FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
[See section 10, Rule 13]
FAILURE SYMPTOM DETECTION DEVICE AND FAILURE SYMPTOM
DETECTION METHOD FOR ELECTRIC MOTOR-PROVIDED EQUIPMENT;
MITSUBISHI ELECTRIC CORPORATION, A CORPORATION ORGANISED AND
EXISTING UNDER THE LAWS OF JAPAN, WHOSE ADDRESS IS 7-3,
MARUNOUCHI 2-CHOME, CHIYODA-KU, TOKYO 100-8310, JAPAN
THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE
INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED.
2
DESCRIPTION
TITLE OF THE INVENTION:
FAILURE SYMPTOM DETECTION DEVICE AND FAILURE SYMPTOM
DETECTION METHOD FOR ELECTRIC MOTOR-PROVIDED EQUIPMENT
5
TECHNICAL FIELD
[0001] The present disclosure relates to a failure symptom
detection device for electric motor-provided equipment and a
failure symptom detection method for electric motor-provided
10 equipment.
BACKGROUND ART
[0002] In general, there are various loads using an
electric motor as a motive-power source, e.g., a pump, a
15 conveyor belt, and a compressor (hereinafter, such loads are
referred to as electric motor-provided equipment).
Conventionally, in a case where abnormality has occurred in
such electric motor-provided equipment, the abnormality is
often diagnosed and determined by senses of a person in a
20 maintenance department. In particular, for electric motorprovided equipment of high importance, diagnosis needs to be
performed regularly, leading to increase in labor and cost
for maintenance and management.
[0003] Accordingly, there has been an increasing interest
25 in technology that makes it possible to monitor electric
3
motor-provided equipment automatically and constantly without
depending on person's senses. However, in many cases,
constant monitoring for an electric motor is based on the
premise that various sensors are attached for each electric
5 motor. Examples of such sensors include a torque meter, an
acceleration sensor, and a temperature sensor. As
conventional technology, it is proposed that current and
voltage signals applied to a stator of an electric motor are
analyzed on the basis of detection outputs from the various
10 sensors, whereby a failure symptom of the electric motorprovided equipment is detected (see, for example, Patent
Document 1).
CITATION LIST
15 PATENT DOCUMENT
[0004] Patent Document 1: Japanese Laid-Open Patent
Publication No. 2007-170411
SUMMARY OF THE INVENTION
20 PROBLEM TO BE SOLVED BY THE INVENTION
[0005] However, three-phase currents outputted to the
electric motor are likely to be influenced by electric noise
due to inverter driving or variations due to an operation
mode which changes depending on the load state or the like,
25 for example. Then, electric signals to be used for diagnosis
4
are distorted due to the above influence, so that a noise
signal might be erroneously detected. Therefore, failure
symptom detection disclosed in Patent Document 1 has a
problem that it is difficult to detect abnormality of the
5 electric motor-provided equipment accurately.
[0006] The present disclosure has been made to solve the
above problem, and an object of the present disclosure is to
provide a failure symptom detection device for electric
motor-provided equipment and a failure symptom detection
10 method for electric motor-provided equipment that can
accurately detect a failure symptom of electric motorprovided equipment while being hardly influenced by noise and
without adding an extra sensor.
15 MEANS TO SOLVE THE PROBLEM
[0007] A failure symptom detection device for electric
motor-provided equipment according to the present disclosure
is a device for detecting a failure symptom of the electric
motor-provided equipment including a load with an electric
20 motor used as a motive-power source and a driving device
which supplies power to the electric motor and drives the
electric motor, the failure symptom detection device
including: a current detection unit which detects current
flowing from the driving device to the electric motor; a
25 diagnosis calculation unit which calculates an index value
5
for determination for presence/absence of abnormality of the
electric motor-provided equipment, on the basis of a result
of detection by the current detection unit; a diagnosis
determination unit which determines presence/absence of
5 abnormality of the electric motor-provided equipment on the
basis of a result of calculation by the diagnosis calculation
unit; and a diagnosis result reporting unit which reports a
result of diagnosis determined by the diagnosis determination
unit, to outside. The diagnosis calculation unit includes a
10 starting current extraction unit which, from the current
detected by the current detection unit, extracts current in
an acceleration period until a constant rotational speed is
reached after starting of the electric motor, a data
generation unit which divides data of the current in the
15 acceleration period extracted by the starting current
extraction unit, into plural pieces of data, and a frequency
analysis unit which performs frequency analysis on each piece
of data divided by the data generation unit.
[0008] A failure symptom detection method for electric
20 motor-provided equipment according to the present disclosure
is a method for detecting a failure symptom of the electric
motor-provided equipment including a load with an electric
motor used as a motive-power source and a driving device
which supplies power to the electric motor and drives the
25 electric motor, the failure symptom detection method
6
including: a first step of detecting current flowing to the
electric motor; a second step of, from the current obtained
in the first step, extracting current in an acceleration
period until a constant rotational speed is reached after
5 starting of the electric motor; a third step of dividing data
of the current in the acceleration period extracted in the
second step, into plural pieces of data, and performing
frequency analysis on each divided piece of data, to generate
a spectrum waveform; a fourth step of detecting an intensity
10 value of a spectrum peak arising in a rotational frequency
band of the electric motor, from the spectrum waveform
obtained in the third step; a fifth step of comparing the
intensity value of the spectrum peak obtained in the fourth
step and a predetermined reference value; a sixth step of
15 determining presence/absence of abnormality of the electric
motor-provided equipment from a result of comparison in the
fifth step; and a seventh step of reporting a result of
determination in the sixth step to outside.
