JP7107632B2 - Rotating machine diagnostic system and rotating machine diagnostic method - Google Patents

Description

The present invention relates to a rotating machine diagnostic system and a rotating machine diagnostic method.

In a rotating machine, deterioration such as cracks and peeling occurs in the insulation layer of the stator winding due to operational stress. Partial discharge occurs in the deteriorated part due to the voltage during operation, and the state of deterioration of the insulation is grasped by detecting this partial discharge. Since the partial discharge of a rotating machine is buried in the radio noise (hereinafter referred to as noise) that accompanies operation, it is necessary to distinguish between the partial discharge and the noise and detect only the partial discharge.
If a motor incorporated to drive a production facility or industrial machine breaks down unexpectedly, unplanned repair or parts replacement work will be required, and the production facility may stop operating. Although it is possible to grasp the degree of deterioration of the motor to some extent by systematically stopping the production equipment, the operating rate of the production equipment is lowered by stopping the motor. Diagnosis during operation makes it possible to prevent a decline in equipment availability, so there is a growing need for motor diagnosis technology during operation. Here, since about 25% of the above high voltage motor failures are caused by insulation deterioration, technology for diagnosing insulation deterioration is being developed. In a high-voltage motor, local insulation deterioration inside the motor is diagnosed from the distribution of the amount of discharge charge with respect to the phase of the applied voltage, called a partial discharge pattern obtained by partial discharge measurement.

In Patent Document 1, a signal detected by a partial discharge sensor provided in a rotating electrical machine is branched and measured simultaneously in two different frequency bands, and the two-frequency intensity ratio is within a certain range based on the two-frequency intensity correlation of the measured signals. Separating into certain signal groups, sorting the separated signal groups into respective occurrence frequency distributions, calculating each signal group shown in the sorted occurrence frequency distribution, and obtaining a distribution pattern of each signal group as a phase characteristic A method for detecting partial discharge in a rotating electrical machine is described. In the partial discharge detection method described in Patent Document 1, external discharge and internal discharge are determined by comparing the magnitude of the positive and negative polarities of the partial discharge charge amount from the obtained partial discharge pattern.

Japanese Patent Application Laid-Open No. 2001-074802

The partial discharge detection method described in Patent Document 1 compares which of the positive polarity and negative polarity of the partial discharge charge amount is larger, and roughly determines the partial discharge generation site. However, it is necessary to analyze the partial discharge pattern in order to determine the partial discharge occurrence locations in detail. Until now, inspectors have visually determined the partial discharge pattern.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a rotating machine diagnostic system and a rotating machine diagnostic method capable of appropriately detecting changes in insulation deterioration.

In order to solve the above problems, a rotating machine diagnosis system of the present invention includes a partial discharge signal distribution acquisition unit that acquires a partial discharge signal distribution with respect to an applied voltage phase of a rotating machine; A maximum value extraction processing unit that extracts the positive and negative maximum values of the quantity, a voltage phase of the maximum value of at least one of the extracted maximum values, and a phase direction around the voltage phase of the maximum value a voltage phase extraction processing unit for extracting a voltage phase based on the acquired partial discharge signal distribution, a magnitude of a maximum value of both positive and negative polarities, and a width from the voltage phase before and after the voltage phase to the voltage phase of the maximum value a partial discharge mechanism determination unit that determines a partial discharge pattern shape appearing in a scatter diagram of charge amount-voltage phase on a two-dimensional plane based on the voltage phase extraction processing unit, the partial discharge signal distribution is characterized in that a voltage phase corresponding to a charge amount of a predetermined ratio of the maximum value is extracted as the preceding and succeeding voltage phases .

Advantageous Effects of Invention According to the present invention, it is possible to provide a rotating machine diagnostic system and a rotating machine diagnostic method capable of appropriately detecting a change in insulation deterioration state.

1 is a diagram showing the configuration of a rotating machine diagnostic system according to an embodiment of the present invention; FIG. It is a block diagram of the data processor of the rotary machine diagnostic system which concerns on the said embodiment. FIG. 5 is a table showing a partial discharge signal distribution acquired by the rotating machine diagnostic system according to the embodiment; FIG. 4 is a diagram for explaining types of partial discharge pattern shapes generated in a high-voltage motor defined by IEC60034-27-2 of the rotating machine diagnostic system according to the embodiment; FIG. 5 is a diagram showing a partial discharge pattern shape determination table for classifying partial discharge pattern shapes of the rotary machine diagnostic system according to the embodiment; It is a figure explaining the outline|summary of the insulation deterioration diagnosis of the rotary machine diagnostic system which concerns on the said embodiment. FIG. 4 is a diagram showing a classification example 1 of the partial discharge pattern shape of the rotating machine diagnostic system according to the above embodiment, where (a) is a diagram showing the partial discharge pattern shape, and (b) is a diagram showing determination of the partial discharge pattern shape. be. FIG. 10 is a diagram showing a classification example 2 of the partial discharge pattern shape of the rotating machine diagnosis system according to the embodiment, (a) is a diagram showing the partial discharge pattern shape, and (b) is a diagram showing determination of the partial discharge pattern shape. be. It is a flow chart which shows processing of insulation degradation diagnosis of a rotating machine diagnosis system concerning the above-mentioned embodiment. FIG. 4 is a diagram showing an application example of the partial discharge mechanism determination of the rotating machine diagnostic system according to the embodiment, (a) is a diagram showing the parameters of the charge amount distribution of the application example 1, and (b) and (c) are the application examples. 2 is a diagram showing charge amount distribution parameters of No. 2. FIG. It is a figure explaining the application example 3 of the partial discharge mechanism determination of the rotary machine diagnostic system which concerns on the said embodiment. It is a figure which shows the determination example of (1-1) Internal void (internal void) of the rotary machine diagnostic system which concerns on the said embodiment. FIG. 5 is a diagram showing an example of determination of (1-2) Internal delamination by the rotating machine diagnosis system according to the embodiment; FIG. 10 is a diagram showing an example of determination of (1-3) Delamination between conductor and insulation in the rotating machine diagnostic system according to the embodiment; FIG. 5 is a diagram showing an example of determination of (2) Slot partial discharges of the rotating machine diagnostic system according to the embodiment; FIG. 7 is a diagram showing an example of determination of (3-1) Corona activity at the S/C and stress grading coating (corona discharge) of the rotating machine diagnostic system according to the embodiment; FIG. 11 is a diagram showing an example of determination of (3-2) Surface discharges tracking (creeping discharge) of the rotating machine diagnosis system according to the embodiment; FIG. 10 is a diagram showing an example of determination of (3-3) Gap type discharges by the rotating machine diagnosis system according to the embodiment;

