本帖最后由 csln 于 2015-6-9 08:34 编辑
规矩湾锦苑 发表于 2015-6-8 14:58
69楼的帖子话虽然不中听，但道理是对的。指示电流表分辨力0.01A，显示0.50A时，分辨力±1个字引入的 ...
69楼的帖子话虽然不中听，但道理是对的。指示电流表分辨力0.01A，显示0.50A时，分辨力±1个字引入的误差将达±0.01/0.5=±2%，说受检点I＝0.5A的最大允许误差是0.4%×0.5+2×0.3%=0.008A的确就扯，就搞笑了，这就是大家常说的生产商把牛皮吹破了。
未必是生产商把牛皮吹破了，生厂商一般不敢这样吹的，这样吹的代价是高昂的索赔，所以一般不会有这样“二”的生产商
如果不是把技术指标看错了（技术指标中至少还会有±1个字的误差项，除非分辨力误差远小于其他误差项），就是为了什么目的故意漏掉了什么，又或者根本就是编出来的
csln 发表于 2015-6-9 08:29
69楼的帖子话虽然不中听，但道理是对的。指示电流表分辨力0.01A，显示0.50A时，分辨力±1个字引入的误差 ...
你说的有一定道理。如果指示电流表分辨力0.01A，量程I＝2A，那么显示Ic＝0.50A时，分辨力±1个字引入的误差将达±0.01/0.5=±2%，这是一个客观事实，生产商一般不会这么“二”，不会也不敢把牛皮吹破。但假设生产商的说明书给出的技术指标只给电流表的最大误差为4%Ic+0.3%I，按此公式计算出的0.5A受检点最大误差是0.4%×0.5+2×0.3%=0.008A，还能说生产商没用误导的责任吗？顾客是关注焦点，生产商至少对顾客会如何理解关注不够，在给出与量程相乘的那个百分数时综合考虑不周。
本帖最后由 csln 于 2015-6-9 11:59 编辑
有些测量仪器，如计数式频率计，在低频段或取样时间较小时（如0.01秒采样），该频率计的误差仅仅取决于分辨力误差一项（其他误差都可略）这时的频率计指标是分辨力误差
这是太老太老的黄历了，现在的计数器，低档的，闸门时间1s时也能分辨8位，好点的，1s分辨10位、11位、12位也很正常，1s分辨15位也不稀奇，无论测量的是1Hz还是100MHz，一般测量时分辨力误差完全可以忽略不计，除非是用于专业的时间频率测量
而U95包含分辨力误差及计量标准的误差，又要求U95小于误差指标的1/3，这是个逻辑错误。这样要求，用铯频标也不能检定数字式频率计。这当然是错误的。
这不是个逻辑错误，是理解错误，这种要求当然不是错误的，是完全正确、合理的，以传统频率计为例，测量误差Δ=±（A*f+1个字）（A为频率计内部晶振频率准确度，假定为1×10^-7，f为测量频率），测量铯钟输出的1Hz信号，闸门时间=1s时，测量值可能为0Hz、1Hz、2Hz，仅以此测量结果能判断被检频率计是否合格吗？当然不能，检定规程不会要求检定员仅就此一个测量点判定频率计是否合格，会用充分证据证明频率计合格与否，包括内部晶振和分辨力误差
如果一定要在1Hz测量点判定这类频率计是否合格（如果还能找到这种硕果仅存的设备的化），当然有办法，不需要用铯钟，不需要用铷钟，用一台分辨力足够、内部晶振符合要求的频率合成器就行了，合成器输出（0.999999950～1.000000050）Hz间的信号，很容易判断出这个点频率计是否合格，当然不会有文件要求这样做，因为这没有什么意义
于是现在的情况就是，频率计检定规程与频率计的检定工作，任何人实际上都不理会JJF1094-2002的合格性判别公式。按那个规范，频率计量就没法干事。
这是完全不客观的描述，不需要说太多
本帖最后由 史锦顺 于 2015-6-9 16:36 编辑
sosboxing 发表于 2015-6-8 09:56
1 未说明被测源的频率，这对选择万用表是有影响的
强调一下，校准的是电压源，该电压源具有电流测量功 ...
