Sign determination device, processing method, and program

Abstract

To provide a sign determination device capable of determining whether or not there is an abnormal sign in at least one of an electric motor and a load of the electric motor.SOLUTION: A sign determination device comprises: a measurement result acquisition unit for acquiring a measurement result of a current flowing through an electric motor; an analysis unit for frequency-analyzing the measurement result and decomposing it into frequency components; and a prediction unit for determining whether or not there is an abnormal sign in at least one of the electric motor and a load of the electric motor, based on time series data of the frequency components, and further comprises a specification unit for specifying a site by collating the frequency components to site information obtained by associating the frequency components with the site where the abnormality occurs, when it is determined that there is the sign.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to a sign determination device, a processing method, and a program. [Background technology]

[0002] Patent Document 1 discloses a technique for diagnosing an abnormality in an electric motor, which determines whether or not there is an abnormality in the electric motor based on a reference amplitude probability density function calculated from a reference sine wave signal waveform of the rated current of the electric motor and an inspection amplitude probability density function calculated from the current waveform when the electric motor is in operation. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2011-257362 Summary of the Invention [Problem to be solved by the invention]

[0004] Patent Document 1 discloses a technique for determining whether or not there is an abnormality in the bearing of a load driven by an electric motor. However, there is a demand for a technique that can detect not only abnormalities in bearings but also abnormalities other than bearings. An object of the present disclosure is to provide a sign determination device, a processing method, and a program that are capable of appropriately determining a sign determination target. [Means for solving the problem]

[0005] The sign determination device according to the present disclosure comprises: The apparatus includes a measurement result acquisition unit that acquires measurement results of a current flowing through an electric motor, an analysis unit that performs frequency analysis on the measurement results and resolves them into frequency components, and a prediction unit that determines whether or not there is a sign of an abnormality in at least one of the electric motor and a load of the electric motor based on time-series data of the frequency components, and when it is determined that there is a sign of an abnormality, an identification unit that compares the frequency components with part information that associates the frequency components with a part where the abnormality occurs to identify the part, and further identifies the cause of the abnormality based on an image showing the time-series data of the frequency components and cause information that associates the cause of the abnormality with the part. .

[0006] The processing method according to the present disclosure includes: The method includes: acquiring a measurement result of a current flowing through an electric motor; performing frequency analysis on the measurement result to resolve it into frequency components; and determining whether or not there is a sign of an abnormality in at least one of the electric motor and a load of the electric motor based on time-series data of the frequency components; and if it is determined that there is a sign of an abnormality, comparing the frequency components with part information in which the frequency components are associated with a part where the abnormality occurs to identify the part; and further identifying the cause of the abnormality based on an image showing the time-series data of the frequency components and cause information in which the cause of the abnormality is associated with the part. .

[0007] The program according to the present disclosure is The computer is caused to acquire a measurement result of a current flowing through an electric motor, to perform frequency analysis on the measurement result and to resolve it into frequency components, and to determine whether or not there is a sign of an abnormality in at least one of the electric motor and a load of the electric motor based on time-series data of the frequency components, and if it is determined that there is a sign of an abnormality, to identify the part by comparing the frequency components with part information in which the frequency components and the part in which the abnormality occurs are associated, and to further identify the cause of the abnormality based on an image showing the time-series data of the frequency components and cause information in which the cause of the abnormality is associated. . [Effects of the Invention]

[0008] According to at least one of the above aspects, it is possible to determine whether there is a sign of an abnormality in at least one of the electric motor and the load of the electric motor. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a diagram showing the configuration of a sign determination system according to a first embodiment. [Figure 2] 1 is a schematic block diagram showing the configuration of a sign determination device according to a first embodiment. [Figure 3] FIG. 2 is a diagram showing an example of an image according to the first embodiment. [Figure 4] FIG. 4 is a diagram showing an example of cause information according to the first embodiment. [Figure 5] FIG. 4 is a diagram illustrating an example of time information according to the first embodiment. [Figure 6] 4 is a flowchart showing an example of the operation of the sign determination system according to the first embodiment. [Figure 7] FIG. 10 is a diagram illustrating the configuration of a sign determination system according to a second embodiment. [Figure 8] FIG. 10 is a first diagram showing an example of training data in the second embodiment. [Figure 9] FIG. 10 is a second diagram showing an example of training data in the second embodiment. [Figure 10] 10 is a flowchart showing an example of the operation of the sign determination system according to the second embodiment. [Figure 11] FIG. 10 is a diagram showing the configuration of a sign determination system according to a fifth embodiment. [Figure 12] FIG. 1 is a schematic block diagram illustrating the configuration of a computer according to at least one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] First Embodiment <Configuration of the symptom determination system> The configuration of the sign determination system 1 according to the embodiment will be described in detail below with reference to the drawings. The symptom determination system 1 determines a symptom of an abnormality in a load 13 driven by an electric motor 11.

[0011] FIG. 1 is a diagram showing the configuration of a sign determination system 1 according to the first embodiment. The sign determination system 1 includes a power source 10 , an electric motor 11 , an electric wire 12 , a load 13 , a measuring instrument 16 , a converter 17 , and a sign determination device 100 .

[0012] A power source 10 supplies current to an electric motor 11 via an electric line 12 . The electric motor 11 receives a current from the power source 10 via the electric wire 12. The electric motor 11 that receives the current rotates a shaft 14A provided in the electric motor 11, and rotates a shaft 14B provided in the load 13.

[0013] Shaft 14B is rotated by electric motor 11. That is, load 13 is driven by electric motor 11. Load 13 also includes bearings 15A and 15B that support shaft 14B. Lubricating oil is supplied to bearings 15A and 15B. The lubricating oil reduces friction between bearings 15A and 15B and shaft 14B.

[0014] The measuring instrument 16 measures the current flowing through the electric wire 12. That is, the measuring instrument 16 measures the current flowing through the electric motor 11 via the electric wire 12. An example of the measuring instrument 16 is a current transducer (CT). The measuring instrument 16 measures the current and acquires an analog current waveform.

[0015] Converter 17 converts the analog current waveform acquired by measuring instrument 16 into digital current data. Converter 17 transmits the converted digital current data to sign determination device 100. In other words, converter 17 transmits the measurement result of the current flowing through electric motor 11 to sign determination device 100.

[0016] Configuration of the sign determination device The configuration of the sign determination device 100 will be described below. The symptom determination device 100 determines whether or not there is a symptom of an abnormality in the bearings 15A and 15B of the load 13 driven by the electric motor 11. An example of an abnormality in the bearings 15A and 15B is a change in the shape of the bearings 15A and 15B due to friction between the shaft 14B and the bearings 15A and 15B.

[0017] FIG. 2 is a schematic block diagram showing the configuration of the sign determination device 100. The sign determination device 100 includes a measurement result acquisition unit 101, an analysis unit 102, an image generation unit 103, a detection unit 104, a first determination unit 105 (an example of a prediction unit), a calculation unit 106, a second determination unit 107, an identification unit 108, an update unit 109, an output unit 110, and a memory unit 111.

[0018] The measurement result acquisition unit 101 acquires the digital current data transmitted from the converter 17. That is, the measurement result acquisition unit 101 acquires the measurement result of the current flowing through the electric motor 11. The measurement result is digital current data. The analysis unit 102 decomposes the measurement results acquired by the measurement result acquisition unit 101 into a plurality of frequency components using FFT (Fast Fourier Transform).

[0019] The image generating unit 103 generates an image showing time-series data for each of a plurality of frequency components and time-series data for energy values ​​associated with the frequency components. The image generated by the image generating unit 103 shows the frequency components as a color graph with time and frequency as axes, and shows energy values ​​on the color graph. The color graph is represented by a set of points. FIG. 3 is an example of an image generated by the image generating unit 103. As shown in FIG.

