1
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
[See section 10, Rule 13]
DEVICE ANALYSIS APPARATUS, DEVICE ANALYSIS METHOD, AND
DEVICE ANALYSIS PROGRAM;
MITSUBISHI ELECTRIC CORPORATION, A CORPORATION ORGANISED
AND EXISTING UNDER THE LAWS OF JAPAN, WHOSE ADDRESS IS 7-3,
MARUNOUCHI 2-CHOME, CHIYODA-KU, TOKYO 1008310, JAPAN
THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE
INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED
2
DESCRIPTION
Field
[0001] The present disclosure relates 5 to a device
analysis apparatus, a device analysis method, and a device
analysis program for analyzing a state of a device.
Background
10 [0002] Efforts have been widely made to collect and
accumulate operation data from each device of a railway
vehicle, and visualize and analyze a current state of a
soundness degree of each device. As a method for
visualizing and analyzing a state a soundness degree of
15 each device, there is a method of analyzing a change in
device soundness degree, that is, deterioration, for
example, by cutting out feature quantity data in which the
device soundness degree can be checked from time-series
data, and performing difference comparison or the like with
20 graph drawing on feature quantity data for a certain period,
that is, for each term. Such a technique is disclosed in
Patent Literature 1.
Citation List
25 Patent Literature
[0003] Patent Literature 1: International Publication No.
2019/230282
Summary
30 Technical Problem
[0004] However, according to the above-described
conventional technique, for example, when it is desired to
cut out a change in operation data for several seconds at a
3
time of departure from a station as a feature quantity data
and compare the feature quantity data for each month,
feature quantity data for each term, that is, for each
month becomes an enormous number of samples. Therefore,
there has been a problem in that a processing 5 load at a
time of graph drawing increases. In addition, there has
been a problem in that data comparison cannot be easily
performed when the number of samples is enormous.
[0005] The present disclosure has been made in view of
10 the above, and an object is to obtain a device analysis
apparatus capable of performing visualization to allow data
to be easily compared, while preventing an increase in
processing load in visualizing a state of a device.
15 Solution to Problem
[0006] In order to solve the above problem and achieve
the object, a device analysis apparatus in the present
disclosure includes: an operation data storage unit to
store operation data indicating an operation state of a
20 device installed on a railway vehicle; a feature quantity
data generation unit to generate feature quantity data of
the device by using the operation data; a feature quantity
data storage unit to store the feature quantity data; a
first computation unit to generate first data indicating
25 behavior of the feature quantity data in units of a term
that is set, by using the feature quantity data stored in
the feature quantity data storage unit; a second
computation unit to generate second data indicating
behavior of latest feature quantity data by using one or
30 more pieces of the latest feature quantity data newer than
the feature quantity data used in generating the first data
by the first computation unit, among the feature quantity
data stored in the feature quantity data storage unit; and
4
a display unit to display one or more pieces of the first
data and the second data in one graph.
Advantageous Effects of Invention
[0007] According to the present disclosure, 5 there is an
effect that the device analysis apparatus can perform
visualization to allow data to be easily compared, while
preventing an increase in processing load in visualizing a
state of a device.
10
Brief Description of Drawings
[0008] FIG. 1 is a diagram illustrating an exemplary
configuration of a device analysis apparatus according to a
first embodiment.
15 FIG. 2 is a flowchart illustrating an operation of the
device analysis apparatus according to the first embodiment.
FIG. 3 is a diagram illustrating an example of a case
where processing circuitry included in the device analysis
apparatus according to the first embodiment is configured
20 with a processor and a memory.
FIG. 4 is a diagram illustrating an example of a case
where processing circuitry included in the device analysis
apparatus according to the first embodiment is configured
with dedicated hardware.
25 FIG. 5 is a diagram illustrating an exemplary
configuration of a first computation unit of a device
analysis apparatus according to a second embodiment.
FIG. 6 is a flowchart illustrating an operation of the
first computation unit of the device analysis apparatus
30 according to the second embodiment.
FIG. 7 is a graph illustrating an example of band line
information generated by a band line information
summarizing unit of the device analysis apparatus according
5
to the second embodiment.
FIG. 8 is a diagram illustrating an exemplary
configuration of a second computation unit of the device
analysis apparatus according to the second embodiment.
FIG. 9 is a flowchart illustrating an 5 operation of the
second computation unit of the device analysis apparatus
according to the second embodiment.
FIG. 10 is a graph illustrating an example of band
line information and current band line information
10 displayed by a display unit of the device analysis
apparatus according to the second embodiment.
FIG. 11 is a diagram illustrating an exemplary
configuration of a first computation unit of a device
analysis apparatus according to a third embodiment.
15 FIG. 12 is a flowchart illustrating an operation of
the first computation unit of the device analysis apparatus
according to the third embodiment.
FIG. 13 is a diagram illustrating an exemplary
configuration of a second computation unit of the device
20 analysis apparatus according to the third embodiment.
FIG. 14 is a flowchart illustrating an operation of
the second computation unit of the device analysis
apparatus according to the third embodiment.
FIG. 15 is a graph illustrating an example of the
25 outlier score total value and the current outlier score
total value displayed by a display unit of the device
analysis apparatus according to the third embodiment.
FIG. 16 is a diagram illustrating an exemplary
configuration of a first computation unit of a device
30 analysis apparatus according to a fourth embodiment.
FIG. 17 is a diagram illustrating an exemplary
configuration of the second computation unit of the device
analysis apparatus according to the fourth embodiment.
6
Description of Embodiments
[0009] Hereinafter, a device analysis apparatus, a
device analysis method, and a device analysis program
according to embodiments of the present 5 disclosure will be
described in detail with reference to the drawings.
[0010] First Embodiment.
