From: AAAI Technical Report SS-99-04. Compilation copyright © 1999, AAAI (www.aaai.org). All rights reserved.
Condition-Based Monitoring of Motor-Pump Systems
Using Model-Based Reasoning
Yi-Liang Chen and Gregory Provan
Rockwell Science Center
1049 Camino Dos Rios
Thousand Oaks, California 91360
{ylchen, gmprovan}@rsc.rockwell.com
Abstract
This article presents a system-level, model-based framework for
machinery diagnosis that combines the signal processing and
domain knowledge. Based on causal network diagnosis, this
framework provides an integrated, condition-based monitoring
system with full machinery prediction capabilities. We describe
in detail a preliminary diagnostic model of a motor-pump system
in this framework.
1. Introduction
Current systems for machinery monitoring and fault
diagnosis are labor intensive and machine specific.
Furthermore, they only diagnose the local condition of a
machine and do not treat it as part of a system. In this
paper we describe an approach aimed at developing an
integrated, condition-based monitoring system based on
open systems technology that provides full machinery
prediction capabilities to allow the accurate and reliable
determination of the remaining useful life of equipment.
The primary focus of this effort will be to fuse disparate
data and information types within an open architecture for
machinery prognostics, and to validate the prognostics
system. This will be done by combining the signal
processing and domain knowledge within a system-level,
model-based framework for machinery mechanics.
We base our knowledge-integration mechanism on a set of
tools for constructing causal networks, which provide an
excellent platform for knowledge fusion. Because of the
rigorous framework in which the causal model is
constructed, completeness of the knowledge fusion is
guaranteed, essentially providing automatic inclusion of all
the possible combinations of fault conditions without the
necessity of their explicit identification [Darwiche, 1995].
Research supported in part by The Office of Naval Research under
contract number N00014-98-3-0012.
Copyright © 1999, American Association for Artificial Intelligence
(www.aaai.org). All rights reserved.
This article presents a diagnostic model of a motor-pump
system, which is modeled in terms of dynamic causal
networks. The main focus of this preliminary model is to
replicate the diagnoses of certain faults when some
peculiar vibration signatures are observed, as described in
various places in the literature [Bloch & Geitner 1997,
Eshleman 1998, Sohre 1980, TAC 1994, Wang 1997,
Wowk 1991]. This approach thus relies on constructing
system models, which replace the role rules would play in
a traditional approach. These models can integrate the
output from several sensors, and if the system is altered the
models can be altered to reflect the changes, such that the
diagnostics computed are automatically updated. The
majority of faults modeled thus far are bearing faults.
Physical descriptions/properties of the system are modeled
only when they are critical for the diagnostic purpose and
at a minimal detail. We plan to expand this model later
with more detailed physical descriptions/properties for
more accurate diagnoses and/or broader coverage of faults.
We first briefly explain our modeling mechanism that will
be followed by a description of key aspects of the motorpump system. We then present the details of this
preliminary model. Diagnostic results generated by our
model through simulation will also be shown.
2. Software Architecture
The key software novelty, as compared to approaches that
use a single sensor output to compute diagnostics/
prognostics for a device (pure signal-processing-based
methods), is that we employ a system-level model to drive
the diagnostic and prognostic reasoning. This approach
specifies a model for the device or set of devices, and can
incorporate any number of sensors, actuators, etc. This
model then can fuse the outputs of all the sensors to obtain
a more detailed and accurate prediction of component
health and expected lifetime. Our system-level model uses
the causal network model-based approach [Darwiche
1998], which has been under development for over a
decade, and has successfully been applied to conditionbased monitoring, as well as diagnosis of systems ranging
Diagnosis
from factory automation, avionics systems to the Space
Shuttle [Darwiche & Provan 1996, El Fattah & Provan
1997].
Sensor
This paper describes our initial efforts towards the
construction of models for a complete pump loop that
consists of motor, pump, tank and pipes, with sensors for
measuring pump/motor vibration, fluid flow, fluid pressure
and temperature. This “redundant” sensor suite facilitates
higher-fidelity diagnosis/prognosis, plus the ability to
identify existing or potential faults in all key aspects of the
pump loop.