20 EFFECT OF THE INVENTION
[0009] The failure symptom detection device for electric
motor-provided equipment and the failure symptom detection
method for electric motor-provided equipment according to the
present disclosure can accurately detect a failure symptom of
25 electric motor-provided equipment while being hardly
7
influenced by noise and without adding an extra sensor.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] [FIG. 1] FIG. 1 is a block diagram showing a
5 schematic configuration of electric motor-provided equipment
and a failure symptom detection device according to the
present disclosure.
[FIG. 2] FIG. 2 is a block diagram showing a
schematic configuration of an air conditioner as an example
10 of electric motor-provided equipment according to embodiment
1 of the present disclosure.
[FIG. 3] FIG. 3 is a block diagram showing a
configuration of a control device for controlling a driving
device in electric motor-provided equipment according to
15 embodiment 1 of the present disclosure.
[FIG. 4] FIG. 4 is a block diagram showing a
schematic configuration of the failure symptom detection
device according to embodiment 1 of the present disclosure.
[FIG. 5] FIG. 5 illustrates an example of a
20 spectrum waveform obtained through frequency analysis on
phase current.
[FIG. 6] FIG. 6 illustrates an example of a
frequency spectrum waveform obtained through frequency
analysis on q-axis current.
25 [FIG. 7] FIG. 7 is a flowchart showing a failure
8
symptom detection method according to embodiment 1 of the
present disclosure.
[FIG. 8] FIG. 8 is a waveform diagram showing
temporal changes in a rotational speed and a current value
5 when an electric motor is started, according to embodiment 1
of the present disclosure.
[FIG. 9] FIG. 9 illustrates an example of a
frequency analysis result for q-axis current according to
embodiment 1 of the present disclosure.
10 [FIG. 10] FIG. 10 is a block diagram showing a
schematic configuration of electric motor-provided equipment
and a failure symptom detection device according to
embodiment 2 of the present disclosure.
[FIG. 11] FIG. 11 is a block diagram showing
15 another schematic configuration of electric motor-provided
equipment and a failure symptom detection device according to
embodiment 2 of the present disclosure.
[FIG. 12] FIG. 12 is a block diagram showing a
hardware configuration of a control device and the like
20 according to the embodiments of the present disclosure.
DESCRIPTION OF EMBODIMENTS
[0011] Embodiment 1
FIG. 1 is a block diagram showing a schematic
25 configuration of electric motor-provided equipment and a
9
failure symptom detection device according to the present
disclosure.
[0012] Electric motor-provided equipment 1 includes a load
2 with an electric motor 3 used as a motive-power source, a
5 driving device 4 which supplies power to the electric motor 3
and drives the electric motor 3, and a control device 8 for
controlling operation of the driving device 4. An AC power
supply 5 is connected to the driving device 4.
[0013] Here, the electric motor-provided equipment 1 is,
10 for example, a water pump, a vacuum pump, a conveyor belt, an
air conditioner, and the like. In embodiment 1, a case of
taking an air conditioner as an example of the electric
motor-provided equipment 1 and detecting a failure symptom
thereof will be described.
15 [0014] FIG. 2 is a block diagram showing a schematic
configuration of an air conditioner as an example of electric
motor-provided equipment according to embodiment 1 of the
present disclosure.
The air conditioner operates as a refrigeration
20 cycle apparatus and includes a compressor 11, a condenser 12,
an expansion valve 13, and an evaporator 14. In this
refrigeration cycle, a refrigerant circulates through the
compressor 11, the condenser 12, the expansion valve 13, and
the evaporator 14 in this order.
25 [0015] The compressor 11 compresses and discharges a gas
10
refrigerant, and corresponds to the load 2 in FIG. 1. The
compressor 11 compresses a gas refrigerant sucked using a
driving force of the electric motor 3. The electric motor 3
is connected to a compression mechanism (not shown) that
5 compresses the gas refrigerant in the refrigeration cycle.
The condenser 12 condenses the gas refrigerant discharged
from the compressor 11 and discharges a liquid refrigerant.
The expansion valve 13 undergoes valve opening control to
expand the refrigerant from the condenser 12, thus reducing
10 the pressure thereof. The evaporator 14 evaporates a liquidstate refrigerant (liquid refrigerant) discharged from the
expansion valve 13 and discharges a gas-state refrigerant
(gas refrigerant).
[0016] The driving device 4 includes a converter 41 and an
15 inverter 42. In FIG. 1, the converter is abbreviated as CNV
and the inverter is abbreviated as INV.
The converter 41 receives AC current from the AC
power supply 5, converts the AC current to DC current, and
outputs the DC current to the inverter 42. The frequency of
20 the AC power supply 5 is, for example, 50 Hz or 60 Hz.
[0017] The inverter 42 includes an inverter main circuit
including a plurality of switching elements (not shown). The
inverter 42 receives a pulse width modulation (PWM) signal
from the control device 8 and performs ON/OFF switching of
25 the switching elements to output three-phase currents (for U,
11
V, W phases) to the electric motor 3 for driving the
compressor 11 which is the load 2.
