CN101243366A - 具有诊断的过程变量变送器 - Google Patents
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Abstract
提供了一种具有诊断的过程变量变送器(60,82,230,232,234,238),该诊断基于过程变量传感器信号的功率谱密度(PSD)分析。在一个实施例中,该过程变量变送器(60,82,230,232,234,238)是压力变送器(82),而且诊断用于诊断导压管线阻塞或迫近阻塞。其它诊断方法也是有用的,例如诊断主元件的恶化。对传感器信号进行数字化,并且把数字化的信号变换为频域。检查传感器信号的频率处的功率以提供改进的诊断。在一个方面,直接利用传感器PSD数据来产生诊断。在另一方面,使用PSD分析来调谐滤波器,以改进传统的诊断算法。
Description
背景技术
过程变量变送器用于工业过程环境并耦合到过程流体,而且提供关于过程的测量。过程变量变送器能够被配置以监控与加工厂中的流体相关的一个或更多个过程变量,诸如化学、纸浆、石油、医药品、食品和其它流体处理工厂中的泥浆、液体、蒸汽、气体。被监控的过程变量可以是流体的压力、温度、流量、水平、pH、传导率、浊度、浓度、密度、化学组成或其它属性。取决于加工厂的安装需要,过程变量变送器包括能够包括变送器内部或外部的一个或更多个传感器。过程变量变送器产生表示所感测的过程变量的一个或更多个变送器输出。变送器输出被配置为经由通信总线242长距离地传输到控制器或指示器。在典型的流体加工厂中,通信总线242可以是为变送器供电的4-20mA电流环路,或是面向控制器、控制系统或读出装置的现场总线连接、HART协议通信或光纤连接。在由2线环路供电的变送器中,电力必须保持较低以提供在爆燃性空气中的固有安全性。
一种类型的过程变量变送器被称作压力变送器。典型地,压力变送器将通过导压管线(impulse line)耦合到过程流体。如果导压管线的一条或两条发生阻塞,则压力变送器的操作容易发生恶化。
拆卸和检查导压管线是一种用于检测和纠正管线阻塞的方法。另一种用于检测阻塞的已知方法是周期地将“检验脉冲”加到来自压力变送器的测量信号。这个检验脉冲导致与变送器相连的控制系统干扰流。如果压力变送器未能精确感测流干扰,则产生指示管线阻塞的报警信号。另一用于检测阻塞的已知方法是感测静压和差压。如果静压和差压的摆动之间的相关性不足,则产生指示管线阻塞的报警信号。另一用于检测管线阻塞的已知方法是感测静压,并使它们经过高通和低通滤波器。把从滤波器获得的噪声信号与阀值进行比较,并且如果噪声变化小于阀值,则报警信号指示线路被阻塞。
这些已知方法使用了会增加设备复杂性并减小设备可靠性的技术。此外,虽然这些方法有时能够检测阻塞的导压管线,但它们通常无法检测沉积物何时开始在导压管线内集中但还不致阻塞导压管线。因此,即使压力变送器感测压力的能力在一定程度上被折衷,操作仍可能继续。因此,存在对提供更多预测性、更少反应性维护以减小成本或提高可靠性的更好的诊断技术的需要。
发明内容
提供了一种具有诊断的过程变量变送器,基于过程变量传感器信号的功率谱密度(PSD)分析。在一个实施例中,所述过程变量变送器是压力变送器,并且所述诊断用于诊断导压管线的阻塞或即将发生的阻塞。其他的诊断也是有用的,例如诊断主元件的恶化。对所述传感器信号进行数字化,并且把数字化后的信号转换到频域。检查传感器信号的频率处的功率,以提供增强的诊断。在一个方面,直接利用传感器PSD数据产生诊断。在另一方面,PSD分析用于调谐滤波器,以增强传统的诊断算法。
附图说明
图1是诊断压力变送器的典型流体处理环境的图示。
图2是用于诊断其导压管线和/或主元件的情况的流体流量计中的差压变送器的实施例的图示。
图3是根据本发明实施例的提供诊断的流体流量计的框图。
图4-6是示出指示导压管线阻塞的传感器数据的PSD分析的曲线图。
图7是根据本发明实施例的用于训练过程变量变送器基于PSD的诊断的方法的流程图。
图8是根据本发明实施例的基于PSD分析来选择数字滤波器的特性的方法的流程图。