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
(embodiment)
FIG. 1 is a diagram showing the configuration of a rotating machine diagnostic system according to an embodiment of the present invention. The rotating machine diagnosis system of this embodiment is an example applied to insulation deterioration diagnosis of a rotating machine.
As shown in FIG. 1, the power supply 101 supplies electric power to the rotating machine 102 through feeder lines 103a, 103b, and 103c. Rotating machine diagnostic system 100 includes lead wire 104 , coupling capacitor 105 , current sensor 106 , current sensor 107 , voltage sensor 108 , and data processor 110 .
Electric power is supplied from the power source 101 to the rotary machine 102 via feeder lines 103a, 103b, and 103c. A lead wire 104 is drawn out from the feeder line 103a and is grounded via a coupling capacitor 105 .
A current sensor 106 measures a current signal waveform flowing through the power supply line 103 a , and a current sensor 107 measures fluctuations in the current flowing through the lead wire 104 . The two current sensors 106 and 107 are installed in different locations and detect the current at two locations, so that the location of insulation deterioration can be estimated based on the directionality of the flowing current, and measurements can be performed with high accuracy (without the influence of noise). be able to.
A voltage sensor 108 measures a voltage waveform applied to the rotating machine 102 via the power supply line 103a.
Current sensor 106 and voltage sensor 108 can be installed on any of feeder lines 103a, 103b, 103c. Similarly, lead line 104, coupling capacitor 105 and current sensor 107 can be connected to any of feed lines 103a, 103b and 103c. The current sensors 106 to 108 detect signals from the installed feeder lines 103a, 103b, 103c. The power supply lines 103a, 103b, and 103c are collectively referred to as the power supply line 103. FIG.

In FIG. 1, the current sensor 107 is installed between the feeder line 103 and the coupling capacitor 105, but it may be installed between the coupling capacitor 105 and the ground line depending on the installation space. Also, the current sensor is installed at two locations, the power supply line 103 and the lead wire 104 from the power supply line 103, for each phase, but the current sensor 106 may be installed only on the power supply line 103. FIG. In this case, although the device is susceptible to noise, the number of current sensors can be reduced and the configuration of the device can be simplified. Any type of current sensor may be used, and for example, a through-type current sensor, a clamp-type current sensor, a split-type current sensor, an optical fiber sensor using a magneto-optic effect, or the like can be used. The type of voltage sensor is also not limited, and for example, a probe type sensor, a non-contact type sensor, or the like can be used.

FIG. 2 is a configuration diagram of the data processing device of the rotating machine diagnostic system according to the embodiment of the present invention.
As shown in FIG. 2 , data processing device 110 includes partial discharge signal distribution acquisition section 111 , local maximum value extraction processing section 112 , voltage phase extraction processing section 113 , and partial discharge mechanism determination section 114 .
The data processing device 110 is realized by a computer into which a predetermined program for realizing each function related to the rotating machine diagnosis system 100 is installed. It functions as a rotating machine diagnostic method for the diagnostic system 100 .

<Partial discharge signal distribution acquisition unit 111>
The partial discharge signal distribution acquisition unit 111 acquires partial discharge signal distribution data (hereinafter referred to as partial discharge signal distribution) with respect to the applied voltage phase of the rotating machine 102 (hereinafter referred to as voltage phase). Specifically, the partial discharge signal distribution acquisition unit 111 acquires the partial discharge signal distribution by synchronizing the measured current fluctuation caused by partial discharge with the voltage phase. In the present embodiment, the partial discharge signal distribution acquisition unit 111 synchronously extracts current signals caused by partial discharge detected by the current sensors 106 and 107 attached to the rotating machine 102 and voltage signals detected by the voltage sensor 108. Thus, the partial discharge signal distribution is obtained.

The partial discharge signal distribution can be acquired as digital data as shown in FIG. FIG. 3 is a table showing the obtained partial discharge signal distribution. As shown in FIG. 3, the partial discharge charge amount [pC] for the voltage phase [deg] is stored.
Here, by integrating the measured current, it is possible to acquire the charge amount distribution (PRPD pattern, Phase Resolved Partial Discharge pattern) discharged by partial discharge. For example, as indicated by symbol e in FIG. 6B, the horizontal axis represents the voltage phase and the vertical axis represents the amount of charge. distribution.

In this embodiment, the rotating machine diagnostic system 100 includes a pass bandpass filter 300 that separates partial discharge signals. The pass frequency band filter 300 is a plurality of filters with different pass frequency bands, and from current signals caused by partial discharge detected by the current sensors 106 and 107 attached to the rotating machine 102, partial discharge signals for each pass frequency band are obtained. To separate.
The partial discharge signal distribution acquisition unit 111 uses a pass frequency band filter 300 having a different pass frequency band for current signals caused by partial discharge measured by the current sensors 106 and 107 attached to the rotating machine 102 to obtain a plurality of can be separated to obtain a partial discharge signal.

<Maximum value extraction processing unit 112>
The maximum value extraction processing unit 112 extracts the positive and negative maximum values of the charge amount (positive and negative maximum values) based on the acquired partial discharge signal distribution.

<Voltage phase extraction processing unit 113>
The voltage phase extraction processing unit 113 extracts the voltage phase of the maximum value of at least one of the polarities of the extracted maximum values (the voltage phase when the maximum value occurs), and the phase direction around the maximum value. , a voltage phase based on the acquired partial discharge signal distribution is extracted.
The voltage phase extraction processing unit 113 extracts voltage phases before and after the extracted maximum value is set to a predetermined ratio. The voltage phase before and after is a value (half value) when the maximum value is halved. Furthermore, the voltage phase before and after is a voltage phase of a value (3/4 value) other than the half value, for example, when the value is 3/4.
In addition, as shown in a modified example below, the voltage phase extraction processing unit 113 extracts the voltage phase of each of the extracted positive and negative maximum values, and the obtained partial discharge signal before and after the phase direction centering on each of the positive and negative maximum values. You may make it extract the voltage phase based on distribution.

