NZ599241B - Methods and systems for monitoring operation of equipment - Google Patents
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- NZ599241B NZ599241B NZ599241A NZ59924112A NZ599241B NZ 599241 B NZ599241 B NZ 599241B NZ 599241 A NZ599241 A NZ 599241A NZ 59924112 A NZ59924112 A NZ 59924112A NZ 599241 B NZ599241 B NZ 599241B
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 23
- 238000000034 method Methods 0.000 title claims description 40
- 238000012545 processing Methods 0.000 claims description 33
- 230000008859 change Effects 0.000 claims description 11
- 238000012423 maintenance Methods 0.000 claims description 10
- 238000005070 sampling Methods 0.000 claims description 8
- 230000007613 environmental effect Effects 0.000 claims description 7
- 230000006870 function Effects 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 6
- 230000015654 memory Effects 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 229910000897 Babbitt (metal) Inorganic materials 0.000 description 1
- 230000002547 anomalous effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
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Abstract
599241 Disclosed is a condition assessment system (10) for monitoring the operation of at least one of a plurality of units included within a fleet of substantially similar units. The system (10) is comprised of a fleet of units (72-80, 110-118, 140-148), an input device (96) configured to receive a selection of at least one source of data related to the operation of at least first and second (72 and 74, respectively) units of the fleet of units (72-80, 110-118, 140-148), at least one sensor (82-90, 122-130, 152-160) and a condition assessment device (12, 70, 120, 150, 170). The fleet of units (72-80, 110-118, 140-148) includes the first and second units (72, 74) which are of the same type which is different from the type of the other units. The at least one sensor (82-90, 122-130, 152-160) is associated with the at least one source of data and is configured to sense data related to the operation of at least the first and second units (72, 74). The condition assessment device (12, 70, 120, 150, 170) is configured to receive data from said at least one sensor (82-90, 122-130, 152-160), sample data associated with at least one data parameter from the received data and generate a baseline parametric curve from the data associated with the at least one sampled data parameter. a selection of at least one source of data related to the operation of at least first and second (72 and 74, respectively) units of the fleet of units (72-80, 110-118, 140-148), at least one sensor (82-90, 122-130, 152-160) and a condition assessment device (12, 70, 120, 150, 170). The fleet of units (72-80, 110-118, 140-148) includes the first and second units (72, 74) which are of the same type which is different from the type of the other units. The at least one sensor (82-90, 122-130, 152-160) is associated with the at least one source of data and is configured to sense data related to the operation of at least the first and second units (72, 74). The condition assessment device (12, 70, 120, 150, 170) is configured to receive data from said at least one sensor (82-90, 122-130, 152-160), sample data associated with at least one data parameter from the received data and generate a baseline parametric curve from the data associated with the at least one sampled data parameter.
Description
METHODS AND SYSTEMS FOR MONITORING OPERATION OF EQUIPMENT
This application claims priority from United States
Application No. 13/082,086 filed on 7 April 2011, the contents of which are to be
taken as incorporated herein by this reference.
BACKGROUND OF THE INVENTION
The embodiments described herein relate generally to
monitoring equipment, and more specifically to condition assessment systems used to
analyze operation of the equipment.
A condition assessment system may be used to monitor the
operation of equipment. At least some known condition assessment systems include
stored parametric curves that represent a baseline of normal operation of the
equipment. Sensors included within the equipment provide outputs that are compared
to the parametric baseline to detect anomalous operating conditions and/or shifts in
equipment operations.
For fleet equipment, defined herein as equipment
manufactured and sold for use in a plurality of remotely-located facilities (i.e., not
one-off custom equipment), a fleet rule package includes generic parametric baseline
curves for the equipment. Such generic parametric baseline curves may be based on
average conditions in which the equipment may operate. However, variations in the
local environment, variations in maintenance schedules, and/or other operational
variations may cause equipment installed in a first facility to operate significantly
differently than equipment installed in a second facility or within an individual site.
In such cases, the actual operation of the equipment may mistakenly be interpreted as
faulty operation if the operation varies enough from the parametric baseline curves
within the fleet rule package, even though the operating variation may be caused by
factors related to the local environment, rather than a result of equipment malfunction.
False alarm signals may be generated when the sensor outputs from such equipment
are compared to a baseline curve representative of normal operation of that
equipment. Typically, to counteract such inaccuracies in the parametric baseline
curves, the parametric baseline curves are manually maintained to ensure that the
parametric baseline curves accurately represent proper operation of the equipment in
the environment in which the equipment is installed. However, depending on the
location of the equipment, the local environment in which the equipment is operating,
the maintenance schedules, and other factors, maintaining the accuracy of such curves
may be a time-consuming and difficult task.
A reference herein to a patent document or other matter which
is given as prior art is not to be taken as an admission that that document or matter
was known or that the information it contains was part of the common general
knowledge as at the priority date of any of the claims.