[0020] For example, the analysis unit 102 decomposes the measurement results into two frequency components. A color graph of the two decomposed frequency components is shown in Figure 3. Assume that an abnormality occurs in the bearing 15 of the load 13 at time T2. The frequency values ​​for the two energy values ​​are constant band values ​​until time T1. If there is no abnormality in the load 13, there is no change in the current flowing through the motor 11, so the values ​​of the two frequency components obtained by decomposing the current measurement results are constant band values ​​until time T1. The value of the frequency component after time T2 changes by Z from the value of the constant band shown before time T1. This is because an abnormality occurs in bearing 15 at time T2, and when the measurement results of the current flowing through electric motor 11 are analyzed, the frequency component has a different value from that at time T1. In FIG. 3, the horizontal axis represents frequency and the vertical axis represents time, but the horizontal axis may represent time and the vertical axis may represent frequency.

[0021] On the color graph of two frequency components, the energy value of each frequency component is represented by a color. Examples of colors include blue, green, yellow, and red. Low energy values ​​are represented by blue, and as the energy value increases, the colors are green, yellow, and red, in that order. The energy value of the frequency component indicating the bearing condition has a value above a certain level, and therefore is displayed in the same color (for example, red) before time T1, from T1 to T2, and after T2. The image generating unit 103 may display colors not only on the color graph but also in areas other than the color graph. For example, the image generating unit 103 may display areas other than the color graph in a dark blue color in the generated image, thereby indicating that the energy value at the corresponding frequency is zero or close to zero.

[0022] The detection unit 104 detects a change value of the frequency component based on the image generated by the image generation unit 103. For example, if the image is a graph as shown in Fig. 3, the detection unit 104 detects Z as the change value.

[0023] The first determination unit 105 compares the change value detected by the detection unit 104 with a preset first threshold value to determine whether or not there is a sign of abnormality in the bearing 15. For example, if the change value detected by the detection unit 104 is equal to or greater than the first threshold value, the first determination unit 105 determines that there is a sign of abnormality in the bearing 15. Furthermore, if the change value detected by the detection unit 104 is not equal to or greater than the first threshold value, the first determination unit 105 determines that there is no sign of abnormality in the bearing 15. If an abnormality occurs in the bearing 15, the value of the frequency component changes, and therefore, by comparing the change value with a certain threshold value, it is possible to determine whether or not there is a sign of abnormality in the bearing 15.

[0024] The calculation unit 106 calculates the similarity based on the feature amount of the image associated with the cause of the abnormality in the bearing 15 and the feature amount of the image generated by the image generation unit 103. Examples of the cause include poor lubrication, poor installation, intrusion of foreign matter, rust, and insufficient clearance. A user of the sign determination system 1 records in advance factor information, which is information associating factors with images, in the storage unit 111. The image is an image showing time-series data of frequency components and energy values. 4 shows an example of an image in the factor information. For example, a user of the sign assessment system 1 records factor information in which the image A in FIG. 4 is associated with factor A in the storage unit 111. The user of the sign assessment system 1 also records factor information in which the image B in FIG. 4 is associated with factor B in the storage unit 111.

[0025] For example, the calculation unit 106 extracts image features associated with factors, as shown in FIG. 4, using a convolutional neural network (CNN) technique. The calculation unit 106 also extracts feature features of the image generated by the image generation unit 103 using a convolutional neural network technique. The calculation unit 106 calculates similarities between the extracted feature features. The calculation unit 106 calculates similarities between feature features of multiple images associated with factors in the factor information and feature features of the image generated by the image generation unit 103. That is, the calculation unit 106 calculates multiple similarities between each of the images in the multiple pieces of factor information. The calculation unit 106 may operate in two ways: extracting based on known factors and known images before the user of the sign assessment system 1 uses the system; and extracting based on newly occurring factors and newly occurring images when the user of the sign assessment system 1 uses the system.

[0026] When the first determination unit 105 determines that there is a sign, the second determination unit 107 determines whether the similarity calculated by the calculation unit 106 is equal to or greater than a second threshold set in advance. When the calculation unit 106 calculates multiple similarities, the second determination unit 107 determines whether each of the multiple similarities is equal to or greater than the second threshold.

[0027] If second determination unit 107 determines that the similarity is equal to or greater than the second threshold, identification unit 108 identifies a factor associated with the similarity calculated by calculation unit 106. For example, if the similarity with image A in Fig. 4 is equal to or greater than the second threshold, second determination unit 107 determines that the similarity is equal to or greater than the second threshold. Identification unit 108 identifies a factor associated with image A in Fig. 4. Furthermore, it is also possible to identify the area when the second judgment unit 107 judges that the value is equal to or greater than the second threshold, and it is also possible to identify the area when at least one of the first judgment unit 105 or the second judgment unit 107 judges that there is a sign or that the value is equal to or greater than the second threshold.

[0028] Furthermore, when the first determination unit 105 determines that there is a sign of an abnormality, the identification unit 108 compares the frequency component with part information in which the frequency component is associated with the part where the abnormality occurs, to identify the part. Examples of the parts include the inner ring of the bearing 15, the outer ring of the bearing 15, and the ball between the inner ring and the outer ring of the bearing 15. A user of the sign determination system 1 records body part information in advance in the storage unit 111. Note that the user of the sign determination system 1 may also record newly generated body part information in the storage unit 111 when using the sign determination system 1.

[0029] 3 has changed by Z from F1, and the first determination unit 105 has determined that there is a sign of an abnormality in the bearing 15. In this case, the identification unit 108 compares the frequency component value F1 with the part information. If the part information associates the frequency component value F1 with the inner ring of the bearing 15, the identification unit 108 identifies the inner ring of the bearing 15 as the part where an abnormality will occur.

[0030] Furthermore, the identifying unit 108 checks the identified cause against time information that associates the cause with the time until the abnormality occurs, and further identifies the time. The user of the sign determination system 1 records time information in advance in the storage unit 111. Note that the user of the sign determination system 1 may also record newly generated time information in the storage unit 111 when using the sign determination system 1.

[0031] 5 is a diagram illustrating an example of time information in the first embodiment. For example, assume that the identification unit 108 identifies a cause A. As shown in FIG. 5, if the cause A is associated with a time T3 until an abnormality occurs in the time information, the identification unit 108 identifies the time T3. The identifying unit 108 may identify the time when the abnormality occurs instead of the time T3 until the abnormality occurs.

[0032] If the second determination unit 107 determines that the similarity is not equal to or greater than the second threshold, the update unit 109 accepts an input from the outside and updates the factor information. For example, suppose that it is determined that the similarity of the image generated by the image generation unit 103 with the image in the factor information is not equal to or greater than the second threshold. The output unit 110 outputs the image generated by the image generation unit 103 to a display device (not shown) included in the sign determination system 1. A user of the sign determination system 1 checks the output image through the display device. The user of the sign determination system 1 identifies the cause of the abnormality in the bearing 15 using a device other than the sign determination device 100. The user of the sign determination system 1 associates the newly identified cause with the output image and inputs this as factor information to the sign determination system 1. The update unit 109 accepts the input and updates the factor information. This allows the factor information to be updated even for factors that the second determination unit 107 cannot determine, thereby increasing the number of factors that the second determination unit 107 can determine.

[0033] The output unit 110 outputs the content identified by the identification unit 108 to a notification device included in the sign determination system 1. Examples of the notification device include a display device and a speaker. The signals output by the output unit 110 include a signal representing an image and a signal related to sound. For example, the output unit 110 outputs the cause, location, and time identified by the identification unit 108 to a display device. A user of the symptom determination system 1 can check the cause of an abnormality occurring in the bearing 15, the location where the abnormality will occur in the bearing 15, and the time until the abnormality will occur in the bearing 15, from the display on the display device. In this way, the output unit 110 outputs a signal showing an image to the display device, so that the user can easily understand the content identified by the identification unit 108.

