JP7060432B2 - Diagnostic support device, rotary machine system and diagnostic support method - Google Patents

Description

The present invention relates to a technique of a diagnostic support device, a rotary machine system, and a diagnostic support method.

If a rotating machine such as a motor (motor) built into production equipment suddenly breaks down, unplanned repair work or replacement work is required. As a result, it is necessary to reduce the operating rate of production equipment and review the production plan. Then, by examining the signs of failure, it is possible to prepare replacement parts in advance for the device that is likely to fail, or to plan a repair schedule. In this way, it is possible to minimize the decrease in the operating rate of production equipment and the revision of production plans.

As a method for preventing a sudden failure of the rotating machine and its ancillary equipment in the rotating machine system, there are the following methods and the like. The rotary machine and its ancillary equipment are an inverter, a gear, a coupling, a load device, and the like.
(A1) A vibration sensor is attached to each part of the rotating machine system, and an increase in the effective vibration value is investigated.
(A2) An increase in the characteristic frequency component of vibration is investigated in each part of the rotating machine system.
By executing the methods (a1) and (a2), it is possible to diagnose the abnormal state of each part and prevent sudden failure to some extent.

However, the methods (a1) and (a2) described above have the following problems.
(B1) The price of the vibration sensor is high.
(B2) Sensitivity largely depends on the mounting position of the vibration sensor.
(B3) It is necessary to attach a vibration sensor to each part where an abnormality may occur.

Therefore, a method has been proposed in which the number of sensors installed is reduced by using an inexpensive current sensor instead of using a vibration sensor, and robust diagnosis is possible regardless of the installer. In this method, the current waveform acquired from the rotating machine system is Fourier transformed, and the component of the frequency characteristic for the anomaly (abnormal characteristic frequency) is extracted from the result of the Fourier transform. Then, the diagnosis is made based on the amplitude of the component of the abnormal characteristic frequency. MCSA (Motor Current Signature Analysis) is known as one of such methods.

It is known that the component of the anomalous characteristic frequency is expressed by the following equation (1) in the case of a mechanical anomaly.

fc = | f0 ± fm | ... (1)

In equation (1), fc is the anomalous characteristic frequency of the current, f0 is the fundamental wave frequency of the current, and fm is the frequency of mechanical vibration. With this formula, it is possible to diagnose deterioration of bearings, damage to gears and couplings, abnormalities in load devices, and the like. For example, in the case of a scratch on the inner ring of a bearing, it is known that periodic vibration occurs depending on the size of the bearing, the number of bearing balls, and the like, and the vibration caused by the vibration appears as a sideband wave of the fundamental wave frequency of the current.

However, it is still necessary to install an additional current sensor, and it is considered to utilize the internal value for control of the inverter connected to the motor for diagnosis.

According to Patent Document 1, "The rotation abnormality detection unit 67 calculates a value corresponding to the input power of the motor 60 based on the actually detected value during the position estimation operation, and the rotation speed / angle estimation unit 66 uses the rotation speed / angle estimation unit 66. A value corresponding to the output power of the motor 60 is calculated based on the estimated rotation speed ωest, a rotation abnormality index corresponding to these comparison results is obtained, and if this rotation abnormality index is larger than the threshold value, it is determined to be rotation abnormality. A rotating machine control device and a washing machine are disclosed (see summary).

Japanese Unexamined Patent Publication No. 2009-65764

However, the technique described in Patent Document 1 has the following problems. That is, in the technique disclosed in Patent Document 1, the abnormality of the entire motor system is diagnosed by the ratio of the estimated value of the input power and the output power. With this method, it is not possible to easily identify the abnormal site. If the abnormal part is not known, it is difficult to identify the parts to be prepared for replacement in advance. In addition, it becomes difficult to schedule repairs that need to be planned according to the abnormal part.

The present invention has been made in view of such a background, and an object of the present invention is to facilitate identification of an abnormal portion in a rotating machine and ancillary equipment.

In order to solve the above-mentioned problems, the present invention calculates the amplitude of each frequency related to the q-axis current from the acquisition unit that acquires the q-axis current of the power conversion device that controls the rotary machine and the acquired q-axis current. Information on the frequency at which the amplitude increases when an abnormality occurs in at least one of the amplitude calculation unit, the output unit that outputs information on the amplitude for each frequency, the rotating machine, and the auxiliary equipment of the rotating machine. By comparing the storage unit having the abnormal feature frequency information stored for each part, the amplitude for each frequency calculated by the amplitude calculation unit, and the abnormal feature frequency information, the rotating machine and the incidental device are used. It is characterized by having an abnormal site specifying portion for identifying a site where an abnormality occurs in at least one of the above.
Other solutions will be described later in the embodiment.

