A Rule-Based Fault Detection Algorithm for a Purge System of a ...

Volume 2, Issue 1, 2008
A Rule-Based Fault Detection Algorithm for a Purge System of a Ventricular Assist Device
Yih-Choung Yu, Assistant Professor, Lafayette College, yuy@lafayette.edu
Abstract
A simple fault detection algorithm was designed to monitor the purge pump operation of a rotary type
ventricular assist device (AB-180 Circulatory Support System [AB-180 CSS], CardiacAssist, Inc.,
Pittsburgh, PA, USA). This algorithm receives the fluid pressure of the purge system, calculates the
pressure derivative, and then compares with the preset thresholds to detect potential abnormal
conditions, such as leaking and kinking along the purge tubing, abnormal purge rate, and stoppage of the
purge pump. The thresholds were identified from bench top experiments simulating the possible failure
conditions in the purge system. The algorithm was then implemented in a data acquisition system and
verified with different pumps and controllers. Because of its simplicity, the algorithm only takes an
insignificant computation time to process the measured signal. Therefore, it can be incorporated into the
existing controller without any major design change in the controller hardware. The same concept is also
applicable as an alternative way to monitor an extremely low flow rate in other devices in which directly
monitoring flow rate from the devices is important, but either too expensive or too difficult to implement.
Keywords: Fault detection, purge system, rotary blood pump, left ventricular assist device.
I. Introduction
Rotary blood pumps have numerous advantages over existing left ventricular assist devices (LVAD),
smaller size, better efficiency, and less complicated for implantation. One of the major challenges is to
maintain an effective and reliable bearing that can sustain the rotating impeller without the risk of
hemolysis and thrombus formation (1). Several existing rotary blood pumps mount the impeller directly to
the motor shaft. Purge liquid is introduced at a constant rate into the LVAD to lubricate the seal and the
rotating element to reduce heat generation due to mechanical friction (2). Proper operation of the purge
system is crucial in keeping the rotary blood pump operation reliable and safe. Therefore, the purge
system must be monitored continuously during the rotary LVAD operation.
One of the key variables to be monitored in a purge system is the purge flow rate. Integrating a flow
sensor along the purge fluid path and comparing the flow measurement with the preset flow rate would be
a straightforward way to monitor the proper operation of a purge system. However, as the preset purge
flow rate is extremely low in most LVAD designs, the use of a flow sensor to measure such a small
quantity would be either inaccurate or expensive. An alternative way to cost-effectively monitor a purge
system is to derive or estimate the key variables of the system using other signals that can be measured
through reliable and inexpensive sensors.
Several fault detection methods have been developed in the past for different heart assist devices.
Jammu et al. performed statistics, power spectrum, and ensemble average analyses on the sensing
signals to characterize the changes in signals due to abnormal purge system operation (3). Power
spectrum analysis and artificial neural network were used to monitor the mechanical vibration of an axial
flow pump (4) and total artificial hearts (5,6). These approaches usually require large data sets from
various sensors and high-speed computation, which is difficult to implement in real-time without a highend
processor. Replacing the microprocessor in an existing LVAD controller could lead to a completely
new design, which would be expensive and time consuming.

In this paper, a fault detection algorithm was designed to identify the possible fault conditions of the
infusion system (purge system): blockage, leakage, high/low purge rate, and purge pump stoppage, for
the AB-180 CSS blood pump (7) using only purge pressure as the measurement. The algorithm
compares the measured purge pressure and the calculated pressure derivative to the preset thresholds to
detect the possible faults in the system. The thresholds were determined by the differences between the
normal and fault signals under experimental conditions. This algorithm is simple and can be easily
integrated with the existing device controller without major hardware or software design changes.
II. System Description
The AB-180 CSS is an implantable LVAD for patients with post-cardiotomy cardiogenic shock who are
refractory to standard treatment, such as intra-aortic balloon pump support, pharmacological treatment, or
both. The intent of the AB-180 CSS is to provide temporary mechanical support by pumping blood from
the left atrium to the ascending aorta until native heart function recovers. As shown in Figure 1, the AB-
180 CSS is a continuous-flow LVAD, which consists of a centrifugal pump operated by a three-phase
permanent magnet brushless DC motor, a purge system delivering purge fluid (infusate) for lubrication,
and an occluder system preventing retrograde flow into the heart in the event of device failure (7). The
pump is connected to the external control unit by a power cable that passes through the patient’s skin.
Figure 1, AB-180 CSS pump assembly (7)
2

