a functional approach to fixing flow oscillation
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A Functional Approach to Fixing Flow OscillationJeremy Hammond a , Angela Haiss a , Shawn Lavigne a , Beverly Daniels a & John Allen ba IDEXX Laboratories, Inc. , Westbrook , Maineb The New Science of Fixing Things, Ltd. , Portsmouth , New HampshirePublished online: 02 Sep 2013.
To cite this article: Jeremy Hammond , Angela Haiss , Shawn Lavigne , Beverly Daniels & John Allen (2013) A FunctionalApproach to Fixing Flow Oscillation, Quality Engineering, 25:4, 385-391, DOI: 10.1080/08982112.2013.790739
To link to this article: http://dx.doi.org/10.1080/08982112.2013.790739
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A Functional Approach to FixingFlow Oscillation
Jeremy Hammond1,
Angela Haiss1,
Shawn Lavigne1,
Beverly Daniels1,
John Allen2
1IDEXX Laboratories, Inc.,
Westbrook, Maine2The New Science of Fixing
Things, Ltd., Portsmouth, New
Hampshire
ABSTRACT An automated instrument for veterinary diagnostics utilizes a
positive displacement syringe pump to move fluid within the system. Large
fluctuations in flow rate have been identified in some instruments, resulting
in system error flags. A problem-solving approach is described to show
the methodology used to understand and mitigate the failure. Multiple
problem-solving tools were incorporated where appropriate to solve this
problem. Measurement systems analysis and assembly=disassembly were
used to focus the investigation to the point where it was clearly determined
that the assembly process impacted the failure mode across the full range of
measured results. Structural and functional decomposition techniques
were used to identify the causal mechanism for the failure, leading to several
possible resolutions.
KEYWORDS assembly-disassembly, functional decomposition, measurement
systems analysis, source-load model
INTRODUCTION
An automated veterinary diagnostic instrument creates dilutions and
moves fluids utilizing a syringe pump. The syringe is mechanically linked
to a lead screw, which is driven by a geared pulley, as shown in Figure 1.
The gear pulley is coupled to a drive pulley by a timing belt. A stepper
motor increments rotation of the drive pulley, moving the belt and thus
the plunger as a function of the angular displacement and pitch of the lead
screw.
The flow from the syringe pump, shown in Figure 2, has an oscillating
pattern that causes a system fault when the flow oscillates with extreme
amplitude. This failure mode results in yield loss and rework.
PROBLEM-SOLVING APPROACH
The first step is to make sure that the measurement system is capable of
supporting the diagnostics by characterizing precision (repeatability) and
accuracy of the test device. A measurement systems analysis (MSA; Wheeler
and Lyday 1988) was performed, with results shown in Figure 3. The Y-axis
in Figure 3 is the quantitative value of the oscillation amplitude depicted in
Figure 2.
Address correspondence to AngelaHaiss, IDEXX Laboratories, Inc., 1IDEXX Drive, Westbrook, ME 04092.E-mail: [email protected]
Quality Engineering, 25:385–391, 2013Copyright # Taylor & Francis Group, LLCISSN: 0898-2112 print=1532-4222 onlineDOI: 10.1080/08982112.2013.790739
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This MSA procedure consisted of selecting multiple
pairs of instruments demonstrating the limits of flow
rate variation, pairing one high and one low instru-
ment and testing them iteratively and randomly.
The within-instrument scatter was small with respect
to the captured variation between high and low instru-
ments. The problem was repeatable within an instru-
ment and failing and functional instruments were
distinctly different, as exhibited in Figure 3. Probabilis-
tic confirmation of the measurement system provided
the confidence to move ahead with the search.