<Partial discharge mechanism determination unit 114>
The partial discharge mechanism determination unit 114 determines the partial discharge based on the magnitude of the maximum value of both positive and negative polarities and the width from the voltage phase before and after the maximum value to the voltage phase of the maximum value. Determine pattern shape. Based on the determined partial discharge pattern shape, the partial discharge mechanism determination unit 114 determines the partial discharge mechanism indicating the partial discharge generation site. That is, the partial discharge mechanism determination unit 114 determines the partial discharge mechanism indicating the left-right imbalance of the distribution centered on the maximum value from the magnitude of the difference between the voltage phases before and after the voltage phase of the maximum value.

When the voltage phase extraction processing unit 113 extracts the voltage phases of the extracted positive and negative maximum values and the voltage phases before and after the extracted positive and negative maximum values, the partial discharge mechanism determination unit 114 determines the magnitude of each of the positive and negative maximum values and the voltage phases before and after the voltage phases. to the voltage phase of the maximum value, and the shape of the partial discharge pattern is determined.

In this embodiment, the partial discharge mechanism determination unit 114 determines the shape of the partial discharge pattern classified by the magnitude of the maximum value of both positive and negative polarities and the width from the voltage phase before and after the voltage phase to the voltage phase of the maximum value. A table 200 (storage unit) (see FIG. 5) is stored.
The partial discharge mechanism determination unit 114 refers to the partial discharge pattern shape determination table 200 based on the magnitude of the obtained positive and negative maximum values and the width from the voltage phase before and after the voltage phase of the maximum value. A discharge pattern shape is determined.
The partial discharge mechanism determination unit 114 collates the determined partial discharge pattern shape with the partial discharge pattern shape of the partial discharge pattern shape determination table 200, and determines whether or not the rotating machine 102 is suitable.
The partial discharge mechanism determination unit 114 compares the acquired local maximum value with a threshold determined for each classified partial discharge pattern shape, and diagnoses insulation deterioration of the rotating machine 102 .
The partial discharge mechanism determination unit 114 acquires parameters from the determined partial discharge pattern shape, applies machine learning to the parameters, and detects the state of insulation deterioration of the rotating machine and changes in the state. In machine learning, VQC (Vector Quantization Clustering) may be applied.

When it is considered that a plurality of partial discharge phenomena are occurring simultaneously, the partial discharge mechanism determination unit 114 uses the passband filter 300 when acquiring the partial discharge signal, and determines the partial discharge pattern for each frequency band. and determine the partial discharge mechanism for each. This makes it possible to determine even if partial discharge is caused by multiple mechanisms.

As described above, in the rotating machine diagnostic system 100 , the maximum value extraction processing unit 112 extracts the magnitude relationship between the positive and negative maximum values of the partial discharge signal distribution acquired by the partial discharge signal distribution acquisition unit 111 . The voltage phase extraction processing unit 113 extracts the voltage phase when the maximum value occurs and two voltage phases before and after this maximum value in the phase direction (four voltage phases before and after using a 3/4 value). and obtain the voltage phase width of the maximum value. The partial discharge mechanism determination unit 114 determines the partial discharge pattern based on the magnitude of the maximum value of both positive and negative polarities and the width from the voltage phase before and after the maximum value to the voltage phase of the maximum value, which is shifted in the phase direction around the maximum value. The shape determination table 200 is referenced to determine the shape of the partial discharge pattern. Since the partial discharge mechanism can be determined, it becomes possible to select a threshold value for determination according to the partial discharge mechanism, and it is possible to determine external discharge and internal discharge. Further, by being able to determine whether the distribution has a steep shape, it is possible to determine whether the partial discharge is due to voids or peeling.

Further, the rotating machine diagnostic method of the present embodiment includes the step of acquiring the partial discharge signal distribution with respect to the applied voltage phase of the rotating machine 102, and extracting the positive and negative maximum values of the charge amount based on the acquired partial discharge signal distribution. extracting a voltage phase of a maximum value of at least one of the polarities among the extracted maximum values, and a voltage phase based on the obtained partial discharge signal distribution before and after the phase direction centering on the maximum value; and determining the shape of the partial discharge pattern based on the magnitude of the positive/negative maximum value and the width from the preceding and following voltage phases to the voltage phase of the maximum value.

[Types of partial discharge]
Next, types of partial discharge pattern shapes generated in a high voltage motor will be described.
FIG. 4 is a diagram for explaining types of partial discharge pattern shapes generated in a high-voltage motor specified by IEC (International Electrotechnical Commission) 60034-27-2. The horizontal axis is the voltage phase, and the vertical axis is the partial discharge signal distribution.
The types of partial discharge pattern shapes generated in high-voltage motors can be classified into the following seven typical types.
(1-1) Internal void (discharge in internal void) (see Fig. 4(a))
(1-2) Internal delamination (discharge due to internal peeling) (see Fig. 4(b))
(1-3) Delamination between conductor and insulation: (discharge between conductor and insulation layer) (see Fig. 4(c))
(2) Slot partial discharges (see Fig. 4(d))
(3-1) Corona activity at the S/C and stress grading coating (see Fig. 4(e))
(3-2) Surface discharges tracking (see Fig. 4(f))
(3-3) Gap type discharges (see Fig. 4(g))

[Classification of partial discharge pattern shape]
Next, classification of partial discharge pattern shapes will be described.
FIG. 5 is a diagram showing a partial discharge pattern shape determination table 200 for classifying partial discharge pattern shapes.
The partial discharge pattern shape can be classified into seven types according to the comparison of positive and negative maximum charges and the distribution shape of the partial discharge pattern.
The classifications (1-1), (1-2), (1-3), (2), (3-1), (3-2), and (3-3) of the partial discharge pattern shape determination table 200 are Corresponds to types (1-1), (1-2), (1-3), (2), (3-1), (3-2), and (3-3) of partial discharge in FIG. is doing.
For example, when Q ^(p) max = Q ⁽ⁿ⁾ max, when p ⁽ⁿ⁾ 5 - p ⁽ⁿ⁾ 1 ≪ ^{p (p)} 5 - p ^(p) 1, Gap type discharges (3-3 ), the distribution state p3－p1≒p5－p3 is classified as Internal void (1-1), and the distribution state p3－p1≪p5－p3 is classified as Internal delamination (1-2). be. In the case of Q ^(p) _max =Q ⁽ⁿ⁾ _max , corona discharge is selected when the number of positive partial discharges N ^(p) is greater than the number of negative partial discharges N ^(N) .