1xMag
Discrete
Indicators
Causal Networks
S/N
For this application, we are developing an openarchitecture, distributed diagnostics and prognostics
system, as shown in Figure 1. This modular system is
Figure 2: Stages of processing for typical sensor data
Middleware communications channel
Satcom
Sensor
Collection
Module
Centralized
Diagnostic
Engine
Presenter
Remote
Maint..
Interface
PMAT
RF from
distributed
smart
wireless
sensors
Figure 1: Distributed condition-based monitoring system
planned to consist of a number of components that are
connected via CORBA/COM middleware (which can be
run over the system’s communications channel as shown in
Figure 1). The components are as follows:
• smart wireless sensors: collect data and perform
signal processing on the data;
• sensor collection module: collect the processed
sensor data and send it on the middleware
communications channel;
• centralized diagnostics engine: perform sensor
fusion and, using a model of the system, compute
high-fidelity
diagnostics
and
prognostics
information based on the processed sensor data;
• diagnostics and prognostics user interface (or
presenter): transmit the diagnostics and
prognostics information to the user and provide
the user an easy tool to modify/reconfigure the
system model;
• remote maintenance interface: send the
diagnostics and prognostics information to remote
control center (or logistics/maintenance base), so
that maintenance operations can be planned and
prepared in advance.
Figure 2 shows the type of data processing typical in our
approach. The sensing unit, such as an accelerometer, of a
smart wireless sensor will transmit data to the digital signal
processing (DSP) unit of the sensor. The DSP unit
computes a set of discrete diagnostic indicators based on
analyses such as measurements of amplitude peaks at
particular frequencies in the spectra and signal-to-noise
ratio. Through the middleware communication channel,
these indicators will then be sent as inputs to the
centralized diagnostic engine. The diagnostic engine uses
the causal network model and the input indicators to
compute the most likely broken components of the system,
if any such failures are indicated.
In this document we focus on the diagnostics aspects of
this architecture. We have implemented the middleware,
Centralized Diagnostics Engine and Presenter; we are
developing smart wireless sensors that perform basic
signal-processing tasks within the sensor, sending
“diagnostic indicators”, discrete-valued representations
necessary to determining fault conditions, along the
middleware channel.
3. Overview of Causal Networks and Modelbased Diagnosis
This section introduces an example that we use to describe
our approach, and the representation that we adopt for
modeling, causal networks [Darwiche 1997]. The example
we will describe is a simple control hardware system, since
it is one of the simplest systems to describe. Note that the
causal network modeling language allows discrete
specifications of any physical system, including digital
systems and mechanical systems such as a pump or motor,
in addition to functional systems, such as software
systems.
those faults, which variables can be observed, such as
variables for sensors, etc. In this report we focus on
symbolic causal networks.
3.1 Causal Network Representation
A causal network is a graph-based representation that is
used for diagnosing failures of a system. Causal networks
provide probabilistic, order of magnitude, and symbolic
representations. They also have predictable scaling
properties for the eventual embedded runtime diagnostic
system.
To specify a causal network model we need to define:
1. the variables in the model, which represent the
components, e.g., Tx, Act1, Act2, Act3, and Act4
from Figure 4;
2. the values for the variables, which represent the data
that flows through various parts of the system;
3. the assumables, which are the variables that describe
the operating characteristics of the components, such
as ok or broken;
4. the quantifications, which relate the variables and
assumables;
5. the evidence variables, which are the variables that can
be observed, typically the system sensors; and
6. the weights for the assumables, which specify the
likelihood or relative ranking of the assumable fault
modes.
A causal network encodes the causal relations of a system;
e.g., in Figure 3, the controller/transmitter in Tx causes
Actuators Act1 through Act4 to turn on. A causal network
is thus a good way of representing the flow of data in such
systems, and hence reasoning about the root causes of data
flow. Figure 3 shows the causal network structure for this
simple example. Conversely, if data is not flowing as
intended, the causal network can help track down the
reason for the “breakdowns” of correct data flow; e.g., Tx
could be the root cause of Act1 through Act4 not turning
on. We call this diagnosing the faults in the system.