图9是根据本发明实施例的执行基于PSD的诊断的方法的流程图。
图10是过程变量信号的幅度与频率和时间的关系的曲线图。
图11是离散小波变换的框图。
图12是示出从离散小波变换输出的信号的曲线图。
具体实施方式
通常,本发明的实施例执行频谱分析以产生关于过程变量变送器的诊断信息。这种分析被描述为在过程变量变送器内的微处理器系统内发生,但能够由任何适合的处理系统执行。该处理系统88能够执行小波变换、离散小波变换、傅立叶变换,或使用其它技术以确定传感器信号的频谱。通过监控这个转换后的信号随时间的变化来确定分布的频率处的功率。其一个实例是功率谱密度(PSD)。功率谱密度可被定义为时间序列的功率(或方差),并能够被描述为时间序列的功率(或方差)如何随频率而分布。例如,这能够被定义为时间序列的自相关序列的傅立叶变换。功率谱密度的另一定义是由适合的常数项进行定标(scale)的时间序列的傅立叶变换的平方模(squared modulus)。
在图1中,在220处示出了用于诊断流或压力测量的典型环境。诸如流量计230、罐236上的水平(压力)变送器232和234、整体式管口流量计238的过程变量变送器被示出为与控制系统240相连。
在图1中,整体式管口流量计238设有诊断输出,该输出沿着与之相连的通信总线242而耦合。控制系统240能够被编制为向操作员显示诊断输出,或能够被编制为当存在来自流量计238的诊断警告时更改其操作。控制系统240控制诸如控制阀244、泵电机或其它控制设备的输出设备的操作。
在图2中,大体示出了根据本发明的典型诊断变送器82的分解图。变送器82包括:用于承受差压的凸缘83;差压传感器31;电子装置,包括模数转换器84、微处理器系统88、数模转换器96以及数字通信电路100。变送器82被拧到凸缘适配器87。在这里显示的实施例中,传感器31能够包括绝对压力传感器、计量(gage)传感器、差压传感器或其它类型的压力传感器。本发明的实施例可用于许多应用中,但在过程设备经由导压管道耦合到过程的情况下尤其有利。微处理器88被编程有诊断算法,这将在下面更详细的说明。凸缘适配器87连接到导压管道,而导压管道又连接到主要的流元件周围的流(未在图2中示出)。图2中变送器82的布置在图3中进行了更详细的说明。
图3是示出适合感测管24中的流体流22的流体流量计60的框图。流体流量计60包括压力发生器26,压力发生器26包括主元件28和导压管线30,导压管线30将围绕主元件28的流体流中产生的压力耦合到压力变送器82中的差压传感器31。在本申请中使用的术语″压力发生器″是指主元件(例如,管孔盘、空速管平均空速管管道系统、喷嘴、文氏管、梭口杆(shedding bar)、管中的弯曲或适于引起流中的压降的其它流间断)和把来自主元件附近位置的压降耦合到流管之外的位置的导压管或导压通路。在面向所连接的压力变送器82的流管之外的位置处,由这个所定义的“压力发生器”表示的该压力的频谱特性会受到主元件的情况以及导压管的情况的影响。所连接的压力变送器82可以是自包含单元,或它能够根据需要与远程密封(remote seal)相适合以适于应用。压力变送器82(或其远程密封)上的凸缘83耦合到导压管线30上的凸缘适配器87,以完成压力的连接。压力变送器82经由导压管线30耦合到主流元件28以感测流。压力变送器82包括差压传感器31,差压传感器31适于经由凸缘装置耦合到导压管线30。模数转换器84耦合到压力传感器31,并产生所感测的压力的一系列数字表示。流电路34使用这些数字表示来以计算流,并提供沿管线36的流指示。
在本发明的一个实施例中,模数转换器是已知的Sigma-Delta转换器,其提供每秒22次的转换。在这个实施例中,过程变量的每个转换后的数字表示成为用于功率谱密度(PSD)分析的数据点。优选地,把32点快速傅立叶变换(FFT)应用于数字过程数据点,以产生PSD信息。由于使用以已知方式操作的已知的模数转换器来执行PSD分析,因而通过调整微处理器系统88的操作,本发明的这个实施例能够整体上以软件来实现。因此,本发明的实施例能够应用于当前已在现场安装或已制造的过程变量变送器,无需修改其电路。下文给出用于执行PSD分析的算法。