According to the present invention, it is possible to easily identify an abnormal portion in a rotating machine and ancillary equipment.

It is a figure which shows the structural example of the rotary machine system 100 which concerns on 1st Embodiment. It is a figure which shows the example (normal state) of the frequency spectrum of the q-axis current. It is a figure which shows the example (at the time of abnormality occurrence) of the frequency spectrum of the q-axis current. It is a figure which shows the example (normal state) of the vibration frequency spectrum of a rotary machine 3. It is a figure which shows the example (at the time of abnormality occurrence) of the vibration frequency spectrum of a rotary machine 3. It is a figure which shows the example (normal state) of the frequency spectrum of the load current flowing through each phase of a rotary machine 3. An example (when an abnormality occurs) of the frequency spectrum of the load current flowing in each phase of the rotary machine 3 is shown. It is a figure which shows the structural example of the rotary machine system 100a which concerns on 2nd Embodiment. It is a figure explaining the definition of an abnormality degree. It is a figure explaining the abnormality determination based on the abnormality degree. It is a figure which shows the structural example of the rotary machine system 100b which concerns on 3rd Embodiment. It is a figure which shows the structural example of the rotary machine system 100c which concerns on 4th Embodiment. It is a figure which shows the structural example of the rotary machine system 100d which concerns on 5th Embodiment. It is a figure which shows the structural example of the rotary machine system 100e which concerns on 6th Embodiment. It is a figure which shows the structural example of the rotary machine system 100f which concerns on 7th Embodiment. It is a figure which shows the hardware configuration of the diagnostic support apparatus 1, 1a to 1d which concerns on 1st to 5th Embodiment. It is a figure which shows the structural example of the rotary machine system 100g in the comparative example (Patent Document 1).

Hereinafter, embodiments for carrying out the present invention (referred to as “embodiments”) will be described with reference to the drawings as appropriate. Further, what is described below is merely an example of the embodiment, and the scope of the present invention is not intended to be limited to the following embodiment.

[Comparison example]
First, a comparative example with respect to this embodiment is shown.
FIG. 14 is a diagram showing a configuration example of the rotary machine system 100 g in the comparative example (Patent Document 1).
Among the abnormalities that occur in the rotating machine 3 such as an electric motor and the ancillary equipment of the rotating machine 3, there are various factors such as where the abnormality occurs and the factors thereof. For example, deterioration of the rotary machine bearing, chipping of gears, deterioration of the bearing of the load device 4, foreign matter biting of the load device 4, scratches on the inner ring of the rotary machine bearing, and the like can be considered as causes of abnormality. The auxiliary equipment of the rotary machine 3 is a power conversion device 2, a gear, a coupling, a load device 4, and the like as described above.

As shown in FIG. 14, in the rotating machine system 100g, the power conversion device 2 and the rotating machine 3 are electrically connected to each other. Further, the rotary machine 3 and the load device 4 are mechanically connected. As a mechanical connection method between the rotary machine 3 and the load device 4, a method of directly connecting the rotating shaft, a method of connecting via a gear, and the like can be considered.
Further, a diagnostic support device 1g for diagnosing an abnormality in the rotary machine 3 and its ancillary equipment by acquiring an internal value from the power conversion device 2 is installed.

In the conventional diagnostic support device 1g shown in FIG. 14, first, the power ratio estimation unit 17 estimates the ratio of the input power and the output power of the rotating machine 3 from the internal value of the power conversion device 2. Then, the abnormality determination unit 18 determines an abnormality of the rotary machine system 100 g from the estimated change in the power ratio.

However, it is not possible to estimate the location where deterioration has progressed only from the ratio of the input power to the output power of the rotary machine 3. Therefore, there is a problem that it is difficult to specify the parts to be prepared for replacement in advance, and it is necessary to plan the repair schedule according to the deteriorated part.

The present embodiment solves such a problem.

[First Embodiment]
FIG. 1 is a diagram showing a configuration example of the rotary machine system 100 according to the first embodiment.
In FIG. 1, the same components as those in FIG. 14 are designated by the same reference numerals, and the description thereof will be omitted.
Here, the inventors have found that, among the internal values of the power conversion device 2, vibration of a specific frequency component is generated in the q-axis current component according to an abnormal portion due to mechanical deterioration of the rotary machine system 100 or the like. Clarified. Furthermore, the inventors have found that the state can be tenderly monitored for each abnormal site by changing the specific frequency component of the q-axis current with time.

