Hospitality Suite

Monday





Dolphin Beach Area - Swan & Dolphin
Hotel
6:00 to 8:00
Fluke Sponsored Presidents Reception
5:45 for Met-Support-Gold members
Don’t forget
Morning Agenda
Introductions & your goals for today?
 New Products, Promotions, Procedures,
etc…
 MET/CAL Version 7.2 Service Pack 1







What is available in SP1?
Using the List Box Math Function
Reading and Writing to a INI file
Building Perfect reports
A Basic SQL Tutorial
Utility
Afternoon Agenda



MET/CAL and Z540.3 – Risk Assessment
User Forum – Tony Giannasca – Analog Devices
Drawing & Prizes
Introductions…



Name…
Company…
What you do…
What version of MET/CAL…
 What you hope to learn today…
 Anything you want to share…

Training
http://training.fluke.com
Training

Classroom Training:
New

New

New








MET-101 Basic Hands-on Metrology
MET-301 Advanced Hands-on Metrology
MET-302 Hands-on Metrology Statistics
Cal Lab Management for the 21st Century
MET/CAL Database and Reports
MET/CAL Procedure Writing
MET/CAL Advanced Programming Techniques
Metrology for Laboratory Personnel (CCT Exam
Preparation)
Product Specific Training
On-Site Training is available for most of the above classes
Training

Classroom Training:






Principles of Temperature Metrology
Advanced Topics in Temperature Metrology
Infrared Temperature Metrology
Precision Pressure Calibration
Setting Up and Using COMPASS Pressure Software
Gas Flow Calibration Using MOLBLOC/MOLBOX
Training

Web-Based Training:



Self-Paced Web-Based Training:




New
MET/CAL Database & Reports
MET/CAL Procedure Development

Introduction to Measurement and Calibration
Precision Electrical Measurement
Measurement Uncertainty
AC/DC Calibration and Metrology
Metrology for Laboratory Personnel (CCT Exam Preparation)
Training
New
MET-302 Basic Hands-on Metrology Uncertainty
•
•
•
•
•
•
•
Three Days with Hands-on Labs
Reviews Basic Elements of Uncertainty
Methods of Calculating Measurement Uncertainty
How to Create an Uncertainty Budget
Guardbanding and Risk Analysis Techniques
 A discussion of Z540.3
Uncertainty in Temperature Measurement
Uncertainty in Pressure & Flow Measurement
Only Given in Everett WA
Instructor - Led
Metrology for Laboratory Personnel
(CCT Preparation Course)






Created with permission of ASQ
Six sections based on CCT Primer by QCI
Includes CCT Primer
Includes ASQ’s “The Metrology Handbook”
5 Days
US – Canada only
Also available
As Self-Paced
Web Course
Questions…
Priority Gold Care Plan

Features…








Available on all FPM instruments
Includes one, three or 5 annual calibrations
Free repairs complete with calibration
3 day in-house turnaround for calibration
Priority turnaround for repairs
Discounts on upgrades -10%
Discounts on training -20%
Free two-day return freight on calibrations
and repairs
Gold Procedures…
Questions…
Metrology Users Community
The User Community Web Site