Tx
Act1
Tx
Act2
Act1
Act2
Act3
Act4
Act3
Figure 4: Causal structure for the
simple hardware system
Act4
The assumable associated with a variable characterizes the
ways the component works under an exhaustive set of
scenarios. Act1 has an associated assumable, Act1-mode,
with two possible values, ok and broken, and a
quantification as follows: if [Act1-mode = ok], Act1
receives data from Tx, but if [Act1-mode=broken]
receives no data and the actuator will not activate. In
propositional logic, we might write this as:
Figure 3: A simple hardware system
More formally, a causal network specifies the causal
relationships among a set V of variables by encoding each
variable in V with a node, and encoding the causal
influence of V1 on V2 by a directed arc from V1 to V2.
Hence a causal network is a directed graph (N,A) of nodes
N and directed arcs A.
⇒ [Act1 = on]
[Act1-mode = ok] ∧ [Tx = transmit]
[Act1-mode = broken] ∨ [Tx = not-transmit] ⇒ [Act1 = off].
3.2 Causal Network Specification Language
A causal network can encode much more than just the
cause and effect relationships among a set of variables. It
can encode the nature of these relationships, such as
probabilistic or symbolic/logical (using a multi-valued
propositional logic), static or temporal relationships, faults
that may occur in the system and the relative frequency of
If Act1-mode=OK and Tx=transmit
then Act1=on
If Act1-mode=broken or Tx=not-transmit
Act2-mode
then Act1=off
Figure 5 shows the causal network model for the simple
hardware example with the assumables and quantification
defined.
Note that the causal network representation can describe
not only deterministic systems as in the above example,
Tx-mode
Tx
If Tx-mode=OK then Tx=transmit
If Tx-mode=broken then Tx=not-transmit
Act3-mode
Act4-mode
Act1-mode
Act1
If Act2-mode=OK and Tx=transmit
then Act2=on
If Act2-mode=broken or Tx=not-transmit
then Act2=off
If Act4-mode=OK and Tx=transmit
then Act4=on
If Act4-mode=broken or Tx=not-transmit
If Act3-mode=OK and Tx=transmit
then Act4=off
then Act3=on
If Act3-mode=broken or Tx=not-transmit
then Act3=off
Act2
Act3
Act4
Figure 5: Causal network model for the simple hardware system
Tx-mode
Act2-mode
Tx
Act4-mode
Act3-mode
Act1-mode
Act1
Act2
Act3
Act41
Act42
Act43
Act44
Figure 6: Temporal causal network for hardware system with time-dependent actuator Act4,
shown for time steps t=1,2,3.
but also stochastic systems (using probabilities and orderof-magnitude probabilities (OMP) [Darwiche 1998]), and
discrete-event systems [Cassandra 1993].
3.3 Dynamic Causal Networks
We have extended the static causal networks just described
to handle dynamic systems [Darwiche & Provan 1996, El
Fattah & Provan 1997]. In such an extension, we assign a
temporal index to each causal network variable, and
describe the evolution of the variables over time.
Consider an extension of the hardware example, in which
Act4 displays time-dependent behavior: the value of Act4
depends on Tx and on the previous value of Act4.
Figure 6 shows the causal relationships for this temporal
t
model. If we denote the temporal variable as Act4 , where t
denotes the time, then for such a system we may have
some temporal equations including the generic relations:
∀ t ([Act4-mode=ok] ∧ [Tx=transmit] ∧ [Act4 =on]
t
⇒ [Act4 =on]);
∀ t (([Act4-mode=broken] ∨ [Tx=not-transmit]) ∧
t-1
t
[Act4 =on] ⇒ [Act4 =off]);
t-1
∀ t ([Act4-mode=ok] ∧ [Tx=transmit] ∧ [Act4 =nott
on] ⇒ [Act4 =off]) .
t-1
Note that in the ensuing motor-pump modeling, we
describe only the static structure for the system. The
temporal expansion of our model will not be described, due
to the limitation of the space.