[0034] The storage unit 111 stores the cause information, body part information, and time information recorded by the user of the sign determination system 1. An example of the storage unit 111 is a hard disk.

[0035] <<Operation of the Premonition Judgment System>> The operation of the sign determination system 1 will be described below. FIG. 6 is a flowchart showing the operation of the sign determination system 1.

[0036] The measuring instrument 16 measures the current and acquires an analog current waveform (step S1). Converter 17 converts the analog current waveform acquired in step S1 into digital data (step S2).

[0037] The measurement result acquisition unit 101 acquires the measurement result, which is digital data, from the converter 17 (step S3). The analysis unit 102 resolves the measurement results acquired in step S3 into frequency components using FFT (step S4).

[0038] The image generating unit 103 generates an image of a color graph showing the time-series data of the frequency components and the energy values ​​(step S5). The detection unit 104 detects the change value of the frequency component based on the image generated in step S5 (step S6).

[0039] The first determination unit 105 compares the change value detected in step S6 with a first threshold value to determine whether or not there is a sign of an abnormality in the bearing 15 (step S7). If it is determined that there is no sign of abnormality (step S7: NO), the sign determination system 1 returns to step S1 and performs the operations from step S1. On the other hand, if it is determined that there is a sign of abnormality (step S7: YES), the calculation unit 106 calculates the similarity between the feature amount of the image in the factor information and the feature amount of the image generated in step S5 (step S8).

[0040] The second determination unit 107 determines whether the similarity calculated in step S8 is equal to or greater than a second threshold value (step S9). If it is determined that the similarity is equal to or greater than the second threshold (step S9: YES), the identifying unit 108 identifies factors related to the similarity (step S10). The identifying unit 108 identifies a portion of the bearing 15 where an abnormality occurs (step S11). The identifying unit 108 also identifies the time until the abnormality occurs in the bearing 15 (step S12).

[0041] The output unit 110 outputs the content identified by the identification unit 108 to the notification device (step S13). The notification device displays the content identified by the identification unit 108 to the user of the sign determination system 1 (step S14).

[0042] On the other hand, if it is determined that the similarity is less than the second threshold value (step S9: NO), the output unit 110 outputs the image generated in step S5 to the display device (step S15). After that, the identification unit 108 identifies the part of the bearing 15 where the abnormality occurs (step S11).

[0043] Based on the display output in step S15, the user of the sign assessment system 1 identifies the cause of the abnormality using a device other than the sign assessment device 100. The user of the sign assessment system 1 inputs new factor information related to the factor identified in step S16 to the sign assessment system 1. The update unit 109 updates the cause information recorded in the storage unit 111 with the cause information input in step S17.

[0044] Actions and Effects The symptom determination device 100 according to the present disclosure includes a measurement result acquisition unit 101 that acquires measurement results of the current flowing through the electric motor 11, an analysis unit 102 that performs frequency analysis on the measurement results and breaks them down into frequency components, an image generation unit 103 that generates an image showing time series data of the frequency components, and a first determination unit 105 that determines, based on the image, whether or not there are signs of an abnormality in the bearing 15 of the load 13 driven by the electric motor 11.

[0045] The symptom assessment device 100 can determine whether or not there are signs of an abnormality in the bearing 15 of the load 13 driven by the electric motor 11, based on an image showing the frequency components of the current of the electric motor 11. Furthermore, the symptom assessment device 100 can determine whether or not there are signs of an abnormality from a long distance, even if it is not installed near the bearing 15 where vibrations occur and which is affected by temperature changes.

[0046] Furthermore, the analysis unit 102 of the sign determination device 100 performs FFT to decompose the signal into a plurality of frequency components, and the image represents time-series data for each of the plurality of frequency components.

[0047] The sign determination device 100 can determine whether or not there is a sign of abnormality in the bearing 15 of the load 13 driven by the electric motor 11, based on an image showing a plurality of frequency components resolved by FFT.

[0048] In addition, the image of the sign determination device 100 further shows time series data of energy values ​​associated with frequency components, and is equipped with a detection unit 104 that detects change values ​​of the frequency components based on the image, and a first determination unit 105 compares the change value with a predetermined first threshold value to determine whether or not a sign is present.

[0049] The sign determination device 100 can determine whether or not there is a sign of abnormality in the bearing 15 of the load 13 driven by the electric motor 11, based on the image showing the frequency components and energy values.

[0050] The image of the sign determination device 100 shows the frequency versus energy value as a color graph with time and frequency as axes, and the energy value is indicated by color on the color graph.

[0051] The sign determination device 100 can determine whether or not there is a sign of abnormality in the bearing 15 of the load 13 driven by the electric motor 11, based on the image of the color graph showing the frequency components and energy values.

[0052] Furthermore, the sign determination device 100 includes an identification unit 108 that, when it is determined that a sign exists, identifies the part by comparing the frequency component with part information that associates the frequency component with the part where the abnormality occurs.

[0053] The sign assessment device 100 identifies the part where an abnormality will occur based on the part information, thereby enabling the user of the sign assessment device 100 to identify the part of the bearing 15 where an abnormality will occur.

[0054] Furthermore, when it is determined that a sign exists, the identifying unit 108 of the sign assessment device 100 further identifies the cause based on the cause information that associates the image with the cause of the abnormality.

[0055] The sign assessment device 100 identifies the cause of the abnormality based on the cause information, thereby enabling the user of the sign assessment device 100 to identify the cause of the abnormality in the bearing 15.

[0056] The sign determination device 100 also includes a calculation unit 106 that calculates a similarity based on the image feature associated with the factor and the feature of the image generated by the image generation unit 103, and a second determination unit 107 that determines whether the similarity is equal to or greater than a second threshold value set in advance when it is determined that there is a sign, and an identification unit 108 identifies the factor related to the calculated similarity when it is determined that the similarity is equal to or greater than the second threshold value.

[0057] The sign assessment device 100 calculates the similarity based on the feature amount of the image and identifies the cause of the abnormality, thereby enabling the user of the sign assessment device 100 to identify the cause of the abnormality in the bearing 15.

[0058] Furthermore, the sign assessment device 100 includes an update unit 109 that receives input from the outside and updates the cause information when it is determined that the similarity is not equal to or greater than the second threshold value.

[0059] If the sign assessment device 100 cannot identify a cause, it accepts input from outside and updates the cause information, thereby enabling the sign assessment device 100 to identify more causes.

[0060] Furthermore, the identifying unit 108 of the sign assessment device 100 compares the identified cause with time information that associates the cause with the time until the abnormality occurs, and further identifies the time.

[0061] The sign assessment device 100 determines the time until an abnormality occurs based on the time information, thereby enabling the user of the sign assessment device 100 to determine the time until an abnormality in the bearing 15 occurs.

[0062] The sign determination device 100 also includes an output unit 110 that outputs the content identified by the identification unit .

[0063] The sign assessment device 100 outputs the cause, part, time, and other details identified by the identification unit 108. This allows the user of the sign assessment device 100 to confirm the cause, part, time, and other details identified by the identification unit 108.

[0064] The method for determining abnormalities according to the present disclosure includes obtaining measurement results of the current flowing through the electric motor 11, performing frequency analysis on the measurement results to decompose them into frequency components, generating an image showing time series data of the frequency components, and determining, based on the image, whether there are any abnormality warning signs in the bearings 15 of the load 13 driven by the electric motor 11.

[0065] By using the symptom determination method, a user can determine whether there are signs of an abnormality in the bearing 15 of the load 13 driven by the electric motor 11 based on an image showing the frequency components of the current of the electric motor 11.

[0066] The program according to the present disclosure causes a computer to acquire measurement results of the current flowing through the electric motor 11, perform frequency analysis on the measurement results to decompose them into frequency components, generate an image showing time series data of the frequency components, and determine, based on the image, whether there are any signs of abnormality in the bearing 15 of the load 13 driven by the electric motor 11.