Total registered users: 610
Total forum subscribers: 224
Total forum messages: 136
Unique visitors per month (addresses): 1,100
Unique visits per month (sessions): 2,000
Page views (humans): 8,000
Hits (humans): 31,000
Total number of FAQ's: 160
Total downloads: 852
Total MET/CAL procedures: 837
Total related links (URLs): 16
Questions…
Fluke Papers…
Session/Date
Author
Paper Title
2E
Mon, Aug 4
2:30-4:00 pm
Paul Roberts
Understanding Phase Noise in Calibration Applications
4C
Tues, Aug 5
10:45-12:15 pm
Rick Walker
Evaluation of Out-of-Tolerance Risk in Measuring and Test
Equipment
4E
Tues, Aug 5
10:45-12:15 pm
Michael Bair, Martin Girard
and Pierre Delajoud
The Design And Implementation Of A Fully Automated
Crossfloat System For The Comparison Of Piston
Gauges In Both Gauge And Absolute Measurement
Modes
6E
Wed, Aug 6
8:30-10:00 am
Mingjian Zhao
How Does Temperature Non-uniformity of an Annealing
Furnace Affect SPRT Stability?
7E
Wed, Aug 6
10:45-12:15 pm
Thomas Wiandt
Low Voltage Traceability and Uncertainty Evaluation for
High Accuracy Thermocouple Calibration Utilizing a
Modern Automated Potentiometer
7E
Wed, Aug 6
10:45-12:15 pm
Frank Liebmann
Infrared Uncertainty Budget Determination in an Industrial
Application
TBD (Reserve)
Dave Deaver
Characterizing the Power Coefficient of Current Shunts
TBD (Reserve)
Rong Ding
Quality Control of Fixed-Point Cells During Manufacturing
Fluke Tutorials…
Session/Date
Author
Tutorial Title
T13
Sun, Aug 3
8 am – 12 noon
Tom Wiandt and Mike
Coleman
Fundamentals of Temperature Calibration
T14
Sun, Aug 3
8 am – 12 noon
Michael Bair and Karl Kurtz
Very Low Pressure Calibrations
T18
Sun, Aug 3
8 am – 12 noon
Dave Deaver
Good, Bad or Indeterminate: Who Makes the Call?
T19
Sun, Aug 3
1:00-5:00 pm
Tom Wiandt and Mike
Coleman
Temperature Calibration Uncertainty Analysis
T20
Sun, Aug 3
1:00-5:00 pm
Michael Bair and Karl Kurtz
Very Low Pressure Applications
New MATH Functions
List Box Window Functions
lbAdd
add item to list box
lbAddList
add list of items to list box
lbAddV
add item to list box, with associated variable
lbButton
add button to list box window
lbButtonFocus set default button (with keyboard focus)
lbClose
close list box window
lbConf
configure list box window
lbGetSel
get name of n-th selected list box item
lbGetSelX
get index of n-th selected list box item
New MATH Functions
List Box Window Functions
lbNew
lbResp
lbSel
lbSelCount
lbSelX
lbShow
lbText
lbUnSel
lbUnSelX
create new list box window
get user's text response
initially select list box item (by name)
get count of selected list box items
initially select list box item (by index)
display list box window
add text line to list box window
remove initial selection of item (by name)
remove initial selection of item (by index)
New MATH Functions
CRND
Purpose: Rounds a number to the closest
multiple of a specified increment.
Example 1:
MATH MEM = CRND(1.234, 0.01)
DISP [MEM]
CRND rounds 1.234 to 1.23.
Example 2:
MATH MEM = CRND(94.0, 10)
DISP [MEM]
CRND rounds 94.0 to 90.
New MATH Functions
NUM
Purpose: NUM extract the 1st numeric value
from a string.
Example:
MATH v = NUM("abc1.2def")
The call to NUM above assigns the
value 1.2 to the variable "v".
New MATH Functions
NUMN
Purpose: NUMN extracts the n-th embedded
numeric value from a string.
Examples:
MATH v = NUMN("abc1.2def3.4ghi5.6jkl", 2)
The call to NUMN above assigns the value 3.4
to the variable "v" because 3.4 is the 2nd embedded
numeric value in the string.
MATH v = NUMN("abc1.2def3.4ghi5.6jkl", 0)
The call to NUMN above assigns the value 3
to the variable "v" because the string contains 3
embedded numeric values (namely 1.2, 3.4, and 5.6).
Using the List Box Functions
Creates a New List Box
1.003
1.004
1.005
1.006
1.007
1.008
1.009
MATH
MATH
MATH
MATH
MATH
MATH
MATH
Window
andtext
names
it list box