Also, when Q ^(p) _max > Q ⁽ⁿ⁾ _max , p ⁽ⁿ⁾ 5−p ⁽ⁿ⁾ 1 << ^{p (p)} 5−p ^(p) 1 does not exist, and the distribution state p3−p1 When ≒p5－p3, it is classified as Corona activity at the S/C and stress grading coating (3-1), and when distribution p3－p1≪p5－p3, it is classified as Slot partial discharges (2).
In the case of Q ^(p) _max < Q ⁽ⁿ⁾ _max , when p ⁽ⁿ⁾ 5 - p ⁽ⁿ⁾ 1 ≪ ^{p (p)} 5 - p ^(p) 1, Surface discharges tracking (3-2 ), and when the distribution state p3-p1≈p5-p3, it is classified as Delamination between conductor and insulation (1-3), and the distribution state p3-p1 <<p5-p3 does not exist.

An insulation deterioration diagnosis method of the rotary machine diagnosis system 100 configured as described above will be described below.
[Outline of insulation deterioration diagnosis]
First, an outline of the insulation deterioration diagnosis of the rotary machine diagnosis system will be described.
FIG. 6 is a diagram for explaining an outline of insulation deterioration diagnosis of the rotary machine diagnosis system.

<Measurement of PD signal>
As indicated by symbol a in FIG. 6A, current sensors 106 and 107 and a voltage sensor 108 provided on the feeder line 103 and the lead wire 104 of the rotating machine 102 detect the voltage and current of the rotating machine 102 during operation. and measured as a PD (Partial Discharge) signal (see symbol b in the figure). In the present embodiment, the partial discharge signal distribution acquisition unit 111 synchronously extracts current signals caused by partial discharge detected by the current sensors 106 and 107 attached to the rotating machine 102 and voltage signals detected by the voltage sensor 108. Thus, the partial discharge signal distribution is obtained. As indicated by symbol c in FIG. 6(a), the PD signal is separated from noise (Separation of PD signal from noise).

<PD signal to PRPD pattern transformation>
Assume that the PD signal indicated by symbol d in FIG. 6B is obtained, and the partial discharge signal distribution indicated by symbol e in FIG. 6B is obtained. If the horizontal axis is the voltage phase and the vertical axis is the charge amount, the charge amount distribution (PRPD pattern) is shown by overlapping the applied voltage waveform 101 and the distribution of the partial discharge signal 102 .
As described above, by integrating the measured current, it is possible to obtain the charge amount distribution (PRPD pattern) emitted by the partial discharge.

<Extraction of parameters indicating the shape of PRPD pattern>
As shown in FIG. 6(c), the maximum positive value Q ^(p) _max and the maximum negative value Q ⁽ⁿ⁾ _max of the partial discharge signal distribution are calculated, and the maximum positive value Q ^(p) _max and the negative maximum value Q(p)max are calculated. Compare with the maximum value Q ⁽ⁿ⁾ _max . Then, a plurality of points indicating the outline of the partial discharge signal distribution are extracted from the partial discharge signal distribution with the positive maximum value Q ^(p) _max having a large absolute value of the maximum value.
The points representing the outline of the partial discharge signal distribution are the point p3 extracted with the positive maximum value Q ^(p) _max and the point Q ^(p) _max points p1-p5 extracted at 1/2×Q ^(p) _max (that is, Q ^(p) _half ) and half (1/2) of the above 1/2×Q ^(p) _max on the convex side points p2-p4 extracted with 3/4×Q ^(p) _max , and
In the case of FIG. 6(c), the point p3 extracted at the maximum positive value Q ^{(p) max} _and the point p3 extracted at 1/2×Q (p) _max of Q ^(p) _max (i.e. Q ^(p) _half ) Points p1-p5 and points p2-p4 extracted by 3/4×Q ^(p) _max obtained by further halving the above 1/2×Q ^(p) _max on the convex side are used. It is also possible to use only the point p3 extracted with the positive maximum value Q ^(p) _max and the points p1-p5 extracted with Q ^(p) _half .

<Diagnosis with VQC>
As shown in FIG. 6(d), VQC (Vector Quantization Clustering) is applied to detect the state of insulation deterioration of the rotating machine 102 and changes in that state. In this embodiment, VQC is used as a learning method for machine learning.
As indicated by symbol f in FIG. 6D, clustering parameters are set and analyzed, and diagnosis is made based on the analysis results. Symbol h in FIG. 6(d) is a diagram for explaining vector quantization.
1478250654997_0
and put it together as
1478250654997_1
do. VQC first captures N samples each and makes it an N-dimensional vector.
1478250654997_2
I do. After that, one "representative vector" is determined from each cluster, and the vectors other than the representative vector are replaced with the representative vector of the assigned cluster. Finally, the representative vector is
1478250654997_3
to complete the quantization.

[Classification example of partial discharge pattern shape]
Next, an example of classification of partial discharge pattern shapes will be described.
<Classification example 1>
7A and 7B are diagrams showing classification example 1 of the partial discharge pattern shape, FIG. 7A is a diagram showing the partial discharge pattern shape, and FIG. 7B is a diagram showing determination of the partial discharge pattern shape. The hatched portion in FIG. 7(a) schematically shows the points where the measured charge amounts are plotted. In practice, noise points are removed (filtering) from the plotted points of the measured charge amount, and the distribution pattern of the charge amount is drawn (contour processing). A partial discharge signal distribution of the drawn charge amount is called a partial discharge pattern shape.
Assume that the parameters indicating the partial discharge pattern shape shown in FIG. 7(a) are acquired by the <PD signal to PRPD pattern transformation> processing shown in FIG. 6(b).
Next, by the < _Extraction of parameters indicating the shape ^{of PRPD} _pattern > process shown in FIG. Calculate and compare the maximum positive and negative values. Here, as shown in FIG. 7(a), Q ^(p) _max >Q ⁽ⁿ⁾ _max .

Next, by the same <Extraction of parameters indicating the shape of PRPD pattern> process, a plurality of points indicating the outer shape of the partial discharge pattern shape are extracted for the positive partial discharge pattern shape having a large absolute value of the maximum value. Specifically, as shown in FIG. 6C, the point p3 extracted with the positive maximum value Q ^{(p) max} _and 1/2 of Q (p) _max × Q ^(p) _max (that is, Q ^(p) _half ) extracted points p1-p5. In addition, points p2-p4 extracted by 3/4×Q ^(p) _max , which is half (1/2) of the above 1/2×Q ^(p) _max on the convex side, may be used. .