4. Key Aspects of the Motor-Pump System
Figure 7 shows a generic motor-pump system that we
model. The system components are horizontally mounted.
We assume that the motor is a three-phase AC induction
motor and the pump is a centrifugal pump. The motor and
the pump are rigidly coupled.
Figure 7: A motor-pump system
Two sets of bi-axial accelerometers are mounted onto the
housing of the driving end bearing of the motor and the
housing of the pump bearings, respectively. The bi-axial
accelerometers are oriented to measure the axial and radial
vibrations. Note that, for simplicity, we do not measure the
vibrations from the motor bearing at the free end. Such
measurements could be added later on for more extended
and/or more accurate diagnoses.
As mentioned previously, we focus mainly on the
modeling and diagnosis of bearing faults for this
preliminary model. We describe the types of faults
modeled thus far as follows. For motor/pump bearings, we
are interested in the misalignment, mechanical looseness,
and wearing of the bearings. Four types of wearing are
covered: inner race, outer race, balls, and cage. For the
motor, only the imbalance of the rotor is of interest for
now. Similarly, for the pump, our concern is only the
imbalance of the pump shaft. For the coupling, we focus on
the misalignment problem, both angular and parallel.
Please note that, as we expand the model with more
physical descriptions/properties, we will extend our
coverage to most of the motor and pump faults.
5. Dynamic Causal Network Model for the
Motor-Pump System
In this section, we present our preliminary model for the
motor-pump system in a hierarchical manner. We divide
the system into seven subsystems/components: motor,
pump, shaft/coupling, motor bearing (at the driving end),
pump bearings, and the signals from accelerometers on
housings of both motor and pump bearings. Note that we
do not model the motor bearing at the free end thus far.
Figure 8 depicts a high-level causal structure of the system.
This figure shows the causal relationships among the
subsystems. For example, the rotation of the motor rotor
and the condition/health of the motor affect the rotation of
the shaft/coupling. The rotation of the shaft/coupling then
drives the pump and affects the condition of motor and
pump bearings and their housings. Meanwhile, the
condition of the pump can also affect the rotation of
shaft/coupling.
Motor
Observed that the quantizations of the variable values are
coarse. We will change to finer quantizations, if necessary,
when we expand the model.
Pump
Shaft/Coupling
Motor Bearing
Pump Bearing
Sensors at
Motor Bearing
Sensors at
Pump Bearing
There is only one assumable in Figure 9 as indicated by a
solid shade of the node:
•
Figure 8: High level causal structure
of the system
The subsystems are also modeled in terms of dynamic
causal networks. These dynamic causal network models
are implemented in both Rockwell’s CNETS [Darwiche
1992] system (textual) and CDMB [Provan & Chen 1998]
user interface (graphical) formats. Note that the interface
between related subsystems can contain multiple nodes in
their causal structures. We present the detail causal
structures of these subsystems in the following
subsections.
MRotorBalMode; {ok, unbal}; balance mode of
motor rotor, ok or unbalanced.
Note that the assumable MRotorBalMode represents
whether the motor rotor is balanced or not while the
unobservable MRotorBalCond, which is speed dependent,
serves as a ramification of the rotor balance situation to the
other subsystem, i.e., shaft/coupling.
5.2 Pump
Figure 10 shows the causal structure of the preliminary
pump model. Similar to the motor model, the pump model
is much simplified and will be expanded in the future.
PumpRPM
PImpelBalMode
LoadTorque
PImpelBalCond
5.1 Motor
The causal structure of our preliminary motor model is
illustrated in Figure 9. This is a much-simplified model
where we only model the motor torque and rotor balance
condition. As mentioned previously, in future work we will
expand the model with more physical descriptions of the
motor and cover more motor faults.
Figure 10: Causal structure for pump
Two unobservable that were not previously described are:
•
ElectricTorque
LoadTorque
•
DynamicTorque
MRotorBalMode
MotorRPM
MRotorBalCond
Figure 9: Causal structure for motor
There are six nodes in the causal structure of the motor.
