工业例如炼油厂中,以便控制过程的操作。发送器(transmitter)是控制环的一个典型部分,并且位于现场,以便测量和发送如压力、流量或温度那样的过程变量给控制室设备。如阀控制器之类的控制器也是过程控制环的一部分,它根据在控制环上接收的或在内部产生的控制信号,控制阀的位置。其它的控制器控制例如电动机或螺线管。控制室设备也是过程控制环的一部分,以便控制室内的操作者或计算机能够根据从现场的发送器接收的过程变量监测过程,以及作为响应通过发送控制信号给适当的控制设备来控制过程。另一种可能是控制环一部分的过程设备是能够在过程控制环上监测和发送信号的便携式通信装置。典型地,这些部分用于配置形成控制环的设备。
已有各种技术用于监测过程控制环的操作以及诊断和识别控制环中的故障。然而,还期望例如通过识别系统中的作为过程操作偏差(aberration)来源的特殊设备或部件,来确定故障的来源或“根本原因(rootcause)”。这将向操作者提供关于过程中的哪个设备需要修理或替换的附加信息。
发明内容
在本发明的各个方面,提供一种能够确定工业过程中偏差的来源或“根本原因”的工业过程诊断设备。在本发明的一个方面,该设备包括多个过程配置模型,每个模型与工业过程的物理(或实际)实现相关。可以选择多个模型之一,并且可以利用选择的模型以及与过程相关的至少一个过程信号来执行诊断。根据诊断,确定偏差的根本原因。
附图说明
图1所示的简图显示了一个包括发送器、控制器、手持通信装置和控制室的过程控制环。
图2所示的示意图显示了液位控制环的过程控制环模型。
图3所示的示意图显示了流速控制环的过程控制环模型。
图4所示的框图显示了用于实现本发明一个实施例的设备。
图5所示的框图显示了图4的一个硬件实现例子。
具体实施方式
本发明可以用于工业过程,以便确定在过程中出现的偏差的“根本原因”。图1所示的简图显示了用于控制过程液体流量的工业过程控制系统2的例子,该控制系统2包括用于传送过程液体的过程管道4以及用于传送回路电流I的两线过程控制环6。过程控制系统2包括以下部分:发送器8;控制器10,其连接到控制环中的最终控制元件例如致动器、阀、泵、电动机或螺线管;通信装置12;以及控制室14。如果在过程操作中出现了偏差,本发明可以用于确定观察到的偏差的原因。
结构中显示的控制环6是用于说明目的,可以使用任何合适的过程控制环,例如4-20mA环,2、3或4线环,多点环(multi-drop loop),以及按照HART、现场总线或者其它数字或模拟通信协议操作的环。在操作中,发送器8利用传感器16检测过程变量例如流量,并在环6上发送检测到的过程变量。可以通过控制器/阀致动器10、通信装置12和/或控制室设备14接收过程变量。控制器10与阀18连接,并且能够通过调节阀18来控制过程,由此改变管道4中的流量。控制器10接收环6上的、来自例如控制室14、发送器8或通信装置12的控制输入,并相应地调节阀18。在另一个实施例中,控制器10根据在环6上接收的过程信号,在内部产生控制信号。通信装置12可以是图1所示的便携式通信装置,或者是被固定安装的、用于监视过程和执行计算的过程单元。过程设备包括,例如图1所示的发送器8(例如可从Rosemount公司得到的3095发送器)、控制器10、通信装置12以及控制室14。另一种过程设备是个人计算机(PC)、可编程逻辑单元(PLC)或其它计算机,它们利用合适的I/O电路连接到控制环,以允许在环上进行监测、管理和/或发送。
图2所示的简图50显示了用于控制罐52中的液位的过程控制环50的图形模型。如下所讨论的,可以选择并利用这种模型去诊断过程操作中偏差的根本原因。液位发送器54测量罐52中的液位,并提供一个基本过程变量(PV)给控制器56。图2所示的控制器56是一个比例积分微分(PID)控制器,然而,控制器56可以是任何类型的控制器。控制器56还接收一个与罐52内的期望液位相关的设定点(SP)。控制器56利用已知的控制算法,提供控制需求量(CD)输出给阀58。可选的阀位置传感器60可以用于测量阀58的阀杆的实际位置。该特殊的示例模型的其它可选部件包括:被配置用于把液体从罐52中抽出的泵62,被配置用于测量入口流速的发送器64,以及被配置用于测量出口流速的发送器66。如下所述,可以将模型和模型的可选部件存储在存储器中,并且可以通过操作者或其它选择技术来选择它们。在本发明的各个方面,存储器可以位于连接到过程或可使用过程信号的任何设备,或者可以被该任何设备存取。