FIG. 1 is a diagram illustrating an exemplary
configuration of a device analysis apparatus 1 according to
10 a first embodiment. In the example of FIG. 1, the device
analysis apparatus 1 analyzes a state of a device 3
installed on a railway vehicle 2. Note that application of
the device analysis apparatus 1 is not limited to the
device 3 installed on the railway vehicle 2. A
15 configuration and an operation of the device analysis
apparatus 1 will be described in detail. The device
analysis apparatus 1 includes an operation data acquisition
unit 11, an operation data storage unit 12, a feature
quantity data generation unit 13, a feature quantity data
20 storage unit 14, a first computation unit 15, a second
computation unit 16, a setting unit 17, and a display unit
18. FIG. 2 is a flowchart illustrating an operation of the
device analysis apparatus 1 according to the first
embodiment.
25 [0011] The operation data acquisition unit 11 acquires,
from the railway vehicle 2, operation data indicating an
operation state of the device 3 installed on the railway
vehicle 2 (step S11). In the example of FIG. 1, one device
3 is installed on the railway vehicle 2, but in practice,
30 it is assumed that a plurality of the devices 3 are
installed on the railway vehicle 2. In a case where a
train including a plurality of the railway vehicles 2 are
targeted, the operation data acquisition unit 11 acquires
7
operation data from each railway vehicle 2. The operation
data acquisition unit 11 may acquire operation data for a
plurality of trains, from a plurality of the railway
vehicles 2 constituting each train. The device 3 installed
on the railway vehicle 2 is, for 5 example, an air
conditioner, a motor, or the like, but is not limited
thereto. The operation data is, for example, a difference
between a set temperature and an actual temperature, an
operation mode, and the like when the device 3 is an air
10 conditioner, and is an applied voltage of a motor, a
current flowing through the motor, and the like when the
device 3 is a motor. The operation data may be a
measurement value measured by a sensor (not illustrated)
installed on the railway vehicle 2, operation data by a
15 driver (not illustrated) of the railway vehicle 2, or the
like. As a method for acquiring the operation data, the
operation data acquisition unit 11 may acquire the
operation data from the railway vehicle 2 by using wireless
communication, by using wired communication, or via a
20 storage medium or the like. Further, as for a timing of
acquiring the operation data, the operation data
acquisition unit 11 may acquire the operation data for one
day after daily operation of the target railway vehicle 2
ends, or may sequentially acquire the operation data in a
25 case where wireless communication is used. The operation
data acquisition unit 11 causes the operation data storage
unit 12 to store the operation data acquired from the
railway vehicle 2. The operation data storage unit 12
stores the operation data acquired by the operation data
30 acquisition unit 11.
[0012] The feature quantity data generation unit 13
generates feature quantity data of the device 3 installed
on the railway vehicle 2, by using time-series operation
8
data stored in the operation data storage unit 12 (step
S12). For example, when the operation data is acquired
every day by the operation data acquisition unit 11, the
feature quantity data generation unit 13 generates the
feature quantity data of the target device 5 3 once a day by
using the added operation data. In a case where there are
a plurality of target devices 3, the feature quantity data
generation unit 13 generates the feature quantity data for
each device 3. A method of generating the feature quantity
10 data in the feature quantity data generation unit 13 is not
particularly limited, and may be a conventional general
method. The feature quantity data generation unit 13
causes the feature quantity data storage unit 14 to store
the generated feature quantity data. The feature quantity
15 data storage unit 14 stores the feature quantity data
generated by the feature quantity data generation unit 13.
[0013] The first computation unit 15 generates first
data indicating behavior of the feature quantity data in
units of a set term, by using the feature quantity data
20 stored in the feature quantity data storage unit 14 (step
S13). The set term is set by default in the first
computation unit 15 or set by a user 4 via the setting unit
17. The term may be in units of years, months, weeks, or
days. In addition, the term may be a period different
25 depending on a type of the device 3. Here, the first
computation unit 15 generates the first data indicating a
past state of the device 3 by using feature quantity data
of a multiple of the set term among the feature quantity
data stored in the feature quantity data storage unit 14.
30 For example, in a case where the unit of the term set for a
certain device 3 is one month, a start date of the term is
the first day of each month, and an end date is the end of
the month, the first computation unit 15 generates the
9
first data indicating a past state of the device 3 by using
feature quantity data from the first day to the end of each
month. Note that, in a case where the unit of the term is
one month and the first data has been generated for a
certain month, the first computation unit 5 15 does not need
to generate the first data for the certain month again.
For example, in the next month, the first computation unit
15 may simply generate the first data for the previous
month once by using the feature quantity data for the
10 previous month. The first computation unit 15 stores one
or more pieces of the generated first data.
[0014] The second computation unit 16 generates second
data indicating behavior of latest feature quantity data,
by using one or more pieces of the latest feature quantity
15 data newer than the feature quantity data used in
generating the first data by the first computation unit 15,
among the feature quantity data stored in the feature
quantity data storage unit 14 (step S14). For example, if
the unit of the term set for a certain device 3 is one
20 month, a start date of the term is the first day of each
month, and an end date of the term is the end of the month
as described above, the second computation unit 16
generates the second data by using feature quantity data
from the first day to the 15th day of the month when the
25 current day is the 15th day of the month. The number of
pieces of feature quantity data used by the second
computation unit 16 is less than the set term. As
described above, the second computation unit 16 generates
the second data by using the latest feature quantity data
30 that is not used by the first computation unit 15 because
the set term is not reached.
[0015] The setting unit 17 receives an operation from
the user 4, and sets the unit of the term, the start date
10
of the term, the end date of the term, and the like
described above for the first computation unit 15 and the
second computation unit 16 (step S15). As described above,
the term may be in units of years, months, weeks, or days.
In addition, the term may be a period different 5 depending
on a type of the device 3. The start date of the term is,
for example, ○ month ○ day ○○ hour ○○ minute every year
when the unit of the term is one year, and ○ day ○○ hour ○○
minute every week when the unit of the term is one week.