BRIEF DESCRIPTION OF THE INVENTION
In one aspect, a condition assessment system for monitoring
the operation of at least one of a plurality of units included within a fleet of similar
units, said system comprising: a fleet of units wherein at least a first and second unit
of the fleet of units are of a first type which is different to other units of the fleet of
units; an input device configured to receive a selection of at least one source of data
related to the operation of at least the first and second units; at least one sensor
associated with the at least one source of data, said at least one sensor configured to
sense data related to the operation of at least the first and second units; and a
condition assessment device configured to: receive data from said at least one sensor,
wherein the received data represents current environmental conditions that said at
least one sensor is subject to; sample data associated with at least one data parameter
from the received data; and generate a customized baseline parametric curve from the
data associated with the at least one sampled data parameter.
In another aspect, a method of maintaining a customized
baseline parametric curve representing normal operation of at least a first unit and a
second unit of a fleet of units, wherein the first unit and the second unit are one of a
first type which is different to other units in the fleet of units, said method comprising:
receiving a selection of at least one source of data related to the operation of the first
and second units; receiving data from the at least one selected source of data, wherein
the received data represents current environmental conditions that the first and second
units are subject to; sampling, using a computer program embodied on a non-
transitory computer readable medium, data associated with at least one data parameter
from the received data; and determining the customized baseline parametric curve by
fitting a curve to the data associated with the at least one sampled data parameter.
In yet another aspect, a condition assessment device for
monitoring the operation of at least one of a plurality of units included within a fleet
of similar units, wherein, at least a first and second unit of the fleet of units are of a
first type which is different to other units of the fleet of units; said device comprising
a processing device configured to store at least one code segment configured to
instruct said processing device to: receive a selection, from an input device, of at least
one source of data related to the operation of at least the first and second units; receive
data from at least one sensor associated with the at least one source of data, wherein
the received data represents current environmental conditions that the at least one
sensor is subject to; sample data associated with at least one data parameter from the
received data; and generate a customized baseline parametric curve from the data
associated with the at least one sampled data parameter.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 is a block diagram of an exemplary condition
assessment system.
Figure 2 is an exemplary flow diagram of a method that may
be used to automatically maintain at least one baseline curve for use by the condition
assessment system shown in Figure 1.
Figure 3 is a block diagram of an exemplary processing
device that may be used with the condition assessment system shown in Figure 1.
DETAILED DESCRIPTION OF THE INVENTION
The methods and systems described herein enable a baseline
maintained, wherein the unit is one of a plurality of substantially similar units
included in an equipment fleet. More specifically, the methods and systems described
herein enable a generic fleet rule package to be modified to more accurately represent
the operation and performance of an individual unit within the fleet. The
modifications may be based on an operating environment and/or machine conditions
of the individual unit. The methods and systems described herein enable the
generation and use of individualized baseline parametric curves, without manually
updating and/or maintaining the baseline parametric curves for each individual unit.
Automatically maintaining the baselines and associated alarm set points, provides a
commercial advantage by not requiring a facility’s maintenance engineer to manually
maintain baseline curves of units within their associated facility. The disclosure is
described as applied to exemplary embodiments, namely, systems and methods for
maintaining parametric baseline curves for a fleet of equipment. However, it is
contemplated that this disclosure has general application to condition assessment
systems in industrial, commercial, and residential applications.
Technical effects of the methods and systems described
herein include at least one of: (a) receiving a selection of at least one source of data
related to the operation of the unit; (b) receiving data from the at least one selected
source of data; (c) sampling data associated with at least one data parameter from the
received data; and (d) determining the baseline parametric curve by fitting a curve to
the data associated with the at least one sampled data parameter.
Figure 1 is a block diagram of an exemplary condition
assessment system 10 for use in monitoring performance of equipment and for
maintaining baseline parametric curves used to analyze operation and performance of
the equipment. In the exemplary embodiment, condition assessment system 10
includes at least one condition assessment device 12 and a plurality of data sensors 14.
Moreover, in the exemplary embodiment, condition assessment system 10 monitors
the operation of fleet equipment. Fleet equipment is defined herein as equipment
manufactured and sold for use in a plurality of remotely-located facilities (i.e., not
one-off custom equipment), such as, for example, a first facility 40, a second facility
42, a third facility 44, a fourth facility 46, a fifth facility 48, a sixth facility 50, a
seventh facility 52, an eighth facility 54, and a ninth facility 56. In the exemplary
embodiment, condition assessment system 10 may be configured to monitor the
performance of a first type of unit, a second type of unit, and a third type of unit.
Although described herein as monitoring performance of three types of units included
within nine facilities, condition assessment system 10 may monitor performance of
any suitable number of unit types included within any suitable number of facilities.