The purge system delivers the infusate to the AB-180 CSS centrifugal pump. The infusate serves mainly
to provide a hydrodynamic bearing for the internal moving parts of the AB-180 CSS pump. This reduces
friction, heat generation, and wear. The infusate also carries anticoagulant (heparin) into the pump upper
housing at the impeller shaft seal interface to reduce the risk of thrombus formation. The configuration of
the purge system, shown in Figure 2, consists of a reservoir containing infusate (a mixture of sterile water
and heparin), a constant speed eccentric cam rotating peristaltic purge pump, a pressure transducer,
associated tubing, and a bacteriologic filter. The intravenous (IV) bag supplies infusate to the system
through a pediatric drip set connected to a length of infusion tubing. The infusion tubing is threaded
through the eccentric cam infusion pump mounted in the back of the AB-180 CSS controller. Once inside
the lower housing, shown in Figure 3, the infusate fills the area between the lower housing, journal, and
rotor. Channels in the lower housing and journal allow the infusate to pass across the rotor surfaces,
providing lubrication. The interface between the seal and the impeller functions as a check valve, allowing
infusate to pass into the upper housing, while preventing blood from entering the lower housing.
Figure 2, AB-180 CSS infusion system (7)
3

The rotational speed of the purge pump determines the infusion rate, which is set to be within ±0.1 ml/hr
of the nominal 10 ml/hr specification when the AB-180 CSS controller is manufactured. Several conditions
may cause an abnormal infusion rate when the AB-180 CSS pump is under operation, such as
obstruction or leaking in the infusion line, purge pump speed drift from its preset speed, and purge pump
stoppage. In order to increase the safety feature of the AB-180 CSS, a rule-based fault detection
algorithm was designed and implemented to monitor the purge system during operation using only the
measurement of infusion pressure, which can be obtained through an inexpensive pressure transducer.
Figure 3, AB-180 CSS pump internal infusate flow (7)
III.
Method
Since the infusate is delivered by a constant rotational speed of the eccentric cam purge pump, the
infusion pressure measurement is a cyclic waveform. This waveform can be decomposed into two parts:
the dc level and the major frequency content. The dc level, represented by the mean infusion pressure, is
affected by the vacuum created by the rotation of the AB-180 pump and the fluid resistance due to the
infusion tubing geometry and the gap between the seal and the impeller shaft. Mean infusion pressure
decreases when AB-180 pump speed increases because a higher pump speed creates a stronger
vacuum effect to the purge system. A tighter seal gap creates a higher fluid resistance, which results in a
higher mean infusion pressure at a constant purge rate. However, the variation of mean infusion pressure
due to pump speed changes and seal geometric tolerance should be within a pre-defined range (7). An
excessive high infusion pressure represents high fluid resistance, which implies that an obstruction
occurs along the infusion tubing. On the other hand, an extremely low infusion pressure might be due to a
leakage along the infusion tubing.
The major frequency of the infusion pressure waveform is related to the rotating speed of the purge
pump, which represents the infusion (purge) rate. A high major frequency means high infusion rate while
low major frequency implies low infusion rate. Obtaining the major frequency from a waveform in real-time
usually requires computing the power spectrum of the waveform (8), which requires a high-end
microprocessor to execute the complicated algorithm. In order to save computation time, counting the
number of zero-crossings of the instantaneous infusion pressure derivative within a certain period of time
4