[0067] By executing the program, a user of the program can determine whether there are any signs of abnormality in the bearing 15 of the load 13 driven by the electric motor 11 based on an image showing the frequency components of the current of the electric motor 11.

[0068] Second Embodiment <Configuration of the symptom determination system> The configuration of the sign determination system 1 according to the embodiment will be described in detail below with reference to the drawings. The symptom assessment system 1 according to the first embodiment assesses signs of abnormalities in the bearings 15A and 15B of the load 13. The symptom assessment system 1 according to the second embodiment assesses signs of abnormalities including at least one of bearing 15, breakage of the rotor bar in the electric motor 11, changes in tension of the belt connecting the electric motor 11 and the load 13, and cavitation.

[0069] The configuration of the sign determination system 1 according to the second embodiment is the same as the configuration of the sign determination system 1 according to the first embodiment shown in Figure 1, so explanations of each component will be omitted, and from here on, the same symbols will be used to describe this embodiment.

[0070] Configuration of the sign determination device The configuration of the sign determination device 100 will be described below. The symptom determination device 100 determines whether there is a symptom of an abnormality including at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of a belt connecting the electric motor 11 and load 13, and cavitation.

[0071] The configuration of the sign assessment device 100 is similar to the configuration of the sign assessment device 100 according to the first embodiment shown in Fig. 2, so a description of each component will be omitted and only the different components will be described. Furthermore, the same components will be denoted by the same reference numerals hereinafter in the description of this embodiment.

[0072] When there is a sign of abnormality in the image generated by the image generating unit 103, the frequency components change significantly as in the image shown in FIG. 3, similar to the sign of abnormality in the bearing 15 according to the first embodiment.

[0073] For example, the analysis unit 102 decomposes the measurement results into two frequency components. It is assumed that the image generated by the image generation unit 103 shows a color graph of the two decomposed frequency components shown in Fig. 3, and that an abnormality occurs at time T2, including at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation. In this case, as in the first embodiment, the value of the frequency component after time T2 changes by Z from the value of the fixed band shown before time T1. This is because at time T2, an abnormality occurs including at least one of bearing 15, breakage of a rotor bar in electric motor 11, a change in tension of the belt connecting electric motor 11 and load 13, and cavitation, and when the measurement results of the current flowing through electric motor 11 are resolved, the frequency component has a different value from that at time T1.

[0074] The energy value of the frequency component that indicates the state of bearing 15, breakage of a rotor bar in electric motor 11, and change in tension of the belt connecting electric motor 11 and load 13 has a value above a certain level. If there is an abnormality including at least one of bearing 15, breakage of a rotor bar in electric motor 11, and change in tension of the belt connecting electric motor 11 and load 13 before time T1, between T1 and T2, or after T2, the energy value of this frequency component may change. Cavitation can be detected by an increase in the energy values ​​of multiple frequency components. If cavitation occurs at time T1, it can be detected by an increase in the energy values ​​of multiple frequency components after time T1 (e.g., from blue to yellow).

[0075] The detection unit 104 detects a change value of the frequency component based on the image generated by the image generation unit 103. For example, if the image is a graph as shown in Fig. 3, the detection unit 104 detects Z as the change value.

[0076] The first determination unit 105 compares the change value detected by the detection unit 104 with a preset first threshold value and determines whether there is a sign of an abnormality including at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation. For example, if the change value detected by the detection unit 104 is equal to or greater than the first threshold value, the first determination unit 105 determines that there is a sign of an abnormality including at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation. On the other hand, if the change value detected by the detection unit 104 is not equal to or greater than the first threshold value, the first determination unit 105 determines that there is no sign of an abnormality including at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation. If an abnormality occurs, including at least one of bearing 15, breakage of a rotor bar in electric motor 11, a change in tension in the belt connecting electric motor 11 and load 13, and cavitation, the value of the frequency component will change. Therefore, by comparing the change value with a certain threshold value, it is possible to determine whether there are signs of an abnormality, including at least one of bearing 15, breakage of a rotor bar in electric motor 11, a change in tension in the belt connecting electric motor 11 and load 13, and cavitation.

[0077] The calculation unit 106 calculates the similarity based on the feature amount of the image associated with the cause of an abnormality, which includes at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation, and the feature amount of the image generated by the image generation unit 103. Examples of the cause include poor lubrication, improper installation, intrusion of foreign matter, rust, and insufficient clearance for bearing 15; aging deterioration for breakage of a rotor bar in the electric motor 11 and a change in tension of the belt connecting the electric motor 11 and the load 13; and an abnormality in a pump (not shown) operated by the electric motor 11 for cavitation.

[0078] As in the first embodiment, when the first determination unit 105 determines that there is a sign of an abnormality, the identification unit 108 compares the frequency component with part information that associates the frequency component with the part where an abnormality occurs, to identify the part. Examples of the above parts include the inner ring of the bearing 15, the outer ring of the bearing 15, and the ball between the inner ring and the outer ring of the bearing 15 for the bearing 15, the rotor bar for a broken rotor bar in the electric motor 11, the belt for a change in tension of the belt connecting the electric motor 11 and the load 13, and the pump operated by the electric motor 11 for cavitation.

[0079] For example, suppose that the value of the frequency component in Fig. 3 changes by Z from F1, and the first determination unit 105 determines that there is a sign of an abnormality regarding breakage of a rotor bar in the electric motor 11. In this case, the identification unit 108 compares the frequency component value F1 with the part information. If the frequency component value F1 and the rotor bar are associated with each other in the part information, the identification unit 108 identifies the rotor bar as the part where an abnormality will occur.

[0080] As in the first embodiment, when the second determination unit 107 determines that the similarity is not equal to or greater than the second threshold, the update unit 109 receives an input from the outside and updates the factor information. For example, suppose it is determined that the similarity of the image generated by the image generation unit 103 to the image in the cause information is not equal to or greater than the second threshold. If this determination is made, the user of the sign assessment system 1 can use a device other than the sign assessment device 100 to identify the cause of the abnormality, which includes at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation.

[0081] As in the first embodiment, the output unit 110 outputs the details identified by the identification unit 108 to the notification device included in the sign determination system 1. For example, the output unit 110 outputs the cause, location, and time identified by the identification unit 108 to a display device. A user of the symptom determination system 1 can see from the display on the display device the cause of the abnormality, which includes at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation, the location where the abnormality will occur, and the time until the abnormality will occur. In this way, the output unit 110 outputs a signal showing an image to the display device, so that the user can easily understand the content identified by the identification unit 108.

[0082] <<Operation of the Premonition Judgment System>> The operation of the sign determination system 1 will be described below. The flowchart showing the operation of the sign determination system 1 in this case is the same as the flowchart shown in Figure 6, which shows the operation of the sign determination system 1 according to the first embodiment, so we will omit the explanation of each process and only explain the different process contents.

[0083] In step S7, the first judgment unit 105 compares the change value detected in step S6 with the first threshold value and determines whether there are any signs of abnormality including at least one of bearing 15, breakage of the rotor bar in the electric motor 11, change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation. In step S11, which follows step S10, the identifying unit 108 identifies a location where an abnormality occurs, including at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation. In step S12, the identifying unit 108 identifies the time until an abnormality occurs, including at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation.

[0084] On the other hand, in step S11 after passing through step S15, the identification unit 108 identifies the location where an abnormality occurs, including at least one of the bearing 15, breakage of the rotor bar in the electric motor 11, change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation.

[0085] In this embodiment, the analysis unit 102 decomposes the signal into a plurality of frequency components, and the configuration using the frequency components has been described. However, it is possible to obtain a peak frequency component from the frequency components decomposed by the analysis unit 102, and adopt a configuration using the peak frequency component. At this time, a threshold value is set for the frequency component that becomes a peak, and the frequency component that becomes a peak when the threshold value is exceeded is acquired. Furthermore, by identifying the energy value of the peak frequency component by color, it is possible to determine whether or not there is a sign of an abnormality even if a peak occurs in an unexpected frequency component.