[0018] Of the three-phase currents (iu, iv, iw) outputted
to the electric motor 3, for example, U-phase current iu and
5 V-phase current iv are detected by a current sensor (current
detection unit) 6 and outputted to the control device 8. A
rotation angle θ of a rotor of the electric motor 3 is
detected by an angle sensor 7 provided to the electric motor
3 and is outputted to the control device 8.
10 [0019] FIG. 3 is a block diagram showing a configuration
of the control device for controlling operation of the
driving device according to embodiment 1.
The control device 8 outputs a PWM signal to the
inverter 42, to perform vector control, and includes a d-q
15 transformation unit 81, a voltage command value calculation
unit 82, an output voltage vector calculation unit 83, and a
PWM signal generation unit 84. The d-q transformation unit
81 includes a phase current calculation unit 811, a Clarke
transformation unit 812, and a Park transformation unit 813.
20 [0020] Here, the d-q transformation unit 81, the voltage
command value calculation unit 82, the output voltage vector
calculation unit 83, and the PWM signal generation unit 84
may be each implemented by dedicated hardware, or may be
implemented by a computer such as a central processing unit
25 (CPU) executing a program stored in a memory.
12
That is, the control device 8 is composed of a
processor 1000 and a storage device 1010, as shown in a
hardware example in FIG. 12. The storage device 1010 is
provided with a volatile storage device such as a random
5 access memory and a nonvolatile auxiliary storage device such
as a flash memory (not shown). An auxiliary storage device
of a hard disk may be provided instead of the flash memory.
The processor 1000 executes a program inputted from the
storage device 1010. In this case, the program is inputted
10 from the auxiliary storage device to the processor 1000 via
the volatile storage device. The processor 1000 may output
data such as a calculation result to the volatile storage
device of the storage device 1010 or may store such data into
the auxiliary storage device via the volatile storage device.
15 [0021] The phase current calculation unit 811 receives the
U-phase current iu and the V-phase current iv detected by the
current sensor 6 and calculates W-phase current iw. Here, Vphase current iv and W-phase current iw may be detected to
calculate U-phase current iu, or W-phase current iw and U20 phase current iu may be detected to calculate V-phase current
iv. The phase currents (iu, iv, iw) calculated by the phase
current calculation unit 811 are outputted to the Clarke
transformation unit 812 at the subsequent stage.
[0022] The phase currents (iu, iv, iw) change along with
25 change in the rotation angle θ (mechanical angle) of the
13
rotor of the electric motor 3. In the following description,
the rotation angle θ is described as a value measured by the
angle sensor 7. However, the angle sensor 7 is not an
essential component in the present disclosure and the
5 rotation angle θ may be calculated by another method. For
example, the rotation angle θ may be calculated from the
phase currents (iu, iv, iw) and a voltage command value as
performed in known position sensorless control.
[0023] The Clarke transformation unit 812 transforms the
10 phase currents (iu, iv, iw) to two-phase currents (iα, iβ) in
a two-axis coordinate system (α-β coordinate system), and
outputs the two-phase currents (iα, iβ) to the Park
transformation unit 813 at the subsequent stage.
[0024] The Park transformation unit 813 receives the
15 rotation angle θ of the rotor detected by the angle sensor 7
provided to the electric motor 3, and transforms the twophase currents (iα, iβ) in the two-axis coordinate system (αβ coordinate system) to dq-axis currents (id, iq)
corresponding to coordinates in a rotating coordinate system
20 (d-q coordinate system). The Park transformation unit 813
outputs the values of the dq-axis currents (id, iq) to the
voltage command value calculation unit 82 at the subsequent
stage and outputs the value of the q-axis current iq to a
failure symptom detection device 9.
25 [0025] Here, the d-axis current id is an excitation
14
current component and produces a rotating magnetic field in
the electric motor 3. The q-axis current iq is a torque
current component and produces torque of the electric motor
3. The dq-axis currents (id, iq) correspond to values
5 obtained when αβ-phase currents (iα, iβ) rotating by the
rotation angle θ in the coordinate system at rest are
measured in the rotating coordinate system that follows the
rotation, and therefore the dq-axis currents (id, iq) have no
change in the rotation angle θ.
10 [0026] The voltage command value calculation unit 82
calculates a difference between an actual voltage command
value and the values of the dq-axis currents (id, iq)
outputted from the Park transformation unit 813. Next, the
output voltage vector calculation unit 83 calculates a
15 correction value for correcting the calculated difference.
Finally, the PWM signal generation unit 84 generates a PWM
signal on the basis of the corrected voltage command value.
Thus, the electric motor 3 is controlled into an ideal
rotation state in accordance with the command value.
20 [0027] FIG. 4 is a block diagram showing a configuration
of the failure symptom detection device according to
embodiment 1.
The failure symptom detection device 9 is for
detecting a failure symptom of the electric motor-provided
25 equipment 1 (load 2 or electric motor 3) and includes a
15
diagnosis calculation unit 91, a diagnosis determination unit
92, and a diagnosis result reporting unit 93.
[0028] Here, the diagnosis calculation unit 91 includes a
starting current extraction unit 911, a d-q transformation
5 unit 912, an equipment information storage unit 913, a data
generation unit 914, and a frequency analysis unit 915. The
frequency analysis unit 915 includes a spectrum analysis unit
915a and a spectrum feature quantity detection unit 915b.