can be used instead. When the infusion pump is stopped, the variation of the infusion pressure
measurement should be insignificant. This implies that a small amplitude of pressure derivative is an
indication of infusion pump stoppage.
The algorithm requires computing the time derivative of the pressure measurement. This calculation was
implemented by backward Euler method (9),
P(t
K
) - P(t
K-1
)
P(t
K
) =
T
, (1)
where P is the infusion pressure measurement and T is the sampling period.
IV.
Experiments
A series of experiments were designed to identify the thresholds of the infusion pressure and its time
derivative to the faults in the purge system. Six conditions, listed in the following,
(1) Baseline: the purge system is operated normally,
(2) Kinked infusion line: blockage in the infusion line at a normal purge pump speed,
(3) Leaking infusion line: leakage in the infusion line at a normal purge pump speed,
(4) Purge pump stoppage,
(5) High infusion rate: the infusion rate is 25% higher than normal due to a high purge pump speed,
(6) Low infusion rate: the infusion rate is 25% lower than normal due to a low purge pump speed,
were created in the tests using a fluid circulation loop as shown in Figure 4. Data collected from these
experiments were used to characterize the thresholds associated with the fault conditions. The rotational
speed of the AB-180 CSS pump was controlled by the AB-180 controller. To assure normal operation of
this blood pump, a pressure transducer (Harvard Apparatus, Holliston, MA) and a flow sensor (Transonic
Systems, Ithaca, NY) were placed downstream from the pump for monitoring purpose. A tubing clamp
was used to control the flow rate through the blood pump.
Although the infusion rate is independent to the pressure (quantified as the mean infusion pressure)
against the purge pump, the six test conditions as stated above were produced at the maximum and
minimum infusion pressure so that the data collected from the experiments for developing the fault
detection algorithm cover the entire pressure range of the infusion pump operation with the AB-180 CSS
pump. The average infusion pressure is affected by the AB-180 CSS pump speed and the pressure
difference across the blood pump. The infusion pressure is lower when the AB-180 CSS pump is
operated at a higher speed due to the vacuum effect created by the blood pump. When the AB-180 CSS
pump is operated at a constant speed, the infusion pressure is higher if the AB-180 CSS pumps less flow
through the pump. Therefore, the highest mean infusion pressure of a purge pump can be obtained by
setting the AB-180 CSS pump at its minimum speed and fully occluding the blood pump outlet. The
lowest mean infusion pressure of a purge pump occurs when the AB-180 pump is operated at its
maximum speed with its outlet port fully open. In order to include the variations between blood pumps and
controllers in the experimental data, four AB-180 CSS pumps and two controllers were used in the
experiments. The infusion pump in each controller was pre-calibrated to the desired (normal, high, and
low) infusion rate prior to the test. The infusion pressure was measured by a Transpac IV disposable
pressure transducer (Abbott Laboratories, North Chicago, IL, USA) and acquired by a Windaq data
acquisition system (Dataq Instruments, Akron, OH, USA) at the sampling rate of 1 Hz for 10 minutes at
each test condition.
5

Fluid
Reservoir
Flow
Probe
Tubing
Clamp
Flow
Meter
Pump
Outlet
Harvard
Pressure
Xducer
AB-180
CSS
3-way
Stopcock
DAQ
System
Pressure
Xducer
3-way
Stopcock
AB-180
Controller
Figure 4, Configuration of experiment setup
Once the thresholds were determined, the fault detection algorithm was implemented in DasyLab
(DasyTec, Amherst, NH, USA) with a data acquisition system (PCM-DAS16S/16, Computer Boards, Inc.,
Mansfield, MA, USA). A different data acquisition system from the previous experiment was used to verify
the consistency of the signal acquired by difference data acquisition hardware and processed by different
software. When a fault was detected by the algorithm, an alarm was activated by the software with a
message of the detected fault on the computer screen. This message was recorded to validate the
effectiveness of the algorithm. The same experiments as described previously were repeated with four
other AB-180 CSS pumps and two other controllers to validate the reliability and repeatability of the
algorithm across different blood pumps and controllers.
V. Results
Equation (1) was implemented in MATLAB (Mathworks Inc., Natick, MA, USA) to calculate the time
derivative of infusion pressure using data acquired in the experiments. Figure 5 shows the typical infusion
pressure waveform and its time derivative in normal operation. When the infusion line was obstructed, the
infusion pressure gradually increased until it saturated the pressure sensor at 750 mmHg as shown in
Figure 6(a). On the other hand, when the infusion line was leaking, the pressure measurement was lower
than 20 mmHg in Figure 6(b). As the infusion pump stopped, the infusion pressure slowly decreased from
the pressure level at the moment the pump stopped. This resulted in the amplitude of the pressure
derivative approaching zero as shown in Figure 7.
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mmHg
mmHg/sec
Infusion Pressure
390
380
370
360
350
340
330
0 1 2 3 4 5 6 7 8 9 10
10
5
0
-5
Pressure Derivative
-10
0 1 2 3 4 5 6 7 8 9 10
minutes
Figure 5, Infusion pressure and its time derivative waveforms in baseline condition
mmHg
mmHg
(a)
800
780
760
740
720
700
0 1 2 3 4 5 6 7 8 9 10
(b)
16
14
12
10
8
6
0 1 2 3 4 5 6 7 8 9 10
minutes
Figure 6, Infusion pressure when the infusion tubing is (a) kinking; (b) leaking
When the infusion rate was 25% higher than the normal rate, the number of zero-crossings of the
pressure derivative was greater than the number counted at normal rate. On the other hand, when the
infusion rate was 25% lower than the normal rate, the number of zero-crossings of the pressure derivative
was less than the number counted at normal rate. Figure 8 shows the pressure derivatives at high,
normal, and low infusion rate. Table 1 shows the average of experimental data obtained from 4 pumps
operated by two controllers under the same test conditions. The fault detection thresholds, defined in
Table 2, were obtained by characterizing the results in Table 1.
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mmHg
380
360
340
320
300
280
260
Infusion Pressure
0 1 2 3 4 5 6 7 8 9 10
mmHg/sec
2
0
-2
-4
-6
-8
-10
Pressure Derivative
0 1 2 3 4 5 6 7 8 9 10
minutes
Figure 7, Infusion pressure and its time derivative waveforms when infusion pump stopped
Table 1, Test result summary
Condition
| P(t) |
MAX
Mean Infusion Pressure
(mmHg)
* No. of zero-crossings
Baseline
Kinking
Leaking
Pump off
High rate
Low rate
2200 rpm 6.10 226.75 45.0
4500 rpm 4.04 158.75 45.8
2200 rpm 0.00 750.00 0.0
4500 rpm 0.00 750.00 0.0
2200 rpm 2.21 12.00 137.5
4500 rpm 2.56 11.75 160.3
2200 rpm 0.32 159.25 221.3
4500 rpm 0.41 78.50 117.0
2200 rpm 9.52 385.75 55.3
4500 rpm 5.81 162.25 55.3
2200 rpm 5.03 213.50 34.0
4500 rpm 3.57 151.50 34.0
* Number of zero-crossings was counted for 10 minutes
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mmHg/sec
mmHg/sec
mmHg/sec
10
5
0
-5
-10
10
5
0
-5
-10
10
5
0
-5
-10
(a)
0 1 2 3 4 5 6 7 8 9 10
(b)
0 1 2 3 4 5 6 7 8 9 10
(c)
0 1 2 3 4 5 6 7 8 9 10
minutes
Figure 8, Infusion pressure derivative: (a) high infusion rate; (b) normal; (c) low infusion rate
Validation of the fault detection algorithm, described in Table 2, was performed with four other pumps and
two other controllers. The same experiment procedures described previously were repeated in the
validation test. All the fault conditions created in the experiment were detectable by the algorithm with all
test pumps and controllers. The average of the results using different pumps and controllers is
summarized in Table 3. These results support the reliability of the fault detection algorithm and its
robustness across pumps and controllers.
Table 2, Rules for infusion system fault detection
Fault
Kinking
Leaking
Thresholds
Mean pressure in a duration of 40 seconds is greater than 700 mmHg
Mean pressure in a duration of 40 seconds is less than 20 mmHg
Pump off | P(t) |