由于Sigma-Delta转换器的快转换时间和高精度,通常将其用于过程测量和控制工业。Sigma-Delta转换器通常使用内部电容器电荷泵方案,该方案通常通过对所设置的间隔中的正1进行计数,以产生被分析的数字比特流。例如,目前使用的一种Sigma-Delta转换器提供包括由50%的1组成的比特流信号,以指示最小压力测量,并且包括由75%的1组成的比特流信号,以指示最大压力测量。在确定流速之前,该数字比特流通常经滤波以去除或衰减波动分量。然后,滤波后的数据与公知的等式一起使用,以计算质量流速(mass flow rate)或体积流速(volumetric flow rate)。
根据本发明的另一实施例,模数转换器内的数字比特流直接用于PSD分析。该比特流通常具有比转换频率高许多个数量级的频率。例如,已知的Sigma-Delta转换器提供具有大约57kHz频率的数字比特流。虽然本领域的技术人员将认识到能够对数字比特流以多种方式进行PSD分析,优选的方法如下。对于给定间隔(例如10秒),收集并保存来自比特流的数字数据。在上述实例中,10秒的57kHz数据产生了570,000个已存储比特。通过从每个已存储比特减去平均比特值(1的个数除以比特的总数),DC分量能够可选地从已存储数据中去除。接下来,对调节后的数据计算功率谱密度。这优选地使用65536点FFT和65536大小的汉宁(Hanning)窗进行。所选择的FFT的大小是因为它是最接近采样比特频率的2的幂,并且在10秒的持续时间,它提供了可接受的频谱的平均。然而,根据本发明的实施例,可以使用其它的大小。
功率谱密度Fi能够使用针对给定数据集的平均周期图的Welch方法来计算。该方法使用每秒fs个采样的测量序列x(n),其中n=1,2,...N。具有小于fs/2的滤波器频率的前端滤波器用于减小谱计算中的混叠(aliasing)。数据集如等式1所示地被分成Fk,i:
(等式1)
存在重叠数据段的Fk,i,并且对于每个段计算周期图,其中M是当前段中点的个数。在对所有段的所有周期图进行求值后,所有这些被平均以计算功率谱:
(等式2)
一旦获得针对训练模式的功率谱,则将该序列存储在存储器中,优选地是EEPROM,作为用于与实时功率谱进行比较的基线功率谱。因此,Pi是功率谱序列,并且i从1到原始数据序列中的点的总数N。N(通常为2的幂)还设置了频谱估计的频率分辨率。因此,Fi也被称作ith频率处的信号强度。功率谱典型地包括具有预定义频率间隔的大量点,将谱功率分布的形状定义为频率的函数。
在使用功率谱密度执行的诊断中,把基线历史情况下的谱密度的相对较大的采样与监控情况下的谱密度的相对较小的采样进行比较。相对较小的采样允许在约1秒中的问题的实时指示。功率谱的有关频率分量的增大能够指示一个或两个导压管线和/或主元件的恶化。图4-6示出了来自数字比特流的PSD数据。这些图示出了三种不同的导压管线情况:完全开放;具有0.0135英寸直径的孔的局部阻塞;以及具有0.005英寸直径的孔的实质阻塞。从图5和6可以看出,对从1到10Hz的比特流数据和/或从10-30Hz的比特流数据进行积分提供了对导压管线阻塞的有效指示。
微处理器系统88接收数字表示的序列(要么是单独的数字转换,要么是数字比特流,或者其任意组合)。微处理器系统88中存有把监控模式期间的PSD数据与训练模式期间获得的PSD数据进行比较的算法。这个比较允许过程变量变送器检测会对过程变量测量带来影响的故障。这种故障可以是压力变送器中的导压管线的阻塞、主元件的恶化或任何其它因素。系统88产生诊断数据62,作为相对于历史的当前数据集的函数。耦合到微处理器系统88的数模转换器96产生表示所感测的流速的模拟变送器输出98。数字通信电路100从微处理器系统88接收诊断数据94,并产生指示该诊断数据的变送器输出102。根据期望,模拟输出98和诊断数据102可以耦合到指示器或控制器。