优选地,在过程操作已经稳定并且处于稳态模式之后,对过程控制系统执行本发明的诊断。通过观测过程信号的平均值和标准偏差来保证这一点。对于每个过程信号(例如过程变量和控制信号),计算其一组N个测量的平均值(μ)和标准偏差(∑),平均值和标准偏差的计算如下:
点数N取决于信号的持续时间和采样率。在公式(1)和(2)中,Xi是在采样号i处取的过程信号值。最初,10分钟的采样周期可以和每秒一个样本的采样率一起使用。在一个例子中,如果过程平均值为100英寸水(inH2O)(标准偏差为1inH2O),并且随后的过程平均值在97inH2O与103inH2O之间,则判定控制环正工作在稳态模式。与启动诊断之前进行过程稳定性判断相关的一个专利是2000年9月12日授权的美国专利No.6,119,047,其在此整个被引入作为参考。
一旦达到稳态操作,还希望丢弃数据瞬变或尖峰信号。一种识别这种数据的技术是连续地比较信号均值与信号标准偏差。两个连续数据块的均值(μ1和μ2)之间的差应该小于标准偏差除以样本数N的平方根。可以表示如下:
其中μ是前一个数据块的均值,μ2是当前数据块的均值,N是数据块中的点数,σ1是前一个数据块的标准偏差。
取决于可用于执行诊断并且和模型一起使用的过程信号,可以确定不同的根本原因。例如,在图2所示的过程模型的情况下,有三种不同的情况:
情况 | 可得的信号 | 监测到的故障 |
1 | SPPVCD | 液位传感器漂移阀问题 |
2 | SPPVCDVP | 液位传感器漂移阀问题 |
3 | SPPVCDVPIFOF | 液位传感器漂移阀问题液体泄漏 |
表1
在初始训练阶段,在用户可选的持续时间例如20分钟内获取所有的过程信号。计算信号的均值和标准偏差。重复该训练阶段,直到过程进入稳态为止。一旦过程处于稳态,就存储每个过程信号的均值(μt)和标准偏差(σt)的训练值(即“标称值”)。
另外,在确定根本原因故障之前,可以估计单个过程信号,以确保过程正在正确地操作。例如,可以计算基本过程变量(PV)。在图2说明的液位的情况下:
状态 | 故障 |
PV>0.95*PV_RANGE | 液位高(罐溢出) |
PV<0.05*PV_RANGE | 液位低(罐干了) |
表2
其中PV_RANGE是液位的范围(最大值和最小值)。可以在配置过程控制系统的时候将PV_RANGE值存储在可以被过程控制系统存取的存储器中,或者可以由用户输入PV_RANGE。类似地,对于控制信号(CD),可以确定以下的故障:
状态 | 故障 |
CD<5% | 控制松弛 |
CD>95% | 控制过紧 |
表3
在表3的例子中,设控制需求量是一个在0与100之间的百分数。如果可以的话,可以对阀位置(VP)过程信号执行类似的测试。
在监测阶段,对各种过程信号进行监测,以确定它们是经历了无变化(NC)、向上偏差(U)(均值信号大于训练均值)还是向下偏差(D)(均值信号小于训练均值)。如果
则判定NC状态,其中μt为训练数据块的均值,μ为当前数据块的均值,N为数据块中的点数,σt为训练数据块的标准偏差,μt和∑t分别为在训练阶段存储的过程信号的均值和标准偏差。N为样本数,μ为过程信号的当前均值。
如果
则判定向上偏差(U)状态,其中μt为训练数据块的均值,μ为当前数据块的均值,N为数据块中的点数,σt为训练数据块的标准偏差。
最后,如果
则判定向下偏差(D)状态,其中μt为训练数据块的均值,μ为当前数据块的均值,N为数据块中的点数,σt为训练数据块的标准偏差。
取决于可得的过程信号的数量,可以确定不同的根本原因作为过程偏差的来源。例如,如果可以获得设定点、基本过程变量和控制需求量过程信号,则可以确定液位传感器漂移或阀相关问题。表4给出了一个示例规则库:
信号 | 故障 |
液位传感器漂移或阀问题 | |
SP | NC |
PV | NC |