As shown ⁱⁿ FIG. ^{7B ,} the _{partial discharge} pattern shape shown in FIG ^. p ^(p) 5−p ^(p) 3. From the partial discharge pattern shape determination table 200 (see FIG. 5), it can be determined that the partial discharge pattern shape shown in FIG. 7A is Delamination between conductor and insulation (1-3). It can be determined that the partial discharge corresponds to the slot partial discharge (2) shown in FIG. 4(d).

<Classification example 2>
8A and 8B are diagrams showing classification example 2 of the partial discharge pattern shape, FIG. 8A is a diagram showing the partial discharge pattern shape, and FIG. 8B is a diagram showing determination of the partial discharge pattern shape.
Assume that the parameters indicating the partial discharge pattern shape shown in FIG. 8(a) are obtained by the <PD signal to PRPD pattern transformation> processing shown in FIG. 6(b).
By the <Extraction of parameters indicating the shape of PRPD pattern> process shown in FIG. 6(c), the maximum positive value Q ^(p) _max and the maximum negative value Q ⁽ⁿ⁾ _max of the partial discharge pattern shape are calculated, Compare maximum positive and negative values. Here, as shown in FIG. 8(a), Q ^(p) _max <Q ⁽ⁿ⁾ _max .

Then, by the <Extraction of parameters indicating the shape of PRPD pattern> process, a plurality of points indicating the outline of the partial discharge pattern shape are extracted for the negative partial discharge pattern shape having a large absolute value of the maximum value. Specifically, the point _p3 extracted at the negative _maximum value Q ^{(n) max and} _the ^point _p1− Use p5 and .
As shown ⁱⁿ FIG. ^{8B ,} the ^partial discharge pattern shape shown in FIG. p ⁽ⁿ⁾ 5−p ⁽ⁿ⁾ 3. From the partial discharge pattern shape determination table 200 (see FIG. 5), it can be determined that the partial discharge pattern shape shown in FIG. 8A is Slot partial discharges (1-3). It can be determined that the partial discharge corresponds to the conductor-insulator interlayer discharge (1-3) shown in FIG. 4(c).
In this way, external discharge and internal discharge are distinguished by comparing the magnitude of the maximum values of the extracted polarities, and whether the distribution is steep or not can be determined whether the partial discharge is void or partial discharge due to exfoliation. It is possible to determine whether

[Insulation deterioration diagnosis processing flow]
FIG. 9 is a flowchart showing insulation deterioration diagnosis processing. This flow is executed by the data processing device 110 (see FIG. 2) of the rotating machine diagnostic system 100 .
When the insulation deterioration diagnosis is started, in step S101, the partial discharge signal distribution acquisition unit 111 detects current signals caused by partial discharge detected by the current sensors 106 and 107 (see FIG. 1) attached to the rotating machine 102 and the voltage sensor 108. A partial discharge signal distribution is obtained by synchronizing with the voltage signal detected from the .
In step S102, the partial discharge signal distribution acquisition unit 111 determines whether or not it is a partial discharge signal. Since the partial discharge of the rotating machine 102 is buried in the noise accompanying the operation, the partial discharge signal is distinguished from the noise, and only the partial discharge signal is detected.
If there is a partial discharge signal (step S102: Yes), the process proceeds to step S103, and if there is no partial discharge signal (step S102: No), the process returns to step S101.

In step S103, the maximum value extraction processing unit 112 extracts the positive and negative maximum values of the charge amount (positive and negative maximum values) based on the acquired partial discharge signal distribution.
In step S104, the voltage phase extraction processing unit 113 extracts the voltage phase of the maximum value of at least one of the polarities of the extracted maximum values, and the obtained partial discharge signal before and after the phase direction centering on the maximum value. Extract the voltage phase based on the distribution.
In step S105, the partial discharge mechanism determination unit 114 determines the magnitude of the maximum value of both positive and negative polarities, and the width from the voltage phase before and after the maximum value to the voltage phase of the maximum value, which is shifted in the phase direction around the maximum value. to determine the shape of the partial discharge pattern.

In step S106, the partial discharge mechanism determination unit 114 compares the determined partial discharge pattern shape with the partial discharge pattern shape of the partial discharge pattern shape determination table 200, and the deterioration abnormality degree increases. determine whether or not
When the degree of deterioration abnormality has increased (step S106: Yes), the process proceeds to step S107, and when the degree of deterioration abnormality has not increased (step S106: No), the process returns to step S101.
In step S107, the partial discharge mechanism determination unit 114 calculates the magnitude relationship of the shape parameters from the determined partial discharge pattern shape. By calculating the size relationship of the shape parameters, it is possible to select a threshold value for determination according to the partial discharge mechanism.
In step S108, the partial discharge mechanism determination unit 114 determines the deteriorated portion by determination according to the partial discharge mechanism.
In step S109, the partial discharge mechanism determination unit 114 determines whether emergency measures are necessary.
If emergency measures are required (step S109: Yes), the process proceeds to step S110, and if emergency measures are not required (step S109: No), the process returns to step S101.
In step S110, the partial discharge mechanism determination unit 114 plans maintenance according to the deteriorated portion, and terminates the insulation deterioration diagnosis processing according to this flow.

[Application example]
FIG. 10 is a diagram showing an application example of partial discharge mechanism determination.
<Application example 1>
Assume that the parameters of the charge amount distribution (PRPD pattern) shown in FIG. 10A are acquired.
The maximum positive value Q ^(p) _max and the maximum negative value Q ⁽ⁿ⁾ _max of the partial discharge pattern shape are calculated, and the maximum positive and negative values are compared. Then, for a positive partial discharge pattern shape having a large absolute value of the maximum value, points indicating the outline of the partial discharge pattern shape are extracted. In this case, the point p3 extracted with the positive maximum value Q ^{(p) max} _and the points p1-p5 extracted with 1/2 of Q(p) _max ×Q ^(p) _max are used.
As shown in FIG. 10(a), with respect to the point p3 extracted with the positive maximum value Q ^(p) _max , which is the maximum charge amount, the width of the voltage phase of the point p1-p3 on the left side is It can be seen that it is smaller than the voltage phase width of p3-p5. Therefore, it can be seen that the voltage phase width of the left point p1-p3 shown in FIG. 10(a) is smaller than the voltage phase width of the right point p3-p5.