Add
line to
+Sel initially selects the first
window
Add item
to in
listthe
box,
item
listwith it’s
Id = lbNew("8508A SPRT/PRT")
associated variable.
lbText(Id, "Select operation to be performed.")
LbConf(Id, “+Sel")
Displays the
a list box
lbAddV(Id, "Review",
"@Review")
window and
waits for the
lbAddV(Id, "Delete",
"@Delete")
user's response
lbAddV(Id, "Insert",
"@Insert")
lbShow(Id)
Using the List Box Functions
1.003 MATH
1.006 MATH
1.007 MATH
1.008 MATH
1.005
1.004 MATH
Id = lbNew("8508A SPRT/PRT")
lbAddV(Id, "Review", "@Review")
lbAddV(Id, "Delete", "@Delete")
lbAddV(Id,LbConf(Id,
"Insert", "@Insert")
MATH
“+Sel")
lbText(Id, "Select operation to be performed.")
1.009 MATH
lbShow(Id)
Using the List Box Functions
1.019
1.020
1.021
1.022
1.023
1.024
1.025
1.026
1.027
1.028
1.029
MATH
MATH
MATH
MATH
MATH
MATH
WHILE
MATH
DISP
MATH
ENDW
id = lbNew("lbSelCount Example")
lbConf(id, "+multi")
lbAddList(id, "A,B,C,D,E,F,G")
lbShow(id)
SelCount = lbSelCount(id
i=1
(i <= SelCount)
str = lbGetSel(id, i)
Selected String #[v i] = [v str]
i=i+1
Using the List Box Functions
#Conversion Example
MATH
Id = LBNEW("Conversions")
MATH
LBTEXT(Id, "Select conversion:")
MATH
LBCONF(Id, "+Multi,+SelReq")
MATH
LBADDV(Id, "dBm to volts", "dBmVolts")
MATH
LBADDV(Id, "volts to dBm", "VoltsdBm")
MATH
LBADDV(Id, "dBm to watts", "dBmWatts")
MATH
LBADDV(Id, "watts to dBm", "WattsdBm")
MATH
LBBUTTON(Id, "Ok")
MATH
LBBUTTON(Id, "Exit")
Using the List Box Functions
# Edit box
1.009
1.010
1.011
1.012
1.013
1.014
1.015
1.016
MATH
MATH
MATH
MATH
MATH
MATH
DISP
MATH
id = lbNew("Error")
lbconf(id, "+Edit")
lbbutton(id, "OK")
lbtext(id, "Enter the error code...")
op = lbShow(id)
Text = lbresp(id)
You entered "[v Text]"
lbclose(id)
The VISA FSC
The VISA FSC provides direct access to instruments using the
National Instruments VISA library (NI-VISA).
To enable the "VISA" FSC in MET/CAL V7.20 SP1 it is necessary to
add the following line to the [Startup] section of the MET/CAL
initialization file ("metcal.ini"):
NI-VISA = Yes
The "VISA" FSC may currently be used with instruments on the
following interface types:
Ethernet
IEEE-488
RS232
USB
The VISA FSC
Licensing Requirements:
MET/CAL users must comply with National Instruments' NI-VISA
licensing requirements.
There are two types of National Instruments NI-VISA licenses,
a "Deployment License" and a "Development License.
To use the VISA FSC in MET/CAL you must have a Development
License.
If you own a National Instruments IEEE-488 interface,
you automatically have the right to use NI-VISA at no
additional cost.
The VISA FSC
Installation:
Before using a MET/CAL procedure that contains VISA statements
it is necessary to install the NI-VISA library.
The library is not distributed with MET/CAL. You must obtain
the library from National Instruments and follow National
Instruments' instructions.
If you have not already installed NI-VISA, go to
"http://www.ni.com/visa" to download the library.
The VISA FSC
When the procedure runs the first
VISA FSC line it will prompt for
An example of how to use the VISA FSC with a SCPI compliant DMM on
type of interface and address of
any of the interfaces:
the UUT.
2.001
2.002
2.003
2.004
2.005
2.006
2.007
2.008
2.009
2.010
2.011
2.012
2.013
2.014
LABEL
VISA
RSLT
RSLT
RSLT
HEAD
TARGET
VISA
VISA
5520
TARGET
VISA
MATH
MEMCX
DC_VOLTAGE
*RST
=
=DC VOLTAGE
=
DC VOLTAGE:{ 100 mV Range}
-p
FUNC "VOLT:DC";:VOLT:DC:RANG 0.1;NPLC 1
FUNC "VOLT:DC";:VOLT:RES MIN
100.0000mV
S 2W
-m
INIT;FETCH?[I]
MEM = MEM / 1e-3
100 100.0000mV 55P% 40P/
Features Available in SP2
• VSET/TSET sets Default UUT COM Port
• MATH Function Generates List of COM Ports on PC
• The PICE FSC now allows "-s <expr> :<results text>“
• The PICE FSC now allows “-s <results text> : <prompt>”
• Functions to Convert Numeric Registers to CSV Lists
New MATH Functions – SP2