sosboxing先生：你们公司的电流源，是你们公司自己生产的吗？
是什么型号？该机的说明书，有关技术指标部分，是怎样写的？
我觉得是你把本来的“标称值指示”(与仪器误差无关)错当成“测得值”（有误差）了，所以才出现“分辨力误差”大于总误差的情况。这是与原机性能无关的不当认识与不当的说法。计量应该严格，但必须合理；对被检仪器的性能指标不能有误解，有误解的要求是不合理的要求。如果抛开“分辨力”不谈，那么该机的实际性能符合指标要求吗？要十分明确：考核“实际性能是否符合指标”是计量的根本任务。计量是凭实测数据说话。
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我也学一学一
本帖最后由 csln 于 2015-6-10 16:19 编辑
不确定度论判别检定资格，用U95。U95包括被检仪器的分辨力，以及被检仪器的重复性。这两项都是检定对象的性能，放在检定能力中，是错误的。
确切地说，U95≤MPEV/3是符合性判断时测量不确定度可忽略条件，或者检定方法符合要求的必要条件
U95包括被检仪器分辨力和重复性是错误的吗？
用一例子来说明
用交流校准源检定1.5级、量程450V指针式交流电压表，电压表分度值15V/格（小格），校准源准确度等级高于被检表10倍
检定员直接输出300V标准电压至被检表(非规程方法），检定员说可以准确估读到1/3格，即5V，只考虑分辨力（分度值）引入的不确定度U95≈4.8V＞MPEV/3=450*1.5/100/3=6.75/3=2.25V
这样的测量结果能用于判定电压表合格与否吗？当然不能，问题出在什么地方，显然不是标准器，不说评定不确定度，估读误差5V测量结果去判断MPEV=6.75V的电压表是否合格，判断结果会有人会相信吗？
显然，U95≤MPEV/3条件，U95包括标准设备、环境条件、测量方法（包括人员）、被检仪器分辨力、被检仪器重复性等是必须的而不是错误的
仅满足MPEV(标准）≤MPEV（被检）/3是不够的
史锦顺 发表于 2015-6-9 16:25
sosboxing先生：你们公司的电流源，是你们公司自己生产的吗？
是什么型号？该机的说明书 ...
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
上图是交流稳压电源的显示界面，红线标注部分为仪器可设定输出的内容，Vac,F,Vdc,
其他六项是测量参量，图中可见到电流 I测量分辨率为0.01A
史锦顺 发表于 2015-6-9 16:25
sosboxing先生：你们公司的电流源，是你们公司自己生产的吗？
是什么型号？该机的说明书 ...
电流测量的指标：（电压源，不是电流源）
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
本帖最后由 史锦顺 于 2015-6-11 15:43 编辑
csln 发表于 2015-6-10 15:48
不确定度论判别检定资格，用U95。U95包括被检仪器的分辨力，以及被检仪器的重复性。这两项都是检定对象的性 ...
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关于计量中的分辨力误差
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史锦顺
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先生所举的例子说明什么呢？
1 按误差理论处理，行得通。
误差理论推导出，计量的误差仅仅决定于检定所用的标准的误差范围，而与被检仪器的分辨力、重复性等性能无关。合格的判别条件是：
|Δ|max ≤ MPEV ― R(标) （5）
根据（5）式，要求计量所用的标准的误差范围越小越好。标准的准确度越高，就是标准的误差范围R(标)越小，则计量的水平越高。现行检定规程《DL/T 980-2005数字多用表检定规程》，执行的就是（5）式的判别标准，要求条件:标准的误差范围是被检仪器误差范围指标的1/5到1/10。这正是误差理论的要求即（5）式的要求。按（5）式，标准的误差可略，如先生给出的实例条件，则检定即可进行。完全符合检定的条件，方法可靠、结果可信。被检测量仪器的指标是误差范围MPEV是6.75伏。用被检电压表测量电压标准，被检电压表的读数与标准的值之差的绝对值就是|Δ|。测量要在量程的数个采样点（通常取10点）上进行，在每点上，都要找差值绝对值的最大值，而最后取各采样点的差值绝对值的最大者，它就是检定得到的|Δ|max的值。因给定条件是R(标)可略，因此，|Δ|max≤6.75伏，则被检仪器合格；若|Δ|max＞6.75伏，则仪器不合格。