For convenience of explanation, the above <application example 1> is based on the point p3 extracted with the positive maximum value Q ^{(p) max} _and the points p1-p5 extracted with 1/2 of Q (p) _max ×Q ^(p) _max . , that is, an example comparing the width of the voltage phase between the points p1-p3 and the width of the voltage phase between the points p3-p5. As a result, the partial discharge pattern shape shown in FIG. 10(a) was determined to be a slot partial discharge. However, slot partial discharge is not the only partial discharge pattern shape in which the width of the voltage phase of the points p1-p3 on the left is smaller than the width of the voltage phase of the points p3-p5 on the right. For example, in the case of the corona discharge shown in FIG. 4(e), the voltage phase width of the points p1-p3 on the left is smaller than the voltage phase width of the points p3-p5 on the right. That is, it cannot be immediately determined that the discharge is the slot discharge only from the width of the voltage phase on the left side and the width of the voltage phase on the right side of the maximum charge amount.

<Application example 2>
10B and 10C are diagrams showing an application example 2 of the partial discharge mechanism determination. FIG. 10(b) is the same as the charge amount distribution (PRPD pattern) shown in FIG. 10(a), and FIG. 10(a) is repeated for explanation. FIG. 10(c) shows parameters of another obtained charge amount distribution (PRPD pattern).
In this embodiment, as shown in FIGS. 10B and 10C, the point p3 extracted with the positive maximum value Q ^{(p) max} _and the point p3 extracted with the positive _maximum value Q (p ⁾ _max In addition to the extracted points p1-p5, points p2-p4 extracted by 3/4×Q ^{(p) max ,} _which is 1/2 of Q (p) _max ×Q (p) _max , are used. . Then, in addition to comparing the voltage phase width of points p1-p3 and the voltage phase width of points p3-p5, the voltage phase width of points p1-p2 and the voltage phase width of points p2-p3, and further point p3 The width of the voltage phase at -p4 and the width of the voltage phase at points p4-p5 are also compared. As a result, the partial discharge pattern shape shown in FIG. It can be determined that it is larger than the width of the voltage phase of p1-p2, that is, it is outwardly convex on the left side of the maximum charge amount. If the width of the voltage phase on the left side of the maximum amount of charge is smaller than the width of the voltage phase on the right side, and the voltage phase on the left side has an outwardly convex shape, then from the partial discharge pattern shape determination table 200 (see FIG. 5), It can be determined that it is the slot partial discharge shown in FIG. 4(d).

On the other hand, in the case of the partial discharge pattern shape shown in FIG. It can be determined that there is If the width of the voltage phase on the left side of the maximum amount of charge is smaller than the width of the voltage phase on the right side, and if the voltage phase on the left side has a concave shape, then from the partial discharge pattern shape determination table 200 (see FIG. 5), It can be determined that it is the corona discharge shown in FIG. 4(e).

In this way, the partial discharge mechanism can be accurately discriminated by using the voltage phase at 3/4 of the maximum value in addition to the voltage phase at half the maximum value of the partial discharge signal. becomes possible. Here, if the partial discharge signal distribution is obtained in an environment without noise, the voltage phase when the signal value is small can be used. By increasing the number of voltage phases to be extracted, the partial discharge pattern shape can be accurately grasped, and the partial discharge mechanism can be determined with high accuracy.

<Application example 3>
In <Application example 1> and <Application example 2>, the maximum value of the partial discharge charge amount was compared, and the partial discharge pattern shape was determined for the signal distribution of one polarity of the positive and negative maximum values. The shape of the polar signal distribution may be used for discrimination. A case in which the shape of the signal distribution of both polarities is used for discrimination will be described below in <application example 3>.
FIG. 11 is a diagram illustrating an application example 3 of the partial discharge mechanism determination. In FIG. 11, the vertical axis indicates the phase width on the left side of the maximum charge amount, and the horizontal axis indicates the phase width on the right side of the maximum charge amount.
By applying machine learning, it is possible to discriminate changes in the shape of the charge amount distribution due to an increase in the maximum amount of discharge (see the solid line in FIG. 11) and changes in the shape of the charge amount distribution due to changes in the discharge mechanism (see the solid line in FIG. 11). can do.

[Example]
12 to 18 are diagrams for explaining the embodiment. In FIGS. 12 to 18, (a) is the maximum positive or negative charge amount extracted as digital data from the partial discharge pattern of (b), the width of the voltage phase on the left side of this maximum charge amount, and the voltage phase on the right side. (b) is a diagram showing the shape of the partial discharge pattern, and (c) is a diagram showing the determination of the partial discharge pattern.

FIG. 12 is a diagram showing an example of determination of (1-1) Internal void.
From the partial discharge pattern of FIG. 12(b), the parameters of the partial discharge pattern shape shown in FIG. 12(a) were obtained.
As shown in FIG. 12 ^{( c )} , the _{partial discharge} pattern shape shown in FIG ^. p ^(p) 5−p ^(p) 3. From the partial discharge pattern shape determination table 200 (see FIG. 5), it was determined that the partial discharge pattern shape shown in FIG. 12(b) is internal void (1-1).

FIG. 13 is a diagram showing a determination example of (1-2) Internal delamination.
From the partial discharge pattern of FIG. 13(b), parameters of the shape of the partial discharge pattern shown in FIG. 13(a) were obtained.
As shown in FIG. 13 ^{( c )} , the _{partial discharge} pattern shape shown in FIG ^. p ^(p) 5−p ^(p) 3. From the partial discharge pattern shape determination table 200 (see FIG. 5), it was determined that the partial discharge pattern shape shown in FIG. 13B is Internal delamination (1-2).

FIG. 14 is a diagram showing a determination example of (1-3) Delamination between conductor and insulation: (discharge between conductor and insulation layer).
From the partial discharge pattern of FIG. 14(b), parameters of the shape of the partial discharge pattern shown in FIG. 14(a) were obtained.
As shown in FIG. ₁₄ ^{( c )} , the _partial discharge pattern shape shown in FIG ^. p ^(p) 5−p ^(p) 3. From the partial discharge pattern shape determination table 200 (see FIG. 5), the partial discharge pattern shape shown in FIG. 1-3).