DELETE
Purpose: Deletes a specified line in a
specified file.
Example:
DELETE("c:/file.txt", 4)
The call to DELETE above deletes
line 4 in the file "c:/file.txt".
New MATH Functions – SP2
APPEND
Purpose: Appends a line to a file.
If the file does not exist, it is first created.
The line is automatically CRLF-terminated.
Example:
APPEND("c:/file.txt", "abc")
The call to APPEND above appends the
line "abc" to the file "file.txt" on the "C:" drive.
New MATH Functions – SP2
INSERT
Purpose: Inserts a line in a file before a
specified line number.
Example:
INSERT("c:/file.txt", "abc", 4)
The call to INSERT above inserts the
line "abc" into the file "c:/file.txt" before line 4.
New MATH Functions – SP2
READ
Purpose: Reads a specified line from a file.
Example: line = READ("c:/file.txt", 4)
The call to READ above reads line
4 from the file "c:/file.txt".
New MATH Functions – SP2
WRITE
Purpose: Writes a line to a file after a
specified line number.
Example:
WRITE("c:/file.txt", "abc", 4)
The call to WRITE above writes the
line "abc" to the file "c:/file.txt"
after line 4.
New MATH Functions – SP2
REPLACE
Purpose: Replaces a specified line in a
specified file.
Example:
REPLACE("c:/file.txt", "def", 4)
The call to REPLACE above replace
line 4 in the file "c:/file.txt" with the
new line “def".
New MATH Functions – SP2
CLOSE
Purpose: Replaces a specified line in a
specified file.
Forces the closing of a file before procedure
termination
New MATH Functions – SP2
LINECOUNT
Purpose:
Example:
Count lines of file.
n = LineCount("c:/file.txt")
Will put the line count into variable n
New MATH Functions – SP2
STRIP
Purpose: STRIP strips leading and trailing
spaces from a string.
Example:
MATH s = STRIP(" a b c ")
The call to STRIP above assigns the
value "a b c" to the variable "s".
The 3 leading spaces and the 3
trailing spaces have been removed.
The embedded spaces (between 'a'
and 'b', and between 'b' and 'c')
are still there.
New MATH Functions – SP2
STRIPT
Purpose: STRIPT strips trailing spaces from
a string.
Example:
MATH s = STRIPT(" a b c ")
The call to STRIPT above assigns the
value " a b c" to the variable "s".
The 3 trailing spaces have been
removed. The leading spaces, and
the embedded spaces (between 'a'
and 'b', and between 'b' and 'c'),
are still there.
New MATH Functions – SP2
STRIPL
Purpose: STRIPL strips leading spaces from
a string.
Example:
MATH s = STRIPL(" a b c ")
The call to STRIPL above assigns the
value "a b c " to the variable "s".
The 3 leading spaces have been removed.
The embedded spaces (between 'a' and
'b', and between 'b' and 'c'), and
the trailing spaces, are still there.
New MATH Functions – SP2
LINREG
Purpose: calculates the linear regression
from a set of (X,Y) data points.
Example:
MATH x = "1, 2, 3"
MATH y = "1.3, 1.9, 3.1"
MATH LinReg(x, y, "SlopeVar", "InterceptVar")
The call to "LinReg" performs a least-squares fit to determine
the line that best fits the specified data points ((1,1.3), (2,1.9),
and (3,3.1)).
New MATH Functions – SP2
FindCSV - find line matching specified
comma-separated values
FindCSVi - case-insensitive version of FindCSV
FindCSVn - find line matching specified commaseparated values at specified indices
FindCSVni - case-insensitive version of FindCSVn
New MATH Functions – SP2
File-Based Accuracy Lookup Functions
ACCF - 1-param accuracy spec lookup
ACCF2 - 2-param accuracy spec lookup
ACCFF - 1-param accuracy floor spec lookup
ACCFF2 - 2-param accuracy floor spec lookup
ACCFG - 1-param accuracy gain spec lookup
ACCFG2 - 2-param accuracy gain spec lookup
Uses the accuracy file name, rather than the name
of a configured instrument, as the first argument.
First Create an INI File
[Sample-742A-1]
Nominal_Value = 1
Certified_Value = 1.0000118
Cal_Date
= 03-Nov-07
[Sample-742A-10]
Nominal_Value = 10