In the exemplary embodiment, each condition assessment
device 12 included within condition assessment system 10 includes a local condition
assessment device 70. Furthermore, in the exemplary embodiment, first facility 40
includes a first unit 72, a second unit 74, a third unit 76, a fourth unit 78, and a fifth
unit 80. More specifically, and as an example only, first unit 72 and second unit 74
are the first type of unit monitored by condition assessment system 10, third unit 76
and fourth unit 78 are the second type of unit monitored by condition assessment
system 10, and fifth unit 80 is the third type of unit monitored by condition
assessment system 10. In the exemplary embodiment, first facility 40 is a power
generation facility and units 72, 74, 76, 78, and 80 are components of the power
generation facility. However, although the condition assessment system 10 is
described in connection with a plurality of power generation facilities, the methods
and systems described herein are applicable to other applications, including, but not
limited to, aircraft, marine, and industrial applications.
In the exemplary embodiment, each unit 72, 74, 76, 78, and
80 includes at least one of the plurality of data sensors 14 configured to sense selected
data parameters related to the operation and performance of the associated unit and/or
first facility 40. For example, first unit 72 includes a first data sensor 82, second unit
74 includes a second data sensor 84, third unit 76 includes a third data sensor 86,
fourth unit 78 includes a fourth data sensor 88, and fifth unit 80 includes a fifth data
sensor 90. Data sensors 14 may include any group or number of sensors 14 that can
monitor data parameters of interest. Such parameters of interest may include, but are
not limited to, ambient temperature, exhaust gas temperature, oil temperature,
component temperatures such as high pressure turbine shroud temperature, fuel flow,
core speed, compressor discharge pressure, turbine exhaust pressure, and fan speed.
Local condition assessment device 70 includes a processing
device 92 that receives data from data sensors 82, 84, 86, 88, and 90. In the
exemplary embodiment, local condition assessment device 70 also includes a memory
device 94. Memory device 94 may be coupled to, or included within, processing
device 92. Furthermore, local condition assessment device 70 may include an
input/output device 96 for use in receiving an input from a user. For example,
input/output device 96 may include a keyboard, a mouse, a touch screen, a track pad,
a button, and/or any other device that enables condition assessment system 10 to
function as described herein. More specifically, input/output device 96 may display a
graphical user interface to the user and/or may receive a selection of at least one of the
plurality of data sensors 14 from the user.
In the exemplary embodiment, local condition assessment
device 70 is programmed to process the data signals received from data sensors 82,
84, 86, 88, and 90 for monitoring performance characteristics of each respective unit
72, 74, 76, 78, and 80. For example, local condition assessment device 70 may be
configured to continuously monitor the output of data sensors 82, 84, 86, 88, and 90
by sampling data associated with a predetermined data parameter sensed by data
sensors 82, 84, 86, 88, and 90. The sampled data parameters are used in diagnostic
trending analysis for monitoring performance of the unit associated with the data
sensor that collected the data. More specifically, the sampled data parameters are
trended against reference data, to monitor performance of the unit. For example, the
sampled data parameters are compared to a stored parametric baseline associated with
the type of data being analyzed.
In the exemplary embodiment, local condition assessment
device 70 may analyze data received from data sensor 82, in order to monitor
performance of first unit 72, and may analyze data received from data sensor 84 to
monitor performance of second unit 74. As described above, first unit 72 and second
unit 74 are the same type of unit. For example, units 72 and 74 may both be the same
model of compressor. Local condition assessment device 70 may also combine data
received from data sensor 82 and data sensor 84, and/or analyze performance units 72
and 74 based on the combined data. For example, if data collected by data sensor 82
varies from a stored parametric baseline, local condition assessment device 70 may
determine that first unit 72 is not functioning properly. However, if data collected
from first unit 72 and data collected from second unit 74 varies from the stored
parametric baseline in a substantially similar manner, such data may be an indicator
that the stored parametric baseline for the associated parameter is not accurately
representing proper operation of the units.
Typically, the original, stored parametric baseline (i.e., the
parametric baseline curve included within the generic fleet rule package) for each type
of unit in a fleet of units describes how that unit should perform in average conditions
and with performance of standard recommended maintenance. However, individual
units of each type of unit may not be installed in the same operating environment or
may not be operated under the same conditions as the units used to determine the
stored parametric baseline. Real-world operation of the units may vary from the
stored parametric baseline, and therefore, comparing real-world operation of the units
to the stored parametric baseline may not provide an accurate indication of a deviation
from proper operation of the unit. Determining an adjusted and/or new baseline, also
referred to herein as “re-baselining”, provides a more accurate baseline that may be
used for comparing with the measured operating data. In other words, re-baselining
converts a baseline included in a generic fleet rule package to a customized baseline
that more accurately represents proper operation of a specific unit.
