CN1493003A - 基于微处理器的发射机的电子线路板寿命预测器 - Google Patents

基于微处理器的发射机的电子线路板寿命预测器 Download PDF

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CN1493003A
CN1493003A CNA028054903A CN02805490A CN1493003A CN 1493003 A CN1493003 A CN 1493003A CN A028054903 A CNA028054903 A CN A028054903A CN 02805490 A CN02805490 A CN 02805490A CN 1493003 A CN1493003 A CN 1493003A
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CN1232833C (zh
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格雷戈里・H・罗梅
格雷戈里·H·罗梅
・埃尔于雷克
埃夫伦·埃尔于雷克
・卡瓦列奥格鲁
卡迪尔·卡瓦列奥格鲁
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Rosemount Inc
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    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/28—Testing of electronic circuits, e.g. by signal tracer
    • G01R31/282—Testing of electronic circuits specially adapted for particular applications not provided for elsewhere
    • G01R31/2829—Testing of circuits in sensor or actuator systems

Abstract

一种现场设备(8)，包括连续测量与提供给预测引擎的电流测量结果相关的参数的电路，所述预测引擎根据多个与电流相关的测量结果，计算诊断输出。由所述预测引擎提供的诊断预测能够提供对设备电子线路剩余寿命的估计。诊断特征提供所述现场发射机电子线路板整体可用状态的在线状况。