Certified_Value = 9.999998
Cal_Date
= 03-Nov-07
[Sample-742A-100]
Nominal_Value = 100
Certified_Value = 100.00044
Cal_Date
= 03-Nov-07
Read from your ini file
Example:
MATH S[1] = “C:\\METCAL\\STATION\\742-X.INI”
MATH M[1] = RIF(S[1], “Sample-742A-1”, “Certified_Value”)
WILL RETURN THE VALUE: 1.0000118
Write to your ini file
Example:
MATH S[1] = “C:\\METCAL\\STATION\\742-X.INI”
MATH S[2] = 1.0000121
MATH M[1] = WIF(S[1], “Sample-742A-1”, “Certified_Value”,S[2] )
WILL WRITE THE VALUE OF S[2] INTO THE INI SECTION
[Sample-742A-1] TO LINE Certified_Value
Write to your ini file
Example:
MATH S[1] = “C:\\METCAL\\STATION\\742-X.INI”
MATH S[2] = 1000.0012
MATH M[1] = WIF(S[1], “Sample-742A-1K”, “Certified_Value”,S[3] )
WILL CREATE A NEW SECTION CALLED [Sample-742A-1K] AND
PUT THE VALUE IN S[3] INTO THE Certified_Value LINE.
Questions…
MET/CAL and Z540.3 Risk Assessment
ANSI/NCSL Z540.3, sub-clause 3.11
defines TUR as:
“Test uncertainty ratio”
“The ratio of the span of the tolerance of a measurement quantity
subject to calibration, to twice the 95% expanded uncertainty of the
measurement process used for calibration.”
T oleranceof UUT
TUR  2 x 2 ExpandedUncertain
ty of theCalibration Process
MET/CAL and Z540.3 Risk Assessment
ANSI/NCSL Z540.3, sub-clause 5.3 b):
“Where calibrations provide for verification that measurement
quantities are within specified tolerances, the probability that
Incorrect acceptance decisions (false accept) will result from
calibration tests shall not exceed 2% and shall be documented.
Where it is not practicable to estimate this probability, the test
uncertainty ratio shall be equal to or greater than 4:1.”
“NOTE: Achieving these requirements may involve adjustment and
management of calibration system parameters such as: measurement
reliability, calibration intervals, measurement uncertainty, calibration
tolerances, and/or guard bands.”
A Little History
• A number of years ago, standards were required
to be at least ten times better than the products
being compared to them; a test uncertainty ratio
(TUR) of 10:1.
• Increased performance in the products being
tested has resulted in a reduction of acceptable
TURs to 4:1 with some arguing that 3:1 is
sufficient.
A Little History
• At a minimum, the points less than a certain TUR
(usually 4:1) must be noted on the calibration
report.
• At the other extreme, a detailed statistical
uncertainty analysis must be undertaken to
establish the uncertainty of the calibration.
A Little History
• Guardbanding has been used by some to
provide some middle ground.
• However, there are many guardband strategies
that become calibration lab policy with little
understanding of their effect on false test
decisions.
Why Worry About Risks?
• There always is a possibility that equipment
parameters tested as ‘in-tolerance’ in a
calibration laboratory are actually ‘out-oftolerance’.
• The probability of this out of tolerance condition
being tested as in-tolerance, is called false
accept risk or consumer’s risk.
• The probability of the in-tolerance condition
being tested as out-of-tolerance is called false
reject or producer’s risk.
Why Worry About Risks?
• The consumer’s risk is aimed at the point of
view of the end user and is the probability
that he/she will accept out-of-tolerance items
• While the producer’s risk is aimed at the
point of view of the calibration laboratory and
is the probability that the lab will reject intolerance items.
Why Worry About Risks?
The producer (calibration lab) will feel the impact of rejecting intolerance items through the cost of adjusting and re-verifying the
item, not to mention the perceived view of quality.
The calibration interval for the instrument may be unnecessarily
shortened, causing more work for the lab.
The consumer’s risk is much greater. The item that was verified