CD | U或D |
表4
如果可以获得附加的过程信号—实际的阀位置(VP),则可以更好地确定根本原因,如表5给出的规则库所示:
信号 | 故障 | |
液位传感器漂移 | 阀问题 | |
SP | NC | NC |
PV | NC | NC |
CD | U或D | U或D |
VP | U或D | NC |
表5
最后,如果可以得到流入速率(IF)和流出速率(OF)过程信号,还有可能确定罐52是否存在泄漏,如表6给出的规则库所示:
信号 | 故障 | ||
液位传感器漂移 | 阀问题 | 液体泄漏 | |
SP | NC | NC | NC |
PV | NC | NC | NC |
CD | U或D | U或D | D |
VP | U或D | NC | D |
IF | NC | NC | NC |
OF | NC | NC | D |
表6
如果过程信号的变化与表4、5和6中给出的任何规则都不匹配,则可提供一个未知的故障输出。而且,如果包括泵62,或者过程50根据用于排干罐52的压差进行操作,这些规则适用。
图3所示的简图100显示了用于控制流速的过程控制环的图形模型。图3说明了另一个示例过程控制环。在图3中,罐102(或者泵103或其它的差压源)可以提供过程液体的流动。发送器104检测流速,并提供基本过程变量(流速)给控制器106。控制器106还接收一个设定点(SP),并提供控制需求量(CD)信号给阀108。阀108可以可选地反向告知其实际的阀杆位置(VP)。附加的选项包括用于检测过程压力(PT)的压力发送器110,以及用于检测过剩流速(FT2)的过剩流量发送器112。
在操作中,在训练阶段以一种类似于按照图2所述的方式,并且如以上公式(1)和(2)所给出的,来确定均值和标准偏差。然而,因为流速控制通常响应得较快,因此可以使用更短的学习时间,例如两分钟。
取决于可得的不同过程信号的数量,可以确定许多不同的根本原因,如表7给出的规则库所示:
情况 | 可获得的信号 | 监测到的故障 |
1 | SPPVCD | 流量传感器漂移阀问题 |
2 | SPPVCDVP | 流量传感器漂移阀问题 |
3 | SPPVCDVPFT2 | 流量传感器漂移阀问题液体泄漏 |
表7
在确定根本原因之前,可以检查基本故障。例如,使用表8中的规则库:
状态 | 故障 |
PT为D | 落差(head)损失 |
表8
进一步,可以如下确定阀的条件:
状态 | 故障 |
CD<5% | 控制过紧 |
CD>95% | 控制松弛 |
表9
利用附加的过程变量可以确定过程偏差的“根本原因”。当可以获得设定点、基本过程变量和控制需求量信号时,可以确定流量传感器漂移或阀问题作为过程偏差的根本原因,如下表10所示:
信号 | 故障 |
流量传感器漂移或阀问题 | |
SP | NC |
PV | NC |
CD | U或D |
表10
如果可获得附加的过程信号—实际的阀位置(VP),则可以确定根本原因为流量传感器漂移或阀问题,如下表11所示:
信号 | 故障 | |
流量传感器漂移 | 阀问题 | |
SP | NC | NC |
PV | NC | NC |
CD | U或D | U或D |
VP | U或D | NC |
表11
最后,如果过剩流量发送器用于测量第二(secondary)流速变量(FT2),则还可以确定过程中的泄漏:
信号 | 故障 | ||
液位传感器漂移 | 阀问题 | 液体泄漏 | |
SP | NC | NC | NC |
PV | NC | NC | NC |
CD | U或D | U或D | D |
VP | U或D | NC | D |
FT2信号 | U或D | NC | D |
表12
图4所示的框图显示了用于实现本发明一个实施例的过程设备100。过程设备100包括一个根本原因分析模块102,该分析模块102通过控制信号输入104接收控制信号CD,通过过程变量输入106接收过程变量PV,以及通过设定点输入108接收设定点SP。取决于可获得的附加过程信号的数量,可以通过其它的输入例如过程信号输入110、111等,接收附加过程信号(PS1,PS2...)。