As shown in FIG. 1, the diagnostic support device 1 according to the first embodiment is characterized by having an amplitude calculation unit 11 and a display unit 12.
The amplitude calculation unit 11 acquires the q-axis current, which is one of the internal values of the power conversion device 2, from the power conversion device 2. Here, the q-axis current is a time change of the q-axis current value. Then, the amplitude calculation unit 11 converts the acquired q-axis current into the amplitude for each frequency. That is, the amplitude calculation unit 11 Fourier transforms the input q-axis current.
The display unit 12 displays the calculation result of the amplitude calculation unit 11. That is, the display unit 12 visualizes the time-dependent change of the frequency spectrum calculated by the amplitude calculation unit 11 and presents the user with an abnormal state.

The user diagnoses the state of the rotary machine system 100 based on the calculation result of the amplitude calculation unit 11 displayed on the display unit 12, that is, the frequency spectrum of the q-axis current.

(Example of frequency spectrum)
Hereinafter, as an example of the diagnostic method using the diagnostic support device 1 of the first embodiment, the function of the diagnostic support device 1 will be described by describing a method of diagnosing a state in which the inner ring of the rotary machine bearing is damaged.

2A and 2B show an example of the frequency spectrum of the q-axis current. Further, FIGS. 3A and 3B show an example of the vibration frequency spectrum of the rotating machine 3. 4A and 4B show an example of the frequency spectrum of the load current flowing in each phase of the rotating machine 3.
Here, the bearing rotation speed is controlled to be 164.32 rad / s (26.17 Hz). Further, for the q-axis current, the internal value of the microcomputer for controlling the power conversion device 2 is output in a text format by modifying the power conversion device 2. For the power conversion device 2 having a q-axis current log output function, the log output function may be used, or a new output function may be added for diagnosis.

Here, FIGS. 2A, 3A, and 4A show frequency spectra in a normal state before scratches occur. Further, FIGS. 2B, 3B, and 4B show frequency spectra in a state after the occurrence of scratches (at the time of occurrence of an abnormality). The fundamental wave frequency f0 of the input current of the rotary machine 3 is 27.77 Hz. Then, as shown in FIG. 2B, the primary component of the characteristic frequency fq of the q-axis current caused by the abnormality is 149.00 Hz. Further, as shown in FIG. 3B, the primary component of the characteristic frequency fv of the vibration caused by the abnormality is 149.00 Hz. Further, as shown in FIG. 4B, the primary component of the characteristic frequency fc of the load current is 121.20 Hz or 176.80 Hz. In each of FIGS. 2B, 3B, and 4B, the characteristic frequency (abnormal characteristic frequency) caused by the abnormality is shown by a vertical line. Further, in each of FIGS. 2B, 3B, and 4B, the primary component of the abnormal characteristic frequency is indicated by an upward arrow.

Here, the frequency spectra shown in FIGS. 2A to 4B compare states driven under the same control conditions. From the viewpoint of diagnostic accuracy, when the degree of abnormality is small, there is a high possibility that a change in control conditions will be erroneously detected as an abnormality, so it is desirable to take out a section driven under the same control conditions for diagnosis.

As is clear from the comparison of FIGS. 2A and 3A, FIGS. 2B and 3B, the change in the frequency spectrum of the q-axis current due to the occurrence of scratches reflects the frequency spectrum of the vibration of the rotating machine 3. That is, when FIG. 2B and FIG. 3B are compared, the abnormal feature frequencies are the same.
On the other hand, as is clear from the comparison of FIGS. 3A and 4A, FIGS. 3B and 4B, the load current does not reflect the frequency spectrum of the vibration of the rotating machine 3. That is, when FIG. 3B and FIG. 4B are compared, the abnormal feature frequencies do not match.
From this, it can be seen that the q-axis current is suitable for abnormality analysis of the rotary machine 3 and incidental equipment.

As described above, according to the q-axis current, it can be confirmed that the characteristic frequency component increases due to the occurrence of scratches. The user can confirm that the abnormality of the rotary machine system 100 can be diagnosed by confirming the increase in the amplitude of the characteristic spectrum of the q-axis current. Furthermore, it was confirmed that the presence or absence of an abnormality and the position where the abnormality occurred (bearing in this case) can be detected by diagnosis focusing on a specific frequency.
In the conventional diagnostic support device 1g, the q-axis current is used as control information. In this embodiment, the q-axis current is used not only for controlling but also for identifying an abnormal portion.
In particular, according to the first embodiment, it is possible to present information for identifying an abnormal portion to the user without requiring an additional sensor such as a current sensor or a vibration sensor.