注意，这种按误差理论的检定条件与检定方法，是历史上一贯应用的方法；也是当今仍在应用的方法。《DL/T 980-2005数字多用表检定规程》就是证明。
分辨力也好，重复性也好，以及机械不良等等因素引入的被检仪器的误差，都将体现在|Δ|max之中。检定是实测被检仪器的测量误差范围的总体，不能去拆分被检仪器的误差范围。实际测量的|Δ|max，只要它不大于指标值6.75伏，该仪器就是合格。
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2 按不确定度论办事，行不通。
先生说过：任何检定规程，不仅仅是DL/T 980-2005数字多用表检定规程，都不会出现不确定（度）评定要求，因为检定规程通过审定、发布实施的前提是U95≤MPEV/3，这是规程编制者在设计检定方法时必须验证的……
这是一句没谱的、不符合实际情况的错话。规程里要求的是标准的性能有多高，DL/T 980-2005要求标准的误差范围小于被检仪器的误差范围的1/5到1/10，并没有要求U95≤MPEV/3。注意，U95中包含有被检仪器的分辨力、重复性等因素，是没法提出要求的。被检仪器的规格层次，可能有几个数量级之差，怎能规定得出？况且一些测量仪器的整体误差（如频标比对器）其指标就是分辨力与重复性，就是说其总指标MPEV基本相当于U95（误差理论总指标是MPEV,不确定度论的总指标就是仪器不确定度U95）要求U95≤MPEV/3，大致就是要求U95≤U95/3，这是一个逻辑错误，不可能的。有些仪器是部分量程出现这种问题，如普通频率计的低频部分或小采样时间测频；游标卡尺与千分表的量程的低段等等。
制定检定规程，无法论证与给出前提条件U95≤MPEV/3。说检定规程必然已经论证过U95≤MPEV/3，且是检定规程的前提条件，是一句空话，完全脱离实际。
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3 检定工作实例
具体检定工作，按不确定度论的要求，则通常没法进行。如先生所举例子，MPEV是6.75伏，而U95是4.8伏。要按下式判别合格性：
|Δ|max ≤ MPEV ― U95 （6）
即
|Δ|max ≤ 6.75伏―4.8伏
|Δ|max ≤ 1.95伏
合格性通道中有71%被待定区占去，只剩29%；这就是说， 100台本来合格的仪器，大约有70台，不能判为合格。这是无故耽误事！
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检定员直接输出300V标准电压至被检表(非规程方法)，检定员说可以准确估读到1/3格，即5V，被检仪器的可能的读数为：
290伏/ 295伏/ 300伏/ 305伏/ 310伏
按误差理论，读数为295伏、300伏、305伏的仪器合格（差值绝对值小于6.75伏）；而按不确定度的方式，只能是读数是300伏的被检仪器合格；而读数295伏、读数305伏的仪器都不能判为合格。
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比较好的办法是利用标准的较高的分辨力。调整标准的输出，使被检仪器的读数值为一整数，看此时的标准读数与被检仪器读数（整数）的最大差值，这就是计量要寻找的|Δ|max。如本案例，调整标准之值，使被检表读数为300伏，这时标准的值（设标准的分辨力为0.25伏）在293.5伏到306.5伏之间，被检仪器都可以判为合格。否则不合格。这是误差理论的作法。注意：被检仪器分辨力的作用，体现在|Δ|max中。
按不确定度论，同样用调标准值的方法，要求用判别公式：
|Δ|max ≤ MPEV ― U95 （6）
标准的值只有为298.05伏到 301.95伏才能判为合格。就是说：不确定度理论的错误，导致合格性通道无故缩小70%，只剩30%. 不确定度论的这一严重的错误，难道不是很清楚吗！
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史锦顺 发表于 2015-6-9 16:25
sosboxing先生：你们公司的电流源，是你们公司自己生产的吗？
是什么型号？该机的说明书 ...
史锦顺 发表于 2015-6-9 16:25
sosboxing先生：你们公司的电流源，是你们公司自己生产的吗？
是什么型号？该机的说明书 ...