根本原因分析模块102还连接到多种过程配置模型112。模型112可以被存储在例如系统存储器中。在所说明的实施例中,共有X个与可能的过程控制配置对应的不同模型。在该实施例中,每个模型都包括用于提供过程的图解说明的图形模型GM1,...GMX。这可用于提供图形用户界面,以方便操作者输入配置数据。例如,图形模型可以类似于图2和图3所示的图。
每个过程模型可以接收任何数量的过程信号(PS1A、PS1B等等)。在图2和图3所示的特定例子中,有确定过程偏差所需的最少三个过程信号,分别为控制需求量CD、基本过程变量PV和设定点SP。在一个实施例中,与模型相关的过程信号是执行根本原因分析所需的最少过程信号,或者是所希望的更多一些过程信号。
接下来,每个模型可以包含任何数量的可选过程信号(OP1A,OP1B...)。每个可选的过程信号对应于通过输入110、111等接收的过程信号(PS1,PS2...)。在图2的例子中,阀位置VP、流入速率IF和流出速率OF是这种可选过程信号的例子。一些模型可以被配置成没有附加的可选过程信号。
接下来,每种模型都包括任何数量的、用于根据接收的过程信号(所需的最少过程信号PS1A、PS1B、...以及任何可选的过程信号OP1A、OP1B、...)确定根本原因的规则库(RB1A、RB1B、...)。以上讨论的表4、5、6、10、11和12中显示了规则库的例子。注意,本发明不限于上述的规则库在执行根本原因分析中的特殊应用。在本发明一个方面,可以使用任何分析技术,包括神经网络、其它规则库、回归学习、模糊逻辑以及其它已知的诊断技术或仍然有待发现的技术。对于在此给出的例子,有最少三个被接收的过程信号,分别为控制需求量CD信号、基本过程变量PV信号和设定点SP信号。然而,可以利用其它过程信号、更少的信号或不同信号组合,来执行根本原因分析。
根本原因分析模块102接收用于选择多种模型112之一的模型选择输入116。模型选择输入116可以来自操作者或来自其它来源。模型选择输入116确定多种模型112之一,供根本原因分析模块102随后使用。另外,在一个例子中,可以选择附加的可选过程(OP)信号与选择的模型一起使用。如果使用了图形用户界面,则模型可以包括可被显示在显示输出118上、并用于配置模型的图形模型。例如,可以利用模型选择输入116把特殊的过程信号分配给过程信号(PS1A、PS1B、...)之一,或者与选择的模型相关的可选过程信号(OP1A、OP1B、...)之一。可以以图解形式说明这种分配。
一旦选定了模型,就把模型规则库使用的过程信号分配给从过程接收的实际过程信号。根本原因分析模块102能够利用任何期望的技术例如上述那些技术,执行根本原因分析。根据根本原因分析,提供根本原因输出120,作为在过程中发生的事件的偏差的根本原因的指示。
根据本发明一个实施例,图5所示的框图显示了过程设备100的物理实现。在图5所示的例子中,设备100通过输入/输出134连接到过程控制环132。控制环132可以是例如图1所示的两线环或其它过程控制环。而且,连接不必是直接连接,而可以只是一个逻辑连接,在该逻辑连接中来自控制环的变量通过逻辑输入/输出134被接收。微处理器136连接到存储器138和图形用户界面140。存储器138可以用于存储变量和编程指令,以及图4所示的模型112。
图形用户界面140提供用于接收模型选择输入116的输入,以及图4的、供模型选择和配置期间使用的显示输出118。微处理器136还可连接到一个可选数据库142,该可选数据库142可以包含与正被监测的过程的配置和操作相关的信息。例如,许多过程控制或监测系统包含这种数据库。一个例子是可以从Minnestor(明尼苏达)的Eden Prairie,Rosemount公司得到的AMS系统。
应该理解,可以在任何过程设备例如图1所示的发送器、控制器、手持通信装置或控制室计算机中,实现根本原因过程设备100。在本发明一个实施例中,过程设备100将在位于控制室内或其它远处的计算机系统或PC上操作。过程控制环132将典型地包括某种类型的基于现场总线的环或多控制环。在这样一种配置中,对于选择的模型,过程设备100可以向连接到控制环的各种设备轮询预期的过程信号。虽然显示了图形用户界面140,但是可以利用任何选择技术选择模型,并且不必由操作员来选择和配置模型。例如,根据通过其它技术提供的、存储在另一个地方的配置信息,设备100可以接收合适的规则库和任何模型选项。作为选择,例如可以在现场实现根本原因过程设备100,并驻留在发送器中。