Five of them are unobservables whose names, possible
values, and descriptions are listed below:
•
•
•
•
•
ElectricTorque; {0, pos}; electrical torque
generated by the motor, zero or positive;
LoadTorque; {0, pos}; torque generated by the
loading (from pump), zero or positive; an
interface node to pump
DynamicTorque; {neg, 0, pos}; net dynamic
torque; negative, zero, or positive;
MotorRpm; {0, 1, 2}; rotor rotation speed; an
interface node to shaft/coupling;
MRotorBalCond; {ok, unbal}; balance condition
of motor rotor, ok or unbalanced; an interface
node to shaft/coupling.
PumpRpm; {0, 1, 2}; rotation speed of pump
shaft; an interface to shaft/coupling;
PImpelBalCond; {ok, unbal}; balance condition
of the pump impeller, ok or unbalanced; an
interface to shaft/coupling.
And the only assumable is:
•
PImpelBalMode; {ok, unbal}; balance mode of
the pump impeller, ok or unbalanced.
5.3 Shaft/Coupling
The causal structure of the shaft/coupling is shown in
Figure 11. Note that causal structure is not connected. This
indicates that there are some attributes of the
shaft/coupling that are not causally related.
We list the new unobservables in the following:
•
•
•
MBRpm; {0, 1, 2}; rotation speed of the shaft
felt by the motor bearing; an interface to the
motor bearing;
PBRpm; {0, 1, 2}; rotation speed of the shaft felt
by the pump bearing; an interface to the pump
bearing;
MBBalCond; {ok, m, p, mp}; balance condition
felt by the motor bearing, ok, motor unbalanced,
•
•
pump unbalanced, or motor and pump
unbalanced; an interface to the motor bearing;
PBBalCond; {ok, m, p, mp}; balance condition
felt by the pump bearing, ok, motor unbalanced,
pump unbalanced, or motor and pump
unbalanced; an interface to the pump bearing;
SAliCond; {ok, misali-a, misali-p, misali-ap};
alignment condition of the coupling, ok, angularly
misaligned, parallely misaligned, or angularly and
parallely misaligned; an interface to both the
motor bearing and the pump bearing.
MotorRPM
PumpRPM
SAliMode
MBRPM
PBRPM
SAliCond
MRotorBalCond
PImpelBalCond
MBBalCond
PBBalCond
Figure 11: Causal structure for
shaft/coupling
threshold. Sensor nodes of this type include:
The only assumable in the shaft/coupling model is:
•
•
SAliMode; {ok, misali-a, misali-p, misali-ap};
alignment mode of the coupling, ok, angularly
misaligned, parallely misaligned, or angularly and
parallely misaligned.
Observe that, although the unobservable SAliCond looks
identical to the assumable SAliMode, it is necessary to
have such an unobservable for technical reasons. This is
because, in our modeling mechanism, an assumable node
cannot
have
any
parent
nodes
while
an
observable/unobservable can have multiple parent nodes.
Hence, the unobservable SAliCond serves as an extension
of SAliMode in order to interface with nodes in both
motor bearing and pump bearing models.
5.4 Bearings and Sensors on the Bearing Housings
In this subsection, we describe the causal network model
for the bearings. We use the same model for both motor
and pump bearings. The causal structure of this model is
depicted in Figure 12. To differentiate the nodes between
two models, we usually add an M prefix and a P prefix to
the name of the nodes for motor and pump bearing models,
respectively.
There are three types of node in the causal structure:
sensors (observables), unobservables, and assumables. The
sensor nodes, denoted as shaded ovals in the figures,
represent the information obtained from the signalprocessing unit that performs FFT analysis and Cepstrum
analysis of the signals from the accelerometers. There are
two types of sensor nodes. The first type of sensor returns a
Boolean value that represents whether a particular
peak/signature in the spectrum is present or has exceed the
|1xR|
|1xA|
1xA>1xR?
|2xR| |2xA|
2xR>.5 1xR?
BBalCond
SubHar
2xA>.5 1xA?