[0086] Actions and Effects The symptom determination device 100 according to the present disclosure includes a measurement result acquisition unit 101 that acquires measurement results of the current flowing through the electric motor 11, an analysis unit 102 that performs frequency analysis on the measurement results and breaks them down into frequency components, an image generation unit 103 that generates an image showing time series data of the frequency components, and a first determination unit 105 that determines, based on the image, whether or not there are signs of an abnormality including at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of a belt connecting the electric motor 11 and load 13, and cavitation.

[0087] The symptom assessment device 100 can determine whether or not there are any signs of abnormality, including at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation, based on an image showing the frequency components of the current in the electric motor 11. Furthermore, the symptom assessment device 100 can determine whether or not there are any of the above signs from a long distance, even if it is not installed in the vicinity of the electric motor 11 or the load 13.

[0088] Furthermore, the analysis unit 102 of the sign determination device 100 performs FFT to decompose the signal into a plurality of frequency components, and the image represents time-series data for each of the plurality of frequency components.

[0089] Based on an image showing multiple frequency components decomposed by FFT, the symptom determination device 100 can determine whether there are any signs of abnormality, including at least one of bearing 15, breakage of the rotor bar in the electric motor 11, a change in tension in the belt connecting the electric motor 11 and the load 13, and cavitation.

[0090] In addition, the image of the sign determination device 100 further shows time series data of energy values ​​associated with frequency components, and is equipped with a detection unit 104 that detects change values ​​of the frequency components based on the image, and a first determination unit 105 compares the change value with a predetermined first threshold value to determine whether or not a sign is present.

[0091] Based on an image showing frequency components and energy values, the symptom determination device 100 can determine whether there are any signs of abnormality, including at least one of bearing 15, breakage of the rotor bar in the electric motor 11, a change in tension in the belt connecting the electric motor 11 and the load 13, and cavitation.

[0092] The image of the sign determination device 100 shows the frequency versus energy value as a color graph with time and frequency as axes, and the energy value is indicated by color on the color graph.

[0093] Based on a color graph image showing frequency components and energy values, the symptom determination device 100 can determine whether there are any signs of abnormality, including at least one of bearing 15, breakage of the rotor bar in the electric motor 11, a change in tension in the belt connecting the electric motor 11 and the load 13, and cavitation.

[0094] Furthermore, the sign determination device 100 includes an identification unit 108 that, when it is determined that a sign exists, identifies the part by comparing the frequency component with part information that associates the frequency component with the part where the abnormality occurs.

[0095] The symptom assessment device 100 identifies the location where an abnormality will occur based on the location information, allowing the user of the symptom assessment device 100 to identify the location where an abnormality will occur, including at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and load 13, and cavitation.

[0096] Furthermore, when it is determined that a sign exists, the identifying unit 108 of the sign assessment device 100 further identifies the cause based on the cause information that associates the image with the cause of the abnormality.

[0097] The symptom assessment device 100 identifies the cause of the abnormality based on the cause information, allowing the user of the symptom assessment device 100 to identify the cause of the abnormality, including at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation.

[0098] The sign determination device 100 also includes a calculation unit 106 that calculates a similarity based on the image feature associated with the factor and the feature of the image generated by the image generation unit 103, and a second determination unit 107 that determines whether the similarity is equal to or greater than a second threshold value set in advance when it is determined that there is a sign, and an identification unit 108 identifies the factor related to the calculated similarity when it is determined that the similarity is equal to or greater than the second threshold value.

[0099] The symptom assessment device 100 calculates the similarity based on the feature amount of the image and identifies the cause of the abnormality, thereby enabling the user of the symptom assessment device 100 to identify the cause of the abnormality, including at least one of bearing 15, breakage of the rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation.

[0100] Furthermore, the sign assessment device 100 includes an update unit 109 that receives input from the outside and updates the cause information when it is determined that the similarity is not equal to or greater than the second threshold value.

[0101] If the sign assessment device 100 cannot identify a cause, it accepts input from outside and updates the cause information, thereby enabling the sign assessment device 100 to identify more causes.

[0102] Furthermore, the identifying unit 108 of the sign assessment device 100 compares the identified cause with time information that associates the cause with the time until the abnormality occurs, and further identifies the time.

[0103] The symptom assessment device 100 identifies the time until an abnormality occurs based on the time information, which enables the user of the symptom assessment device 100 to identify the time until an abnormality occurs, including at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation.

[0104] The sign determination device 100 also includes an output unit 110 that outputs the content identified by the identification unit .

[0105] The sign assessment device 100 outputs the cause, part, time, and other details identified by the identification unit 108. This allows the user of the sign assessment device 100 to confirm the cause, part, time, and other details identified by the identification unit 108.

[0106] The method for determining abnormalities according to the present disclosure includes obtaining measurement results of the current flowing through the electric motor 11, performing frequency analysis on the measurement results to decompose them into frequency components, generating an image showing time series data of the frequency components, and determining, based on the image, whether there are any abnormality warning signs including at least one of bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of a belt connecting the electric motor 11 and a load 13, and cavitation.

[0107] By using the symptom determination method, a user can determine whether there are any signs of abnormality, including at least one of bearing 15, breakage of the rotor bar in the electric motor 11, a change in tension in the belt connecting the electric motor 11 and the load 13, and cavitation, based on an image showing the frequency components of the current in the electric motor 11.

[0108] The program according to the present disclosure causes a computer to acquire measurement results of the current flowing through the electric motor 11, perform frequency analysis on the measurement results to decompose them into frequency components, generate an image showing time series data of the frequency components, and determine, based on the image, whether there are any signs of abnormality including at least one of bearing 15, breakage of the rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation.

[0109] By running the program, a user of the program can determine, based on an image showing the frequency components of the current in the electric motor 11, whether there are any signs of abnormality, including at least one of the following: bearing 15, breakage of the rotor bar in the electric motor 11, a change in tension in the belt connecting the electric motor 11 and the load 13, and cavitation.

[0110] Third Embodiment The sign determination device 100 according to the third embodiment will be described below. In the sign assessment device 100 according to the first and second embodiments, the detection unit 104 detects a change in frequency components based on the generated image, and the first assessment unit 105 determines that a sign of abnormality exists when the change detected by the detection unit 104 is equal to or greater than a first threshold. The calculation unit 106 extracts features of the image generated by the image generation unit 103 using a convolutional neural network technique and calculates a similarity between the extracted features. The second assessment unit 107, when the first assessment unit 105 determines that a sign exists, determines whether the similarity calculated by the calculation unit 106 is equal to or greater than a second threshold set in advance. If the second assessment unit 107 determines that the similarity is equal to or greater than the second threshold, the identification unit 108 identifies a factor related to the similarity calculated by the calculation unit 106. However, in the symptom determination device 100 according to the third embodiment, the processing unit 112 described below determines whether there are any signs of an abnormality, and if there are any signs of an abnormality, determines the time until the abnormality occurs, the location where the abnormality occurred, and the cause of the abnormality.

[0111] As shown in FIG. 7, the sign determination device 100 includes a measurement result acquisition unit 101, an analysis unit 102, a processing unit 112 (an example of a processing unit), an update unit 109, an output unit 110, and a storage unit 111.

[0112] The processing unit 112 predicts whether or not there is a sign of an abnormality, the time until the abnormality actually occurs, the location where the abnormality occurs, and the cause of the abnormality, based on each piece of time-series data decomposed into multiple frequency components by the FFT performed by the analysis unit 102. The processing unit 112 predicts whether or not there is a sign of an abnormality, the time until the abnormality actually occurs, the location where the abnormality occurs, and the cause of the abnormality, for example, by using a trained model (e.g., a convolutional neural network) whose parameters are determined using teacher data, which is a type of machine learning. Here, the trained model used by the processing unit 112 for each prediction will be described.

