Rolling bearing abnormality detection device and abnormality detection method

Abstract

To provide an abnormality detection device and abnormality detection method which can accurately diagnose an abnormality in a rolling bearing.SOLUTION: An abnormality detection device 100 of a rolling bearing 20 used in a rotary machine 30 in which a rotational shaft 31 connected with a gear device 40 is supported by the rolling bearing comprises: a vibration detector 17 which detects the vibration of the rolling bearing 20; and an abnormality diagnostic unit 10 which diagnoses an abnormality in the rolling bearing 20 on the basis of a vibration waveform of a time domain obtained from the vibration detector 17. The abnormality diagnostic unit 10 includes a drive analysis part 14 which determines whether the rotary machine 30 is being driven or being idled on the basis of the vibration waveform, executes abnormality detection of the rolling bearing for driving when the rotary machine 30 is being driven according to the determination result of the drive analysis part 14, and executes abnormality detection of the rolling bearing for idling when the rotary machine 30 is being idled.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an abnormal detection device and an abnormal detection method for a rolling bearing, and particularly to a device and a method for detecting an abnormality of a rolling bearing by determining whether a rotating machine is in operation or idling.

Background Art

[0002] The summary of Patent Document 1 describes the following belt conveyor condition monitoring system. "The data collection device 100 installed for each of one or more belt conveyors 200 has a wireless communication function and can communicate with a diagnostic server 600 via a wireless communication access point 300, a base station antenna 400, and a wired intranet 500. The operation / stop determination unit and the conveyor conveyed material presence / absence determination unit of the diagnostic server 600 perform frequency analysis on the time-series vibration acceleration signal measured by a predetermined vibration acceleration sensor to determine whether the belt conveyor 200 is in operation or stopped, and further determine the presence or absence of the conveyor conveyed material."

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Patent Document 1 discloses a technique for determining whether a belt conveyor is in operation or stopped, and further determining the presence or absence of the conveyor conveyed material by performing frequency analysis on the time-series vibration acceleration signal measured by a vibration acceleration sensor. However, Patent Document 1 does not disclose a technique for determining idling. In order to accurately diagnose an abnormality of a rolling bearing of a rotating machine, it is necessary to perform a diagnosis suitable for each according to whether the rotating machine is Rotation during non-driving in operation or Rotating during driving idling. Rotating during non-driving There is a need to perform appropriate diagnoses accordingly.

[0005] An object of the present invention is to provide an abnormality detection device and an abnormality detection method capable of accurately diagnosing an abnormality in a rolling bearing.

Means for Solving the Problems

[0006] In order to achieve the above object, an abnormality detection device for a rolling bearing according to the present invention is an abnormality detection device for a rolling bearing used in a rotating machine in which a rotating shaft to which a gear device is connected is supported by a rolling bearing, comprising a vibration detector that detects vibration of the rolling bearing, and an abnormality diagnosis unit that diagnoses an abnormality of the rolling bearing based on a vibration waveform in the time domain obtained from the vibration detector. The abnormality diagnosis unit has a drive analysis unit that determines whether the rotating machine is Rotating during driving in operation or Rotating during non-driving idling. According to the determination result of the drive analysis unit, when the rotating machine is said in operation, an abnormality detection for the rolling bearing during operation is executed, and when the rotating machine is said idling, an abnormality detection for the rolling bearing during idling is executed and In the abnormal detection of the rolling bearing for driving, the threshold for detecting the peak related to the abnormal part that occurs in the waveform of the frequency region of the vibration of the rolling bearing is made larger than the threshold for detecting the peak in the abnormal detection of the rolling bearing for idling .