图7是根据本发明实施例的用于训练过程变量变送器进行诊断的方法的流程图。方法250在起始框252处开始。方框252能够在相对地确定过程变量变送器完全可操作并且耦合到在规范内操作的过程的任何时间执行。通常,方框252将由技术人员启动,但方框252在某些情形下可远程地启动。方法250在方框254处继续,在254处接收过程值数据。该数据可以包括多个数字指示。这些指示可以是单独的转换后的过程变量转换;模数转换器内的比特流中的比特;或其任意组合。在方框256处,对数字数据执行FFT。这种FFT可以根据任何已知方法来执行。此外,代替或除方框256处的FFT之外,可以执行用于分析数据的谱分量的备选方法。在方框258处,计算FFT的功率。然后,把这个功率信息存储在过程变量变送器中。在步骤260处,该方法确定是否已出现充分的训练。这可以通过检查是否已经经过充分的时间、是否已取得充分的训练数据或任何其它适合的方法来执行。如果未执行训练,则方法260返回到方框254,并且继续训练。然而,如果在步骤260处确定训练执行时,方法250将结束,并且最后的功率数据集Fi将被存储在过程变量变送器内的非易失性存储器中。
虽然本发明的许多实施例直接采用过程传感器数据的PSD分析来提供诊断,然而一个实施例并非如此。图8示出了使用PSD分析来选择数字滤波器参数的方法。方法270通过执行训练方法272而开始,方法272优选地与方法250相同。在方框274处,检查频率处的功率。在方框276处,基于功率谱密度的分析来选择数字滤波器频率。频率的选择包括选择使用FFT中的哪些“频点(bin)”。问题不仅包括使用哪些频点,而且包括使用多少个频点。该选择可以像选择一个频点那样简单,或更加复杂。例如,可以选择非邻接的频点;可以选择邻接的频点;可以选择有助于整体的所有频点,并根据其各自的幅值而进行加权;或者这些的任意组合。可以使用多个准则来进行频点的选择。例如,可以选择具有最大功率的频点;可以选择功率具有最大方差的频点;可以选择功率具有最小方差的频点;可以选择具有最小幅值的频点;可以选择具有最高标准差的频点;可以选择具有最低标准差的频点;或者可以选择具有类似幅值的一组相邻的频点。一旦选择了频点,相应的滤波器特性用于对传感器数据进行数字滤波,如方框278处所示。如此滤波后的数据可以用于更有效的过程测量和/或诊断。因此,可以基于传感器数据的PSD分析动态地选择滤波器特性。根据本发明的实施例,滤波后的数据甚至能够利用已知的统计管线阻塞算法(tatistical line plugging algorithms)和技术。
图9是根据本发明实施例的执行基于PSD的诊断的方法的流程图。许多因素能够影响数字比特流,由此而影响到过程变量。该导压管线能够变得阻塞和/或主元件能够受到侵蚀或恶化。方法280在方框282处开始,在282处出现训练。方框282优选地与参照图7描述的训练方法250相同。一旦训练已完成,方法280到计算过程值数据的方框284。再一次地,该数据可以是来自转换器84的一组单独的模数转换后的读数,或者该数据包括转换器84内产生的数字比特流的全部或一部分。在方框286处,将数据转换到频域,优选地使用FFT。在方框288处,计算FFT的功率,产生与过程变量有关的功率谱数据的集Fi。在方框290处,把集Fi与已存储的训练数据集Fi进行比较。这种比较能够采用多种形式。例如,该比较能够包括检查针对所选谱范围的幅值之和。该比较还能够包括将被比较的Fi的标准差和平均值(mean)与Fi的标准差和平均值进行比较。另一种比较包括对较高或较低幅值相一致的频率范围进行比较。回过来参照图6,使用数字比特流数据,“完全开放”情况将对应于训练集Fi。因此,把来自所选频率范围的比特流频谱的积分进行比较能够表明:当导压管线开始阻塞时,频谱的积分实质下降。在测试中工作良好的一个频率范围在10和40Hz之间。然而,10与30Hz之间的范围也被认为是有益的。最后,看起来在30-40Hz范围中提供的信息是有用的,其也可用于检测局部或全部的导压管线阻塞。由所选频谱的积分所指示的Fi与Fi之间的差能够与预先选择的阀值进行比较,以确定是否存在故障。在方框292处,基于方框290中的比较来执行故障确定。如果揭示出现故障,则控制到达方框294,在方框294处指示该故障,并且可选地暂停过程变量变送器的操作。该故障指示能够是诸如设备报警的本地指示,或是被传递到诸如控制室或操作员的远程实体的指示。该故障指示可以指示当前的关键故障,或可指示迫近的故障。如果未发现故障,则控制返回到方框284,并且该方法继续执行以监控过程设备的操作。