如在此所使用的,过程变量典型地是在过程中正被控制的基本变量。如在此所使用的,过程变量表示任何描述过程状态的变量,例如压力、流量、温度、产品级、pH、浊度、振动、位置、电动机电流以及其它任何过程特征等等。控制信号表示任何用于控制过程的信号(除了过程变量之外)。例如,控制信号表示通过控制器调节的或用于控制过程的期望过程变量值(即设定点),例如期望的温度、压力、流量、产品级、pH或浊度等等。另外,控制信号表示校准值、报警信号、报警条件、被提供给控制元件的信号例如被提供给阀致动器的阀位置信号、被提供给加热元件的能级,以及螺线管开/关信号等等,或者其它任何与过程控制相关的信号。如在此所使用的诊断信号包括与过程控制环中的设备和元件的操作相关的信息,但是不包括过程变量或控制信号。例如,诊断信号包括阀杆位置、施加的扭矩或力、致动器压力、用于起动阀的增压气体的压力、电压、电流、功率、电阻、电容、电感、设备温度、静摩擦、摩擦力、全开和全关位置、行程、频率、幅度、谱和谱分量、刚度、电场或磁场强度、持续时间、强度、运动、电动机反电动势(emf)、电动机电流、环相关参数(例如控制环电阻、电压或电流),或者其它任何可以在系统中检测或测量的参数。而且,过程信号表示任何与过程或过程中的元件相关的信号,例如过程变量、控制信号或诊断信号。过程设备包括任何形成过程控制环一部分或连接到过程控制环的设备,并且用于控制或监测过程。
虽然已参照优选实施例说明了本发明,但是本领域的工作人员应该认识到,可以不背离本发明的精神和范围在形式和细节上改变本发明。虽然该说明书中显示了两个示例过程和示例模型,但是本发明适用于其它过程配置,并且可以利用已知的技术或将来发现的技术来产生模型。另外,其它类型的规则库或模型配置可以和本发明一起使用。本发明可以在一个独立设备中被实现,或者可以是一个被添加到用于控制或监测工业过程的软件上的软件模块。在本发明一个方面,本发明包括用于实现本发明的计算机指令和/或存储介质。如在此所使用的,“过程模型”是过程的任何逻辑表示,并且不限于在此所述的特定例子。“根本原因”是过程操作的变化或偏差的初始原因。其它可以被模拟的过程控制环包括,但是不限于,流量控制、液位控制、温度控制等等,包括气态、液态、固态或其它形式加工材料的调节器控制和级联控制。控制环的特定例子包括,例如利用差压驱动的阀的流量控制环,利用差压驱动的阀的液位控制环,对流量调节控制的温度调节控制,对从动阀式泵的液位调节控制,利用泵驱动的阀的流量控制,对阀冷却器冷凝器的液位调节控制,对流量调节控制级联馈给的液位调节控制,对阀的液体温度调节控制,对流量调节控制的液体温度调节控制,利用差压驱动的阀的气流控制,对阀的气体温度调节控制,对流量调节控制的气体压力调节控制,对流量调节控制级联重沸器的液位调节控制,对阀的液体压力调节控制,以及对阀重沸器的液位调节控制。受控的各种类型过程元件包括,例如鼓和罐、热交换器、塔台、蒸气系统、冷凝器、锅炉、反应器,以及加热器、压缩机、燃油系统、涡轮机和废气处理系统。
Claims (32)
1.一种工业过程诊断设备,用于确定工业过程偏差的根本原因,该设备包括:
多个过程模型,每个模型与工业过程的物理实现相关;
模型选择输入,其被配置成用于接收选择的模型,该选择的模型唯一确定过程模型之一;
过程信号输入,其被配置成用于接收与过程相关的多个过程信号;以及
根本原因输出,其指示过程偏差的根本原因,该根本原因输出随选择的模型和过程信号而变。
2.根据权利要求1所述的设备,包括模型选项输入,该模型选项输入被配置成用于接收与选择的模型中的可选设备相关的模型选项,其中根本原因输出还随模型选项而变。
3.根据权利要求2所述的设备,其中模型选项包括可选过程信号。
4.根据权利要求1所述的设备,其中每个模型包括规则库。