[Second Embodiment]
FIG. 5 is a diagram showing a configuration example of the rotary machine system 100a according to the second embodiment. In FIG. 5, the same components as those in FIG. 1 are designated by the same reference numerals as those in FIG. 1, and the description thereof will be omitted.
The diagnostic support device 1a of the rotary machine system 100a differs from the diagnostic support device 1 shown in FIG. 1 as follows.
(C1) An abnormal feature frequency storage unit 21 is provided in which a characteristic frequency (abnormal feature frequency; abnormal feature frequency information) of a mechanically abnormal portion of the rotating machine 3 and ancillary equipment is stored.
(C2) An abnormal part specifying unit 13 for specifying an abnormal part is provided by comparing the frequency spectrum output by the amplitude calculation unit 11 with the abnormal feature frequency stored in the abnormal feature frequency storage unit 21. ..
(C3) A notification unit 14 for notifying the user of the identified abnormal portion is provided.

In the first embodiment, an example in which the frequency spectrum is displayed on the display unit 12 and the user is left to judge whether or not there is an abnormality has been described. On the other hand, in the second embodiment, the abnormal feature frequency to be monitored in advance is set. Then, the abnormal portion specifying unit 13 diagnoses the state of the rotary machine system 100 from the amplitude change of the q-axis current at the set abnormal characteristic frequency in the frequency spectrum, and identifies the abnormal portion.

As the frequency to be input to the abnormal feature frequency, a frequency actually measured and obtained in advance may be used. Further, the machine vibration frequency obtained from the geometric shape of the machine part may be set.

Here, the abnormal characteristic frequency corresponding to the scratch on the bearing is represented by each equation shown in Table 1.
In Table 1, fr is the rotation frequency of the rotary machine bearing, _{n b} is the number of balls, bd is the ball diameter, pd is the pitch diameter, and β is the contact angle. Table 1 shows the primary components of each anomalous characteristic frequency.
The abnormal characteristic frequency input to the abnormal portion specifying unit 13 may be one (for example, a primary component). Further, a plurality of primary components of the frequencies of possible abnormal feature frequencies may be input. Alternatively, a higher-order component of the abnormal feature frequency may be input.

The abnormal characteristic frequency shown in Table 1 is an example, and the abnormal characteristic frequency relating to other parts may be input to the abnormal part identification unit 13.
It should be noted that not only the gradually progressing abnormality but also the frequency component related to the installation failure such as the misalignment of the shaft may be input to the abnormality portion specifying unit 13 as the abnormality feature frequency.

The abnormal portion specifying unit 13 identifies the abnormal portion by determining whether or not the amplitude of the input frequency corresponding to each abnormal characteristic frequency is large in the frequency spectrum.
The abnormality portion specifying unit 13 determines whether or not the amplitude of the frequency corresponding to the abnormality feature frequency is large by the following method.
(D1) Whether or not the amplitude exceeds a predetermined threshold value.
(D2) The ratio of the measured frequency amplitude to the pre-known normal amplitude.
(D3) Machine learning. That is, the abnormal / normal state may be determined by machine learning. Here, the machine learning algorithm is not particularly limited, and a method capable of clearly discriminating the difference between the normal state and the abnormal state may be adopted.
(D4) Judgment based on a predetermined index.

It is desirable that the means for notifying the user of the specific result can be appropriately selected.
Here, the following method can be considered as a notification method to the user.
(E1) Display by display.
(E2) Lighting of the lamp. At this time, it is preferable that the color of the lamp that is lit is different for each part.
(E3) Notification by email.

In addition, the following methods can be considered as the notification method.
(F1) Abnormal feature A method of transmitting a change in frequency amplitude to a user with a value obtained by adding some processing (machine learning or the like described above).
(F2) A method of notifying the user when a predetermined threshold value is exceeded.

(Discrimination by degree of abnormality)
Here, as an example of abnormality discrimination by machine learning, discrimination based on the degree of abnormality will be described.
FIG. 6 is a diagram illustrating the definition of the degree of anomaly.
First, as shown in FIG. 6, the axis of x1 and x2 is defined. x1 and x2 are parts of the rotating machine 3 and ancillary equipment, respectively. For example, x1 = outer ring, x2 = inner ring.
And μ1 is the average amplitude in the anomalous feature frequency of x1. Similarly, μ2 is the average amplitude at the anomalous feature frequency of x2.