这些方法中的任意方法都能够以多个指令序列的形式存储在计算机可读介质上,多个指令序列包括这样的序列,即:当由压力变送器中的微处理器系统执行时,致使压力变送器执行关于主元件和可耦合到变送器的导压管线的诊断方法。
在一个实施例中,微处理器系统88包括信号预处理器,该信号预处理器通过模数转换器84耦合到传感器31,而模数转换器84把诸如频率、幅度或与阻塞的导压管线30或恶化的主元件28有关的传感器信号中的信号分量进行隔离。该信号预处理器将隔离的信号输出提供给微处理器88中的信号求值程序。通过滤波、执行小波变换、执行傅立叶变换、使用神经网络、统计分析或其它信号求值技术,该信号预处理器隔离一部分信号。这种预处理优选地在微处理器88中实现,或在专用数字信号处理器中实现。该隔离的信号输出与传感器31所感测到的已阻塞或正在发生阻塞的导压管线30或恶化的主元件28有关。
该信号分量通过信号处理技术进行隔离,其中仅识别期望的频率或诸如幅度的其它信号特性,并且提供对该识别的指示。取决于待检测的强度信号及其频率,信号预处理器能够包括滤波器,例如带通滤波器,以产生隔离的信号输出。对于更灵敏的隔离,使用诸如快速傅立叶变换(FFT)的高级信号处理技术以获得传感器信号的频谱。在一个实施例中,信号预处理器包括小波处理器,这个小波处理器使用离散小波变换,对如图10、11和12所示的传感器信号执行小波分析。小波分析很适合用于分析在时域中具有瞬态或其它非静止特性的信号。与傅立叶变换相比,小波分析保留时域中的信息,即事件出现的时间。
小波分析是一种用于将时域信号变换到频域的技术,类似于傅立叶变换,其允许对频率分量进行识别。然而,不同于傅立叶变换的是,在小波变换中,输出包括与时间有关的信息。这可采用三维曲线图的形式表示,一个轴表示时间,第二轴表示频率,而第三轴表示信号幅度。小波分析的讨论在L.Xiaoli等人的On-Line Tool Condition Monitoring System With Wavelet Fuzzy Neural Network,8 JOURNALOF INTELLIGENT MANUFACTURING pgs.271-276(1997)中给出。在执行连续小波变换中,一部分传感器信号被加窗并与小波函数进行卷积。通过在采样开始处叠加小波函数、将小波函数与信号相乘、然后对该结果在采样周期上进行积分,来执行这个卷积。对该积分的结果进行定标,并提供时间等于零处的连续小波变换的第一值。然后,可以把该点映射到三维平面上。该小波函数然后向右移动(时间上向前),并且重复进行相乘和积分的步骤,以获得被映射到3-D空间的另一组数据点。重复该过程,并使小波移动(卷积)经过整个信号。然后,对小波函数进行定标,这会改变该变换的频率分辨率,并且重复上述步骤。
图10中示出了来自传感器31的传感器信号的小波变换的数据。该数据以三维绘制,并形成表面300。如图10的曲线图中所示,该传感器信号包括时刻t1处在大约1kHz处的小信号峰值,以及时刻t2处在大约100Hz处的另一峰值。通过由信号求值程序的后续处理,对表面300或表面300的一部分进行求值,以确定导压管道或主元件的恶化。