The next step was to identify the component(s) in
selected instrument pairs that drive the variation in
flow oscillation. This was a probabilistic approach
exploiting the power of paired instruments at either
end of the population tails in conjunction with a less
commonly used diagnostic approach, often called
effect-to-cause (Dale 1958). The flow rate variation,
shown in Figure 3, was used to identify instruments
at the population tails. The first step was a confir-
mation test that the problem persisted after a
disassembly–reassembly (Ott 1953) between high and
low instruments. The first action in the disassembly–
reassembly approach was to take the system apart
down to a predetermined granularity and reassemble
the system, keeping all parts contained within the
instrument. If the results were consistent with the
baseline data for each instrument, then subassembly
exchange commenced. In this instance, the instru-
ment assembly process was assumed to be low risk
so the initial step of removing and reinstalling the
pump was skipped. The risk was minimized because
the next step in the exchange procedure was to
return the pump to its original instrument for confir-
mation and, in effect, complete the disassembly–
reassembly step. Confirmation that the problem
was not attributed to the installation of the pump
was obtained when the pump assemblies were
returned to their original instruments and the large
flow rate variation reverted to the baseline levels.
FIGURE 2 Flow rate variation produced by positive-
displacement syringe pump: (a) nominal flow rate variation and
(b) significant flow rate variation from a failing pump.
FIGURE 1 Concept diagram of positive-displacement syringe
pump. (Color figure available online.)
FIGURE 3 Measurement systems analysis.
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At this point, the pump was broken down further to
the syringe drive, shown in Figure 1. The syringe
drive portions of the pumps were then interchanged
between pairs and the high flow rate variation
followed the syringe drive. Figure 4 shows that the
syringe pump was the driving factor for failure within
the instrument, and the syringe drive was the driving
factor for failure within the pump.
After narrowing the potential causal components
down to the syringe drive system, further disassembly–
assembly showed that the assembly method had a
large impact on performance (Figure 5).
Taking the syringe drive system apart and putting it
back together involved removing and replacing
everything up to and including the belt. Retesting
showed a change in flow oscillation amplitude, driven
by the assembly. Given that this was before the parts
were exchanged between pairs, the difference could
not be attributed to any component. In other words,
the full range of performance variation was captured
with the same parts. Therefore, the causal mechanism
could not be the parts alone. At this point another
diagnostic tool was required to move forward.
The lack of repeatability after disassembly–
reassembly was an important clue regarding the fail-
ure mode. In addition, the frequency and shape of
the oscillating signal tied directly to the syringe drive
system’s physical and mechanical properties. This
information identified that the source of the problem
centered on storage and release of potential energy.
Concluding that the problem was in the assembly
process pointed the problem-solving approach to
a functional decomposition method. In order
to understand what was really happening with this
pump failure mode, a source-load model was
applied, as shown in Figure 6 (The New Science of
Fixing Things 2011).
The Thevenin-Norton source-load model describes
that any complex electrical system can be graphically
represented with a power supply, a load, and a
resistor (The New Science of Fixing Things 2011).
The way the components are wired depends on
whether the power supply is a voltage source (Theve-
nin wires the load and resistor in series) or current
source (Norton wires the load in parallel). Any system
that operates based on energetic interactions, regard-
less of the domain, can be represented in this simple
but powerful way, as long as a few simple rules are
followed. One of those rules is that the flow of energy
is described by two conjugate variables, which, when
multiplied together, yield watts. Conjugate variables
for a few domains are volts and amps, pressure and
flow, and torque and angular displacement (The
New Science of Fixing Things 2011). The first variable
in each pair is the effort variable, e, and the second
FIGURE 6 Source-load model (The New Science of Fixing
Things 2011).
FIGURE 5 Assembly process affects pump performance and
failure.
FIGURE 4 Assembly–disassembly testing.
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variable is the flow variable, f, represented in Figure 7.
Source-load is a powerful diagnostic tool when applied
across multiple domains and permits useful energy
accounting by employing SI units. The diagnostics
require little more than separating the ideal input
power into that consumed by the load and that lost
to the source impedance. The solution typically lies
in reducing the impedance and thus the energy
consumption. The source-load model is based on
making machines use less energy. The second law of
thermodynamics indicates that the more efficient the
machine, the better it runs and the longer it lasts.
Source-load shows that efficiency is often the key to
improvement, not component variation reduction.
Probabilistic decomposition requires multiple sam-
ples for comparison, but the source-load approach
calls for only one poor performer. The fundamental
question is ‘‘What is happening and how is this sup-
posed to work?’’ The model shown in Figure 1 is a
good basis to begin analysis for this failure.