10 The end date of the term is, for example, × month × day ××
hour ×× minute every year when the unit of the term is one
year, and is × day ×× hour ×× minute every week when the
unit of the term is one week. The user 4 may appropriately
change the unit of the term, the start date of the term,
15 the end date of the term, and the like via the setting unit
17 in a case where there is a point of interest in the
first data and the second data displayed on the display
unit 18 to be described later. That is, when the user 4
sets the unit of the term, the start date of the term, the
20 end date of the term, and the like via the setting unit 17,
the user 4 may perform the setting in advance before the
start of the operation of the device analysis apparatus 1
before step S11 of the flowchart illustrated in FIG. 2, or
may perform the setting after step S15. Note that the
25 device analysis apparatus 1 may be configured not to
include the setting unit 17 when the term or the like set
by default in the first computation unit 15 and the second
computation unit 16 is used and the setting of the term is
not changed.
30 [0016] The display unit 18 superimposes and displays one
or more pieces of the first data generated by the first
computation unit 15 and one piece of the second data
generated by the second computation unit 16 in one graph,
11
for example (step S16). The first data indicates a past
state summarized in units of a set term for a certain
device 3. The second data indicates a latest state of the
certain device 3. As a result, the user 4 who has checked
a display content of the display unit 18 5 can determine that
there is no change in the state of the device 3 when the
second data indicates a similar feature to the first data,
and determine that a change has occurred in the state of
the device 3, that is, there is a possibility of
10 deterioration when the second data has been changed with
respect to the first data. The user 4 may check original
data, that is, operation data of each device 3 stored in
the operation data storage unit 12 as necessary on the
basis of the display content of the display unit 18.
15 [0017] When acquiring operation data from the railway
vehicle 2 periodically, for example, every day, the device
analysis apparatus 1 may simply perform the operation by
using newly acquired operation data, that is, operation
data of a difference from the previous day. Note that the
20 first computation unit 15 generates the first data for the
latest term after the feature quantity data for the set
term is obtained, that is, for each set term.
[0018] In the device analysis apparatus 1, in a case
where the operation data acquisition unit 11 acquires
25 operation data of only a specific device 3 installed on the
railway vehicle 2, the first computation unit 15 generates
the first data for the specific device 3 installed on the
railway vehicle 2, and the second computation unit 16
generates the second data for the specific device 3
30 installed on the railway vehicle 2. Further, in the device
analysis apparatus 1, in a case where the operation data
acquisition unit 11 acquires operation data of a plurality
of devices 3 of an identical type installed on a specific
12
railway vehicle 2, the first computation unit 15 generates
the first data for the plurality of devices 3 of the
identical type installed on the specific railway vehicle 2,
and the second computation unit 16 generates the second
data for the plurality of devices 3 of 5 the identical type
installed on the specific railway vehicle 2. In addition,
in the device analysis apparatus 1, in a case where the
operation data acquisition unit 11 acquires the operation
data of a plurality of the devices 3 of an identical type
10 installed on different railway vehicles 2, the first
computation unit 15 generates the first data for the
plurality of devices 3 of the identical type installed on
the different railway vehicles 2, and the second
computation unit 16 generates the second data for the
15 plurality of devices 3 of the identical type installed on
the different railway vehicles 2.
[0019] Next, a hardware configuration of the device
analysis apparatus 1 will be described. In the device
analysis apparatus 1, the operation data acquisition unit
20 11 is an interface such as a communication device. The
operation data storage unit 12 and the feature quantity
data storage unit 14 are memories. The setting unit 17 is
an operation device such as a mouse or a keyboard. In the
display unit 18, a portion that displays a display content
25 is a monitor such as a liquid crystal display (LCD). In
the feature quantity data generation unit 13, the first
computation unit 15, the second computation unit 16, and
the display unit 18, a portion that generates a display
content is implemented by processing circuitry. The
30 processing circuitry may be a memory and a processor that
executes a program stored in the memory, or may be
dedicated hardware.
[0020] FIG. 3 is a diagram illustrating an example of a
13
case where processing circuitry 90 included in the device
analysis apparatus 1 according to the first embodiment is
configured with a processor 91 and a memory 92. In a case
where the processing circuitry 90 is configured with the
processor 91 and the memory 92, each 5 function of the
processing circuitry of the device analysis apparatus 1 is
implemented by software, firmware, or a combination of
software and firmware. The software or the firmware is
described as a program and stored in the memory 92. In the
10 processing circuitry 90, the processor 91 reads and
executes the program stored in the memory 92 to implement
each function. That is, the processing circuitry 90
includes the memory 92 for storing a program that results
in execution of processing of the device analysis apparatus
15 1. Further, it can also be said that these programs cause
a computer to execute a procedure and a method of the
device analysis apparatus 1.
[0021] Here, the processor 91 may be a central
processing unit (CPU), a processing device, an arithmetic
20 device, a microprocessor, a microcomputer, a digital signal
processor (DSP), or the like. Further, the memory 92
corresponds to a nonvolatile or volatile semiconductor
memory such as a random access memory (RAM), a read only
memory (ROM), a flash memory, an erasable programmable ROM
25 (EPROM), or an electrically EPROM (EEPROM, registered
trademark), a magnetic disk, a flexible disk, an optical
disk, a compact disk, a mini disk, a digital versatile disc
(DVD), or the like.
[0022] FIG. 4 is a diagram illustrating an example of a
30 case where processing circuitry 93 included in the device
analysis apparatus 1 according to the first embodiment is
configured with dedicated hardware. In a case where the
processing circuitry 93 is configured with dedicated
14
hardware, the processing circuitry 93 illustrated in FIG. 4
corresponds to, for example, a single circuit, a composite
circuit, a programmed processor, a parallel-programmed
processor, an application specific integrated circuit
(ASIC), a field programmable gate 5 array (FPGA), or a
combination thereof. Individual functions of the device
analysis apparatus 1 may be implemented by the processing
circuitry 93 for each of the functions, or the individual
functions may be collectively implemented by the processing
10 circuitry 93.