BAliMode
1xR
1xA
2xR
2xA
RPM
LooseMode
BRPM
ORMode
SAliCond
BAliCond
LooseCond
IRMode
BPFI
BPFO
CageMode
BallMode
BSF FTF
Figure 12: Causal structure for bearing
•
•
•
•
•
•
•
•
1xA: the presence of excessive 1xRPM signal in
the Axial direction;
1xR: the presence of excessive 1xRPM signal in
the Radial direction;
2xA: the presence of excessive 2xRPM signal in
the Axial direction;
2xR: the presence of excessive 2xRPM signal in
the Radial direction;
SubHar: the presence of excessive subharmonic
signals;
BPFI: the presence of BPFI (Ball Pass Frequency
Inner) signal;
BPFO: the presence of BPFO (Ball Pass
Frequency Outer) signal;
BSF: the presence of BSF (Ball Spin Frequency)
signal; and
FTF: the presence of FTF (Fundamental Train
Frequency) signal.
The other type of sensor node shows a quantized value of
the specific signals it represents, covering:
•
•
•
•
•
RPM: the rotation speed detected;
|1xA|: the magnitude of 1xRPM Axial signal;
|1xR|: the magnitude of 1xRPM Radial signal;
|2xA|: the magnitude of 2xRPM Axial signal; and
|2xR|: the magnitude of 2xRPM Radial signal.
Note that the threshold values and the quantization of
specific signals remain context-dependent; these measures
are defined based on relative sensor values.
We list the new unobservable nodes in the bearing models
as shown below:
•
•
•
BAliCond; {ok, misali-a, misali-p, misali-ap};
alignment condition displayed at the bearing, ok,
angularly misaligned, parallely misaligned, or
angularly and parallely misaligned;
LooseCond; {ok, loose}; mechanical looseness
condition at the bearing, ok or loose;
1xA>1xR?; {true, false}; the magnitude of the
1xRPM Axial signal is larger than that of the
1xRPM Radial signal, true or false;
•
•
2xA>.51xA?; {true, false}; the magnitude of the
2xRPM Axial signal is larger than 50% of that of
the 1xRPM Axial signal, true or false; and
2xR>.51xR?; {true, false}; the magnitude of the
2xRPM Radial signal is larger than 50% of that of
the 1xRPM Radial signal, true or false.
Shaft/Coupling
Motor Bearing
Sensors at
Motor Bearing
Finally, the assumables of the bearing model are:
•
•
•
•
•
•
BAliMode; {ok, misali}; alignment mode of the
bearing, ok or misaligned;
LooseMode; {ok, loose}; mechanical looseness
mode of the bearing, ok or loose;
IRMode; {ok, worn}; wearing condition of the
inner race, ok or worn;
ORMode; {ok, worn}; wearing condition of the
outer race, ok or worn;
BallMode; {ok, worn}; wearing condition of the
rolling balls, ok or worn; and
CageMode; {ok, worn}; wearing condition of
the bearing cage, ok or worn.
5.5 Additional nodes for sensor fusion
We have described the causal network models of the
subsystems as depicted in Figure 8. Using the
CNETS/CDMB environment and its simulation capability,
we can combine these subsystem models and generate
diagnostic results given specific observations. We will
present some of the results in the next section.
It is not necessary to have two sets of accelerometers, in
order to generate meaningful diagnostic results. In fact, if
we remove the models of pump bearing and sensors
mounted on the its housing, the remaining models can still
tell whether there is a misalignment or an imbalance
problem.
The main motivation for having two sets of accelerometers
in place is to have a finer “resolution” of diagnoses. For
example, instead of having a diagnosis like “there is a
balance problem”, it would be much better to have a
diagnosis like “there is a balance problem on the motor
rotor”. In order to achieve this objective, information
gathered from different sources needs to be properly
combined/compared (that is, “fused”). This sensor fusion
process can be easily accomplished in our approach by
employing additional “sensor fusion” nodes. Note that
these nodes are often categorized as unobservables.