N1 is a probability density function with respect to the amplitude of the anomalous feature frequency of x1. Similarly, N2 is a probability density function with respect to the amplitude of the anomalous feature frequency of x2. It is assumed that both N1 and N2 have a normal distribution.
Further, L is a curve (curved surface) connecting the average value μ1 of x1 and the average value μ2 of x2. In FIG. 6, L is a curved line, but it may be a straight line. ..
And M1 is a measured value of the amplitude of the anomalous characteristic frequency of x1. Similarly, M2 is a measured value of the amplitude of the anomalous feature frequency of x2. Incidentally, assuming that σ1 is the variance of the probability density function N1, M1 is assumed to be at the position of the mean value μ1 to 3σ1. Similarly, assuming that σ2 is the variance of the probability density function N2, it is assumed that M2 is at the position of σ2 / 3 from the mean value μ2. That is, M1 is an event that occurs with a very low probability.

Next, on the coordinates formed by x1 and x2, the point P whose coordinates are (M1, M2) is defined. M1 and M2 are the amplitudes of the abnormal feature frequencies measured at each part, which can be easily calculated from the information in Table 1.
Next, the vector OP connecting the origin O and the point P is defined. Then, the intersection Q of the vector OP and the curve L is defined. Then, the distance of the vector QP (the size of the thick arrow, that is, the length of the line QP) is defined as the degree of abnormality.

For the sake of simplicity, in FIG. 6, the two-dimensional coordinates of x1 (for example, the outer ring) and x2 (for example, the inner ring) are used. However, in reality, the coordinates are multidimensional and the coordinate axes exist only for the item of the part that identifies the abnormality.

FIG. 7 is a diagram illustrating an abnormality determination based on an abnormality degree.
FIG. 7 describes an example in which the state change of the rotary machine system 100a is diagnosed based on the degree of abnormality based on the amplitude when the abnormal characteristic frequency including the high-order component of the bearing inner ring scratch is input.
As shown in FIG. 7, when the degree of abnormality exceeds a predetermined threshold value, the abnormal portion specifying unit 13 determines that there is an abnormal portion. Such a threshold value may be set by machine learning as described above, or may be set empirically by the user. Then, when the degree of abnormality exceeds a predetermined threshold value, the abnormality portion specifying unit 13 projects the vector OP shown in FIG. 6 on each coordinate axis (x1 axis and x2 axis in FIG. 6). Then, the abnormality portion specifying unit 13 determines that the abnormality has occurred in the portion corresponding to the coordinate axis having the largest magnitude of the orthogonal projection vector in each coordinate axis generated as a result of the projection.

After that, as described above, the notification unit 14 notifies the user of the identified abnormal portion.
According to the methods shown in FIGS. 6 and 7, it can be confirmed that the degree of abnormality increases with the progress of the abnormality. Further, it is possible to easily identify the abnormal portion when an abnormality occurs. Then, by providing an appropriate threshold value, it is possible to convey the abnormality to the user and prompt an appropriate response.

According to the second embodiment, the rotating machine 3 and the ancillary equipment can be diagnosed without any specialized knowledge.

[Third Embodiment]
FIG. 8 is a diagram showing a configuration example of the rotary machine system 100b according to the third embodiment. In FIG. 8, the same components as those in FIG. 5 are designated by the same reference numerals as those in FIG. 5, and the description thereof will be omitted.
The diagnostic support device 1b of the rotary machine system 100b differs from the diagnostic support device 1a shown in FIG. 5 as follows.
(G1) Abnormal feature The input destination of the frequency storage unit 21 is the amplitude calculation unit 11.
(G2) The amplitude calculation unit 11 performs a discrete Fourier transform on only the frequency portion corresponding to the anomalous characteristic frequency with respect to the input q-axis current.

In the second embodiment, after the q-axis current is frequency-converted in the amplitude calculation unit 11, a specific frequency component input from the abnormal feature frequency storage unit 21 is extracted. On the other hand, in the third embodiment, as described above, the amplitude calculation unit 11 swings the input q-axis current only with respect to the preset abnormal feature frequency component by the discrete Fourier transform or the like. Is being extracted. By doing so, it is possible to save the amount of memory used when frequency decomposition is performed by a microcomputer or the like having a limited memory capacity.