In the exemplary embodiment, first facility 40, second facility
42, and third facility 44 are each located in a first region 100, and fourth facility 46,
fifth facility 48, and sixth facility 50 are each located in a second region 102.
Moreover, seventh facility 52, eighth facility 54, and ninth facility 56 are located in a
third region 104. Boundaries of first region 100, second region 102, and third region
104 may be defined geographically to include facilities located within a predefined
distance of one another. Alternatively, first region 100, second region 102, and third
region 104 may be defined as facilities that are located in areas having substantially
similar environments, for example, but not limited to, facilities located in areas having
similar operating temperatures and/or humidity.
In the exemplary embodiment, second facility 42 includes a
first unit 110, a second unit 112, a third unit 114, a fourth unit 116, a fifth unit 118,
and a local condition assessment device 120. Furthermore, the plurality of sensors 14
included within system 10 includes a sixth sensor 122 associated with first unit 110, a
seventh sensor 124 associated with second unit 112, an eighth sensor 126 associated
with third unit 114, a ninth sensor 128 associated with fourth unit 116, and a tenth
sensor 130 associated with fifth unit 118. Similarly, third facility 44 includes a first
unit 140, a second unit 142, a third unit 144, a fourth unit 146, a fifth unit 148, and a
local condition assessment device 150. Moreover, plurality of sensors 14 includes an
eleventh sensor 152 associated with first unit 140, a twelfth sensor 154 associated
with second unit 142, a thirteenth sensor 156 associated with third unit 144, a
fourteenth sensor 158 associated with fourth unit 146, and a fifteenth sensor 160
associated with fifth unit 148.
Condition assessment system 10 may also include a central
condition assessment device 170. As described above with respect to local condition
assessment device 70, central condition assessment device 170 includes a processing
device 172 and a memory device 174. Memory device 174 may be included within,
or coupled to, processing device 172. In the exemplary embodiment, central
condition assessment device 170 is coupled to, and receives data from, local condition
assessment devices 70, 120, and 150. In an alternative embodiment, central condition
assessment device 170 is coupled to plurality of data sensors 14 and receives data
directly from data sensors 14. In the exemplary embodiment, central condition
assessment device 170 analyzes data received from a plurality of facilities, for
example, first facility 40, second facility 42, and/or third facility 44. Analyzing data
from multiple facilities facilitates calculating an adjusted parametric baseline for a
first type of unit based on data collected from multiple units included within first
facility 40, and/or based on data collected from units included within different
facilities, for example, first facility 40 and second facility 42. For example, central
condition assessment device 170 may generate a new parametric baseline for a first
model of compressor based on data received from all compressors of that model that
are coupled to central condition assessment device 170. Central condition assessment
device 170 then transmits the new parametric baseline to each facility that includes a
compressor of that model for use in monitoring operation of the compressors of that
model. Alternatively, central condition assessment device 170 generates a new
parametric baseline for the first model of compressor based on data received from all
compressors of that model that are coupled to central condition assessment device 170
and located within first region 100. The new baseline for first region 100 is
transmitted to each of the facilities located in first region 100 that include a
compressor of that model. By calculating an adjusted baseline for compressors based
on region, the shared environment of those compressors enables generation of a
parametric baseline customized for the environment in which the compressors are
installed.
Figure 2 is a flow chart 200 of an exemplary method 202 that
may be used for maintaining a baseline parametric curve representing normal
operation of a first unit, for example, first unit 72 (shown in Figure 1). As described
above, first unit 72 is one of a plurality of substantially similar units included in an
equipment fleet (i.e., first unit 72 is a first type of unit). In the exemplary
embodiment, initially a selection of at least one source of data related to operation and
performance of first unit 72 is received 210. For example, a selection from a user
may be received 210, at local condition assessment device 70 (shown in Figure 1),
instructing device 70 to base the baseline parametric curve representing normal
operation of first unit 72 on operating data collected by first sensor 82. Furthermore,
a user selection may be received 210, at local condition assessment device 70,
instructing device 70 to base the baseline parametric curve representing normal
operation of first unit 72 on operating data collected by first sensor 82 and second
sensor 84, that is coupled to another of the first type of unit (i.e., second unit 74).
Moreover, a user selection may be received 210, at local condition assessment device
70, to base the baseline parametric curve representing normal operation of first unit 72
on operating data collected by all, or any combination of the sensors coupled to local
condition assessment device 70 (i.e., first sensor 82, second sensor 84, third sensor 86,
fourth sensor 88, and/or fifth sensor 90). Alternatively, a selection from a user may
be received 210, at central condition assessment device 170, instructing device 170 to
base the baseline parametric curve representing normal operation of first unit 72 on
operating data collected by any combination of the plurality of sensors included
within the facilities coupled to central condition assessment device 170 (i.e., any
combination of sensors included within facilities 40, 42, 44, 46, 48, 50, 52, 54 and/or
56).