插入图片没弄好，再发一遍！
上图是交流稳压电源的显示界面，红线标注部分为仪器可设定输出的内容，Vac,F,Vdc,
其他六项是测量参量，图中可见到电流 I测量分辨率为0.01A
电流测量的指标（下图）：（24A是峰值，有效值量程为2A or 4A）
本帖最后由 csln 于 2015-6-11 16:13 编辑
sosboxing 发表于 2015-6-11 15:42
您早点帖出这些东西就不会有那么多争议了，想对您说的是
1、帖出的东西并不能说明显示电流值是电流测量值，并不排除除是程控输出值，这种情况分辨力不会引入不确定度
2、均方根值（有的厂家称有效值）是相当于1σ的值，不是您理解的MPE
本帖最后由 csln 于 2015-6-11 17:13 编辑
比较好的办法是利用标准的较高的分辨力。调整标准的输出，使被检仪器的读数值为一整数，看此时的标准读数与被检仪器读数（整数）的最大差值，这就是计量要寻找的|Δ|max。如本案例，调整标准之值，使被检表读数为300伏，这时标准的值（设标准的分辨力为0.25伏）在293.5伏到306.5伏之间，被检仪器都可以判为合格。否则不合格。这是误差理论的作法。注意：被检仪器分辨力的作用，体现在|Δ|max中。
先生可曾考虑过检定规程为什么要求这样检定？为什么不能象81#举的例子那样检定？
规程要求这样检定的目的是为了把被检表分辨力的不确定度分量降低到最小，实际上被检表表象引入不确定度只剩下了测量重复性，这个重复性包括标准器因素、被检表因素、检定员因素、电源、温度等因素，正是为了满足U95≤MPEV/3，楼上例子测量方法在300V点，测量值假如为295V，读数时估读误差有5V，能判断合格与否吗？例子就是为了说明仅满足 MPEV标准≤MPEV被检/3 是不不够的，同样可以说明|Δ|max ≤ MPEV ― R(标) 是个基本没用的公式，仅仅是在测量分辨力、测量重复性误差远小于其他分量时才行，同|Δ| ≤ MPEV ― U95 没有可比性
按不确定度论，同样用调标准值的方法，要求用判别公式：
|Δ|max ≤ MPEV ― U95 （6）
标准的值只有为298.05伏到 301.95伏才能判为合格。就是说：不确定度理论的错误，导致合格性通道无故缩小70%，只剩30%. 不确定度论的这一严重的错误，难道不是很清楚吗
好象是还没明白测量方法对测量不确定度的影响
本帖最后由 csln 于 2015-6-11 17:11 编辑
这是一句没谱的、不符合实际情况的错话。规程里要求的是标准的性能有多高，DL/T 980-2005要求标准的误差范围小于被检仪器的误差范围的1/5到1/10，并没有要求U95≤MPEV/3。注意，U95中包含有被检仪器的分辨力、重复性等因素，是没法提出要求的。被检仪器的规格层次，可能有几个数量级之差，怎能规定得出？况且一些测量仪器的整体误差（如频标比对器）其指标就是分辨力与重复性，就是说其总指标MPEV基本相当于U95（误差理论总指标是MPEV,不确定度论的总指标就是仪器不确定度U95）要求U95≤MPEV/3，大致就是要求U95≤U95/3，这是一个逻辑错误，不可能的。有些仪器是部分量程出现这种问题，如普通频率计的低频部分或小采样时间测频；游标卡尺与千分表的量程的低段等等。
关于这个问题，多说无益，先生干了那么长时间计量，肯定有不少计量界大腕朋友，联系一下他们就可以证实这是不是空话，先生供职的27所，现在想必还有不少先生的学生，如果有编制过国家规程的，先生可以问一下，即使不编制规程，一定会参加过国家规程宣贯会，看看宣贯材料有没有这一部分内容
关于频标比对器比对不确定度（阿仑标准差）检定方法测量结果不确定度是否能满足U95≤MPEV/3，先生看看马先生编写的书就明白了
csln 发表于 2015-6-11 16:07
您早点帖出这些东西就不会有那么多争议了，想对您说的是
1、帖出的东西并不能说明显示电流值是电流测量 ...