FIG. 15 is a diagram showing an example of determination of (2) Slot partial discharges.
From the partial discharge pattern of FIG. 15(b), the parameters of the partial discharge pattern shape shown in FIG. 15(a) were obtained.
As shown in FIG. 15 ⁽ _c ⁾ , the _partial ^discharge pattern shape shown in FIG ^. p ^(p) 5−p ^(p) 3. From the partial discharge pattern shape determination table 200 (see FIG. 5), it was determined that the partial discharge pattern shape shown in FIG. 15B is Slot partial discharges (2).

FIG. 16 is a diagram showing a determination example of (3-1) Corona activity at the S/C and stress grading coating (corona discharge).
From the partial discharge pattern of FIG. 16(b), parameters of the shape of the partial discharge pattern shown in FIG. 16(a) were obtained.
As shown ⁱⁿ FIG. ^{16C ,} the _{partial discharge} pattern shape shown in FIG ^. p ^(p) 5−p ^(p) 3. From the partial discharge pattern shape determination table 200 (see FIG. 5), the partial discharge pattern shape shown in FIG. 16(b) is Corona activity at the S/C and stress grading coating (corona discharge) (3-1). Judged.

FIG. 17 is a diagram showing an example of determination of (3-2) Surface discharges tracking.
From the partial discharge pattern of FIG. 17(b), parameters of the shape of the partial discharge pattern shown in FIG. 17(a) were obtained.
As shown ⁱⁿ FIG. ^{17C ,} the _{partial discharge} pattern shape shown in FIG ^. p ^(p) 5−p ^(p) 3. From the partial discharge pattern shape determination table 200 (see FIG. 5), it was determined that the partial discharge pattern shape shown in FIG. 17(b) is Surface discharges tracking (creeping discharge) (3-2).

FIG. 18 is a diagram showing an example of determination of (3-3) Gap type discharges.
From the partial discharge pattern of FIG. 18(b), parameters of the shape of the partial discharge pattern shown in FIG. 18(a) were obtained.
As shown in FIG. 18 ^{( c )} , the _{partial discharge} pattern shape shown in FIG ^. p ^(p) 5−p ^(p) 3. From the partial discharge pattern shape determination table 200 (see FIG. 5), it was determined that the partial discharge pattern shape shown in FIG. 18B is gap type discharges (3-3).

As described above, the rotating machine diagnostic system 100 (see FIG. 1) of the present embodiment includes the partial discharge signal distribution acquiring unit 111 that acquires the partial discharge signal distribution with respect to the applied voltage phase of the rotating machine 102, and the acquired partial discharge Based on the signal distribution, the maximum value extraction processing unit 112 extracts the positive and negative maximum values of the charge amount, the voltage phase of the maximum value of at least one of the polarities of the extracted maximum values, and a predetermined ratio of the maximum value A voltage phase extraction processing unit 113 that extracts the voltage phase based on the acquired partial discharge signal distribution in the phase direction around the maximum value in the case of and a partial discharge mechanism determination unit 114 that determines the shape of the partial discharge pattern based on the width from the voltage phase of the maximum value to the voltage phase of the maximum value.

Further, the rotating machine diagnostic method of the present embodiment includes the step of acquiring the partial discharge signal distribution with respect to the applied voltage phase of the rotating machine 102, and extracting the positive and negative maximum values of the charge amount based on the acquired partial discharge signal distribution. extracting a voltage phase of a maximum value of at least one of the polarities among the extracted maximum values, and a voltage phase based on the obtained partial discharge signal distribution before and after the phase direction centering on the maximum value; and determining the shape of the partial discharge pattern based on the magnitude of the positive/negative maximum value and the width from the preceding and following voltage phases to the voltage phase of the maximum value.

In this way, by acquiring the partial discharge pattern shape as a parameter, the type of partial discharge can be automatically classified. In addition, since the pattern shape is extracted in a form suitable for machine learning, it becomes possible to detect changes in the state of insulation deterioration that cannot be detected only by threshold determination of the partial discharge signal.

In this embodiment, the front and rear voltage phases are voltage phases based on the partial discharge signal distribution of values when the maximum values are 1/2 and 3/4. By using the voltage phase at 3/4 of the maximum value in addition to the voltage phase at half the maximum value of the partial discharge signal, the partial discharge can be accurately detected by increasing the voltage phases to be extracted. The pattern shape can be grasped, and the partial discharge mechanism can be determined with high accuracy. Note that if the partial discharge signal distribution is obtained in an environment without noise, the voltage phase at half the value can be used.

In this embodiment, the voltage phase extraction processing unit 113 extracts the voltage phases of the extracted positive and negative maximum values, and the voltage phases before and after the phase directions around the positive and negative maximum values based on the acquired partial discharge signal distribution. , and the partial discharge mechanism determination unit 114 determines the shape of the partial discharge pattern based on the magnitude of each of the positive and negative maximum values and the width from the preceding and following voltage phases to the voltage phase of the maximum value. As a result, although the number of voltage phases to be extracted increases by using both polarities, the partial discharge pattern can be determined more accurately.

In this embodiment, the partial discharge pattern shape determination table 200 classified by the magnitude of the maximum value of both positive and negative polarities and the width from the voltage phase before and after to the voltage phase of the maximum value is stored, and the partial discharge mechanism determination unit 114 judges the partial discharge pattern shape by referring to the partial discharge pattern shape determination table 200 based on the magnitude of the obtained positive and negative maximum values and the width from the voltage phase before and after the voltage phase to the maximum value. . Thereby, the shape of the partial discharge pattern can be determined according to the partial discharge pattern shape determination table 200 . By comparing the magnitude of the extracted polarities, external discharge and internal discharge can be discriminated, and by checking whether the distribution has a steep shape, it is possible to discriminate whether the partial discharge is void or delamination. can. By accumulating the partial discharge pattern shape determination table 200 as a database, it becomes possible to add knowledge obtained by diagnosing the actual machine.

In this embodiment, the partial discharge mechanism determination unit 114 compares the determined partial discharge pattern shape with the partial discharge pattern shape in the partial discharge pattern shape determination table 200, and determines whether the rotating machine is normal or not based on the appropriateness. do. As a result, the insulation deterioration diagnosis can be accurately determined.

In this embodiment, the partial discharge mechanism determination unit 114 determines the partial discharge mechanism indicating the partial discharge occurrence site based on the determined partial discharge pattern shape, thereby obtaining detailed information on the discharge occurrence site specific to the partial discharge mechanism. discrimination becomes possible. Also, in the case where the maximum value extraction processing unit 112 to the partial discharge mechanism determination unit 114 are configured in one circuit, it is possible to input seven typical partial discharge patterns and determine them. can confirm that it is working properly.