In the exemplary embodiment, method 202 also includes
receiving 212 data from the at least one selected source of data. At least one of local
condition assessment device 70 and central condition assessment device 170 receives
212 data from the sensors selected by the user. For example, data is received 212
from first sensor 82, from sensors associated with the plurality of substantially similar
units (i.e., other units of the first type of unit), from all sensors located in a first
region, for example first region 100 (shown in Figure 1), or any combination thereof.
In the exemplary embodiment, method 202 also includes
sampling 220 data associated with at least one data parameter from the received data
and determining 222 the baseline parametric curve by fitting a curve to the data
associated with the at least one sampled data parameter. With respect to a gas turbine,
data parameters may include, but are not limited to, ambient temperature, exhaust gas
temperature, oil temperature, component temperatures such as high pressure turbine
shroud temperature, fuel flow, core speed, compressor discharge pressure, turbine
exhaust pressure, generator output power, voltage, current, frequency, efficiency,
exhaust temperature, firing temperature, vibration, inlet air temperature, ambient
barometric pressure, wheel space temperature, bearing temperature, and bearing metal
temperature. For example, at least one of local condition assessment device 70 and
central condition assessment device 170 calculates the baseline parametric curve from
a stored set of base functions and the data received from the at least one selected
source of data.
In the exemplary embodiment, method 202 also includes
determining 224 whether to generate a new baseline parametric curve. Local
condition assessment device 70 and/or central condition assessment device 170 may
identify a data acquisition logical flag within the received data that indicates that a
change has been made to the first unit. For example, generating a new baseline
parametric curve may be beneficial after performance of a maintenance action on first
unit 72. The logical flag may be created by a user after performance of a maintenance
action and delivered to local condition assessment device 70 and/or central condition
assessment device 170.
Local condition assessment device 70 and/or central condition
assessment device 170 may also determine that a change has been made to first unit
72, for example, a maintenance action has been performed on first unit 72 or a
component of first unit 72 has been fixed or replaced. For example, a detection
algorithm may be configured to determine that a change has been made to first unit 72
by identifying a change that exceeds a predefined level in one of the plurality of data
parameters, by identifying changes in more than one of the plurality of data
parameters, and by applying a rule to the plurality of data parameters.
Furthermore, an expected value of a parameter may be
compared to a measured value to determine 224 whether to generate a new baseline
parametric curve. If the difference between an expected value and a measured value
of a parameter of first unit 72 exceeds a predefined anomaly limit for that parameter, a
determination is made as to whether other parameters of first unit 72 support the
anomaly. If the other parameters support the anomaly (e.g., the other parameters also
vary from expected parameter values), a determination 224 is made that a new
baseline parametric curve should be generated. If the other parameters do not support
the anomaly, it is an indication that the equipment being monitored is not operating in
accordance with the baseline parametric curve.
Furthermore, if a difference between expected values and
measured values of a parameter monitored on multiple units exceeds a predefined
anomaly limit for that parameter, and no cause for the anomaly limit is known, a
determination 224 is made that a new baseline parametric curve should be generated.
For example, a known cause of an anomaly detected in multiple units may include,
but is not limited to, an ambient temperature shared by each unit. Detecting an
anomaly in multiple units that cannot be explained by other sensor values is more
likely an indication that the baseline parametric curve does not accurately represent
proper operation of the units than an indication that multiple units have
simultaneously developed operating anomalies. In an alternative embodiment, if a
difference between expected values and measured values of a parameter monitored on
multiple units exceeds a predefined anomaly limit for that parameter, and a cause for
the anomaly limit is known, a determination 224 may still be made that a new
baseline parametric curve should be generated. For example, if high ambient
temperatures have caused multiple units to deviate from an expected value of a
parameter, condition assessment system 10 may benefit from a calculation of a new
baseline parametric curve that accounts for the higher ambient temperature.
Moreover, a rule or plurality of rules may be defined and
applied by, for example, local condition assessment device 70 and/or central condition
assessment device 170, to determine 224 whether a new baseline parametric curve
should be generated. For example, the rule may include a “fuzzy logic” rule roughly
defining when a new baseline parametric curve should be generated. For example, a
combination of anomalies detected between expected values and measured values and
a relative distribution of the number of units causing such anomalies may trigger
calculation of a new baseline parametric curve.
In the exemplary embodiment, method 202 may also include
storing 226 the baseline parametric curve for use in monitoring operation and
performance of first unit 72 and/or of the plurality of substantially similar units. As
described above, a fleet rule package includes generic baseline parametric curves
generated for a fleet of units. Data associated with a measured data parameter is used
to adjust at least one of the generic baseline parametric curves to better represent
proper operation of first unit 72. The new baseline parametric curve is stored 226 in a
memory unit, for example, memory device 94 and/or 174 (shown in Figure 1), which
may be positioned at local condition assessment device 70 and/or at central condition
assessment device 170.