[0023] Note that some of the functions of the device
analysis apparatus 1 may be implemented by dedicated
hardware, and some of the functions may be implemented by
software or firmware. In this manner, the processing
15 circuitry can implement each of the above-described
functions by dedicated hardware, software, firmware, or a
combination thereof.
[0024] As described above, according to the present
embodiment, the device analysis apparatus 1 generates
20 feature quantity data by using operation data of the device
3 installed on the railway vehicle 2, generates first data
indicating a past state of the device 3 and second data
indicating a current state of the device 3 from the feature
quantity data, and superimposes and displays the first data
25 and the second data in one graph. As a result, the device
analysis apparatus 1 can perform visualization to allow
data to be easily compared, while preventing an increase in
processing load in visualizing a state of the device 3.
The user 4 who has checked the display of the device
30 analysis apparatus 1 can easily determine whether or not a
change has occurred in the state of the device 3.
[0025] Second Embodiment.
In a second embodiment, a case will be described in
15
which band line information is generated as operations of
the first computation unit 15 and the second computation
unit 16 included in the device analysis apparatus 1.
[0026] First, a detailed configuration and operation of
the first computation unit 15 will be described. 5 FIG. 5 is
a diagram illustrating an exemplary configuration of the
first computation unit 15 of the device analysis apparatus
1 according to the second embodiment. The first
computation unit 15 includes a band line information
10 summarizing unit 21, a band line information storage unit
22, and a band line information extraction unit 23. FIG. 6
is a flowchart illustrating an operation of the first
computation unit 15 of the device analysis apparatus 1
according to the second embodiment. The flowchart
15 illustrated in FIG. 6 illustrates details of the operation
in step S13 of the flowchart of the first embodiment
illustrated in FIG. 2.
[0027] The band line information summarizing unit 21
generates, as the first data, band line information
20 obtained by summarizing feature quantity data in units of a
term, by using the feature quantity data stored in the
feature quantity data storage unit 14 (step S21). For
example, in a case where the unit of the term is one month
as described above, the band line information summarizing
25 unit 21 generates one piece of band line information
obtained by summarizing the feature quantity data on a
monthly basis. The term may be set by default or may be
set by the user 4 via the setting unit 17. Note that, in a
case where the unit of the term is one month and the band
30 line information has been generated for a certain month,
the band line information summarizing unit 21 does not need
to generate the band line information of the certain month
again. For example, in the next month, the band line
16
information summarizing unit 21 may simply generate band
line information of the previous month by using feature
quantity data of the previous month. The band line
information summarizing unit 21 causes the band line
information storage unit 22 to store the 5 generated one or
more pieces of band line information. The band line
information storage unit 22 stores the one or more pieces
of the band line information generated by the band line
information summarizing unit 21.
10 [0028] The band line information extraction unit 23
extracts the band line information for a term included in a
designated period, from the band line information storage
unit 22 (step S22). Even when the band line information
for seven months or more is stored in the band line
15 information storage unit 22, the band line information
extraction unit 23 extracts the band line information for
latest six months from the band line information storage
unit 22 when the designated period is the latest six months.
The designated period may be past ○○ days, or a period with
20 a start date designated. In addition, the designated
period may be set by default or may be set by the user 4
via the setting unit 17.
[0029] Here, the band line information generated by the
band line information summarizing unit 21 will be described.
25 FIG. 7 is a graph illustrating an example of the band line
information generated by the band line information
summarizing unit 21 of the device analysis apparatus 1
according to the second embodiment. In FIG. 7, FIG. 7(a)
illustrates feature quantity data that is a source of the
30 band line information, and FIG. 7(b) illustrates band line
information generated by the band line information
summarizing unit 21 using the feature quantity data
illustrated in FIG. 7(a). In FIGS. 7(a) and 7(b), a
17
horizontal axis represents time, and a vertical axis
represents magnitude of a feature quantity. As illustrated
in FIG. 7(a), when the number of pieces of feature quantity
data increases, a processing load when the device analysis
apparatus 1 displays each piece of feature 5 quantity data
increases. Therefore, the band line information
summarizing unit 21 summarizes the feature quantity data
into the band line information in units of a set term. As
a result, the device analysis apparatus 1 can prevent an
10 increase in processing load at a time of display, by
displaying the summarized band line information.
[0030] The band line information summarizing unit 21 may
generate band line information including a plurality of
display patterns on the basis of a quantile obtained from
15 the number of pieces of feature quantity data included in
the band line information generated in units of a term.
For example, in a case of summarizing feature quantity data
including time-series data of 30 points, the band line
information summarizing unit 21 generates the band line
20 information on the basis of quantile information of each
point, that is, the first to 30th points, of the feature
quantity data. For example, in a case of generating
information summary of feature quantity data of one term
with two of a dark color band and a light color band, the
25 band line information summarizing unit 21 indicates from
the first quartile to the third quartile as a quartile area
with the dark color band, and indicates from (the first
quartile-the quartile area×1.5) to (the third quartile+the
quartile area×1.5) as a data distribution area with the
30 light color band. As a result, the user 4 who has checked
the band line information as illustrated in FIG. 7(b) on
the display unit 18 can easily grasp a variation state of
the feature quantity data.
18
[0031] Next, a detailed configuration and operation of
the second computation unit 16 will be described. FIG. 8
is a diagram illustrating an exemplary configuration of the
second computation unit 16 of the device analysis apparatus
1 according to the second embodiment. 5 The second
computation unit 16 includes a current band line
information summarizing unit 31. FIG. 9 is a flowchart
illustrating an operation of the second computation unit 16
of the device analysis apparatus 1 according to the second
10 embodiment. The flowchart illustrated in FIG. 9
illustrates details of the operation in step S14 of the
flowchart of the first embodiment illustrated in FIG. 2.