Here, the d-q transformation unit 912 is a common
10 unit shared as the d-q transformation unit 81 included in the
control device 8, and uses the q-axis current iq obtained
through d-q transformation of the phase currents (iu, iv, iw)
calculated on the basis of a detection output from the
current sensor 6 in the d-q transformation unit 81. Instead
15 of being shared as the d-q transformation unit 81, it is also
possible to perform d-q transformation by the d-q
transformation unit 912 after detecting the phase currents
(iu, iv, iw) by the current sensor 6.
[0029] The diagnosis determination unit 92 includes an
20 initial learning unit 921, a reference value comparison unit
922, and an abnormality count determination unit 923.
The diagnosis result reporting unit 93 includes a
display unit 931 such as a liquid crystal display, a warning
unit 932 such as a lamp, and an external output unit 933 such
25 as a printer.
16
[0030] Specific functions of the diagnosis calculation
unit 91, the diagnosis determination unit 92, and the
diagnosis result reporting unit 93 will become clear when
operation processes in the respective units are described.
5 The diagnosis calculation unit 91 and the diagnosis
determination unit 92 may be implemented by dedicated
hardware, or may be implemented by a computer such as a
central processing unit (CPU) executing a program stored in a
memory.
10 That is, the diagnosis calculation unit 91 and the
diagnosis determination unit 92 are each composed of a
processor 1000 and a storage device 1010, as shown in a
hardware example in FIG. 12. The storage device 1010 is
provided with a volatile storage device such as a random
15 access memory and a nonvolatile auxiliary storage device such
as a flash memory (not shown). An auxiliary storage device
of a hard disk may be provided instead of the flash memory.
The processor 1000 executes a program inputted from the
storage device 1010. In this case, the program is inputted
20 from the auxiliary storage device to the processor 1000 via
the volatile storage device. The processor 1000 may output
data such as a calculation result to the volatile storage
device of the storage device 1010 or may store such data into
the auxiliary storage device via the volatile storage device.
25 [0031] As current used for failure symptom detection,
17
either of the dq-axis currents (id, iq), either of the αβphase currents (iα, iβ), or any of the phase currents (iu,
iv, iw) may be used. In embodiment 1, failure symptom
detection is performed using the q-axis current iq.
5 [0032] In a case where wear of a sliding part of the
compression mechanism, which occupies most of abnormal cases
of the compressor, has occurred, an air gap between the
stator and the rotor of the electric motor vibrates, so that
permeance changes. Therefore, each phase current is suitably
10 used for failure symptom detection of the compressor. Also
in a case where a bearing of the electric motor is worn, gap
vibration occurs similarly, and therefore using each phase
current is a suitable method for abnormality detection of the
electric motor. For detection of each phase current, it
15 suffices that the current sensor 6 is provided to a power
supply cable, and another sensor need not be added. Thus,
there is an advantage in terms of cost as well.
[0033] When the phase current flowing in the stator of the
electric motor 3 or the q-axis current obtained through d-q
20 transformation of the phase current is subjected to frequency
analysis, characteristic spectrum peaks arise because current
variation due to a failure symptom as described above occurs
periodically.
[0034] FIG. 5 illustrates an example of a spectrum
25 waveform when the phase current (e.g., U-phase current iu) is
18
subjected to frequency analysis.
In a case where current variation due to a failure
symptom as described above occurs periodically,
characteristic spectrum peaks Is as sideband waves arise on
5 both sides near a peak Ip of the power supply frequency, at
positions different among abnormality types. For example, in
a case of abnormality such as misalignment or imbalance,
sideband-wave peaks Is arise on both sides of the peak Ip of
the power supply frequency, at positions away therefrom by a
10 rotational frequency. In a case of abnormality due to a
bearing of the electric motor 3, sideband-wave peaks Is arise
on both sides of the power supply frequency, at positions
away therefrom by a natural frequency of the bearing.
[0035] FIG. 6 illustrates an example of a spectrum
15 waveform when the q-axis current iq is subjected to frequency
analysis.
As in the case of FIG. 5, a characteristic spectrum
peak arises at a position different among abnormality types.
For example, in a case where whirling of an electric motor
20 shaft occurs due to deterioration of a bearing or the like, a
characteristic spectrum peak Ir arises in a rotational
frequency band.
[0036] In general, in a method of diagnosing a device
state through frequency analysis on a current signal of the
25 electric motor 3, the analysis has been conventionally
19
performed using the current signal under operation at a
constant rotational speed. However, in a case where the load
2 has a mechanism part such as the compressor 11, a force
having a rotational frequency component might be applied. In
5 particular, in a scroll compressor, a rotational-frequencycomponent force is applied at a compression mechanism of a
scroll part. Along with this, on a similar principle, a
spectrum peak of a rotational frequency component arises in
each phase current of the electric motor 3. Therefore, in
10 such a case, it is difficult to discriminate between this
spectrum peak and the characteristic spectrum peak due to
abnormality of the sliding part of the compression mechanism,
and thus there is a problem that a failure symptom cannot be
detected accurately. The present disclosure is to solve such
15 a problem.
[0037] FIG. 7 is a flowchart showing a failure symptom
detection method by the failure symptom detection device
according to embodiment 1 of the present disclosure. In the
drawing, reference characters S mean processing steps.
20 [0038] First, the d-q transformation unit 81 calculates
the phase currents (iu, iv, iw) on the basis of a detection
output from the current sensor 6 (step S1), and performs d-q
transformation thereof (step S2).