Figure 3 is a block diagram of an exemplary embodiment of a
processing device 250 that may be included within local condition assessment devices
70, 120, or 150, and/or in central condition assessment device 170 (shown in Figure
1). More specifically, Figure 3 is an expanded block diagram of an exemplary
embodiment of processing device 92, processing device 172, memory device 94,
and/or memory device 174 (shown in Figure 1). Processing device 250 is configured
to perform operations associated with method 202 (shown in Figure 2). Processing
device 250 may also be referred to as a system controller and/or a condition
assessment platform, for example, a central condition assessment platform (CCAP).
In some embodiments, processing device 250 includes a bus 260 or other
communications device to communicate information. One or more processor(s) 262
are coupled to bus 260 to process information, including data received from, for
example, but not limited to, plurality of sensors 14 (shown in Figure 1) and/or
input/output device 96 (shown in Figure 1). As used herein, the term processor
broadly refers to a processor, a microcontroller, a microcomputer, a programmable
logic controller (PLC), an application specific integrated circuit, and other
programmable circuits. Furthermore, processor(s) 262 may be included within a
computer. Aspects of the disclosure transform a general-purpose computer into a
special-purpose computing device when configured to execute the instructions
described herein.
Processing device 250 may also include one or more random
access memories (RAM) 264 and/or other storage device(s) 266. RAM(s) 264 and
storage device(s) 266 are coupled to bus 260 to store and transfer information and
instructions to be executed by processor(s) 262. RAM(s) 264 (and/or storage
device(s) 266, if included) can also be used to store temporary variables or other
intermediate information during execution of instructions by processor(s) 262.
Processing device 250 may also include one or more read only memories (ROM) 268
and/or other static storage devices coupled to bus 260 to store and provide static (i.e.,
non-changing) information and instructions to processor(s) 262. For example, static
information may include, but is not limited to, a generic baseline parametric curve,
and/or a previously stored baseline parametric curve. Instructions that are executed
include, without limitation, resident conversion and/or comparator algorithms. The
execution of sequences of instructions is not limited to any specific combination of
hardware circuitry and software instructions.
Processing device 250 may also include, or may be coupled
to, input/output device(s) 270. Input/output device(s) 270 may include, or be coupled
to, any device known in the art to provide input data to processing device 250 and/or
to provide outputs, such as, but not limited to, a baseline parametric curve and/or an
alarm signal. Instructions may be provided to RAM 264 from storage device 266
including, for example, a magnetic disk, a read-only memory (ROM) integrated
circuit, CD-ROM, and/or DVD, via a remote connection that is either wired or
wireless providing access to one or more electronically-accessible media. In some
embodiments, hard-wired circuitry can be used in place of or in combination with
software instructions. Thus, execution of sequences of instructions is not limited to
any specific combination of hardware circuitry and software instructions, whether
described and/or shown herein. Also, in the exemplary embodiment, input/output
device(s) 270 may include, without limitation, computer peripherals associated with
an operator interface (e.g., a human machine interface (HMI)) such as a mouse and a
keyboard (neither shown in Figure 3), and/or input/output device 96 (shown in Figure
1). Furthermore, in the exemplary embodiment, additional output channels may
include, for example, an operator interface monitor and/or alarm device (neither
shown in Figure 3). Processing device 250 may also include a sensor interface 272
that allows processing device 250 to communicate with sensors, for example, plurality
of sensors 14 (shown in Figure 1). Sensor interface 272 may include one or more
analog-to-digital converter that converts analog signals into digital signals that can be
used by processor(s) 262.
Processing device 250 may be included within a personal or
workstation computer. The data signals generated by sensors, for example, the
plurality of sensors 14 (shown in Figure 1), may be transferred to processing device
250 in any suitable manner, for example, but not limited to use of a removable
computer-readable medium, such as a floppy disk, CD-ROM or other optical medium,
magnetic tape or the like, or a wireless communication link. It is also possible to
remotely transmit the data signals directly to processing device 250 for real-time
processing. With any implementation, the monitoring algorithm can be stored on the
condition assessment device and accessed from there, or alternatively, it could be
accessed from a removable computer readable medium inserted into the appropriate
drive of the unit. The monitoring algorithm could also be accessed via the Internet or
another computer network. As used herein, the term "computer-readable medium"
refers generally to any medium from which stored data can be read by a computer or
similar device. This includes not only removable media such as the aforementioned
floppy disk and CD-ROM, but also non-removable media such as a hard disk or
integrated circuit memory device in a local condition assessment device or central
condition assessment device.