Description

基于微处理器的发射机的电子线路板寿命预测器
技术领域
本发明涉及一种过程控制工业中的操作。具体地，本发明涉及根据对电子线路板引出的电流的重复测量，所进行的基于微处理器的现场设备的电子线路板寿命预测。
背景技术
受控过程中对过程变量的有效操作要求迅速地确定现场设备的元件或通信电路中的故障，从而可以执行适当的校正动作或适当的警告。在停止该过程并从过程环路中去除现场设备之前，确定故障或者错误提供了内在的效率。此基于微处理器的发射机包含复杂的电子电路，有时会出现错误，而实际上仅发生了漏电流或分流电流。例如，影响环路电流的电路漏电流可能被报告成发射机故障。
美国专利No.5,481,200提出了一种具有内置测试装置的现场发射机。该现场发射机的一个方面包括得出由电子线路板引出的电流，并利用得出的电流，以提供与发射机电子线路相关的诊断输出。本发明是对美国专利No.5,481,200所述教义的改进。正如后面将从说明书中清楚的那样，本发明的实施例能够更早地检测到问题状态，从而促成更为有效的校正动作。
发明内容
一种现场设备，包括连续测量与所述现场设备的电子线路引出的电流相关的参数的电路。将测量结果提供给预测引擎，所述预测引擎根据多个与电流相关的测量结果，计算诊断输出。由所述预测引擎所提供的诊断预测能够提供对设备电子线路剩余寿命的估计。诊断特征提供所述现场发射机电子线路板整体可用状态的在线状况。
附图说明
图1是本发明实施例可具体应用的过程控制系统的示意图。
图2是描述了典型的4～20mA现场设备的过程控制环路电流的电流分配的图表。
图3是对本发明实施例有用的一对数组(array)的示意图。
图4是描述了多个电流值及其与图3所示的数组之间的关系的图表。
图5是描述了利用线性曲线拟合对电子线路板寿命预测进行计算的图表。
图6描述了在时间Δt之后，获得的第二组电流值。
具体实施方式
过程变量通常是过程中被控制的主要变量。如这里所用，过程变量表示诸如压力、流量、温度、数值、电平、pH值、浊度、振动、位置、发动机电流或过程的任何其他特征等任何描述了过程的条件的变量。控制信号表示用于控制过程的任何信号(除了过程变量之外)。例如，控制信号包括由控制器调整的所需过程变量值(即，给定值)，如所需的温度、压力、流量、电平、pH值或浊度等。此外，控制信号包括校正值、告警、告警条件、如轮流提供给阀动器、加热元件的阀位置信号、螺线管开关信号等提供给控制元件的信号或与过程的控制相关的任何其他信号。这里所使用的诊断信号包括与过程控制环路中的设备和元件的操作相关的信息，但不包括过程变量或控制信号。例如，诊断信号包括阀杆位置、所施加的扭矩或力、阀动器压力、用以驱动阀的压力气体的压力、电压、电流、功率、电阻、电容、电感、器件温度、静摩擦、摩擦力、全开和全关位置、行程、频率、幅度、谱和谱分量、硬度、电场或磁场强度、持续时间、强度、移动、电动机反电动势、电动机电流、与环路相关的参数(如控制环路电阻、电压或电流)或系统中可检测或测量的任何其他参数。此外，过程信号包括与过程或过程中的元件相关的任何信号，如过程变量、控制信号或诊断信号等。现场设备包括形成过程控制环路的部件或与过程控制环路耦合并用在过程的控制或监控中的任何设备。
图1是示出了过程控制系统2的示例的示意图，过程控制系统2包括运输工艺流体的过程管道4和和负载环路电流I的两个线路过程控制环路6。现场设备8、控制器10、通信器12和控制室14是过程控制环路6的全部部件，控制器10与阀动器、阀、泵、发动机或螺线管等环路中的最终控制元件耦合。控制器/阀动器10、通信器12和/或控制室设备14可以接收过程变量。所示控制器10与阀18耦合，并能够通过调整阀18来控制过程，从而改变管道4中的流量Q。控制器10接收来自如控制室14、发射机8或通信器12等的、在环路6上的控制输入，并作为响应调整阀18。在另一实施例中，控制器10根据在环路6上接收到的过程信号H，内部产生控制信号。通信器12可以是图1所示的便携式通信器，或者可以是监控过程并进行计算的永久安装过程单元。例如，“现场设备”包括图1所示的发射机8(如Rosemount Inc.提供的3095发射机)、控制器10、通信器12和控制室14。
本发明的实施例集中在基于微处理器的现场设备的电子线路板。本发明的实施例应用于由4～20mA的信号供电的过程控制系统中的任何现场设备。本发明实施例的诊断特征通过监控总电源电流，提供了现场设备的所有电子线路的在线状况。当电源电流增加到临界电平时，可能会使发射机的电子线路、通信协议和模拟输出范围退化。例如，参见美国专利No.5,481,200。这里所公开的诊断特征，依照本发明的实施例，通过估计电源电流达到预定临界电平的剩余时间，向用户提供对设备电子线路的预测分析。本领域的技术人员将意识到估计电源电流达到预定临界电平的剩余时间仅仅是与电子线路电源电流相关的多个测量结果能与预测诊断输出相关的一种方法。
图2描述了典型的4～20mA现场设备的过程控制环路电流的电流分配。通常，电子线路的电源电流或静态电流(IQ)是低于4mA边界的恒定直流(DC)值。如图2所示，剩余的16mA用于实现给定过程测量的数值的完整范围。在正常的操作条件下，当电流引出更多的电源电流以执行特定功能时，静态电流将从其正常值稍微有所增加。例如，需要实质能量的任务之一是对非易失性存储器的数据写入操作。此外，如温度或湿度等周围环境也可以影响IQ。
异常条件可能使IQ上升到4mA以上的电平，此电平将影响现场设备的输出范围。异常条件可能通过影响其中电气部件(即，无源元件或集成电路)的性能使IQ上升。因而，如果现场设备处于恶劣环境一段时间，可能会加剧电气部件的退化，或者可能发生如泄漏通路或对地的分支电流等其他电气故障。作为示例，例如，如果发生了电气故障，使得IQ的DC值变为6mA，则环路电流不可能小于6mA。即使电子线路中存在这样的故障，对于6～20mA的过程测量结果，发射机仍然可以正常工作(传感器、微处理器、通信等仍然有作用)。相反，如果过程测量结果在4～6mA之间，操作员或使用模拟输出信号的过程控制仪器将接收到不精确的数据。
本发明的实施例采用了预测引擎，此预测引擎获得与电源电流测量结果的序列相关的数据，并计算电源电流(IQ)达到预设上限之前剩余的时间。实质上，装备有此预测诊断的现场设备，依照本发明的实施例，将预测其自身电子线路板的寿命。这不仅预先警告用户可能的错误操作，而且提供给用户额外的时间，或者在危害到具体过程控制环路的性能之前，替换发射机或其电子线路板，或者过程本身。
图3描述了促成本发明实施例的实现的数据结构。具体地，示出了一对长度为N的数组。在诊断期间，采样与电源电流相关的参数或者甚至是电源电流本身，并作为IQ值存储在数组100中。周期性地重复此过程，直到以预定数目(N)的IQ值填充阵列100。概念上，这组IQ值是数组，但可以按照任何格式的数字进行建模和/或存储。类似的数组T，对应于采样每个IQ值的时间。这些数值最好存储在非易失性存储器中。一旦两个数组都填满时，将数组的内容提供给最好以微处理器上运行的软件实现的预测引擎。预测引擎对IQ值对时间的采样实施曲线拟合，如数组(T)所示，并产生连续时间公式IQ(t)的系数。例如，如果将IQ的上限设置在4mA，预测引擎利用IQ(t)的系数，计算到IQ＝IQMAX的绝对时间(TABS-LIFE)。由于TABS-LIFE相对于时间T0(T[1])的初始值，实际剩余时间时TABS-LIFE与t[N]之间的差。此时间被称为电子线路板寿命。
图4描述了在多个时间t获得的多个IQ值。如图所示，每个采样之间的周期最好是恒定的，以便简化计算。但是，可以根据包括如所计算的电子线路板寿命和所计算的电子线路板寿命改变的速度等任意数目的因素，来改变测量之间的周期。
图5是描述了对电子线路板寿命预测进行计算的图表。作为示例，对于任意值N，例如10，在t＝T9将填满数组IQ和T(参见图3)。此时，预测引擎将计算以下数值：
Iq(t)→t(Iq)                             公式1
TABS-LIFE＝t(Iq-max)
TEB-LIFE＝t(Iq-max)-t[N]                    公式2
在本实施例中，预测引擎选择线性曲线拟合，来表示IQ(t)和图5所示的TEB-LIFE的计算。本领域的计算人员将意识到能够提供多种方法，将包含在数组或其他数据结构中的数据点校正为预测诊断输出，而且这些方法是可以考虑的。例如，也可以使用非线性关系。此外，神经网络分析技术和/或模糊逻辑关系可以用于建立测量的电源电流参数和诸如电子线路板的剩余时间等诊断输出之间的关系。
由于采样大小N是固定的，在能够采样IQ的下一数值前，需要移位数组中的每个元素。这被称为先进先出方式。这样，数组的最后一个元素将被IQ的下一数值所取代。这样：
Iq[k]＝Iq[k+1]  k＝1到N-1               公式3
Iq[N]＝下一Iq读取值                     公式4
因此，IQ数组作为寿命FIFO(先进先出)数组或序列。以类似的方式，图6描述了对下一组N＝10(T0-0)的IQ和T的数值的获取。如上所述，在数组中移位先前的IQ值，并在T10获得新的IQ值。随着T10的获得，再次调用预测引擎，如上所述，预测电子线路板寿命的计算。优选地，周期性地进行电子线路板寿命预测。但是，也可以或者通过在现场设备本身的本地用户接口，或者通过在控制环路上向现场设备发送适当的通信信号，人工调用电子效率板寿命预测。
尽管已经参照优选实施例对本发明进行了描述，本领域的技术工人将意识到在不偏离本发明的精神和范围的前提下，可以在形式或细节上进行修改。