Next, the starting current extraction unit 911 of
25 the diagnosis calculation unit 91 extracts the q-axis current
20
iq in an acceleration period until a constant rotational
speed is reached after starting, except for a time just after
starting of the electric motor 3 (step S3). The starting
current extraction unit 911 may extract any of the phase
5 currents (iu, iv, iw) in the acceleration period and then the
d-q transformation unit 912 may perform d-q transformation of
the extracted phase current.
[0039] FIG. 8 is a waveform diagram showing temporal
changes in the rotational speed and the current value when
10 the electric motor is started. The lower-side graph shows
temporal change in the rotational speed until the constant
rotational speed is reached after starting of the electric
motor 3, and the upper-side graph shows temporal change in
the phase current (e.g., U-phase current iu) in this case.
15 [0040] A region A is a period in which inrush current
flows just after starting of the electric motor 3, and the
phase current greatly varies, leading to erroneous detection.
Therefore, the region A is excluded in this analysis method.
A region B is the acceleration period until the
20 rotational speed of the electric motor 3 reaches the constant
value. The current in the acceleration period B is extracted
and used as analysis data for failure symptom detection.
A region C is a period after the electric motor 3
reaches the constant rotational speed.
25 [0041] Then, after the q-axis current iq in the
21
acceleration period B of the electric motor 3 is extracted by
the starting current extraction unit 911, the data generation
unit 914 divides data of the q-axis current iq into two or
more pieces of data for data analysis on the extracted q-axis
5 current iq (step S4). The division method is determined by
the acceleration and the data sampling number. That is, in a
case where the acceleration is great, a range where the power
supply frequency and the spectrum peak vary is expanded, and
therefore the number of divided pieces of data needs to be
10 increased.
[0042] Next, the spectrum analysis unit 915a of the
frequency analysis unit 915 performs frequency analysis on
each of the plural pieces of data of the q-axis current iq
divided by the data generation unit 914, to generate a
15 spectrum waveform (step S4). As a method for the frequency
analysis, for example, a current fast Fourier transform (FFT)
analysis is known.
[0043] Meanwhile, in a variable-speed operation based on
inverter driving, the number of pieces of data that can be
20 used for frequency analysis is small and therefore there is a
problem of being readily influenced by noise. In this
regard, filter processing is performed so as to emphasize
only feature components by applying compressed sensing to a
current frequency characteristic having sparsity, whereby
25 frequency analysis can be accurately performed even in a case
22
of variable speed.
In the frequency analysis, without performing d-q
transformation of the phase current, data obtained by
detecting any of the phase currents (iu, iv, iw) may be
5 directly divided into two or more pieces of data to perform
frequency analysis.
[0044] As shown in the frequency spectrum waveforms in
FIG. 5 and FIG. 6, whichever data of the phase current or the
q-axis current iq is used, a spectrum peak of a rotational
10 frequency component is detected due to eccentricity of the
electric motor shaft based on abnormality of the sliding part
of the compression mechanism. The rotational frequency
component and the intensity value of the corresponding
spectrum peak calculated by the diagnosis calculation unit 91
15 serve as index values for determining presence/absence of
abnormality of the electric motor-provided equipment 1.
[0045] Separately from the above processing steps S1 to
S4, information about the power supply frequency, the number
of poles, and the rated rotational speed which are
20 specifications of the electric motor 3, and information about
an operation mode with which driving operation greatly
varies, are inputted and stored into the equipment
information storage unit 913 (step S00).
[0046] The rotational speed under no load of the electric
25 motor 3 can be calculated as 120·fs/p (fs: power supply
23
frequency, p: number of poles). Therefore, the rotational
speed of the electric motor 3 has a value between the
rotational speed under no load and the rated rotational
speed, and thus a rotational frequency band can be specified.
5 In addition, for example, in the compressor 11, the operation
mode greatly differs between summer and winter. In this way,
analysis data can be associated with each of a plurality of
operation modes.
[0047] In the failure symptom detection device 9, in a
10 case of determining presence/absence of a failure symptom in
the electric motor-provided equipment 1 (load 2 or electric
motor 3), as a premise therefor, data obtained initially at
the start of diagnosis, i.e., in a state in which the
electric motor-provided equipment 1 is new and has not been
15 deteriorated due to aging yet, is regarded as normal, and
from the normal data, reference values Ib for determining
presence/absence of a failure symptom are set for respective
power supply frequencies (see solid line in FIG. 9).
[0048] For this purpose, first, initially at the start of
20 diagnosis, the spectrum feature quantity detection unit 915b
detects intensity values of spectrum peaks arising in a
rotational frequency band, which are obtained as a result of
frequency analysis performed on each of the plural pieces of
data divided in step S4 (step S5).
25 Subsequently, from initial data obtained through
24
operation in each of set operation modes, the intensity
values of spectrum peaks detected in step S5 and information
about the operation modes and the power supply frequencies
(or the rotational frequencies calculated from the power
5 supply frequencies) stored in the equipment information
storage unit 133, are associated with each other and then
stored as learning data in the initial learning unit 921
(step S01).
[0049] Subsequently, the initial learning unit 921
10 generates the reference values Ib for the respective power
supply frequencies from the stored learning data (step S02).
For example, the reference values Ib are set at values such
as two or three times a variation σ of the learning data,
whereby an influence due to operation variation can be
15 excluded. The reference values Ib may be set from outside.
[0050] As described above, after initial learning in which
the initial learning unit 921 generates the reference values
Ib for the respective power supply frequencies using initial
data at the start of diagnosis as normal data is finished,
20 next, actual diagnosis is started.