5.根据权利要求4所述的设备,其中规则库提供过程信号与过程偏差的根本原因之间的关系。
6.根据权利要求4所述的设备,其中每个模型包括多个规则库,每个规则库与过程信号的数量有关。
7.根据权利要求1所述的设备,其中该设备在个人计算机中实现。
8.根据权利要求1所述的设备,其中该设备在过程设备中实现。
9.根据权利要求8所述的设备,其中过程设备包括发送器。
10.根据权利要求8所述的设备,其中过程设备包括控制器。
11.根据权利要求1所述的设备,其中模型包括用于提供过程的物理实现的图解表示的图形模型。
12.根据权利要求1所述的设备,其中多个过程信号包括基本过程变量(PV)、控制需求量(CD)信号以及设定点(SP)。
13.根据权利要求12所述的设备,其中多个过程信号还包括指示被提供用于响应控制需求量的实际控制值的过程信号。
14.根据权利要求10所述的设备,其中多个过程信号还包括过剩基本过程变量。
15.根据权利要求1所述的设备,其中多种过程模型至少之一表示液位过程控制环。
16.根据权利要求1所述的设备,其中多个过程模型至少之一表示过程液流控制环。
17.一种用于确定工业过程偏差的根本原因的工业过程诊断方法,该诊断方法包括:
从多个过程模型中选择过程模型,每个模型与工业过程的物理实现相关,选择的模型唯一确定过程模型之一;
接收与过程相关的多个过程信号;以及
确定指示过程偏差来源的根本原因,所述对根本原因的确定随选择的模型和过程信号而变。
18.根据权利要求17所述的方法,其包括接收与选择的模型中的可选设备相关的模型选项,其中确定根本原因还随模型选项而变。
19.根据权利要求18所述的方法,其中模型选项包括可选过程信号。
20.根据权利要求17所述的方法,其中每个模型包括规则库。
21.根据权利要求20所述的方法,其中规则库提供过程信号与过程偏差的根本原因之间的关系。
22.根据权利要求20所述的方法,其中每个模型包括多个规则库,每个规则库与过程信号的数量有关。
23.用于实现根据权利要求17所述方法的个人计算机。
24.用于实现根据权利要求17所述方法的过程设备。
25.根据权利要求17所述的方法,其中模型包括图形模型,且该方法包括显示过程的物理实现的图解表示。
26.根据权利要求17所述的方法,其中多个过程信号包括基本过程变量(PV)、控制需求(CD)量以及设定点(SP)。
27.根据权利要求26所述的方法,其中多个过程信号还包括指示被提供用于响应控制需求量的实际控制值的过程信号。
28.根据权利要求26所述的方法,其中多个过程信号还包括过剩基本过程变量。
29.根据权利要求17所述的方法,其中多个过程模型至少之一表示液位过程控制环。
30.根据权利要求17所述的方法,其中多个过程模型至少之一表示过程液流控制环。
31.一种被配置成实现权利要求1所述方法的过程设备。
32.一种工业过程诊断设备,用于确定工业过程偏差的根本原因,该设备包括:
用于存储多个过程模型的装置,每个过程模型与工业过程的物理实现相关;
用于接收唯一确定过程模型之一的模型选择输入的装置;
用于接收与过程相关的多个过程信号的装置;以及
用于确定根本原因的装置,该根本原因指示过程偏差的来源,并且随选择的模型和过程信号而变。
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WO2003032100A1 (en) | 2003-04-17 |
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EP1436678A1 (en) | 2004-07-14 |
CN1564971A (zh) | 2005-01-12 |
US20020038156A1 (en) | 2002-03-28 |
JP4635167B2 (ja) | 2011-02-16 |
JP2008269640A (ja) | 2008-11-06 |
DE60226757D1 (de) | 2008-07-03 |
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