[0032] The current band line information summarizing
unit 31 generates, as the second data, current band line
15 information obtained by summarizing latest feature quantity
data, by using one or more pieces of the latest feature
quantity data newer than feature quantity data used in
generating band line information by the first computation
unit 15 (step S31). The number of pieces of feature
20 quantity data used by the current band line information
summarizing unit 31 is less than the set term. As
described above, the current band line information
summarizing unit 31 generates the current band line
information by using the latest feature quantity data that
25 is not used by the band line information summarizing unit
21 because the set term is not reached. A method of
generating the current band line information in the current
band line information summarizing unit 31 is similar to the
method of generating the band line information in the band
30 line information summarizing unit 21 described above.
[0033] After the band line information is generated by
the first computation unit 15 and the current band line
information is generated by the second computation unit 16,
19
the display unit 18 displays, in one graph, one or more
pieces of the band line information extracted by the band
line information extraction unit 23 and the current band
line information generated by the current band line
information summarizing unit 31. That is, 5 the display unit
18 superimposes one or more pieces of the band line
information and the current band line information to be
plotted on a graph. FIG. 10 is a graph illustrating an
example of the band line information and the current band
10 line information displayed by the display unit 18 of the
device analysis apparatus 1 according to the second
embodiment. In FIG. 10, a horizontal axis represents time,
and a vertical axis represents magnitude of a feature
quantity. In the example of FIG. 10, the band line
15 information generated by the first computation unit 15 is
indicated by “term #1”, and the current band line
information generated by the second computation unit 16 is
indicated by “term #2”. For example, the user 4 checks how
much the current band line information has shifted from the
20 band line information displayed on the display unit 18, how
much a shape of the current band line information has
changed, and the like. As a result, the user 4 can grasp
what kind of state the latest state of a certain device 3
is. Note that, in the example of FIG. 10, a case is
25 illustrated in which the number of pieces of the band line
information generated by the first computation unit 15 is
one, but the present disclosure is not limited thereto.
The display unit 18 can simultaneously display a plurality
of pieces of the band line information generated by the
30 first computation unit 15 and one piece of the current band
line information generated by the second computation unit
16.
[0034] In the second embodiment, the device analysis
20
apparatus 1 periodically executes the operations up to the
band line information summarizing unit 21 offline, and
executes the operations in and after the band line
information extraction unit 23 and the current band line
information summarizing unit 31 online by 5 an operation from
the user 4.
[0035] As described above, according to the present
embodiment, the device analysis apparatus 1 generates
feature quantity data by using operation data of the device
10 3 installed on the railway vehicle 2, generates band line
information as first data indicating a past state of the
device 3 and current band line information as second data
indicating a current state of the device 3 from the feature
quantity data, and superimposes and displays the band line
15 information and the current band line information in one
graph. In this case, similarly to the first embodiment,
the device analysis apparatus 1 can perform visualization
to allow data to be easily compared, while preventing an
increase in processing load in visualizing a state of the
20 device 3. The user 4 who has checked the display of the
device analysis apparatus 1 can easily determine whether or
not a change has occurred in the state of the device 3.
[0036] Third Embodiment.
In a third embodiment, a case will be described in
25 which band line information is generated as operations of
the first computation unit 15 and the second computation
unit 16 included in the device analysis apparatus 1.
[0037] First, a detailed configuration and operation of
the first computation unit 15 will be described. FIG. 11
30 is a diagram illustrating an exemplary configuration of the
first computation unit 15 of the device analysis apparatus
1 according to the third embodiment. The first computation
unit 15 includes a normal-time model learning unit 41, a
21
learned normal-time model storage unit 42, an outlier score
calculation unit 43, an outlier score storage unit 44, an
outlier score totalization unit 45, an outlier score total
value storage unit 46, and an outlier score total value
extraction unit 47. FIG. 12 is a flowchart 5 illustrating an
operation of the first computation unit 15 of the device
analysis apparatus 1 according to the third embodiment.
The flowchart illustrated in FIG. 12 illustrates details of
the operation in step S13 of the flowchart of the first
10 embodiment illustrated in FIG. 2.
[0038] The normal-time model learning unit 41 learns a
normal-time model representing a state of the device 3 in a
normal time by using feature quantity data in a defined
period as feature quantity data in the normal time of the
15 device 3, among the feature quantity data stored in the
feature quantity data storage unit 14 (step S41). For
example, the normal-time model learning unit 41 uses
feature quantity data in a period that is set, that is,
defined by the setting unit 17, as the feature quantity
20 data in a normal time of the device 3, and learns a normaltime
model by artificial intelligence (AI) learning or the
like. A method of AI learning in the normal-time model
learning unit 41 may be a conventional general method, and
is not particularly limited. Note that the normal-time
25 model learning unit 41 may learn the normal-time model by a
method other than AI learning. The normal-time model
learning unit 41 causes the learned normal-time model
storage unit 42 to store the learned normal-time model
obtained as a result of learning. The learned normal-time
30 model storage unit 42 stores the learned normal-time model
learned by the normal-time model learning unit 41.
[0039] The outlier score calculation unit 43 uses the
learned normal-time model stored in the learned normal-time
22
model storage unit 42, to calculate an outlier score
indicating a degree of deviation from a state of the device
3 in the normal time with respect to the feature quantity
data stored in the feature quantity data storage unit 14
(step S42). The outlier score calculation 5 unit 43 causes
the outlier score storage unit 44 to store the calculated
outlier score. The outlier score storage unit 44 stores
the outlier score calculated by the outlier score
calculation unit 43.