Described herein are exemplary methods and systems for use
in monitoring operation and performance of at least one of a plurality of units
included within a fleet of substantially similar units. More specifically, the methods
and systems described herein enable a generic fleet rule package to be modified to
more accurately represent operation and performance of an individual unit within the
fleet based on an operating environment and/or condition of the individual unit. The
methods and systems described herein enable the generation and use of individualized
baseline parametric curves, without requiring a manual update and/or without
maintaining the baseline parametric curves for each individual unit. Automatically
maintaining the baselines and associated alarm set points provides a commercial
advantage by freeing a maintenance engineer or knowledge engineer from having to
manually maintain baseline curves of units within a facility.
The methods and systems described herein facilitate efficient
and economical maintenance of baseline parametric curves associated with fleet
equipment. Exemplary embodiments of methods and systems are described and/or
illustrated herein in detail. The methods and systems are not limited to the specific
embodiments described herein, but rather, components of each system, as well as
steps of each method, may be utilized independently and separately from other
components and steps described herein. Each component, and each method step, can
also be used in combination with other components and/or method steps.
When introducing elements/components/etc. of the methods
and apparatus described and/or illustrated herein, the articles “a”, “an”, “the”, and
“said” are intended to mean that there are one or more of the
element(s)/component(s)/etc. The terms “comprising”, “including”, and “having” are
intended to be inclusive and mean that there may be additional
element(s)/component(s)/etc. other than the listed element(s)/component(s)/etc.
This written description uses examples to disclose the
invention, including the best mode, and also to enable any person skilled in the art to
practice the invention, including making and using any devices or systems and
performing any incorporated methods. The patentable scope of the invention is
defined by the claims, and may include other examples that occur to those skilled in
the art. Such other examples are intended to be within the scope of the claims if they
have structural elements that do not differ from the literal language of the claims, or if
they include equivalent structural elements with insubstantial differences from the
literal language of the claims.
Where the terms “comprise”, “comprises”, “comprised” or
“comprising” are used in this specification (including the claims) they are to be
interpreted as specifying the presence of the stated features, integers, steps or
components, but not precluding the presence of one or more other features, integers,
steps or components, or group thereto.
PARTS LIST
condition assessment system
12 condition assessment device
14 plurality of data sensors
40 first facility
42 second facility
44 third facility
46 fourth facility
48 fifth facility
50 sixth facility
52 seventh facility
54 eighth facility
56 ninth facility
70 local condition assessment device
72 first unit
74 second unit
76 third unit
78 fourth unit
80 fifth unit
82 first data sensor
84 second data sensor
86 third data sensor
88 fourth data sensor
90 fifth data sensor
92 processing device
94 memory device
96 input/output device
100 first region
102 second region
104 third region
110 first unit
112 second unit
114 third unit
116 fourth unit
118 fifth unit
120 local condition assessment device
122 sixth data sensor
124 seventh data sensor
126 eighth data sensor
128 ninth data sensor
130 tenth data sensor
140 first unit
142 second unit
144 third unit
146 fourth unit
148 fifth unit
150 local condition assessment device
152 eleventh data sensor
154 twelfth data sensor
156 thirteenth data sensor
158 fourteenth data sensor
160 fifteenth data sensor
170 central condition assessment device
172 processing device
174 memory device
200 flow chart
202 method for maintaining a baseline
parametric curve
210 receiving a selection of at least one source
of data
212 receiving data from the at least one
selected source of data
220 sampling data associated with at least one
data parameter from the received data
222 determining the baseline parametric curve
224 determining whether to generate the
baseline parametric curve
226 storing the baseline parametric curve
250 processing device
260 bus
262 processor
264 RAM
266 storage device
268 ROM
270 input/output device
272 sensor interface
Claims (20)
1. A condition assessment system for monitoring the operation of at least one of a plurality of units included within a fleet of similar units, said system comprising: a fleet of units wherein at least a first and second unit of the fleet of units are of a first type which is different to other units of the fleet of units; an input device configured to receive a selection of at least one source of data related to the operation of at least the first and second units; at least one sensor associated with the at least one source of data, said at least one sensor configured to sense data related to the operation of at least the first and second units; and a condition assessment device configured to: receive data from said at least one sensor, wherein the received data represents current environmental conditions that said at least one sensor is subject to; sample data associated with at least one data parameter from the received data; and generate a customized baseline parametric curve from the data associated with the at least one sampled data parameter.
2. A system in accordance with Claim 1, wherein the fleet of similar units includes at least a first unit, a second unit, and a third unit, wherein the first and second units are located in a first region, and wherein said condition assessment device is further configured to at least one of: receive data from the first unit; receive data from the plurality of similar units; and receive data from units located in the first region.
3. A system in accordance with Claim 2, wherein said condition assessment device is further configured to store the customized baseline parametric curve for use in at least one of monitoring operation of the first unit, and monitoring operation of the plurality of similar units.