[0113] The trained model used by the processing unit 112 to predict signs of abnormality in the bearing 15 is referred to as the first trained model. The trained model used by the processing unit 112 to predict signs of abnormality including at least one of the bearing 15, breakage of a rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation is referred to as the second trained model. In the following description, the first trained model that predicts signs of abnormality in the bearing 15 is cited as a specific example of a trained model to make it easy to understand how to determine the parameters.

[0114] (First trained model) First, the first trained model will be described. The processing unit 112 predicts signs of abnormality in the bearing 15 based on each piece of time series data decomposed into multiple frequency components by the analysis unit 102 using FFT. Here, a trained model will be described for the case where the processing unit 112 predicts signs of abnormality in the bearing 15 based on each piece of time series data decomposed into multiple frequency components by the analysis unit 102 using FFT.

[0115] In this case, each piece of time-series data decomposed into multiple frequency components by the FFT performed by the analysis unit 102 becomes one piece of input data. Furthermore, the time until an abnormality occurs for the input data, the location of the abnormality, and the cause of the abnormality become one piece of output data. A combination of input data and output data corresponding to the input data becomes one piece of training data. For example, before the sign assessment device 100 predicts an abnormality sign, the output data (i.e., data indicating the time until the abnormality actually occurs, the location of the abnormality, and the cause of the abnormality) is identified for each piece of input data, which is time-series data decomposed into multiple frequency components by the FFT performed by the analysis unit 102 when used by another device to predict an abnormality sign. Alternatively, for example, by conducting experiments or simulations, the output data (i.e., data indicating the time until the abnormality actually occurs for the time-series data decomposed into multiple frequency components by the FFT performed by the analysis unit 102) is identified for each piece of input data. In this way, training data consisting of multiple data combining input data and output data can be prepared. The training data is data used to determine parameter values ​​in a learning model in which the parameter values ​​have not yet been determined.

[0116] Fig. 8 is a diagram showing an example of training data. One set of data is input data, which is time-series data decomposed into a plurality of frequency components by FFT performed by the analysis unit 102, and output data for the input data (i.e., data indicating the time until an abnormality actually occurs, the location where the abnormality occurred, and the cause of the abnormality). In the example shown in Fig. 8, the training data includes 10,000 sets of data.

[0117] For example, consider the case where parameters in a learning model are determined using training data consisting of 10,000 sets of data as shown in FIG. 8. In this case, the training data is divided into, for example, training data, evaluation data, and test data. Examples of the ratios of training data, evaluation data, and test data include 70%, 15%, 15%, 95%, 2.5%, and 2.5%. For example, suppose that the training data consisting of data #1 to #10,000 is divided into data #1 to #7000 as training data, data #7001 to #8500 as evaluation data, and data #8501 to #10,000 as 15% test data. In this case, data #1, which is training data, is input to a convolutional neural network, which is a learning model. The convolutional neural network outputs either that no abnormality has occurred, or the time until an abnormality actually occurs, the location where the abnormality occurred, and the cause of the abnormality. The training data input data is input to the convolutional neural network, and each time the convolutional neural network outputs the absence of an abnormality, the time until an abnormality actually occurs, the location of the abnormality, and the cause of the abnormality (in this case, each time each of data #1 to #7000 is input to the convolutional neural network), the parameters indicating the weighting of the data connections between nodes are changed (i.e., the convolutional neural network model is changed) by performing, for example, backpropagation in accordance with the output. In this way, the training data is input to the neural network and the parameters are adjusted.

[0118] Next, the input data of the evaluation data (data #7001 to #8500) is input in order to the convolutional neural network whose parameters have been changed using the training data. Depending on the input evaluation data, the convolutional neural network outputs either that no abnormality has occurred, or the time until the abnormality actually occurs, the location where the abnormality occurred, and the cause of the abnormality. Here, if the data output by the convolutional neural network differs from the output data associated with the input data in FIG. 8, the parameters are changed so that the output of the convolutional neural network becomes the output data associated with the input data in FIG. 8. The convolutional neural network (i.e., the learning model) whose parameters have been determined in this way is the first trained model.

[0119] Next, as a final check, the input data of the test data (data #8501 to #10000) is input sequentially to the convolutional neural network of the first trained model. Depending on the input test data, the convolutional neural network of the trained model outputs either that no abnormality has occurred, or the time until the abnormality actually occurs, the location where the abnormality occurred, and the cause of the abnormality. If the output data output by the convolutional neural network of the trained model for all test data matches the output data associated with the input data in FIG. 8, the convolutional neural network of the first trained model is the desired model. Furthermore, if the output data output by the convolutional neural network of the first trained model for even one of the test data does not match the output data associated with the input data in FIG. 8, the parameters of the training model are determined using new training data. The above-described determination of the parameters of the training model is repeated until a first trained model having the desired parameters is obtained. When a first trained model having the desired parameters is obtained, the first trained model is recorded in the storage unit 111.

[0120] (Second trained model) Next, the second trained model will be described. FIG. 9 is a diagram showing an example of training data. The parameters of the second trained model can be determined in the same manner as the first trained model described above. However, as shown in FIG. 9, abnormalities can occur not only in the bearings but also in other places, such as broken rotor bars in the electric motor 11, changes in tension in the belt connecting the electric motor 11 and the load 13, and cavitation, and there are multiple causes of abnormalities.

[0121] <<Operation of the Premonition Judgment System>> The operation of the sign determination system 1 will be described below. FIG. 10 is a flowchart showing the operation of the sign determination system 1.

[0122] The measuring instrument 16 measures the current and acquires an analog current waveform (step S1). Converter 17 converts the analog current waveform acquired in step S1 into digital data (step S2).

[0123] The measurement result acquisition unit 101 acquires the measurement result, which is digital data, from the converter 17 (step S3). The analysis unit 102 resolves the measurement results acquired in step S3 into frequency components using FFT (step S4).

[0124] The processing unit 112 inputs each piece of time-series data decomposed into a plurality of frequency components by the FFT performed by the analysis unit 102 to the trained model (step S21). The trained model of the processing unit 112 outputs the time until an abnormality actually occurs, the location where the abnormality occurred, and the cause of the abnormality (step S22).

[0125] The output unit 110 outputs the details identified by the processing unit 112 (that is, the time until the abnormality actually occurs, the location where the abnormality occurred, and the cause of the abnormality) to the notification device (step S23). The notification device notifies the user of the sign determination system 1 of the content identified by the processing unit 112 (step S24).

[0126] Actions and Effects In the sign determination device 100 according to the present disclosure, the processing unit 112 predicts the presence or absence of signs of an abnormality, the time until the abnormality actually occurs, the location where the abnormality has occurred, and the cause of the abnormality, based on each of the time series data decomposed into multiple frequency components by the FFT performed by the analysis unit 102.

[0127] Based on the prediction results from the processing unit 112, the symptom determination device 100 can determine whether there are any signs of abnormality, including at least one of bearing 15, breakage of the rotor bar in the electric motor 11, a change in tension in the belt connecting the electric motor 11 and the load 13, and cavitation.

[0128] Fourth Embodiment The sign determination device 100 according to the fourth embodiment will be described below. The symptom assessment device 100 may provide, as the cause information, information that associates a cause of an abnormality occurring in the bearing 15, an image, and a countermeasure for the cause. This allows the user of the symptom assessment device 100 to check not only the cause but also an appropriate countermeasure for the cause.

[0129] Furthermore, the update unit 109 of the sign assessment device 100 may receive input from the outside and update the time information or body part information.