[0007] Also, in order to achieve the above object, an abnormality detection method for a rolling bearing according to the present invention is an abnormality detection method for a rolling bearing used in a rotating machine in which a rotating shaft to which a gear device is connected is supported by a rolling bearing, having an abnormality diagnosis step of diagnosing an abnormality of the rolling bearing based on a vibration waveform in the time domain obtained from a vibration detector that detects vibration of the rolling bearing, the abnormality diagnosis step including a drive analysis step of determining whether the rotating machine is Rotating during driving in operation or Rotating during non-driving idling. According to the determination result of the drive analysis step, when the rotating machine is said in operation, an abnormality detection for the rolling bearing during operation is executed, and when the rotating machine is said idling, an abnormality detection for the rolling bearing during idling is executed and In the abnormal detection of the rolling bearing for driving, the threshold for detecting the peak related to the abnormal part that occurs in the waveform of the frequency region of the vibration of the rolling bearing is made larger than the threshold for detecting the peak in the abnormal detection of the rolling bearing for idling Do.

Advantages of the Invention

[0008] According to the present invention, it is possible to provide a technique capable of determining whether a rotating machine supported by a rolling bearing and having a changing rotational speed and load is in operation or idling.

[0009] Problems, configurations, and effects other than those described above will be clarified by the following description of the embodiments.

Brief Description of the Drawings

[0010]

Figure 1

Figure 2

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Figure 8

Modes for Carrying Out the Invention

[0011] Abnormalities (such as damage or defects) in a rolling bearing can be detected based on the rotational speed and vibration. When the rotational speed or load of a rotating machine changes, the vibration measured by the rolling bearing also changes. For example, in the case of an electric motor, the vibration generated also changes depending on whether the power is on or off. When the power is on, it is the vibration in the driving state with a load applied, and when the power is off, it is the vibration in the Non-driving rotation state( idling state ) without a load. Therefore, when detecting abnormalities in a rolling bearing, in order to accurately analyze the measured vibration data, it is necessary to determine whether the rotating machine is Rotating during driving (hereinafter, operating is called) or Rotating during non-driving (hereinafter, idling is called) . However, in order to determine whether the rotating machine is operating or idling, it is necessary to incorporate a drive control signal from a control device or install a torque sensor on the rotating machine. Adding a control device to a rotating machine without a control device retrospectively incurs costs such as the cost of the device and the labor involved in adding wiring from the control device. Also, when using a torque sensor, since the torque sensor needs to be arranged so as to be sandwiched between two rotating bodies, it increases costs and reduces work efficiency. In this embodiment, while solving the above-mentioned problems, by determining whether the rotating machine is operating or idling and performing an abnormality diagnosis of the rolling bearing, the accuracy of the abnormality diagnosis is improved.

[0012] An abnormality detection device and method for a rolling bearing according to an embodiment of the present invention determine whether a rotating machine supported by a rolling bearing and having a changing rotational speed and load is operating or idling by using the vibration of the rolling bearing in a rotating machine to which a gear device is connected, and detect an abnormality of the rolling bearing by specifying an abnormal component of the rolling bearing based on the determination result of whether it is operating or idling.

[0013] In the rolling bearing abnormality detection device and abnormality detection method according to this embodiment, the determination of whether it is in driving or idling is performed as follows. That is, in the intensity distribution data in the frequency domain obtained by performing fast Fourier transform (hereinafter also referred to as "FFT") processing after performing envelope processing on the vibration waveform of the rolling bearing, the meshing frequencies of the first-order and higher-order gears proportional to the rotational frequency of the rotating machine are obtained, and the vibration intensity at the obtained meshing frequencies of the first-order and higher-order gears is obtained. Then, when the vibration intensity at the meshing frequencies of the first-order and higher-order gears is equal to or greater than the threshold value, it is determined that it is in driving, and when it is less than the threshold value, it is determined that it is in idling, and it is determined whether the rotating machine is in driving or idling.

[0014] With the abnormality detection device and abnormality detection method of this embodiment described above, the above-described problems can be solved.

[0015] Hereinafter, the rolling bearing abnormality detection device and abnormality detection method according to this embodiment will be described with reference to the drawings.

[0016] FIG. 1 is a schematic diagram showing a rolling bearing abnormality detection device 100 according to Embodiment 1 of the present invention. FIG. 1 also shows a rolling bearing 20 for which the abnormality detection device 100 detects an abnormality, a rotating machine 30 supported by the rolling bearing 20, and a gear device 40 connected to the rotating machine 30.