4. A system in accordance with Claim 2, wherein said condition assessment device is further configured to at least one of identify a data acquisition logical flag within the received data that indicates that a change has been made to the first unit, and determine that a change has been made to the first unit.
5. A system in accordance with Claim 4, wherein said condition assessment device is further configured to at least one of: identify a change that exceeds a predefined level in data associated with one of the plurality of data parameters; identify changes in data associated with more than one of the plurality of data parameters; and apply a logic rule to the data associated with the plurality of data parameters.
6. A system in accordance with Claim 1 or 2, wherein said condition assessment device is further configured to calculate the customized baseline parametric curve from a stored set of base functions and from the current environmental condition data received from the at least one selected source of data.
7. A system in accordance with Claim 1 or 2, wherein said condition assessment device is configured to determine whether to generate the customized baseline parametric curve.
8. A method of maintaining a customized baseline parametric curve representing normal operation of at least a first unit and a second unit of a fleet of units, wherein the first unit and the second unit are one of a first type which is different to other units in the fleet of units, said method comprising: receiving a selection of at least one source of data related to the operation of the first and second units; receiving data from the at least one selected source of data, wherein the received data represents current environmental conditions that the first and second units are subject to; sampling, using a computer program embodied on a non-transitory computer readable medium, data associated with at least one data parameter from the received data; and determining the customized baseline parametric curve by fitting a curve to the data associated with the at least one sampled data parameter.
9. A method in accordance with Claim 8, wherein the plurality of similar units included in the fleet includes a second unit and a third unit, wherein the first and second units are located in a first region, and wherein receiving data from the at least one selected source of data comprises at least one of: receiving data from the first unit; receiving data from the plurality of similar units; and receiving data from units located in the first region.
10. A method in accordance with Claim 8 or 9, further comprising storing the customized baseline parametric curve for use in at least one of monitoring the operation of the first unit and monitoring the operation of the plurality of similar units.
11. A method in accordance with Claim 8 or 9, wherein determining the customized baseline parametric curve by fitting a curve to the data associated with the at least one sampled data parameter comprises calculating the customized baseline parametric curve from a stored set of base functions and from the data received from the at least one selected source of data.
12. A method in accordance with Claim 8 or 9, further comprising determining whether to generate the customized baseline parametric curve.
13. A method in accordance with Claim 12, wherein determining whether to generate the customized baseline parametric curve comprises at least one identifying a data acquisition logical flag within the received data that indicates that a change has been made to the first unit; and determining that a change has been made to the first unit.
14. A method in accordance with Claim 12 or 13, wherein sampling data associated with the at least one data parameter from the received data comprises sampling data associated with a plurality of data parameters from the received data, and wherein determining that a change has been made to the first unit comprises at least one of: identifying a change that exceeds a predefined level in one of the plurality of data parameters; identifying changes in more than one of the plurality of data parameters; and applying a logic rule to the plurality of data parameters.
15. A method in accordance with any one of Claims 8 to 14, wherein receiving data comprises receiving data representing a schedule of maintenance performed on the first unit, and data representing usage of the first unit.
16. A condition assessment device for monitoring the operation of at least one of a plurality of units included within a fleet of similar units, wherein, at least a first and second unit of the fleet of units are of a first type which is different to other units of the fleet of units, said device comprising a processing device configured to store at least one code segment configured to instruct said processing device to: one code segment configured to instruct said processing device to: receive a selection, from an input device, of at least one source of data related to the operation of at least the first and second units; receive data from at least one sensor associated with the at least one source of data, wherein the received data represents current environmental conditions that the at least one sensor is subject to; sample data associated with at least one data parameter from the received data; and generate a customized baseline parametric curve from the data associated with the at least one sampled data parameter.
17. A device in accordance with Claim 16, wherein the at least one code segment is configured to instruct said processing device to at least one of: receive data from a first unit of the plurality of units; receive data from the plurality of similar units; and receive data from units located in a first region.
18. A device in accordance with Claim 17, wherein the at least one code segment is configured to instruct said processing device to store the customized baseline parametric curve for use in at least one of monitoring the operation of said first unit, and monitoring the operation of the plurality of similar units.
19. A device in accordance with Claim 16 or 17, wherein the at least one code segment is configured to instruct said processing device to calculate the customized baseline parametric curve from a stored set of base functions and from the data received from said at least one selected source of data.
20. A device in accordance with Claim 16 or 17, wherein the at least one code segment is configured to instruct said processing device to determine whether to generate the customized baseline parametric curve.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US13/082,086 US9053468B2 (en) | 2011-04-07 | 2011-04-07 | Methods and systems for monitoring operation of equipment |
US13/082,086 | 2011-04-07 |
Publications (2)
Publication Number | Publication Date |
---|---|
NZ599241A NZ599241A (en) | 2013-10-25 |
NZ599241B true NZ599241B (en) | 2014-01-28 |
Family
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