10 [0040] The outlier score totalization unit 45 totalizes
the outlier scores stored in the outlier score storage unit
44 in units of a term included in a designated period, to
generate an outlier score total value (step S43). The
outlier score totalization unit 45 calculates, for example,
15 an average value, a standard deviation, or the like of the
outlier scores in units of the term as the outlier score
total value. Note that, in a case where the unit of the
term is one month and the outlier score total value has
been generated for a certain month, the outlier score
20 totalization unit 45 does not need to generate the outlier
score total value for the certain month again. For example,
in the next month, the outlier score totalization unit 45
may simply generate an outlier score total value of the
previous month by using an outlier score of the previous
25 month. The outlier score totalization unit 45 causes the
outlier score total value storage unit 46 to store the
generated one or more outlier score total values. The
outlier score total value storage unit 46 stores one or
more of the outlier score total values generated by the
30 outlier score totalization unit 45.
[0041] The outlier score total value extraction unit 47
extracts the outlier score total value for a term included
in a designated period, from the outlier score total value
23
storage unit 46 (step S44). Even in a case where the
outlier score total value for seven months or more is
stored in the outlier score total value storage unit 46,
the outlier score total value extraction unit 47 extracts
the outlier score total value for latest 5 six months from
the outlier score total value storage unit 46 when the
designated period is the latest six months. The designated
period may be past ○○ days, or a period with a start date
designated. In addition, the designated period may be set
10 by default or may be set by the user 4 via the setting unit
17.
[0042] Next, a detailed configuration and operation of
the second computation unit 16 will be described. FIG. 13
is a diagram illustrating an exemplary configuration of the
15 second computation unit 16 of the device analysis apparatus
1 according to the third embodiment. The second
computation unit 16 includes a current outlier score
totalization unit 51. FIG. 14 is a flowchart illustrating
an operation of the second computation unit 16 of the
20 device analysis apparatus 1 according to the third
embodiment. The flowchart illustrated in FIG. 14
illustrates details of the operation in step S14 of the
flowchart of the first embodiment illustrated in FIG. 2.
[0043] The current outlier score totalization unit 51
25 generate, as the second data, a current outlier score total
value indicating behavior of the latest feature quantity
data, by using one or more pieces of latest feature
quantity data newer than feature quantity data used in
generating the outlier score total value by the first
30 computation unit 15 (step S51). The number of pieces of
feature quantity data used by the current outlier score
totalization unit 51 is less than the set term. As
described above, the current outlier score totalization
24
unit 51 generates the current outlier score total value by
using the latest feature quantity data that is not used by
the outlier score totalization unit 45 because the set term
is not reached. A method of generating the current outlier
score total value in the current outlier 5 score totalization
unit 51 is similar to the method of generating the outlier
score total value in the outlier score totalization unit 45
described above.
[0044] After the outlier score total value is generated
10 by the first computation unit 15 and the current outlier
score total value is generated by the second computation
unit 16, the display unit 18 displays, in one graph, one or
more of the outlier score total values extracted by the
outlier score total value extraction unit 47 and the
15 current outlier score total value generated by the current
outlier score totalization unit 51. That is, the display
unit 18 superimposes one or more of the outlier score total
values and the current outlier score total value to be
plotted on a graph. FIG. 15 is a graph illustrating an
20 example of the outlier score total value and the current
outlier score total value displayed by the display unit 18
of the device analysis apparatus 1 according to the third
embodiment. In FIG. 15, a horizontal axis represents time,
and a vertical axis represents a soundness degree of the
25 device 3. Soundness is higher and maintenance is less
necessary as the soundness degree is larger, and
maintenance is more necessary as the soundness degree is
smaller. Note that the first two of outlier score total
values illustrated in FIG. 15 are outlier score total
30 values corresponding to normal time, and a last part
corresponding to “current” is the current outlier score
total value. For example, the user 4 checks how much the
outlier score total value displayed on the display unit 18
25
deviates from the normal time as a reference, a progress
status of an outlier degree, that is, how a change rate
changes, and the like. As a result, the user 4 can grasp
what kind of state the latest state of a certain device 3
5 is.
[0045] In the third embodiment, the device analysis
apparatus 1 periodically executes the operations up to the
outlier score totalization unit 45 offline, and executes
the operations in and after the outlier score total value
10 extraction unit 47 and the current outlier score
totalization unit 51 online by an operation from the user 4.
[0046] As described above, according to the present
embodiment, the device analysis apparatus 1 generates
feature quantity data by using operation data of the device
15 3 installed on the railway vehicle 2, generates an outlier
score total value as first data indicating a past state of
the device 3 and a current outlier score total value as
second data indicating a current state of the device 3 from
the feature quantity data, and superimposes and displays
20 the outlier score total value and the current outlier score
total value in one graph. In this case, similarly to the
first embodiment, the device analysis apparatus 1 can
perform visualization to allow data to be easily compared,
while preventing an increase in processing load in
25 visualizing a state of the device 3. The user 4 who has
checked the display of the device analysis apparatus 1 can
easily determine whether or not a change has occurred in
the state of the device 3.
[0047] Fourth Embodiment.
30 While the device analysis apparatus 1 generates and
displays band line information in the second embodiment and
the device analysis apparatus 1 generates and displays an
outlier score total value in the third embodiment, it is
26
also possible to generate and display both the band line
information and the outlier score total value.
[0048] FIG. 16 is a diagram illustrating an exemplary
configuration of the first computation unit 15 of the
device analysis apparatus 1 according 5 to a fourth
embodiment. In the fourth embodiment, the first
computation unit 15 includes all the configurations
illustrated in FIGS. 5 and 11. An operation of each
configuration is as described above. FIG. 17 is a diagram
10 illustrating an exemplary configuration of the second
computation unit 16 of the device analysis apparatus 1
according to the fourth embodiment. In the fourth
embodiment, the second computation unit 16 includes all the
configurations illustrated in FIGS. 8 and 13. An operation
15 of each configuration is as described above.