to be in-tolerance is actually out-of-tolerance and perhaps will
affect an entire production line run, causing a massive recall, or
worse, cause a failure that results in death!
Product Uncertainty
The output of many manufacturing processes can be described
with a normal probability distribution. The probability distribution
about the mean is shown below.
-3
-2
-1
1
2
3
The probability that the performance of the unit under test
(UUT) is within its specifications is the area under the curve
between the specification limits (SL), assumed to be centered
about the mean.
Product Uncertainty
Specification Limits
Probability Unit
Conforms (%)
Probability Unit Does
Not Conform (%)
±1.0s
68.3
31.7
±1.5s
86.6
13.4
±2.0s
95.4
4.6
±2.5s
98.8
1.2
±3.0s
99.7
0.3
The Measurement Problem
UUT
Measured Value
Non
Conformance
-3
Non
Conformance
Conformance
Zone
-2
Lower
Spec
Limit
-1
1
2
3
Upper
Spec
Limit
If we naively assume the test standard measures a
true value, we will make a pass/fail decision based
solely on the UUT test limits.
The Measurement Problem
Producer
Risk
Consumer
Risk
The Measurement Problem
Note that decreasing the TUR increases the risk for
both consumer and producer. The risk is also
sensitive to the specifications of the UUT.
The Measurement Problem
4:1 TUR,toand
limits set
Even if the TURWith
werea reduced
1:1,specification
the
With a more
conservatively
specified unit at
at 2s,
consumer
consumer risk would
bethe
only
0.5 %. risk is 0.8 %.
SL=2.5s, the chance of accepting defective
units would be 0.25 %.
Guardbanding
Uncertain
Zone
UUT
Measured Value
Non-Conformance
Zone
Non-Conformance
Zone
Calibrator
Uncertainty at
Nominal Output
Conformance
Zone
-3
-2
Lower
Spec
Limit
-1
1
2
3
Upper
Spec
Limit
When the UUT measurement reading is close to
the test limits, the uncertainty of the calibrator must
be considered.
Guardbanding
What happens when you can not meet the 4:1 requirement?
Your choices are:
 choose to lower the level of confidence in the
measurement,
 invest in more precise standards, or
 undergo an analysis of the uncertainties and
document the deviations from the required TUR
 set test limits different from specification limits
(Guardband)
Guardbanding
Guardband limits cannot be used to reduce both false accept
risk and false reject risk. The reduction of one increases the
other.
The price to be paid for controlling the consumer
The probability of making
increases
with
riskfaulty
is thattest
the decisions
producer risk
can be much
decreasing TURs
higher than for a 4:1 TUR
When the TUR is less than 4:1 the test limits can be placed to
set the desired level of consumer risk or producer risk.
For example, it is possible, with a 2:1 TUR, to keep the same
risk of accepting defective units as a 4:1 TUR by setting the
test limits (TL) inside the specification limits.
Guardbanding Strategies
CR = CR4:1
The guardband factor (k) which is defined as a multiplier
of the specification limit (SL) to produce the test limit
(TL):
TL = k * SL
The usual factor is 0.8 or 80 % of the specification limit.
In this strategy, the test limit is set to maintain the same
risk of false accepts as a 4:1 TUR would produce.
Guardbanding Strategies
CR = CR4:1
MET/CAL Example:
1.001
1.002
1.003
1.004
VSET
VSET
VSET
VSET
Allows the procedure to
Set the test spec limits.
tighten
the Test
specSummary
limits by
Sets
the Post
In this case it is set to
a to
specified
factor
Window
only show
after a PASS,
80%
PASS INDETERMINATE,
or FAIL INDETERMINATE result.
NMEAS = 5
GB = DIRECT
GBF = .8
GB_PTS = P
Guardbanding Strategies
k 1-
1
TUR
When the TUR is less than 4:1, this strategy subtracts
the uncertainty of the UUT from the specification limit to
obtain the test limit.
This method suffers from being quite conservative from a
false accept perspective at the expense of rejecting a