Claims (13)

1、一种现场设备，具有两个能够与4～20mA过程控制环路耦合的接线端，所述现场设备包括：
    设备电子线路，从4～20mA过程控制环路引出电源电流；
    电源电流测量电路，适于测量与设备电子线路消耗的电源
电流相关的参数；
    存储结构，适于接收和存储与连续测量的电源电流参数相
关的多个数据；以及
    预测引擎，与存储器结构耦合，根据多个电流电源参数，
计算针对设备电子线路而估计的寿命。
2、按照权利要求1所述的现场设备，其特征在于所述现场设备是过程控制发射机。
3、按照权利要求1所述的现场设备，其特征在于所述现场设备是阀。
4、按照权利要求1所述的现场设备，其特征在于所述预测引擎对多个存储的电源电流参数采用线性曲线拟合。
5、按照权利要求1所述的现场设备，其特征在于所述预测引擎对多个存储的电源电流参数采用非线性曲线拟合。
6、按照权利要求1所述的现场设备，其特征在于所述存储结构是数组。
7、按照权利要求1所述的现场设备，其特征在于所述存储结构包括多个数组，第一数组存储与多个连续测量的电源电流相关的数据，而第二数组包含与第一数组的测量时间相关的数据。
8、按照权利要求1所述的现场设备，其特征在于所述预测引擎针对寿命估计的计算，适合于使用神经网络分析。
9、按照权利要求1所述的现场设备，其特征在于所述预测引擎针对寿命估计的计算，适合于使用模糊逻辑。
10、一种预测现场设备中的板电子线路寿命的方法，所述方法包括：
    连续地测量与电子线路电源电流相关的多个参数；
    对连续测量的电源电流参数采用数学模型；以及
    计算电源电流期望达到选定阈值时的未来时间。
11、按照权利要求10所述的方法，其特征在于计算未来时间包括采用神经网络分析。
12、按照权利要求10所述的方法，其特征在于计算未来时间包括采用模糊逻辑。
13、一种现场设备，该现场设备能够与两线4～20mA过程控制环路耦合，所述现场设备包括：
    设备电子线路；
    电源电流测量电路，能够与4～20mA过程控制环路耦合；以
及
    预测装置，根据多个连续的电源电流测量结果，预测电子
线路板的残余寿命估计。
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