In this embodiment, the partial discharge mechanism determination unit 114 compares the acquired local maximum value with a threshold value determined for each classified partial discharge pattern shape, and diagnoses the insulation deterioration of the rotating machine, thereby determining the partial discharge mechanism. can be used, and the insulation deterioration diagnosis can be accurately determined.

In this embodiment, the partial discharge mechanism determination unit 114 acquires parameters from the determined partial discharge pattern shape, applies machine learning including VQC to the parameters, and detects the insulation deterioration state of the rotating machine and changes in the state. By doing so, by applying machine learning to the parameters representing the shape of the partial discharge pattern, it is possible to detect minute changes in the partial discharge pattern. In addition, since it becomes possible to detect signs of insulation deterioration failure by machine learning, it is possible to determine progress of insulation deterioration that cannot be clarified by threshold determination.

In this embodiment, the pass frequency band filter 300 that separates the partial discharge signal is provided, and the partial discharge signal distribution acquisition unit 111 acquires the partial discharge signal distribution for each pass frequency band, thereby obtaining a partial discharge pattern for each frequency band. , and the partial discharge mechanism determination is performed for each of them, it becomes possible to determine even when partial discharge is caused by multiple mechanisms (insulation deterioration at different occurrence sites). Further, it becomes possible to detect the progress of deterioration from the change in pattern shape.

Each of the embodiments described above has been described in detail in order to explain the present invention in an easy-to-understand manner, and is not necessarily limited to those having all the configurations described. In addition, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. . Moreover, it is possible to add, delete, or replace a part of the configuration of each embodiment with another configuration.

1 vacuum tube unit 100 rotating machine diagnostic system 101 power supply 102 rotating machine 103a, 103b, 103c feeding line 104 lead wire 105 coupling capacitor 106, 107 current sensor 108 voltage sensor 110 data processor 111 partial discharge signal distribution acquisition unit 112 maximum value extraction processing Unit 113 Voltage phase extraction processing unit 114 Partial discharge mechanism determination unit 200 Partial discharge pattern shape determination table (storage unit)

Claims (9)

a partial discharge signal distribution acquisition unit that acquires a partial discharge signal distribution with respect to the applied voltage phase of the rotating machine;
a maximum value extraction processing unit that extracts positive and negative maximum values of the amount of charge based on the acquired partial discharge signal distribution;
Extracting the voltage phase of the maximum value of at least one of the polarities among the extracted maximum values, and the voltage phase based on the acquired partial discharge signal distribution in the phase direction around the voltage phase of the maximum value. a voltage phase extraction processing unit;
A partial discharge pattern shape that appears in a scatter diagram of charge amount-voltage phase on a two-dimensional plane based on the magnitude of the maximum value of both positive and negative polarities and the width from the preceding and following voltage phases to the voltage phase of the maximum value. and a partial discharge mechanism determination unit that determines
The voltage phase extraction processing unit is
A rotary machine diagnostic system, wherein a voltage phase corresponding to a charge amount of a predetermined ratio of the maximum value in a partial discharge signal distribution is extracted as a voltage phase before and after the partial discharge signal distribution. The voltage phase extraction processing unit is
Extracting the voltage phase of the voltage phase of the extracted positive and negative maximum values and the voltage phase based on the acquired partial discharge signal distribution in the phase direction around the positive and negative maximum values,
The partial discharge mechanism determination unit
2. The rotating machine according to claim 1, wherein the shape of the partial discharge pattern is determined based on the magnitude of each of the positive and negative maximum values and the width from the preceding and following voltage phases to the voltage phase of the maximum value. diagnostic system. a storage unit that stores a partial discharge pattern shape determination table classified by the magnitude of the maximum value of both positive and negative polarities and the width from the voltage phase before and after the voltage phase to the voltage phase of the maximum value;
The partial discharge mechanism determination unit
The partial discharge pattern shape is determined by referring to the partial discharge pattern shape determination table based on the magnitude of the obtained positive and negative maximum values and the width from the voltage phase before and after the voltage phase to the voltage phase of the maximum value. 2. The rotating machine diagnostic system according to claim 1, characterized in that it determines. The partial discharge mechanism determination unit
4. The method according to claim 3, wherein the determined partial discharge pattern shape and the partial discharge pattern shape in the partial discharge pattern shape determination table are collated, and whether or not the rotating machine is normal is determined based on the suitability of the partial discharge pattern shape. Rotating machine diagnostic system. The partial discharge mechanism determination unit
2. The rotating machine diagnostic system according to claim 1, wherein a partial discharge mechanism indicating a partial discharge generation site is determined based on the determined partial discharge pattern shape. The partial discharge mechanism determination unit
2. The rotating machine diagnosis system according to claim 1, wherein the acquired maximum value is compared with a threshold value determined for each of the classified partial discharge pattern shapes, and insulation deterioration diagnosis of the rotating machine is performed. The partial discharge mechanism determination unit
A parameter is obtained from the determined partial discharge pattern shape, and machine learning including VQC (Vector Quantization Clustering) is applied to the parameter to detect an insulation deterioration state of the rotating machine and a change in that state. The rotating machine diagnostic system according to . with a passband filter for isolating the partial discharge signal;
2. The rotating machine diagnostic system according to claim 1, wherein the partial discharge signal distribution acquiring unit acquires the partial discharge signal distribution for each pass frequency band. obtaining a partial discharge signal distribution with respect to the applied voltage phase of the rotating machine;
a step of extracting positive and negative maximum values of the amount of electric charge based on the acquired partial discharge signal distribution;
Extracting the voltage phase of the maximum value of at least one of the polarities among the extracted maximum values, and the voltage phase based on the acquired partial discharge signal distribution in the phase direction around the voltage phase of the maximum value. a process ,
a step of extracting a voltage phase corresponding to a charge amount of a predetermined ratio of the maximum value in the partial discharge signal distribution as preceding and succeeding voltage phases;
A partial discharge pattern shape that appears in a scatter diagram of charge amount-voltage phase on a two-dimensional plane based on the magnitude of the maximum value of both positive and negative polarities and the width from the preceding and following voltage phases to the voltage phase of the maximum value. A rotating machine diagnostic method, comprising: a step of determining

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