The function of each subsystem is written using a set
of rules developed by Hartshorne and Allen (The New
Science of Fixing Things 2011). There are only seven
functions (supply power, dampen power, contain
energy, release energy, transmit power, direct power,
and convert power) performed by machines and only
three properties (inertance, compliance, and resist-
ance) that govern functional behavior. The functions
and properties are used to build the diagram shown
in Figure 8. The arrows always show the direction of
power flow. The notation above the arrow is the effort
variable and below the arrow is its conjugate flow
variable from the same domain.
Once the E-FAST diagram (Bytheway 2007) is
finished, functions can be isolated and tested in
informative ways. Altering the system inputs and
monitoring the response can often provide direct
insight to performance. For example, in stepper
motors the torque drops off rapidly as the velocity
increases (Solarbiotics 2011). This torque decay is
not as radical in permanent magnet DC motors.
The oscillation frequency and amplitude provide sig-
nificant information regarding pump performance. Is
motor stepping the source of the oscillations? To
answer this question, a 24V DC motor was substi-
tuted for the stepper motor, but the flow oscillation
persisted. Because motor stepping was not the
causal mechanism, the source of the oscillations
could not be from the functions shown in Figure 8.
The next piece evaluated was the fluidic load at
the syringe. It was simple and fast to remove the
syringe, essentially removing the functions covered
in Figure 9. The oscillating pattern and amplitude
remained despite syringe removal. At this point,
Figures 8 and 9 were ruled out (the failing conditions
were not significantly impacted by the motor or
fluidic load because the oscillating pattern and
amplitude remained). The only remaining functions
were performed by the belt.
Figure 10 shows a diagrams of how the power gets
from the pulley into the belt as potential energy and is
then released. This represents an effort-based serial
power transmission, with effort dropped as the belt con-
tains and releases energy, and the parasitic loss of effort.
In order to understand belt function, the system
was evaluated utilizing a range of belt tensions and
stepper motor speeds. A narrow band of belt tension
FIGURE 8 E-FAST shows power to the first gear, up to the belt.
x is a function of switching speed, I is a function of impedance.
FIGURE 10 Power from the stepper motor to the second
pulley.
FIGURE 7 Effort and flow workspace (The New Science of
Fixing Things 2011). (Color figure available online.)
FIGURE 9 Pulley to the syringe.
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was identified that turned out to drive a standing
wave. A standing wave is an energy sink, an undesir-
able element of contain-and-release potential energy
functions. Likewise, a small range of stepper motor
speeds resulted in increased flow rate variation,
shown in Figure 11.
CAUSAL EXPLANATION
The system proved to be operating in a resonance
condition, as shown in Figure 11, for one particular
belt tension. Resonance occurs when a system in
motion with a small vibration near the fundamental
frequency of the system grows to large amplitude
(HyperPhysics 2011). Resonance has been shown
to occur in belt-driven systems (Stevens 2011) when
the fundamental natural frequency of the belt
matches the motor drive or load frequency. The
power in understanding the physics of the system
yields a number of options to mitigate the failing
condition. For example, modifying the tension or
length of the belt will change its natural frequency.
Modifying the drive speed of the motor or the mech-
anical impedance of the load will also move the sys-
tem away from resonance. The causal explanation
requires knowledge of functional decomposition in
order to analyze system behavior in a way that is
consistent with first principles, sound diagnostics,
and convergence.
ELIMINATING THE PROBLEM
Once a valid causal explanation for system
behavior has been identified and tested, technicalFIGURE 13 Potential schematic without belt to remove source
of resonance.
FIGURE 12 Direct drive system without timing belt. (Color
figure available online.)
FIGURE 11 System resonance response by varying motor
drive speed.
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and business decisions can be made to determine
appropriate modifications to eliminate failures. In
order to mitigate resonance, the characteristics of the
belt can be changed, the mechanical impedance of
the load can be changed, or the motor drive speed
can be changed, all while maintaining the syringe pump
in its current configuration. An alternative solution
would be to remove the belt drive, thus changing func-
tion dependencies. The decision on which avenue to
follow came from an analysis of cost of implementation,
time to implement, risk to the system, and physical lim-
itations related to the syringe pump and its application.