[Fourth Embodiment]
FIG. 9 is a diagram showing a configuration example of the rotary machine system 100c according to the fourth embodiment. In FIG. 9, the same components as those in FIG. 5 are designated by the same reference numerals as those in FIG. 5, and the description thereof will be omitted.
The difference between the diagnostic support device 1c of the rotary machine system 100c and the diagnostic support device 1a shown in FIG. 5 is as follows.
(H1) A filter unit 15 is provided on the upstream side of the amplitude calculation unit 11. The filter unit 15 is, for example, a low-pass filter, a high-pass filter, or the like.

In the first to third embodiments, the input q-axis current is directly decomposed into the amplitudes for each frequency. On the other hand, in the fourth embodiment, after the specific frequency band in the q-axis current is removed, it is converted into the amplitude for each frequency. For example, by removing the DC component and the noise component in advance, the resolution of the current amplitude can be improved and the diagnostic accuracy can be improved.

[Fifth Embodiment]
FIG. 10 is a diagram showing a configuration example of the rotary machine system 100d according to the fifth embodiment. In FIG. 10, the same components as those in FIG. 5 are designated by the same reference numerals as those in FIG. 5, and the description thereof will be omitted.
The difference between the diagnostic support device 1d of the rotary machine system 100d and the diagnostic support device 1a shown in FIG. 5 is as follows.
(I1) The abnormal characteristic frequency storage unit 21d stores the abnormal characteristic frequency in each part of the rotating machine 3 and the ancillary equipment in association with the model number (identification information) of the rotating machine 3 and the ancillary equipment. .. For example, the abnormal feature frequency storage unit 21d stores the abnormal feature frequencies in the parts of the rotating machine 3 and the ancillary devices as a table associated with the model numbers of the rotating machine 3 and the ancillary devices.
(I2) The diagnostic support device 1d is provided with a model number input unit (identification information input unit) 16 for inputting the model number of the rotary machine 3 and the ancillary equipment into the abnormal feature frequency storage unit 21d.
(I3) The model number input unit 16 inputs the abnormal feature frequency associated with the input model number from the abnormal feature frequency storage unit 21d to the abnormal part identification unit 13.

With such a configuration, it is possible to identify the abnormal portion according to the type of the rotating machine 3 and the incidental equipment, and it is possible to improve the accuracy of identifying the abnormal portion.

[Sixth Embodiment]
FIG. 11 is a diagram showing a configuration example of the rotary machine system 100e according to the sixth embodiment. In FIG. 11, the same components as those in FIG. 1 are designated by the same reference numerals as those in FIG. 1, and the description thereof will be omitted.
In the rotary machine system 100e shown in FIG. 11, the power conversion device 2e is provided with the functions of the diagnostic support devices 1, 1a to 1d according to the first to fifth embodiments.
With such a configuration, it is possible to support the abnormality diagnosis of the rotating machine 3 and the incidental equipment in the power conversion device 2e.

[7th Embodiment]
FIG. 12 is a diagram showing a configuration example of the rotary machine system 100f according to the seventh embodiment. In FIG. 12, the same components as those in FIG. 1 are designated by the same reference numerals as those in FIG. 1, and the description thereof will be omitted.
In the rotary machine system 100f shown in FIG. 12, the power conversion device 2 and the diagnostic support devices 1, 1a to 1d according to the first to fifth embodiments are connected via the Internet NW.
With such a configuration, it is possible to support the diagnosis of the rotating machine 3 existing in a remote place and ancillary devices.

[Hardware configuration]
FIG. 13 is a diagram showing the hardware configurations of the diagnostic support devices 1, 1a to 1d according to the first to fifth embodiments.
The diagnostic support devices 1, 1a to 1d are composed of a PC (Personal Computer), a PLC (Programmable Logic Controller), and the like.
The diagnostic support devices 1, 1a to 1d include a memory 201, a CPU (Central Processing Unit) 202, a storage device 203, an input device 204, and an output device 205.
Then, the program stored in the storage device 203 is loaded into the memory 201, and the loaded program is executed by the CPU 202. As a result, the amplitude calculation unit 11, the abnormality portion identification unit 13, the notification unit 14, the filter unit 15, and the like shown in FIGS. 1, 5, and 8 to 10 are embodied.

Further, the storage device 203 stores an abnormal feature frequency (FIG. 5, FIG. 8, FIG. 9), an abnormal feature frequency corresponding to the model number (FIG. 10), and the like.
Further, the output device 205 corresponds to the display unit 12 of FIG. 1, the notification unit 14 of FIGS. 5 and 8 to 10.

According to the diagnostic support devices 1, 1a to 1d in the present embodiment, since the sensor is not required, inexpensive diagnostic support devices 1, 1a to 1d can be provided.