Table 3, Summary of validation results
Condition
| P(t) |
MAX
Mean Infusion Pressure
(mmHg)
No. of zerocrossing
Fault Detected
Normal 9.71 167 45 No
Kinking 0 750 0 Yes
Leaking 1.17 -15 113 Yes
Pump off 0.82 99 10 Yes
High rate 7.91 199 60 Yes
Low rate 6.34 191 29 Yes
VI.
Discussions and Conclusions
A fault detection algorithm was designed to monitor the operation of the purge system of the AB-180
CSS. This algorithm uses the measured infusion pressure, calculates the pressure derivative, and then
compare with the pre-defined thresholds to detect the possible faults in the purge system. Since the
infusion pressure sensor is located in the controller unit, no invasive sensor is required for this algorithm.
Additionally, implementing this algorithm in the controller does not require an expensive high-end
processor because of its simplicity.
Although all the fault conditions created in the validation experiment were detectable, nuisance alarms
might be possible under certain conditions, particularly when a noisy infusion pressure signal is received.
Infusion pressure is produced by the infusate flowing through the infusion tubing and the seal lip inside
the pump. Infusion pressure could fall below the threshold when the seal lip is excessively worn. A worn
seal could cause device damage and risk patient’s life. Therefore, a nuisance alarm on the flow rate might
serve as a monitor for the seal and the device safety. A non-smooth surface inside the infusion tubing
could produce fluid turbulence and result in a noisy infusion pressure signal. Shifting the section of the
infusion tubing, laced around the eccentric cam, regularly as suggested in the device operation manual
can prevent the pressure noise due to a non-smooth tubing surface. The noise accompanied with the
pressure derivative was not significant under normal pump operation because of a low sampling rate and
a built-in low-pass filter in the pressure instrumentation circuitry. A digital low-pass filter (10) could be
added to improve the quality of the pressure derivative signal if needed.
The fault detection algorithm described herein is dedicated to the AB-180 CSS system. However, the
same concept is applicable to other devices with similar type of pumping mechanism in which monitoring
an extremely low flow rate is crucial but expensive, for example, insulin pumps and intravenous (IV)
therapy devices.
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