[0017] In particular, the rotating machine 30 of this embodiment targets a rotating machine to which a gear device 40 is connected and whose rotational speed and load change.

[0018] The abnormality detection device 100 includes an abnormality diagnosis unit 10 that performs abnormality diagnosis of the rolling bearing 20, a vibration detector 17 installed in the rolling bearing 20, a computer (control device) 19 such as a personal computer that operates the abnormality diagnosis unit 10, and a data logger 18. The rolling bearing 20 supports a rotating machine 30 to which a gear device 40 is connected and whose rotational speed and load change.

[0019] The abnormality diagnosis unit 10 includes a data acquisition unit 11, a rotation analysis unit 12, a rotation speed calculation unit 13, a drive analysis unit 14, an abnormality identification unit 15, and an output unit 16, and is connected to the data logger 18.

[0020] The data acquisition unit 11 acquires the time-domain waveform (vibration waveform) of the vibration detected by the vibration detector 17.

[0021] The rotation analysis unit 12 determines whether the rotating machine 30 is rotating or stopped from the waveform acquired by the data acquisition unit 11. In this case, the rotation analysis unit 12 calculates the root mean square (RMS) of the vibration waveform in the time domain (S3 in FIG. 4), and estimates whether the rotating machine 30 is rotating or stopped from this root mean square (RMS) and the like. For this purpose, the rotation analysis unit 12 has a vibration waveform RMS calculation unit 12a that calculates the root mean square (RMS).

[0022] The rotation speed calculation unit 13 obtains the rotation speed of the rotating machine 30 from the waveform acquired by the data acquisition unit 11. In this case, the rotation speed calculation unit 13 converts the vibration waveform in the time domain into intensity distribution data in the frequency domain (vibration waveform frequency intensity distribution data) by fast Fourier transform processing, and estimates the rotation speed of the rotating machine 30 using the intensity distribution data of the vibration waveform in the frequency domain. For this purpose, the rotation speed calculation unit 13 has a vibration waveform frequency intensity distribution data conversion unit 13a that converts the vibration waveform in the time domain into intensity distribution data in the frequency domain (vibration waveform frequency intensity distribution data).

[0023] The drive analysis unit (drive determination unit) 14 determines whether the rotating machine is in operation or idling based on the waveform acquired by the data acquisition unit 11. In this case, the drive analysis unit 14 obtains the envelope of the vibration waveform in the time domain (vibration waveform envelope), converts the vibration waveform envelope in the time domain into intensity distribution data in the frequency domain (vibration waveform envelope frequency intensity distribution data) by performing fast Fourier transform processing, and further estimates whether the rotating machine 30 is in operation or idling based on the vibration waveform envelope intensity at one or more frequencies proportional to the rotational speed of the rotating machine 30 using the vibration waveform envelope frequency intensity distribution data in the frequency domain. For this purpose, the drive analysis unit 14 includes a vibration waveform envelope processing unit 14a that obtains the envelope of the vibration waveform in the time domain (vibration waveform envelope), and a vibration waveform envelope frequency intensity distribution data conversion unit 14b that converts the vibration waveform envelope in the time domain into intensity distribution data in the frequency domain (vibration waveform envelope frequency intensity distribution data) by performing fast Fourier transform processing.

[0024] More specifically, the drive analysis unit 14 obtains the intensity (frequency intensity) A at one or more frequencies that are integer (m) multiples of the rotational frequency fs estimated by the rotational speed calculation unit 13 from the vibration waveform envelope frequency intensity distribution data, and determines whether the rotating machine 30 is in operation or idling using the obtained one or more intensities (frequency intensities) A. For this purpose, the drive analysis unit 14 includes a frequency intensity determination unit 14c that obtains the intensity (frequency intensity) A at one or more meshing frequencies of the gears.