[0049] The display unit 18 displays, in one graph, one
or more pieces of the band line information extracted by
the band line information extraction unit 23 and the
current band line information generated by the current band
20 line information summarizing unit 31. In addition, the
display unit 18 displays, in one graph, one or more outlier
of the score total values extracted by the outlier score
total value extraction unit 47 and a current outlier score
total value generated by the current outlier score
25 totalization unit 51. As a result, the user 4 can grasp
what kind of state the latest state of a certain device 3
is. Furthermore, in a case where the display unit 18 can
receive an operation from the user 4, the user 4 can check
a state of the device 3 by appropriately selecting a
30 display content.
[0050] As described above, according to the present
embodiment, the device analysis apparatus 1 generates
feature quantity data by using operation data of the device
27
3 installed on the railway vehicle 2, generates band line
information and an outlier score total value as first data
indicating a past state of the device 3 and current band
line information and a current outlier score total value as
second data indicating a current state of 5 the device 3 from
the feature quantity data, superimposes and displays the
band line information and the current band line information
in one graph, and superimposes and displays the outlier
score total value and the current outlier score total value
10 in one graph. In this case, similarly to the first
embodiment, the device analysis apparatus 1 can perform
visualization to allow data to be easily compared, while
preventing an increase in processing load in visualizing a
state of the device 3. The user 4 who has checked the
15 display of the device analysis apparatus 1 can easily
determine whether or not a change has occurred in the state
of the device 3.
[0051] The configuration illustrated in the above
embodiments illustrates one example and can be combined
20 with another known technique, and it is also possible to
combine embodiments with each other and omit and change a
part of the configuration without departing from the
subject matter of the present disclosure.
25 Reference Signs List
[0052] 1 device analysis apparatus; 2 railway vehicle;
3 device; 4 user; 11 operation data acquisition unit; 12
operation data storage unit; 13 feature quantity data
generation unit; 14 feature quantity data storage unit; 15
30 first computation unit; 16 second computation unit; 17
setting unit; 18 display unit; 21 band line information
summarizing unit; 22 band line information storage unit;
23 band line information extraction unit; 31 current band
line information summarizing unit; 41 normal-time model
learning unit; 42 learned normal-time model storage unit;
outlier score calculation unit; 44 outlier score
storage unit; 45 outlier score totalization unit; 46
outlier score total value storage unit; 5 47 outlier score
total value extraction unit; 51 current outlier score
totalization unit.
We Claim:
1. A device analysis apparatus comprising:
an operation data storage unit to store 5 operation data
indicating an operation state of a device installed on a
railway vehicle;
a feature quantity data generation unit to generate
feature quantity data of the device by using the operation
data;
a feature quantity data storage unit to store the
feature quantity data;
a first computation unit to generate first data
indicating behavior of the feature quantity data in units
of a term that is set, by using the feature quantity data
stored in the feature quantity data storage unit;
a second computation unit to generate second data
indicating behavior of latest feature quantity data by
using one or more pieces of the latest feature quantity
data newer than the feature quantity data used in
generating the first data by the first computation unit,
among the feature quantity data stored in the feature
quantity data storage unit; and
a display unit to display one or more pieces of the
first data and the second data in one graph.
2. The device analysis apparatus according to claim 1,
wherein
the first computation unit includes:
a band line information summarizing unit to generate, as
the first data, band line information obtained by
summarizing the feature quantity data in units of the term,
by using the feature quantity data stored in the feature
quantity 5 data storage unit;
a band line information storage unit to store the band
line information; and
a band line information extraction unit to extract the
band line information for a term included in a designated
period, from the band line information storage unit,
the second computation unit includes:
a current band line information summarizing unit to
generate, as the second data, current band line information
obtained by summarizing latest feature quantity data, by
15 using one or more pieces of the latest feature quantity
data newer than the feature quantity data used in
generating the band line information by the first
computation unit, and
the display unit displays, in one graph, one or more
pieces of the band line information extracted by the band
line information extraction unit and the current band line
information generated by the current band line information
summarizing unit.
3. The device analysis apparatus according to claim 2,
wherein
the band line information summarizing unit generates
the band line information including a plurality of display
patterns, based on a quantile obtained from the number of
30 pieces of the feature quantity data included in the band
line information generated in units of the term.
4. The device analysis apparatus according to any one of
claims 1 to 3, wherein
the first computation unit includes:
a normal-time model learning unit to learn a normaltime
model representing a state of the 5 device in a normal
time by using the feature quantity data in a defined period
as the feature quantity data in a normal time of the device,
among the feature quantity data stored in the feature
quantity data storage unit;
a learned normal-time model storage unit to store the
learned normal-time model learned by the normal-time model
learning unit;
an outlier score calculation unit to use the learned
normal-time model stored in the learned normal-time model
storage unit to calculate an outlier score indicating a
degree of deviation from a state of the device in a normal
time with respect to the feature quantity data stored in
the feature quantity data storage unit;
an outlier score storage unit to store the outlier
score;
an outlier score totalization unit to totalize the
outlier score stored in the outlier score storage unit in
units of a term included in a designated period and to
generate an outlier score total value;
25 an outlier score total value storage unit to store the
outlier score total value; and
an outlier score total value extraction unit to
extract the outlier score total value for a term included
in a designated period, from the outlier score total value
storage unit,
the second computation unit includes:
a current outlier score totalization unit to generate,
as the second data, a current outlier score total value
indicating behavior of latest feature quantity data, by
using one or more pieces of the latest feature quantity
data newer than the feature quantity data used in
generating the outlier score total value by the first
computation 5 unit, and
the display unit displays, in one graph, one or more
of the outlier score total values extracted by the outlier
score total value extraction unit and the current outlier
score total value generated by the current outlier score
totalization unit.
5. The device analysis apparatus according to any one of
claims 1 to 4, wherein
the first computation unit generates the first data
15 for the specific device installed on the railway vehicle,
and
the second computation unit generates the second data
for the specific device installed on the railway vehicle.