[0051] Then, regarding the spectrum peaks arising in the
rotational frequency band, which are obtained as a result of
frequency analysis performed on each of the plural pieces of
data divided in the above step S4, when the intensity values
25 are detected in step S5, the reference value comparison unit
25
922 compares each detected intensity value with the reference
value Ib set by the initial learning unit 921 as described
above (step S6).
[0052] As a result of comparison by the reference value
5 comparison unit 922, when the detected intensity value
exceeds the reference value Ib, the abnormality count
determination unit 923 determines that there is abnormality
and stores the comparison result for each of the divided
pieces of data (step S7).
10 Then, in a case where abnormality determination is
consecutively repeated for a specific rotational speed and a
predetermined threshold is exceeded, the abnormality count
determination unit 923 determines that the electric motorprovided equipment 1 has a failure symptom, thus determining
15 that there is abnormality (step S8).
[0053] FIG. 9 illustrates an example of a result of
frequency analysis on the q-axis current in a case where
abnormality has occurred at the sliding part of the
compression mechanism of the compressor 11.
20 [0054] In FIG. 9, filled circles indicate a result of
initial learning using data obtained initially at the start
of diagnosis as normal data, and a solid line indicates a
result of initial learning performed by providing the
reference values Ib for the respective power supply
25 frequencies from the normal data. In addition, blank circles
26
indicate data obtained after deterioration by aging and
determined as abnormality, and a broken line indicates an
average value thereof.
[0055] In operation of the electric motor 3, acceleration
5 is performed to reach a power supply frequency of 120 Hz, and
thereafter, operation at a constant rotational speed is
performed in a state of 120 Hz. A rated rotational speed N
(r/min) of the electric motor 3 is calculated as 120·fs/p
(fs: power supply frequency, p: number of poles (6)), and as
10 the power supply frequency increases, the rotational speed
also increases proportionally.
[0056] As is found from the result shown in FIG. 9, in
comparison between the reference value Ib and data obtained
when the electric motor-provided equipment 1 has been
15 deteriorated due to aging after continuous usage, it is
difficult to perform abnormality detection in the constantrotational-speed operation (120 Hz), but it is possible to
perform discrimination for whether abnormal or normal in a
low-speed region (40 Hz to 80 Hz) during acceleration from
20 starting.
[0057] Thus, a force applied to the electric motor shaft
by the scroll part of the compressor and a force applied to
the electric motor shaft due to failure are clearly
discriminated from each other, whereby a spectrum peak
25 arising due to the failure can be detected.
27
[0058] In a case where, in the above step S8, the
abnormality count determination unit 923 determines that the
electric motor-provided equipment 1 (load 2 or electric motor
3) has a failure symptom and thus determines that there is
5 abnormality, the diagnosis result reporting unit 93
accordingly causes the display unit 931 to display
abnormality of the electric motor-provided equipment 1 as an
alarm on a screen and causes the warning unit 932 to issue a
warning. In addition, processing such as printing out is
10 performed by the external output unit 933 (step S9).
[0059] In embodiment 1, the failure symptom detection
method has been described using the electric motor-provided
equipment 1 in which the load 2 is the compressor 11, as an
example. However, without limitation thereto, even if the
15 load 2 is other than the compressor 11, the present
disclosure is applicable to the electric motor-provided
equipment 1 (e.g., a vacuum pump, a water pump, or a conveyor
belt) for which failure symptom detection is difficult
because a component other than that due to abnormality is
20 superimposed.
[0060] In embodiment 1, the configuration in which the
control device 8 and the failure symptom detection device 9
are provided as independent devices has been described as a
premise. However, the control device 8 and the failure
25 symptom detection device 9 may be combined integrally as a
28
single device.
[0061] As described above, according to embodiment 1, a
failure symptom is detected through analysis on current
signals obtained by the control device 8 which controls
5 operation of the electric motor-provided equipment 1. Thus,
it becomes possible to assuredly detect a failure symptom
without adding an extra sensor.
[0062] By using data in the acceleration period (region B
in FIG. 8) until reaching a constant stable rotational speed
10 from transitional variation occurring just after starting of
the electric motor 3, it becomes possible to assuredly detect
a failure symptom of a sliding part such as a bearing for
which detection has been conventionally difficult due to the
operation state of the electric motor-provided equipment 1.
15 [0063] Embodiment 2
FIG. 10 is a block diagram showing a schematic
configuration of electric motor-provided equipment and a
failure symptom detection device according to embodiment 2 of
the present disclosure.
20 In the configuration shown in FIG. 10, the
diagnosis calculation unit 91 and a diagnosis result
reporting unit 94 are connected to the control device 8 of
the electric motor-provided equipment 1, and meanwhile, the
diagnosis determination unit 92 and the diagnosis result
25 reporting unit 93 are provided on an external calculation
29
area 100 side such as a personal computer (PC), a server, or
a cloud. Then, the control device 8 and the external
calculation area 100 are connected via a communication
network 110 such as Ethernet, whereby data is mutually
5 transmitted/received via the communication network 110.
[0064] The diagnosis calculation unit 91 connected to the
control device 8 calculates index values (a rotational
frequency component and the intensity value of a
corresponding spectrum peak) for determination for
10 presence/absence of abnormality of the electric motorprovided equipment 1, and the calculation result is
transmitted to the external calculation area 100 side via the
communication network 110. The diagnosis determination unit
92 provided on the external calculation area 100 side
15 accumulates the transmitted data, performs diagnosis
determination on the basis of the data, and outputs a result
thereof to the diagnosis result reporting unit 93. In a case
where the diagnosis determination unit 92 determines that
there is abnormality, information thereof is transmitted to
20 the control device 8 via the communication network 110.