high number of conforming units.
This method has a large discontinuity at 4:1 TUR. At 4:1,
the UUT may be tested at the specification limit;
however, at a TUR of 3.999, the test limit must be
reduced to 75 % of the specification limit.
Guardbanding Strategies
k  1.25 -
1
TUR
When the TUR is less than 4, the test limit is set to 1.25
times the specification limit minus the uncertainty of the
standard.
NCSL’s Recommended Practice RP-10 recommends this
method, which improves on some of the limitations of the
last method.
It is continuous at 4:1 TUR and does not require near the
producer risk penalty, especially for the higher TURs.
For Example at 2:1
k  1.25 -
1
 .75
2
Guardbanding Strategies
k  1.25 -
1
TUR
MET/CAL Example:
1.001
1.002
1.003
1.004
VSET
VSET
VSET
VSET
NMEAS = 5
GB = DIRECT
GBF = .75
GB_PTS = P
Guardbanding Strategies
k  1-
1
TUR 2
This is the RSS strategy, the test limit is determined by
taking the square root of the specification limit squared
less the square of the uncertainty of the standard.
Used by the Fluke Corporation and others for a number
of years, this strategy has only a slight false reject
penalty over the constant risk strategy.
For a confidence interval of ±2.0, it has a false accept
risk of about 0.6 % as compared to the 0.8 % risk for the
first strategy of maintaining the same risk as 4:1.
For Example at 2:1
k  1-
1
 .87
4
Guardbanding Strategies
k  1-
1
TUR 2
MET/CAL Example:
1.001 VSET
1.002 VSET
1.003 VSET
NMEAS = 5
GB = RDS
GB_PTS = P
Guardbanding Strategies
Measurement Uncertainty Method
MET/CAL Example:
1.001
1.002
1.003
1.004
VSET
VSET
VSET
VSET
NMEAS = 5
GB = MU
GB_PTS = P
GBF = .8
The guardband limits are determined by tightening the
specification limits by the expanded measurement
uncertainty (or a % of the MU). – Method used in
Europe
Guardbanding Strategies
CR = CR3:1
This strategy is the same as the first strategy except that
guardband factors are selected to maintain the risk the
same as for a TUR of 3:1.
Guardbanding Strategies
Strategy
K
TUR = 4
CR
PR
K
TUR = 2
CR
PR
1)
CR=CR4:1
1.0
0.8 %
1.5 %
0.91
0.8 %
6.6 %
2)
1-1/TUR
0.75
0.02 %
10 %
0.5
0.03 %
33 %
3)
1.25-1/TUR
1.0
0.8 %
1.5 %
0.75
0.3 %
14 %
4)
RSS
0.97
0.6 %
2%
0.86
0.63 %
8.2 %
5)
CR=CR3:1
---
---
---
0.95
1.0 %
5.4 %
If you are going to be compliant with Z540.3, you must
keep the consumer risk to less than 2 % while minimizing
the producer risk or maintain a TUR greater than 4:1.
Note: The 2 % false accept applies to individual
measurement points, not to the whole instrument.
This is not a SQL class
There are lots of good SQL books available.
Search your hard disk for…
ASA SQL Reference Manual.pdf
Read about the “Select” statement
What is SQL
and why should I care about it?
SQL = Structured Query Language
SQL is a standard computer language
for accessing and manipulating databases.
Met/Track Uses it.
Crystal Reports uses it,
Bar Code Magician uses it.
Seen the Questions-Answers utility program? It uses it
Any program that gets data from Met/Track uses it.
You too can put it to good use. And I’m here to show you how.
Things that can be done with
SQL statements…
View hard to get information
Export information to data files
Import into Word® & Excel®
Quickly create new reports
What tools are available?
Sybase ISQL
This is the “Interactive SQL” editor
Filename = dbisql.exe
"C:\sybase\SQL Anywhere 8\win32\dbisql.exe"
Enter
The
Query
View
The
data
The Official way
Is there an easier way…
Use Steve’s quick & dirty ISQL
It understands the Met/Track database
Helps build and test SQL statements
Use the sample queries to learn
It’s on
your Memory Stick
Sample Queries
They’re on
your Memory Stick
Questions - Answers
It’s on
your Memory Stick
http://support.fluke.com
Sample Queries
They’re on
your Memory Stick
Use Queries
to build Crystal Reports