[0130] Fifth Embodiment The sign determination device 100 according to the fifth embodiment will be described below. 11 is a diagram showing an example of a sign assessment system 1 according to a fifth embodiment. In the sign assessment system 1, the sign assessment device 100 may be configured as a system capable of communicating with a terminal device 200 having a request unit 2001. In this case, the request unit 2001 of the terminal device 200 may be configured to request the sign assessment device 100 to assess signs of abnormalities in the electric motor 11 and the load 13 of the electric motor 11. Furthermore, the terminal device 200 may have the function of the output unit 110, and may be configured to output the abnormality sign determination result of the sign determination device 100 as the aforementioned notification device.

[0131] <Computer Configuration> FIG. 12 is a schematic block diagram illustrating the configuration of a computer according to at least one embodiment. The computer 1100 includes a processor 1110 , a main memory 1120 , storage 1130 , and an interface 1140 . The above-described sign determination device 100 is implemented in a computer 1100. The operations of the above-described processing units are stored in the form of a program in a storage 1130. The processor 1110 reads the program from the storage 1130, loads it into the main memory 1120, and executes the above-described processing in accordance with the program. The processor 1110 also allocates storage areas in the main memory 1120 corresponding to the above-described storage units in accordance with the program.

[0132] The program may be for realizing some of the functions to be performed by the computer 1100. For example, the program may be combined with other programs already stored in the storage 1130 or other programs implemented in other devices to perform the functions. In other embodiments, the computer 1100 may include a custom LSI (Large Scale Integrated Circuit) such as a PLD (Programmable Logic Device) in addition to or instead of the above configuration. Examples of PLDs include a PAL (Programmable Array Logic), a GAL (Generic Array Logic), a CPLD (Complex Programmable Logic Device), and an FPGA (Field Programmable Gate Array). In this case, some or all of the functions realized by the processor 1110 may be realized by the integrated circuit.

[0133] Examples of storage 1130 include a magnetic disk, a magneto-optical disk, and a semiconductor memory. Storage 1130 may be an internal medium directly connected to the bus of computer 1100, or an external medium connected to the computer via interface 1140 or a communication line. Furthermore, when this program is distributed to computer 1100 via a communication line, computer 1100 that receives the program may load the program into main memory 1120 and execute the above-described processing. In at least one embodiment, storage 1130 is a non-transitory tangible storage medium.

[0134] The program may also be a program for realizing part of the above-described functions. Furthermore, the program may be a so-called differential file (differential program) that realizes the above-described functions in combination with another program already stored in storage 1130.

[0135] <Additional Notes> The sign determination device 100 described in each embodiment can be understood, for example, as follows.

[0136] (1) The symptom determination device 100 according to the present disclosure includes a measurement result acquisition unit 101 that acquires measurement results of the current flowing through the electric motor 11, an analysis unit 102 that performs frequency analysis on the measurement results and breaks them down into frequency components, and a prediction unit 105 that determines whether there are signs of an abnormality in at least one of the electric motor 11 and the load 13 of the electric motor 11 based on time series data of the frequency components.

[0137] The sign assessment device 100 can determine whether or not there is a sign of an abnormality in at least one of the electric motor 11 and the load 13 of the electric motor 11, based on the frequency component of the current of the electric motor 11. Furthermore, the sign assessment device 100 can determine whether or not there is a sign of an abnormality from a long distance, even if it is not installed in the vicinity of the electric motor 11 and the load 13, which are affected by the generation of vibrations, temperature changes, etc.

[0138] (2) In addition, the analysis unit 102 of the symptom determination device 100 decomposes the signal into multiple frequency components using FFT, and the prediction units (105, 112) determine whether there are any signs of abnormality in at least one of the motor 11 and the load 13 based on the time series data of each of the multiple frequency components.

[0139] The sign determination device 100 can determine whether or not there is a sign of an abnormality in at least one of the electric motor 11 and the load 13, based on a plurality of frequency components resolved by FFT.

[0140] (3) Furthermore, the signs of abnormality in the electric motor 11 determined by the prediction unit (105, 112) of the sign determination device 100 include at least one of breakage of the rotor bar in the electric motor 11, a change in tension of the belt connecting the electric motor 11 and the load 13, and cavitation.

[0141] The symptom determination device 100 can determine whether there is a symptom of an abnormality including at least one of breakage of a rotor bar in the electric motor 11, a change in tension of a belt connecting the electric motor 11 and the load 13, and cavitation.

[0142] (4) Furthermore, the prediction unit (112) of the sign determination device 100 includes a trained model whose parameters are determined using training data, in which time series data of frequency components is used as input data and the determination result as to whether or not there is a sign of an abnormality in the input data is used as output data.

[0143] The sign determination device 100 can determine whether or not there is a sign of an abnormality in at least one of the electric motor 11 and the load 13, using a trained model whose parameters are determined using teacher data.

[0144] (5) The symptom determination device 100 also includes an image generation unit 103 that generates an image showing time series data of frequency components, and a prediction unit 105 determines whether there are any signs of abnormality in at least one of the electric motor 11 and the load 13 based on the image.

[0145] The sign assessment device 100 can determine whether or not there is a sign of an abnormality in at least one of the electric motor 11 and the load 13, based on an image showing the frequency components of the current in the electric motor 11. The sign assessment device 100 can determine whether or not there is a sign of an abnormality from a long distance, even if it is not installed in the vicinity of the electric motor 11 and the load 13, which are affected by the generation of vibrations, temperature changes, etc.

[0146] (6) Furthermore, the analysis unit 102 of the sign determination device 100 decomposes the signal into a plurality of frequency components using FFT, and the image represents time-series data for each of the plurality of frequency components.

[0147] The sign determination device 100 can determine whether or not there is a sign of an abnormality in at least one of the electric motor 11 and the load 13, based on an image showing a plurality of frequency components resolved by FFT.

[0148] (7) The image of the sign determination device 100 further shows time series data of energy values ​​associated with frequency components, and the device is equipped with a detection unit 104 that detects change values ​​of the frequency components based on the image, and a prediction unit 105 that compares the change value with a predetermined first threshold value to determine whether or not a sign is present.

[0149] The sign determination device 100 can determine whether or not there is a sign of an abnormality in at least one of the electric motor 11 and the load 13, based on the image showing the frequency components and energy values.

[0150] (8) Furthermore, the image of the sign determination device 100 shows frequency components as a color graph with time and frequency as axes, and energy values ​​are indicated by colors on the color graph.

[0151] The sign determination device 100 can determine whether or not there is a sign of an abnormality in at least one of the electric motor 11 and the load 13, based on the image of the color graph showing the frequency components and energy values.

[0152] (9) Furthermore, the symptom determination device 100 includes an identification unit 108 that, when it is determined that a symptom exists, identifies the part by comparing the frequency component with part information that associates the frequency component with the part where the abnormality occurs.

[0153] The sign assessment device 100 identifies the part where an abnormality will occur based on the part information, thereby enabling the user of the sign assessment device 100 to identify the part where an abnormality will occur in at least one of the electric motor 11 and the load 13.

[0154] (10) Furthermore, when it is determined that a sign exists, the identifying unit 108 of the sign assessment device 100 further identifies the cause based on the cause information that associates the image with the cause of the abnormality.

[0155] The sign assessment device 100 identifies the cause of the abnormality based on the cause information, thereby enabling the user of the sign assessment device 100 to identify the cause of the abnormality in the bearing 15.

[0156] (11) The sign determination device 100 also includes a calculation unit 106 that calculates a similarity based on the image feature associated with the factor and the image feature generated by the image generation unit 103, and a second determination unit 107 that determines whether the similarity is equal to or greater than a second threshold value set in advance when it is determined that a sign exists. The identification unit 108 identifies the factor related to the calculated similarity when it is determined that the similarity is equal to or greater than the second threshold value.

[0157] The sign assessment device 100 calculates the similarity based on the feature amount of the image and identifies the cause of the abnormality, thereby enabling the user of the sign assessment device 100 to identify the cause of the abnormality in at least one of the electric motor 11 and the load 13.