Thus, not only the diagnosis result reporting unit 93
provided on the external calculation area 100 side but also
the diagnosis result reporting unit 94 provided on the
control device 8 side of the electric motor-provided
25 equipment 1 can perform processing such as issuing a warning
30
and performing alarm display, individually.
[0065] FIG. 11 is a block diagram showing another
schematic configuration of electric motor-provided equipment
and a failure symptom detection device according to
5 embodiment 2 of the present disclosure.
In the configuration shown in FIG. 11, the
diagnosis result reporting unit 94 is provided to the control
device 8 of the electric motor-provided equipment 1, and
meanwhile, the diagnosis calculation unit 91, the diagnosis
10 determination unit 92, and the diagnosis result reporting
unit 93 composing the failure symptom detection device 9 are
provided on the external calculation area 100 side. Then,
the control device 8 and the external calculation area 100
are connected via the communication network 110 such as
15 Ethernet, whereby data is mutually transmitted/received via
the communication network 110.
[0066] Data of current signals acquired by the control
device 8 is transmitted to the external calculation area 100
side via the communication network 110. The failure symptom
20 detection device 9 provided in the external calculation area
100 causes the diagnosis calculation unit 91 to extract index
values for determination for presence/absence of abnormality,
through frequency analysis, and causes the diagnosis
determination unit 92 to accumulate data and perform
25 diagnosis determination and output a result thereof to the
31
diagnosis result reporting unit 93. In a case where the
diagnosis determination unit 92 determines that there is
abnormality, information thereof is transmitted to the
control device 8 via the communication network 110. Thus,
5 not only the diagnosis result reporting unit 93 provided on
the external calculation area 100 side but also the diagnosis
result reporting unit 94 provided on the control device 8
side of the electric motor-provided equipment 1 can perform
processing such as issuing a warning and performing alarm
10 display, individually.
[0067] Although the disclosure is described above in terms
of various exemplary embodiments 1 and 2, it should be
understood that the various features, aspects, and
functionality described in these embodiments are not limited
15 in their applicability to the particular embodiment with
which they are described, but instead can be applied, alone
or in various combinations to one or more of the embodiments
of the disclosure.
[0068] It is therefore understood that numerous
20 modifications which have not been exemplified can be devised
without departing from the scope of the present disclosure.
For example, at least one of the constituent components may
be modified, added, or eliminated. At least one of the
constituent components mentioned in at least one of the
25 preferred embodiments may be selected and combined with the
32
constituent components mentioned in another preferred
embodiment.
DESCRIPTION OF THE REFERENCE CHARACTERS
5 [0069] 1 electric motor-provided equipment (air
conditioner)
2 load
3 electric motor
4 driving device
10 41 converter
42 inverter
5 AC power supply
11 compressor (load)
12 condenser
15 13 expansion valve
14 evaporator
6 current sensor
7 angle sensor
8 control device
20 81 d-q transformation unit
9 failure symptom detection device
91 diagnosis calculation unit
911 starting current extraction unit
912 d-q transformation unit
25 913 equipment information storage unit
33
914 data generation unit
915 frequency analysis unit
915a spectrum analysis unit
915b spectrum feature quantity detection unit
5 92 diagnosis determination unit
921 initial learning unit
922 reference value comparison unit
923 abnormality count determination unit
93 diagnosis result reporting unit
10 931 display unit
932 warning unit
933 external output unit

We Claim :
[Claim 1]
A failure symptom detection device for electric
motor-provided equipment, which detects a failure symptom of
5 the electric motor-provided equipment including a load with
an electric motor used as a motive-power source and a driving
device which supplies power to the electric motor and drives
the electric motor, the failure symptom detection device
comprising:
10 a current detection unit which detects current
flowing from the driving device to the electric motor;
a diagnosis calculation unit which calculates an
index value for determination for presence/absence of
abnormality of the electric motor-provided equipment, on the
15 basis of a result of detection by the current detection unit;
a diagnosis determination unit which determines
presence/absence of abnormality of the electric motorprovided equipment on the basis of a result of calculation by
the diagnosis calculation unit; and
20 a diagnosis result reporting unit which reports a
result of diagnosis determined by the diagnosis determination
unit, to outside, wherein
the diagnosis calculation unit includes
a starting current extraction unit which, from
25 the current detected by the current detection unit, extracts
35
current in an acceleration period until a constant rotational
speed is reached after starting of the electric motor,
a data generation unit which divides data of
the current in the acceleration period extracted by the
5 starting current extraction unit, into plural pieces of data,
and
a frequency analysis unit which performs
frequency analysis on each piece of data divided by the data
generation unit.
10
[Claim 2]
The failure symptom detection device for the
electric motor-provided equipment according to claim 1,
wherein
15 the frequency analysis unit includes
a spectrum analysis unit which performs
frequency analysis for current frequencies of each piece of
data divided by the data generation unit, to generate a
spectrum waveform, and
20 a spectrum feature quantity detection unit
which detects, as the index value, an intensity value of a
spectrum peak in a rotational frequency band of the electric
motor on the basis of the spectrum waveform analyzed by the
spectrum analysis unit.