Directed testing can be performed quickly to confirm
any recommendations for actions. For reference, remov-
ing the belt and directly driving the lead screw by the
stepper motor (schematic shown in Figure 12; results
shown in Figure 13) removed the failing condition
(reduced oscillations were still present, driven by sec-
ondary factors).
The problem-solving approach described in this
article is summarized in Figure 14.
CONCLUSION
The role of scientists and engineers is to design
and manufacture products that are consistent with
the strategic objectives described at the outset of
this article. Fundamental operation is in the determi-
nistic world of first principles, supplemented and
complemented with probabilistic tools. Tools are
used in this process where they provide the largest
benefit and are halted at the point where a different
approach provides the best path toward under-
standing. This failure analysis included probabilistic
techniques to narrow the system down to the
smallest subassembly that yielded clear direction,
and then functional decomposition was incorpor-
ated to describe the system operation and identify
the areas that negatively affect performance. The
power in this solution is that the system is character-
ized and a number of approaches to mitigate the
failure are available and can be selected by business
merit.
ABOUT THE AUTHORS
Jeremy Hammond received a B.S. in Engineering
Physics from Embry Riddle Aeronautical University,
an M.S. in Engineering Physics from The University
of Maine, and a Ph.D. in Biosystems Engineering
from The University of Maine. He has experience
as the Director of Engineering at Sensor Research
and Development Corporation and is currently act-
ing as R&D Manager at IDEXX Laboratories with
emphasis on hematology and urinalysis instrumen-
tation systems.
Angela Haiss received a B.S. in bio-resource
engineering from the University of Maine and an
M.S. in biomedical engineering from Johns Hopkins
University. She has seven years of engineering
experience and is currently a Staff Engineer at IDEXX
Laboratories.
Shawn Lavigne studied Biology at the University
of Southern Maine and the University of New
England. He has experience as an IDEXX Labora-
tories Six Sigma Black Belt. He is currently a Process
Engineer at IDEXX Laboratories.
Beverly Daniels received a BS in electrical engin-
eering from Michigan Technological University.
She has thirty years of experience in Quality Engin-
eering, Six Sigma and Operational Excellence in a
variety of industries including Semiconductors, auto-
motive, aerospace and biomedical. She is currently
the Director of Operational Excellence at IDEXX
Laboratories.
John Allen received a B.A.Sc. from Gannon Uni-
versity and is a partner and founding member at
The New Science of Fixing Things.
FIGURE 14 Problem-solving approach utilized for the flow oscillation problem.
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REFERENCES
Bytheway, C. W. (2007). FASTCreativity and Innovation. FL: J. Ross Publishing.Dale, H. C. A. (1958). Fault finding in electronic equipment. Ergonomics,
1(4):356–385.HyperPhysics. (2011). Standing waves on a string. Available at: http://
hyperphysics.phy-astr.gsu.edu/Hbase/waves/string.html (accessedJanuary 20, 2011).
The New Science of Fixing Things. (2011). Source load model andE-function trifold. Available at: http://www.tnsft.org/restricted/resources/E-Functions%20Trifold%20080227.pdf (accessed January 20, 2011).
Ott, E. R. (1953). A production experiment with mechanical assemblies.Industrial Quality Control, 9(6):124–130.
Solarbotics Ltd. (2011). Industrial circuits application note stepper motorbasics. Available at: http://www.solarbotics.net/library/pdflib/pdf/motorbas.pdf (accessed January 20, 2011).
Stevens, D. (2011). Vibration analysis—Belt drive problems. Availableat: http://www.vibanalysis.co.uk/vibanalysis/belts/belts.html (accessedJanuary 20, 2011).
Wheeler, D., Lyday, R. (1988). Evaluating the Measurement Process,Knoxville, TN: SPC Press.
APPENDIX: VARIABLE DEFINITIONS
a Area
d Displacement
E Electromotive force
eZL Effort: impedance load
F Force
fs Flow: source
fZL Flow: impedance load
I Current
P Pressure
R Resistance
S Entropy
T Temperature
V Volume
v Velocity
ZL Load impedance
Zs Source impedance
s Torque
x Angular velocity
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