The rotary machine systems 100, 100a to 100f in each embodiment can be applied to a high voltage motor, a medium voltage motor, and a low voltage motor. Further, the rotary machine systems 100, 100a to 100f in each embodiment can be applied to industrial equipment such as power conversion devices 2, 2a and transformers. The rotary machine systems 100, 100a to 100f in each embodiment can be applied to an electric device controlled by a q-axis current.
Further, in the second to fifth embodiments, the calculation result of the amplitude calculation unit 11 may be displayed on the display unit 12.

The present invention is not limited to the above-described embodiment, and includes various modifications. For example, the above-described embodiment 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 described configurations. Further, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Further, it is possible to add / delete / replace a part of the configuration of each embodiment with another configuration.

Further, each of the above-mentioned configurations, functions, parts 11, 13 to 16, storage device 203, and the like may be realized by hardware, for example, by designing a part or all of them by an integrated circuit or the like. Further, as shown in FIG. 14, each configuration, function, etc. described above may be realized by software by interpreting and executing a program in which a processor such as a CPU 202 realizes each function. In addition to storing information such as programs, tables, and files that realize each function in HD (Hard Disk), memory 201, recording devices such as SSD (Solid State Drive), or IC (Integrated Circuit) cards It can also be stored in a recording medium such as an SD (Secure Digital) card or a DVD (Digital Versatile Disc).
Further, in each embodiment, the control lines and information lines are shown as necessary for explanation, and not all the control lines and information lines are necessarily shown in the product. In practice, you can think of almost all configurations as interconnected.

1,1a to 1d, 1g Diagnostic support device 2,2a Power conversion device (incidental equipment)
3 Rotating machine 4 Load device (incidental equipment)
11 Amplitude calculation unit 12 Display unit 13 Abnormal part identification unit 14 Notification unit 15 Filter unit 16 Model number input unit (identification information input unit)
21,21d Abnormal feature frequency storage unit (including abnormal feature frequency information)

Claims (9)