25
36
[Claim 3]
The failure symptom detection device for the
electric motor-provided equipment according to claim 2,
further comprising a d-q transformation unit which performs
5 d-q transformation of phase currents detected by the current
detection unit, wherein
from q-axis current obtained through d-q
transformation by the d-q transformation unit, the starting
current extraction unit extracts q-axis current in the
10 acceleration period until the constant rotational speed is
reached after starting of the electric motor.
[Claim 4]
The failure symptom detection device for the
15 electric motor-provided equipment according to claim 2,
wherein
from phase current detected by the current
detection unit, without performing d-q transformation
thereof, the starting current extraction unit extracts phase
20 current in the acceleration period until the constant
rotational speed is reached after starting of the electric
motor.
[Claim 5]
25 The failure symptom detection device for the
37
electric motor-provided equipment according to any one of
claims 2 to 4, wherein
the diagnosis determination unit includes
an initial learning unit which stores an
5 initial analysis result serving as a reference value for
determination comparison,
a reference value comparison unit which
compares the reference value stored in the initial learning
unit and the intensity value of the spectrum peak in the
10 rotational frequency band of the electric motor detected by
the spectrum feature quantity detection unit, and
an abnormality count determination unit which
counts a number of times the reference value comparison unit
determines that the intensity value exceeds the reference
15 value and thus there is abnormality, and determines a
diagnosis result in accordance with the counted number of
abnormality determinations.
[Claim 6]
20 The failure symptom detection device for the
electric motor-provided equipment according to claim 5,
wherein
the initial learning unit learns the reference
values under respective operation conditions corresponding to
25 various operation patterns and outside environments of the
38
electric motor-provided equipment, and the reference value
comparison unit performs comparison with the reference value
stored in the initial learning unit in accordance with the
operation condition.
5
[Claim 7]
The failure symptom detection device for the
electric motor-provided equipment according to any one of
claims 1 to 6, wherein
10 the diagnosis result reporting unit includes at
least one of a display unit which displays presence/absence
of abnormality of the electric motor-provided equipment, an
external output unit which outputs the presence/absence of
abnormality to outside, and a warning unit which issues a
15 warning in a case where there is abnormality of the electric
motor-provided equipment.
[Claim 8]
A failure symptom detection method for electric
20 motor-provided equipment, for detecting a failure symptom of
the electric motor-provided equipment including a load with
an electric motor used as a motive-power source and a driving
device which supplies power to the electric motor and drives
the electric motor, the failure symptom detection method
25 comprising:
39
a first step of detecting current flowing to the
electric motor;
a second step of, from the current obtained in the
first step, extracting current in an acceleration period
5 until a constant rotational speed is reached after starting
of the electric motor;
a third step of dividing data of the current in the
acceleration period extracted in the second step, into plural
pieces of data, and performing frequency analysis on each
10 divided piece of data, to generate a spectrum waveform;
a fourth step of detecting a feature component of a
spectrum peak arising in a rotational frequency band of the
electric motor, from the spectrum waveform obtained in the
third step;
15 a fifth step of comparing an intensity value of the
spectrum peak obtained in the fourth step and a predetermined
reference value;
a sixth step of determining presence/absence of
abnormality of the electric motor-provided equipment from a
20 result of comparison in the fifth step; and
a seventh step of reporting a result of
determination in the sixth step to outside.
[Claim 9]
25 The failure symptom detection method for the
40
electric motor-provided equipment according to claim 8,
further comprising an eighth step of performing d-q
transformation of phase current flowing to the electric motor
and detected in the first step, wherein
5 in the second step, q-axis current obtained in the
eighth step in the acceleration period until the constant
rotational speed is reached after starting of the electric
motor, is extracted, and
in the third step, data of the q-axis current
10 extracted in the second step is divided into plural pieces of
data, and frequency analysis is performed on the divided
pieces of data.
[Claim 10]
15 The failure symptom detection method for the
electric motor-provided equipment according to claim 8,
wherein
in the second step, phase current in the
acceleration period until the constant rotational speed is
20 reached after starting of the electric motor is extracted
without being subjected to d-q transformation, and
in the third step, data of the phase current
extracted in the second step is divided into plural pieces of
data, and frequency analysis is performed on each divided
25 piece of data, to generate a spectrum waveform.
41
[Claim 11]
The failure symptom detection method for the
electric motor-provided equipment according to any one of
5 claims 8 to 10, wherein
in performing the frequency analysis in the third
step, filter processing is performed so as to emphasize only
a feature component by applying compressed sensing to a
current frequency characteristic having sparsity.
10
[Claim 12]
The failure symptom detection method for the
electric motor-provided equipment according to any one of
claims 8 to 11, wherein
15 in the fifth step, prior to comparing the intensity
value of the spectrum peak with the reference value, initial
learning is performed to store an initial analysis result as
the reference value for determination comparison in advance,
and
20 in the sixth step, a number of times it is
determined from the result of comparison in the fifth step
that the intensity value of the spectrum peak exceeds the
reference value obtained through the initial learning and
thus there is abnormality, is counted, and a diagnosis result
25 is determined in accordance with the counted number of
42
abnormality determinations.
[Claim 13]
The failure symptom detection method for the
5 electric motor-provided equipment according to claim 12,
wherein
in the initial learning, the reference values are
learned under respective operation conditions corresponding
to various operation patterns and outside environments of the
10 electric motor-provided equipment.