[0158] (12) Furthermore, the sign determination device 100 includes an update unit 109 that receives an input from outside and updates the cause information when it is determined that the similarity is not equal to or greater than the second threshold value.

[0159] If the sign assessment device 100 cannot identify a cause, it accepts input from outside and updates the cause information, thereby enabling the sign assessment device 100 to identify more causes.

[0160] (13) Furthermore, the identifying unit 108 of the sign determination device 100 compares the identified cause with time information that associates the cause with the time until the abnormality occurs, and further identifies the time.

[0161] The sign assessment device 100 identifies the time until an abnormality occurs based on the time information, thereby enabling the user of the sign assessment device 100 to identify the time until an abnormality occurs in at least one of the electric motor 11 and the load 13.

[0162] (14) The sign determination device 100 also includes an output unit 110 that outputs the content identified by the identification unit 108.

[0163] The sign assessment device 100 outputs the cause, part, time, and other details identified by the identification unit 108. This allows the user of the sign assessment device 100 to confirm the cause, part, time, and other details identified by the identification unit 108.

[0164] (15) Furthermore, the symptom assessment system 1 is an abnormality symptom assessment system that includes a symptom assessment device 100 that can communicate with a terminal device 200. The terminal device 200 includes a request unit 2001 that requests an abnormality symptom assessment for the electric motor 11 and the load 13 of the electric motor 11. The symptom assessment device 100 includes a measurement result acquisition unit 101 that acquires measurement results of the current flowing through the electric motor 11 in response to a request from the terminal device 200, an analysis unit 102 that performs frequency analysis on the measurement results and breaks them down into frequency components, and a prediction unit 105 that determines whether there are any signs of abnormality in at least one of the electric motor 11 and the load 13 of the electric motor 11 based on time series data of the frequency components.

[0165] The sign assessment system 1 can determine whether there is a sign of an abnormality in at least one of the electric motor 11 and the load 13 of the electric motor 11, based on the frequency component of the current of the electric motor 11. Furthermore, the sign assessment device 100 can determine whether there is a sign of an abnormality from a long distance, even if it is not installed near the electric motor 11 and the load 13, which are affected by the occurrence of vibrations, temperature changes, etc.

[0166] (16) The method for determining an abnormality according to the present disclosure includes obtaining measurement results of the current flowing through the electric motor 11, performing frequency analysis on the measurement results to decompose them into frequency components, and determining whether there are any abnormality signs in at least one of the electric motor 11 and the load 13 of the electric motor 11 based on the time series data of the frequency components.

[0167] By using the sign determination method, a user can determine whether or not there is a sign of abnormality in at least one of the electric motor 11 and the load 13 of the electric motor 11, based on the frequency component of the current of the electric motor 11. Furthermore, by using the sign determination method, a user can determine whether or not there is a sign of abnormality from a long distance, even if the user is not located near the electric motor 11 and the load 13, which are affected by the occurrence of vibrations, temperature changes, etc.

[0168] (17) The program according to the present disclosure causes a computer to acquire measurement results of the current flowing through the electric motor 11, perform frequency analysis on the measurement results to decompose them into frequency components, and determine whether there are signs of an abnormality in at least one of the electric motor 11 and the load 13 of the electric motor 11 based on the time series data of the frequency components.

[0169] By having a computer execute the program, a user of the program can determine whether there are signs of an abnormality in at least one of the electric motor 11 and the load 13 of the electric motor 11, based on the frequency component of the current of the electric motor 11. Furthermore, by having a computer execute the program, a user of the program can determine whether there are signs of an abnormality from a long distance, even if the computer is not installed near the electric motor 11 and the load 13, which are affected by the occurrence of vibrations, temperature changes, etc.

[0170] (18) The sign determination method according to the present disclosure includes a request step in which a terminal device 200 requests a sign determination for an abnormality regarding the electric motor 11 and the load of the electric motor 11, a measurement result acquisition step in which an abnormality determination device 100 capable of communicating with the terminal device 200 acquires measurement results of the current flowing through the electric motor 11 in response to a request from the terminal device 200, an analysis step in which the measurement results are subjected to frequency analysis to resolve them into frequency components, and a prediction step in which it is determined whether or not there is a sign of an abnormality in at least one of the electric motor 11 and the load 13 of the electric motor 11 based on time series data of the frequency components.

[0171] By using the sign determination method, a user can determine whether or not there is a sign of abnormality in at least one of the electric motor 11 and the load 13 of the electric motor 11, based on the frequency component of the current of the electric motor 11. Furthermore, by using the sign determination method, a user can determine whether or not there is a sign of abnormality from a long distance, even if the user is not located near the electric motor 11 and the load 13, which are affected by the occurrence of vibrations, temperature changes, etc. [Explanation of symbols]

[0172] 1. Prediction assessment system 10 Power source 11 Electric motor 12 Electric wire 13 Load 14 axes 15 Bearings 16 Measuring Instruments 17 Converter 100 Premonition Judgment Device 101 Measurement result acquisition unit 102 Analysis Department 103 Image generation unit 104 Detector 105 1st Judgment Section 106 Calculation Unit 107 Second Judgment Section 108 Specific section 109 Update section 110 Output section 111 Storage section 112 Processing section 200 Terminal Device 1100 Computer 1110 processor 1120 main memory 1130 Storage 1140 Interface 2001 Request part

Claims

1. a measurement result acquisition unit that acquires a measurement result of a current flowing through the electric motor; an analysis unit that performs frequency analysis on the measurement result and resolves it into frequency components; a prediction unit that determines whether or not there is a sign of an abnormality in at least one of the electric motor and a load of the electric motor based on the time series data of the frequency components; Equipped with an identification unit that, when it is determined that the sign exists, compares the frequency component with part information in which the frequency component is associated with a part in which the abnormality occurs to identify the part, and further identifies the cause of the abnormality based on an image showing time-series data of the frequency component and cause information in which the cause of the abnormality is associated with the cause; An indication determination device comprising:

2. a calculation unit that calculates a similarity based on the feature amount of the image associated with the factor and the feature amount of the image; a second determination unit that, when it is determined that the sign exists, determines whether the similarity is equal to or greater than a second threshold value that is set in advance; the specifying unit specifies the factor related to the calculated similarity when it is determined that the similarity is equal to or greater than the second threshold. The sign determination device according to claim 1 .

3. an update unit that receives an input from an external device and updates the factor information when it is determined that the similarity is not equal to or greater than the second threshold; The sign determination device according to claim 2 , further comprising:

4. the identifying unit compares the identified cause with time information that associates the cause with the time until the abnormality occurs, and further identifies the time. The sign determination device according to claim 1 .

5. an output unit that outputs the content identified by the identification unit; The sign determination device according to claim 1 , comprising:

6. obtaining a measurement of the current through the electric motor; performing frequency analysis on the measurement results to resolve them into frequency components; determining whether or not there is a sign of an abnormality in at least one of the electric motor and a load of the electric motor based on the time series data of the frequency components; Including, If it is determined that the sign exists, comparing the frequency component with part information in which the frequency component is associated with a part where the abnormality occurs, to identify the part, and further identifying the cause of the abnormality based on an image showing time-series data of the frequency component and cause information in which the cause of the abnormality is associated with the image; A processing method comprising:

7. On the computer, obtaining a measurement of the current through the electric motor; performing frequency analysis on the measurement results to resolve them into frequency components; determining whether or not there is a sign of an abnormality in at least one of the electric motor and a load of the electric motor based on the time series data of the frequency components; Execute If it is determined that the sign exists, comparing the frequency component with part information in which the frequency component is associated with a part where the abnormality occurs, to identify the part, and further identifying the cause of the abnormality based on an image showing time-series data of the frequency component and cause information in which the cause of the abnormality is associated with the image; A program that executes the following.

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