Watch me do it
Use Queries
to populate MS Excel & Word

Watch me do it
Use Queries
to create data files

Watch me do it
This is not a presentation on
procedure writing
or report writing…
It is assumed that you are reasonably skilled in both
Making Procedures & Reports Work Together
for the Perfect Report
Creating comprehensive, easy to read calibration reports
Making Procedures & Reports Work Together
for the Perfect Report
Creating comprehensive, easy to read calibration reports
Knowing what data is available and how it got there
Focus on Calibration reports
Calibration reports
Reports you run after a calibration
Reports that prompt for asset number and cal date
Reports that have names like
“rt_report_of_cal_ver_7.rpt”
Calibration reports
Complex
Least understood
Use a stored procedure
Little understanding of what data is available
Cannot modify without knowing the “CTAG”
1.
2.
3.
4.
Run a procedure
Run a report
View data in the Met/Track results viewer
Understand what is there
Experiment, Experiment, Experiment
Write Special Test Procedures…
Only used to generate data
Force tests to…
Pass
Fail
Be marginal
TUR > 4
TUR < 4
Be Indeterminate
Use {Brace} statements
Use OBPR statements
Have different procedures for
Meters
Scopes
Counters
Generators
Etc…
# Measurement Uncertainty
# ----------------------1.001 VSET
nmeas = 1
1.002 VSET
gb = mu
1.003 VSET
ufmt = prefix
# DC Voltage Tests
1.006 RSLT
1.007 HEAD
1.008 5500
2.001 HEAD
2.002 5500
3.001 HEAD
3.002 5500
4.001 HEAD
4.002 5500
=DC Volts
Enter 1.0022
1.0000A
Enter 1.0048
1.0000A
Enter 1.0052
1.0000A
Enter 1.0055
1.0000A
# Blank Line
5.001 RSLT
=
# AC Voltage Tests
5.002 RSLT
5.003 HEAD
5.004 5500
=AC Volts
1.0000v Test
1.0000V
# Blank Line
6.001 RSLT
=
# Frequency Accuracy
6.002 RSLT
6.003 HEAD
6.004 5500
=Frequency Accuracy
Freq = 1.000 Mhz
1.000MH
0.3%
0.5%
2W
0.5%
2W
0.5%
2W
0.5%
2W
0.3%
10kH
SI
2W
500mV
SI
2W
Write Special Test Reports…
Run reports…
Build reports
that show
available data
What information is in the database &
where do I find it?
Met/Track Results Viewer
Thou shall consider your reports
when developing procedures
Line spacing
Section Headers & Labels
Formatted numerics
Doug Hot Buttons
1. Not upper casing key words
Wrong: rslt =text
Right: RSLT =This is my text
2. Not entering the number of decimal places in the nominal
Wrong: nominal = 5
Right: Nominal = 5.000
3. Not using comments in a procedure to explain what a test is doing
4. Not spacing procedure rows for readability – or report rows
5. No references to the UUT serial number, version, Manual date, etc
6. Not using the key word colors for readability
7. Ramming together MATH expressions
Wrong: 1.058 MATH
MEM=MEM+MarkCorr
Right: 1.058 MATH
MEM = MEM + MarkCorr
8. Using lower-case names for specified MET/CAL variables. Notice how
easy it is to confuse a lower-case "L" with the number "1"!
Wrong: 1.001 MATH
l[1] = mem / m[1]
Right: 1.001 MATH
L[1] = MEM / M[1]
Use RSLT = to create blank lines
Use RSLT =xx or HEAD {xx} statements to label tests
Use “VSET UFMT = prefix” to format uncertainty
Remember, there is no MU with Slew or Go-No-Go
Remember to put all the Zero’s in the nominal
Use report formulas to combine data fields - 1.234 mV
Use report formulas to control color – Pass, Fail, Marginal
It is better to modify than create a new report
Build a template for future needs
Results Fields:
System_Actual is always the value of the source
UUT_Indicated is always the reading of the UUT
What do all those
database fields mean?
ResultsTable7.pdf
Manual Met/Cal 2.0
New look & feel
 New design tools
 New run-time tools
 New creature comforts

Manual Met/Cal 2.0

Simpler user interface
Datasheet Designer






New Wizards
New Test Types
Approval
Set # Places
Enter TUR
Enter MU
Calibration Run-Time





Hibernation
Batch reporting
Add notes
Set Actual
Averaging
Resume Calibration





Hibernation
Batch reporting
Add notes
Set Actual
Averaging
Manual Met/Cal 2.0





Hibernation
Batch reporting
Add notes
Set Actual
Averaging
Questions?…
Oops…
One more Question…
External Services Module
Monitors
Controls
Reports
Who is using?