An acquisition unit that acquires the q-axis current of the power converter that controls the rotating machine,
An amplitude calculation unit that calculates the amplitude of each frequency related to the q-axis current from the acquired q-axis current.
An output unit that outputs information about the amplitude of each frequency,
A storage unit having anomalous characteristic frequency information that stores information on a frequency at which the amplitude increases when an abnormality occurs in at least one of the rotating machine and the auxiliary equipment of the rotating machine.
By comparing the amplitude for each frequency calculated by the amplitude calculation unit with the abnormal feature frequency information, the abnormal part identification unit that identifies the part where the abnormality occurs in at least one of the rotary machine and the incidental equipment. When,
A diagnostic support device characterized by having. An acquisition unit that acquires the q-axis current of the power converter that controls the rotating machine,
An amplitude calculation unit that calculates the amplitude of each frequency related to the q-axis current from the acquired q-axis current.
An output unit that outputs information about the amplitude of each frequency,
A storage unit having anomalous characteristic frequency information that stores information on a frequency at which the amplitude increases when an abnormality occurs in at least one of the rotating machine and the auxiliary equipment of the rotating machine.
By comparing the amplitude for each frequency calculated by the amplitude calculation unit with the abnormal feature frequency information, the abnormal part identification unit that identifies the part where the abnormality occurs in at least one of the rotary machine and the incidental equipment. When,
Have,
The amplitude calculation unit calculates the amplitude only for the frequency stored in the abnormal feature frequency information.
A diagnostic support device characterized by this. An acquisition unit that acquires the q-axis current of the power converter that controls the rotating machine,
An amplitude calculation unit that calculates the amplitude of each frequency related to the q-axis current from the acquired q-axis current.
An output unit that outputs information about the amplitude of each frequency,
A storage unit having anomalous characteristic frequency information that stores information on a frequency at which the amplitude increases when an abnormality occurs in at least one of the rotating machine and the auxiliary equipment of the rotating machine.
By comparing the amplitude for each frequency calculated by the amplitude calculation unit with the abnormal feature frequency information, the abnormal part identification unit that identifies the part where the abnormality occurs in at least one of the rotary machine and the incidental equipment. When,
Have,
The abnormal feature frequency information is stored in the storage unit in association with at least one identification information of the rotary machine and the incidental device.
Identification information input unit that sends the abnormality feature frequency information corresponding to the identification information of at least one of the input rotary machine and the incidental device to the abnormality part identification unit.
A diagnostic support device, characterized in that it further possesses. An acquisition unit that acquires the q-axis current of the power converter that controls the rotating machine,
An amplitude calculation unit that calculates the amplitude of each frequency related to the q-axis current from the acquired q-axis current.
An output unit that outputs information about the amplitude of each frequency,
A storage unit having anomalous characteristic frequency information that stores information on a frequency at which the amplitude increases when an abnormality occurs in at least one of the rotating machine and the auxiliary equipment of the rotating machine.
By comparing the amplitude for each frequency calculated by the amplitude calculation unit with the abnormal feature frequency information, the abnormal part identification unit that identifies the part where the abnormality occurs in at least one of the rotary machine and the incidental equipment. When,
Have,
The abnormal part identification part is
In the rotating machine and the ancillary equipment, set the coordinate axes related to the frequency amplitude of each of the parts, and set the coordinate axes.
The average value of the amplitude for each part is set, and the plane connecting the average value of the amplitude in each part is set.
When the amplitude corresponding to each of the parts calculated from the measured q-axis current is calculated, the point P in which the amplitude corresponding to each of the parts is plotted at the coordinates defined by the coordinate axis is set. ,
A line OP connecting the origin O of the coordinates and the point P is generated.
Generate an intersection Q between the line OP and the plane,
The length of the line QP is defined as the degree of abnormality.
When the length of the degree of abnormality exceeds a predetermined threshold value, it is determined that an abnormality has occurred in at least one of the rotating machine and the ancillary equipment.
Based on the result of projecting the line QP on each of the coordinate axes, the site where the abnormality occurs is specified.
A diagnostic support device characterized by this. The diagnostic support device according to any one of claims 1 to 4 , wherein the output unit outputs an amplitude for each frequency. The diagnostic support device according to any one of claims 1 to 4 , wherein a filter unit for removing a predetermined frequency is provided between the power conversion device and the amplitude calculation unit. With a rotating machine,
The power conversion device that controls the rotating machine and
A diagnostic support device that supports abnormality diagnosis of the rotary machine and the equipment incidental to the rotary machine, and
Is a rotating machine system with
The diagnostic support device is
An acquisition unit that acquires the q-axis current of the power converter, and
An amplitude calculation unit that calculates the amplitude of each frequency related to the q-axis current from the acquired q-axis current.
An output unit that outputs information about the amplitude of each frequency,
A storage unit having anomalous characteristic frequency information that stores information on a frequency at which the amplitude increases when an abnormality occurs in at least one of the rotating machine and the auxiliary equipment of the rotating machine.
By comparing the amplitude for each frequency calculated by the amplitude calculation unit with the abnormal feature frequency information, the abnormal part identification unit that identifies the part where the abnormality occurs in at least one of the rotary machine and the incidental equipment. When,
A rotating machine system characterized by having. With a rotating machine,
The power conversion device that controls the rotating machine and
A diagnostic support device that supports abnormality diagnosis of the rotary machine and the equipment incidental to the rotary machine, and
Is a rotating machine system with
The diagnostic support device is
An acquisition unit that acquires the q-axis current of the power converter, and
An amplitude calculation unit that calculates the amplitude of each frequency related to the q-axis current from the acquired q-axis current.
An output unit that outputs information about the amplitude of each frequency,
A storage unit having anomalous characteristic frequency information that stores information on a frequency at which the amplitude increases when an abnormality occurs in at least one of the rotating machine and the auxiliary equipment of the rotating machine.
By comparing the amplitude for each frequency calculated by the amplitude calculation unit with the abnormal feature frequency information, the abnormal part identification unit that identifies the part where the abnormality occurs in at least one of the rotary machine and the incidental equipment. When,
Have,
The amplitude calculation unit calculates the amplitude only for the frequency stored in the abnormal feature frequency information.
A rotating machine system characterized by that . The diagnostic support device that supports the abnormality diagnosis of the rotary machine and the auxiliary equipment of the rotary machine
It has a storage unit having anomalous feature frequency information that stores information on the frequency at which the amplitude increases when an abnormality occurs in at least one of the rotating machine and the auxiliary equipment of the rotating machine.
Acquire the q-axis current of the power converter that controls the rotating machine,
From the acquired q-axis current, the amplitude for each frequency with respect to the q-axis current is calculated.
Information on the amplitude for each frequency is output , and
By comparing the calculated amplitude for each frequency with the abnormal feature frequency information, a portion where an abnormality occurs in at least one of the rotating machine and the ancillary equipment is specified.
A diagnostic support method characterized by this.

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