Figure 13 shows the high-level causal structure of adding
such sensor fusion nodes to our model. We add one sensor
fusion node to our model:
•
1xRMB>1RPB?; {true, false}; the magnitude of
1xRPM Radial signal at motor bearing is larger
than that at pump bearing, true or false;
Pump
Motor
Pump Bearing
Sensor
Fusion
Sensors at
Pump Bearing
Figure 13: High-level causal structure with
sensor fusion
•
1xRMB<1RPB?; {true, false}; the magnitude of
1xRPM Radial signal at motor bearing is larger
than that at pump bearing, true or false.
Both of the nodes have parents from with parent nodes
from MRotorBalCond and PImpelBalCond of the
shaft/coupling model and from |1xR| nodes of both motor
and pump bearing models.
6. Simulation Results
In this section, we present some diagnostic results
generated by our preliminary models when simulated using
both CNETS and CDMB. We use three different settings
of the models. In the first setting, we use only the sensors
at the motor bearing and do not include the models for
pump bearing and sensors attached to it. In the second
setting, we add the models for pump bearing and
corresponding sensors, but there are no sensor fusion
nodes. In the third setting, we add the two sensor fusion
nodes as described in Section 5.5.
Example 1: (imbalance, first setting)
We set the following sensors to true: M1xA, M1xR, and
MRPM. All other sensors are set to false. We also set the
unobservable M1xA>1xR? to false, since the non-binary
valued sensor nodes, M|1xA| and M|1xR|, cannot be
implemented as discussed in Section 4.4. The diagnostic
result is “MRotorBalMode is unbal OR PImpelBalMode
is unbal”, which indicates either motor rotor or the pump
shaft is unbalanced.
Example 2: (imbalance, second setting)
We set sensors M1xA, M1xR, MRPM, P1xA, P1xR, and
PRPM to true and all other sensors to false. We also set
unobservables M1xA>1xR? and P1xA>1xR? to false.
The diagnostic result is the same as that of Example 1.
Example 3: (imbalance, third setting) We set sensors
M1xA, M1xR, MRPM, P1xA, P1xR, and PRPM to true
and all other sensors to false. We also set unobservables
M1xA>1xR?, P1xA>1xR?, and 1xRMB<PB? to false
and 1xRMB>PB? to true. The diagnostic result becomes
“MRotorBalMode is unbal”, which shows that the motor
rotor is unbalanced. This diagnosis is finer than the
diagnoses shown in previous two examples, which shows
the advantage of using a second set of accelerometers and
References
Figure 14: CDMB Simulation results for example 3
sensor fusion technique. Simulation results of this example
on the CDMB is shown in Figure 14.
Example 4: (angular misalignment, third setting)
We set sensors M1xA, M2xA, MRPM, P1xA, P2xA, and
PRPM to true and all other sensors to false. We also set
unobservables M2xA>.5 1xA? and P2xA>.5 1xA? to
false. The diagnosis generated is “SAliMode is misali-a”,
which indicates the angular misalignment of the coupling.
Example 5: (mechanical looseness, third setting)
We set sensors MRPM, PSubHar, and PRPM to true and
all other sensors to false. We obtain the diagnosis of
“PLooseMode is loose” that shows the mechanical
looseness of the pump bearing.
7. Conclusions
We have described a model-based approach for conditionbased monitoring of pump/motor systems. We have shown
how we model these systems, and how we can use our
approach to perform sensor fusion and system-level
diagnostics for such systems. This approach offers several
advantages over traditional methods of condition-based
monitoring. The system models, which replace the role
rules would play in a traditional approach, can integrate the
output from several sensors, and if the system is altered the
models can be altered to reflect the changes, such that the
diagnostics computed are automatically updated. This
process of model updating is much simpler than rule
updating. In addition, our model-based approach provides
analytical results about completeness and soundness of
fault coverage, results that are not possible with a rulebased approach.
This paper describes our preliminary efforts on this topic,
and our future goals include extending the models to be
able to diagnose a wider range of faults, and using
additional signal-processing algorithms to extract
information from the sensors that is more indicative of the
underlying faults.
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