图片已经写的很清楚了：
量测——电流——解析度——0.01A
本帖最后由 csln 于 2015-6-11 19:22 编辑
sosboxing 发表于 2015-6-11 16:56
图片已经写的很清楚了：
量测——电流——解析度——0.01A
源和表类仪器、源 和 表最基本的器件DAC、ADC 使用同一个术语：分辨力（resolution)，你贴那东西能说明什么问题呢？
本帖最后由 csln 于 2015-6-11 20:13 编辑
这是5522A指标的一部分，您能认为这个分辨力是测量值的分辨力吗？这个分辨力会引入不确定度分量吗？
本帖最后由 csln 于 2015-6-12 08:37 编辑
sosboxing 发表于 2015-6-11 16:56
图片已经写的很清楚了：
量测——电流——解析度——0.01A
仅从贴出的这部分看，您的理解也有道理，还是把生产厂和型号帖出来更能说明问题
本帖最后由 csln 于 2015-6-12 09:03 编辑
分辨力也好，重复性也好，以及机械不良等等因素引入的被检仪器的误差，都将体现在|Δ|max之中。检定是实测被检仪器的测量误差范围的总体，不能去拆分被检仪器的误差范围。实际测量的|Δ|max，只要它不大于指标值6.75伏，该仪器就是合格。
对于指针式电工仪表的准确度等级中是否包含分辨力误差，想必先生心知肚明，指针式仪表假如用直接输入标准值的方法检定，是不是只要测量|Δ|不大于MPEV就合格，想必先生也心知肚明
本帖最后由 csln 于 2015-6-12 09:33 编辑
检定员直接输出300V标准电压至被检表(非规程方法)，检定员说可以准确估读到1/3格，即5V，被检仪器的可能的读数为：
290伏/ 295伏/ 300伏/ 305伏/ 310伏
按误差理论，读数为295伏、300伏、305伏的仪器合格（差值绝对值小于6.75伏）；而按不确定度的方式，只能是读数是300伏的被检仪器合格；而读数295伏、读数305伏的仪器都不能判为合格。
准确说，应该是290V、295V、305V、310V均既不能判定合格，也不能判定不合格，因为这本来就是个错误的检定方法，判定模糊限太大，先生认为按误差理论能判定295V、305V合格恰恰证明了不确定评定的重要性，也说明了|Δ|max ≤ MPEV ― R(标) 公式的不可用
如果按先生认为的那样，就没有必要化大价钱去购买高分辨力的校准源，校准源能小小调整指针能到刻线附近就成了，检定员也没必要检定时在度盘上架上放大镜眯着眼睛 把眼眯酸了去看指针、刻线、度盘上镜子是否三者重合了，难不成这种化钱、费时、低效率的检定方法是可以不用的吗
补充内容 (2015-6-12 11:42):
度盘上镜子应为度盘上镜子显示指针
按照本例所用电源，不确定评定中，不应考虑电源的影响，只评定多用表和被检直流电子负荷引入的分量。
规矩湾先生将电源的影响考虑进去，有点画蛇添足！
本帖最后由 规矩湾锦苑 于 2015-6-13 13:23 编辑
我还是要再重复强调一下不确定度评定一定要识别清楚输出量和输入量。被测量就是输出量，被测量不明，不确定度评定就没有目标，评定过程和评定结果也就一定莫名其妙。
我们现在离开了楼主直流电子负载电压测量不确定度评定，转而讨论46楼提出的另一个不确定度评定问题，即“使用标准交流电流表校准交流电源0.5A这个值”的不确定度评定。被测对象变了，被测量也变了，测量模型与楼主的被测量测量模型相比变了没有呢？
我只是提醒，误差是两个值相减，因此误差的测量模型至少有两个输入量。但，示值测量是将标准值赋予被检对象，因此示值的测量模型则很可能只有一个输入量。离开了测量模型谈论不确定度分量的多寡，谈论遗漏了哪些分量，“画蛇添足”重复了哪些分量，将毫无意义。在讲述输出量的各输入量分别引入的标准不确定度分量分析中有没有遗漏或重复时，把正确的测量模型往桌面上一摆，立即清清楚楚，明明白白了。
规矩湾锦苑 发表于 2015-6-13 13:16
我还是要再重复强调一下不确定度评定一定要识别清楚输出量和输入量。被测量就是输出量，被测量不明，不 ...
要讲事实，讲理论。
不确定度论的说教中，模型是一大败笔。
模型是什么东西？就是蒙人，就是误导。
不确定度论的许多错误，来自“模型”。
不确定度评定的许多弊病，也是来自“模型”。
例如对被检仪器分辨力的不当处置，正是来自所谓的模型。且看下文实例。