上面描述的连续小波变换需要大量的计算。因此,在一个实施例中,微处理器系统88执行非常适于在微处理器系统中实现的离散小波变换(DWT)。一种有效的离散小波变换使用Mallat算法,这是一种双通道子带编码器。Mallat算法提供了一系列分离或分解的信号,这些信号表示原始信号的各个频率分量。图11示出了这种系统的示例,其中使用Mallat算法的子带编码器对原始传感器信号S进行分解。该信号S具有从0到最大fMAX的频率范围。该信号同时经过具有自1/2fMAX到fMAX的频率范围的第一高通滤波器和具有从0到1/2fMAX的频率范围的低通滤波器。这个过程被称作分解。高通滤波器的输出提供了“等级1”的离散小波变换系数。等级1系数表示作为在1/2fMAX与fMAX之间的输入信号部分的时间函数的幅度。根据期望的那样,来自0-1/2fMAX低通滤波器的输出经过后续的高通(1/4fMAX-1/2fMAX)和低通(0-1/4fMAX)滤波器,以提供离散小波变换系数的另外等级(超出″等级1″)。来自每个低通滤波器的输出能够经过进一步的分解,根据期望来提供离散小波变换系数的附加等级。这个过程继续,直到实现期望的分辨率为止,或在分解后剩余数据采样的数目没有产生附加信息为止。小波变换的分辨率被选择为与传感器大致相同,或与监控信号所需的最小信号分辨率相同。每个等级的DWT系数表示作为给定频率范围的时间的函数的信号幅度。每个频率范围的系数被连接起来,以形成诸如图10所示的曲线图。
在一些实施例中,通过将数据加到小波分析中使用的窗的边界附近的传感器信号,对信号进行填充。这种填充减小了频域输出的失真。这种技术能够与连续小波变换或离散小波变换一起使用。“填充”被定义为在当前有效的数据窗的任一侧上追加的(appending)额外数据,例如增加延伸超过当前窗的任一窗边缘25%的额外数据点。在一个实施例中,通过重复当前窗中的部分数据以便把增加的数据“填充”到任一侧的现有信号而产生填充。然后,整个数据集被拟合至用于对超过有效数据窗25%的信号进行外插的二次等式。
图12是示出由传感器31产生的信号S和在标示为等级1至等级7的7个分解等级中产生的逼近信号的示例。在这个示例中,信号等级7表示所能够产生的最低频率DWT系数。任何另外的分解产生噪声。提供了所有等级,或仅是那些涉及导压管道或主元件恶化的等级。
微处理器88对从信号预处理接收的隔离信号进行求值,并且在一个实施例中监控所识别的特定频率或频率范围的幅度,并且如果超过阈值,则提供诊断输出。信号求值程序还能够包括更为高级的判决算法,例如模糊逻辑、神经网络、专家系统、基于规则的系统等。共同转让的美国专利No.6,017,143描述了能够在信号求值程序154中实现的多种判决系统,并且通过引用合并于此。
微处理器88使用从差压传感器31导出的信息,执行与导压管道或主元件有关的诊断。下面描述了用于实现诊断电路的多个实施例。该诊断电路能够提供剩余寿命估计、故障指示、迫近故障的指示或用于校正所感测的过程变量中的误差的校准输出。
虽然本发明是参考优选实施例进行描述的,但本领域中的技术工作人员将会意识到,在不背离本发明的精神和范围的前提下,可以做出形式和细节上的变动。例如,本发明的许多功能方框已就电路进行了描述,然而,许多功能方框可以采用诸如数字和模拟电路、软件及其混合的其它形式来实现。当采用软件实现时,微处理器执行功能,并且信号包括软件所操作的数字值。可以使用以致使处理器执行期望过程要素的指令来编程的通用处理器、包含被接线以执行期望要素的专用硬件组件以及对通用处理器和硬件组件的编程的任意组合。根据需要,确定性或模糊逻辑技术能够用于在电路或软件中做出判决。由于复杂数字电路的性质,电路元件可能不会被分隔成所示的单独的方框,而是可以把用于多种功能方框的组件进行混合和共享。同样对于软件来说,某些指令能够以若干功能的一部分而共享,并且与本发明范围内的不相关指令混合。诊断输出能够是未来故障的预测指示器,诸如导压管线未来的局部或完全阻塞。该诊断能够被应用于导压管道和/或主元件。最后,虽然已参照压力变送器描述了本发明的多个实施例,然而本发明的实施例能够利用任何过程设备来实践,其中传感器经模数转换器耦合到过程设备。
Claims (32)
1.一种用于耦合到过程并通过通信总线提供过程变量的指示的过程变量变送器,所述过程变量变送器包括:
过程变量传感器,可耦合到过程以提供变量的模拟指示;
模数转换器,耦合到所述过程变量传感器,并提供表示由所述传感器提供的模拟指示的数字信息;以及
输出电路,被配置为提供与过程变量有关的输出;
微处理器系统,耦合到所述模数转换器,并被配置为计算所述数字信息的功率谱密度,并基于所述功率谱密度做出响应地产生诊断信息。