6. The device analysis apparatus according to any one of
claims 1 to 4, wherein
the first computation unit generates the first data
for a plurality of the devices of an identical type
installed on the specific railway vehicle, and
the second computation unit generates the second data
for a plurality of the devices of an identical type
installed on the specific railway vehicle.
7. The device analysis apparatus according to any one of
30 claims 1 to 4, wherein
the first computation unit generates the first data
for a plurality of the devices of an identical type
installed on the railway vehicles that are different, and
the second computation unit generates the second data
for a plurality of the devices of an identical type
installed on the railway vehicles that are different.
8. The device analysis apparatus according 5 to any one of
claims 1 to 7, comprising:
a setting unit to receive an operation from a user and
set the term.
9. A device analysis method comprising:
a first step of generating feature quantity data of a
device by using operation data, and causing a feature
quantity data storage unit to store the feature quantity
data, by a feature quantity data generation unit, the
operation data being stored in an operation data storage
unit and indicating an operation state of the device
installed on a railway vehicle;
a second step of generating first data indicating
behavior of the feature quantity data in units of a term
20 that is set, by using the feature quantity data stored in
the feature quantity data storage unit, by a first
computation unit;
a third step of generating second data indicating
behavior of latest feature quantity data by using one or
more pieces of the latest feature quantity data newer than
the feature quantity data used in generating the first data
by the first computation unit, among the feature quantity
data stored in the feature quantity data storage unit, by a
second computation unit; and
a fourth step of displaying one or more pieces of the
first data and the second data in one graph, by a display
unit.
10. The device analysis method according to claim 9,
wherein
the first computation unit includes a band line
information summarizing unit, a band line information
storage unit, and a band line information 5 extraction unit,
the second step includes:
a band line information generating step, by the band
line information summarizing unit, of generating, as the
first data, band line information obtained by summarizing
the feature quantity data in units of the term, by using
the feature quantity data stored in the feature quantity
data storage unit, and causing the band line information
storage unit to store the band line information; and
a per-past-term band line information extraction step,
by the band line information extraction unit, of extracting
the band line information for a term included in a
designated period, from the band line information storage
unit,
the second computation unit includes a current band
line information summarizing unit,
the third step includes:
a current-band-line information summarizing step, by
the current band line information summarizing unit, of
generating, as the second data, current band line
25 information obtained by summarizing latest feature quantity
data, by using one or more pieces of the latest feature
quantity data newer than the feature quantity data used in
generating the band line information by the first
computation unit, and
30 in the fourth step, the display unit displays, in one
graph, one or more pieces of the band line information
extracted by the band line information extraction unit and
the current band line information generated by the current
band line information summarizing unit.
11. The device analysis method according to claim 10,
wherein
in the band line information generating 5 step, the band
line information summarizing unit generates the band line
information including a plurality of display patterns,
based on a quantile obtained from the number of pieces of
the feature quantity data included in the band line
information generated in units of the term.
12. The device analysis method according to any one of
claims 9 to 11, wherein
the first computation unit includes a normal-time
model learning unit, a learned normal-time model storage
unit, an outlier score calculation unit, an outlier score
storage unit, an outlier score totalization unit, an
outlier score total value storage unit, and an outlier
score total value extraction unit,
the second step includes:
a normal-time model learning step, by the normal-time
model learning unit, of learning a normal-time model
representing a state of the device in a normal time by
using the feature quantity data in a defined period as the
feature quantity data in a normal time of the device among
the feature quantity data stored in the feature quantity
data storage unit, and causing the learned normal-time
model storage unit to store the learned normal-time model;
an outlier score calculation step, by the outlier
30 score calculation unit, of calculating an outlier score
indicating a degree of deviation from a state of the device
in a normal time with respect to the feature quantity data
stored in the feature quantity data storage unit, by using
the learned normal-time model stored in the learned normaltime
model storage unit, and causing the outlier score
storage unit to store the outlier score;
an outlier score totalization step, by the outlier
score totalization unit, of totalizing 5 the outlier score
stored in the outlier score storage unit in units of a term
included in a designated period to generate an outlier
score total value, and causing the outlier score total
value storage unit to store the outlier score total value;
and
a per-past-term outlier score total value extraction
step, by the outlier score total value extraction unit, of
extracting, from the outlier score total value storage unit,
the outlier score total value for a term included in a
designated period,
the second computation unit includes a current outlier
score totalization unit,
the third step includes:
a current outlier score totalization step, by the
current outlier score totalization unit, of generating, as
the second data, a current outlier score total value
indicating behavior of latest feature quantity data, by
using one or more pieces of the latest feature quantity
data newer than the feature quantity data used in
generating the outlier score total value by the first
computation unit, and
in the fourth step, the display unit displays, in one
graph, one or more of the outlier score total values
extracted by the outlier score total value extraction unit
and the current outlier score total value generated by the
current outlier score totalization unit.
13. The device analysis method according to any one of
claims 9 to 12, wherein
in the second step, the first computation unit
generates the first data for the specific device installed
on the railway vehicle, and
in the third step, the second 5 computation unit
generates the second data for the specific device installed
on the railway vehicle.
14. The device analysis method according to any one of
claims 9 to 12, wherein
in the second step, the first computation unit
generates the first data for a plurality of the devices of
an identical type installed on the specific railway vehicle,
and
in the third step, the second computation unit
generates the second data for a plurality of the devices of
an identical type installed on the specific railway vehicle.
15. The device analysis method according to any one of
claims 9 to 12, wherein
in the second step, the first computation unit
generates the first data for a plurality of the devices of
an identical type installed on the railway vehicles that
are different, and
in the third step, the second computation unit
generates the second data for a plurality of the devices of
an identical type installed on the railway vehicles that
are different.
16. The device analysis method according to any one of
claims 9 to 15, comprising:
a fifth step of receiving an operation from a user and
setting the term by a setting unit.
17. A device analysis program for causing a computer to
execute the device analysis method according to any one of
claims 9 to 16.