2.根据权利要求1所述的变送器,其中,所述变送器是可经由多个导压线路耦合到过程的压力变送器。
3.根据权利要求2所述的变送器,其中,所述诊断表示所述导压管线的情况。
4.根据权利要求3所述的变送器,其中,所述诊断表示至少一条导压管线的局部阻塞。
5.根据权利要求4所述的变送器,其中,所述诊断指示导压管线将完全被阻塞的时刻。
6.根据权利要求1所述的变送器,其中,所述模数转换器以多个离散的数字化转换值来提供所述数字信息。
7.根据权利要求6所述的变送器,其中,以每秒约22次的频率来提供所述离散的数字化值。
8.根据权利要求1所述的变送器,其中,所述数字信息包括数字比特流数据。
9.根据权利要求8所述的变送器,其中,以超过约55kHz的频率来提供所述比特流数据。
10.根据权利要求9所述的变送器,其中,使用快速傅立叶变换(FFT)把所述数字信息变换到频域。
11.根据权利要求10所述的变送器,其中,使用约65536的汉宁窗大小来计算所述功率谱密度。
12.根据权利要求11所述的变送器,其中,所述诊断信息基于范围从约1到约10Hz的频率处的功率的积分。
13.根据权利要求11所述的变送器,其中,所述诊断信息基于范围从约10到约30Hz的频率处的功率的积分。
14.根据权利要求11所述的变送器,其中,所述诊断信息基于范围从约10到约40Hz的频率处的功率的积分。
15.根据权利要求11所述的变送器,其中,所述诊断信息基于范围从约30到约40Hz的频率处的功率的积分。
16.根据权利要求1所述的变送器,其中,使用小波分析把所述数字信息变换到频域。
17.根据权利要求1所述的变送器,其中,基于功率谱密度信息Fi与基线功率谱密度信息Fi的比较来产生所述诊断信息。
18.根据权利要求17所述的变送器,其中,所述比较包括对所选频率上的幅值之和的偏差进行比较。
19.根据权利要求17所述的变送器,其中,所述比较包括对所选频率上的幅值的标准差进行比较。
20.根据权利要求17所述的变送器,其中,所述基线功率谱密度在训练会话期间获得。
21.根据权利要求1所述的变送器,其中,所述诊断信息包括故障指示。
22.根据权利要求21所述的变送器,其中,所述故障指示是本地故障指示。
23.根据权利要求21所述的变送器,其中,所述故障指示通过所述通信总线传输。
24.根据权利要求21所述的变送器,其中,所述故障指示预测未来事件。
25.一种预测性地诊断过程变量变送器的方法,所述过程变量变送器具有耦合到过程变量传感器的模数转换器,所述过程变量传感器耦合到过程,所述方法包括:
从所述模数转换器获得多个数字数据;
计算所述多个数字数据的功率谱密度;
将计算的功率谱密度与基线功率谱密度进行比较;
基于所述比较,产生预测性诊断输出。
26.根据权利要求25所述的方法,其中,所述多个数字数据包括多个数字化的转换。
27.根据权利要求25所述的方法,其中,所述多个数字数据包括数字比特流数据。
28.根据权利要求25所述的方法,其中,所述诊断输出表示至少一条导压管线的情况。
29.一种用于耦合到过程并通过通信总线提供过程变量的指示的过程变量变送器,所述过程变量变送器包括:
过程变量传感器,可耦合到过程以提供过程变量的模拟指示;
模数转换器,耦合到所述过程变量传感器,并提供表示由所述传感器提供的模拟指示的数字信息;以及
微处理器装置,用于产生过程值并使用功率谱密度分析来产生诊断输出。
30.一种在过程变量变送器中配置数字滤波器的方法,所述方法包括:
过程变量传感器,可耦合到过程以提供过程变量的模拟指示;
模数转换器,耦合到所述过程变量传感器,并提供表示由所述传感器提供的模拟指示的数字信息;
微处理器系统,耦合到所述模数转换器,所述微处理器系统使用可配置的滤波器特性来提供所述模数转换器的数字滤波;以及
其中,基于在训练会话期间执行的功率谱密度分析来选择所述可配置的滤波器特性。
31.根据权利要求30所述的方法,其中,滤波后的数字信息用于提供过程变量输出。
32.根据权利要求30所述的方法,其中,滤波后的数字信息用于提供诊断输出。
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US11/205,745 US7949495B2 (en) | 1996-03-28 | 2005-08-17 | Process variable transmitter with diagnostics |
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EP (1) | EP1915660B1 (zh) |
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- 2006-07-14 EP EP06787450.3A patent/EP1915660B1/en active Active
- 2006-07-14 JP JP2008526935A patent/JP5116675B2/ja not_active Expired - Fee Related
- 2006-07-14 RU RU2008110076/09A patent/RU2386992C2/ru active
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CN102879215A (zh) * | 2011-07-15 | 2013-01-16 | 阿自倍尔株式会社 | 导压管的堵塞诊断系统以及诊断方法 |
CN102879215B (zh) * | 2011-07-15 | 2014-12-24 | 阿自倍尔株式会社 | 导压管的堵塞诊断系统以及诊断方法 |
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CN105492874B (zh) * | 2012-11-30 | 2020-02-25 | Ip2Ipo 创新有限公司 | 用于监测携流管道的网络的设备、方法和系统 |
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CN105593647B (zh) * | 2013-09-17 | 2019-08-30 | 恩德斯+豪斯流量技术股份有限公司 | 用于监控自动化技术的测量装置的方法 |
CN106767986A (zh) * | 2015-11-24 | 2017-05-31 | 中国科学院沈阳自动化研究所 | 基于噪声分析的仪表故障在线诊断方法 |
CN108427400A (zh) * | 2018-03-27 | 2018-08-21 | 西北工业大学 | 一种基于神经网络解析冗余的飞机空速管故障诊断方法 |
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Also Published As
Publication number | Publication date |
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US7949495B2 (en) | 2011-05-24 |
RU2386992C2 (ru) | 2010-04-20 |
CA2615293C (en) | 2016-03-29 |
JP5116675B2 (ja) | 2013-01-09 |
CA2615293A1 (en) | 2007-02-22 |
EP1915660A1 (en) | 2008-04-30 |
WO2007021419A1 (en) | 2007-02-22 |
US20060036404A1 (en) | 2006-02-16 |
EP1915660B1 (en) | 2018-09-05 |
JP2009505276A (ja) | 2009-02-05 |
RU2008110076A (ru) | 2009-09-27 |
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