[0025] The meshing frequency of the gears can be used as the frequency that is an integer (m) multiple of the rotational frequency fs, and the intensity at this meshing frequency of the gears (meshing frequency intensity) A can be obtained (in this case, 14c is called the meshing frequency intensity determination unit), and it is advisable to determine whether the rotating machine 30 is in operation or idling based on the meshing frequency intensity A. As the frequency used to determine whether the rotating machine 30 is in operation or idling, one or more frequencies can be used from among the meshing frequencies of the primary and higher-order gears.

[0026] When the torque in the gear device 40 is small and the force applied to the gear meshing portion is weak, vibrations of the harmonics of the meshing frequency are likely to occur. This is because when the torque in the gear device 40 is small and the force applied to the gear meshing portion is weak, the spring due to the elastic deformation of the meshing teeth becomes non-linear, making it easier for vibrations of the harmonics of the meshing frequency to occur. In such cases, it is better to use the intensity A at the higher-order meshing frequency than the intensity A at the first-order meshing frequency.

[0027] Note that the "gear meshing frequency" may sometimes be simply referred to as the "meshing frequency" for explanation.

[0028] The abnormality identification unit 15 identifies the abnormal components of the rolling bearing 20 using the rotational speed obtained by the rotational speed calculation unit 13 and the drive analysis result (drive determination result) of the drive analysis unit 14. When identifying the abnormal components, the abnormality identification unit 15 uses the characteristic frequency indicating the cause of the vibration of the rolling bearing 20. In other words, the abnormality identification unit 15 identifies the abnormal components of the rolling bearing 20 using the drive analysis result in the drive analysis unit 14 and the characteristic frequency indicating the cause of the vibration of the rolling bearing 20, and the rotational speed obtained by the rotational speed calculation unit 13 is taken into account when identifying the abnormal components.

[0029] The output unit 16 can be composed of a display screen and an interface to an external device, and outputs the result of the abnormality detection.

[0030] The abnormality diagnosis unit 10 is a device that can be operated by a computer 19 such as a personal computer. Alternatively, the abnormality diagnosis unit 10 can also be composed of a computer 19. The computer 19 such as a personal computer that operates the abnormality diagnosis unit 10 constitutes the control device of the abnormality diagnosis unit 10.

[0031] The vibration detector 17 is composed of, for example, an acceleration sensor, and detects the vibration of the rolling bearing 20.

[0032] The data logger 18 is a storage device that stores data on the vibration of the rolling bearing 20 detected by the vibration detector 17, that is, data on the waveform in the time domain of the vibration of the rolling bearing 20 (vibration waveform).

[0033] The data acquisition unit 11 of the abnormality diagnosis unit 10 acquires the waveform in the time domain of the vibration of the rolling bearing 20 (vibration waveform) from the data logger 18.

[0034] The rotating machine 30 includes a rotating shaft (rotating axis) 31, and the shaft 31 is supported by the rolling bearing 20. The rotating speed of the rotating machine 30 (that is, the rotating speed of the shaft 31) changes during operation. The rotating machine 30 is, for example, an electric motor, a centrifugal compressor, a pump, or the like. A gear device 40 is connected to the rotating machine 30.

[0035] FIG. 2 is a schematic diagram showing the configuration of the rolling bearing 20. In FIG. 2, a state of the rolling bearing 20 viewed from a direction parallel to the axial center line 31A of the shaft 31 of the rotating machine 30 is schematically shown.

[0036] The rolling bearing 20 includes an inner ring 21, an outer ring 22, a plurality of rolling elements 23, and a cage 24. The inner ring 21 is an annular member fixed to the shaft 31 side of the rotating machine 30 and rotates together with the shaft 31. The outer ring 22 is an annular member fixed to the housing side of the rolling bearing 20 and is arranged concentrically with the inner ring 21. In the present embodiment, it is assumed that the inner ring 21 is fixed to the shaft 31 of the rotating machine 30 and the outer ring 22 is fixed to the housing of the rolling bearing 20. The plurality of rolling elements 23 are members arranged in the space between the inner ring 21 and the outer ring 22 and rotate (revolve) in the same circumferential direction as the inner ring 21 while rotating on their own axes. The cage 24 is a member that holds the plurality of rolling elements 23 so as to maintain their relative positions in the circumferential direction.

[0037] FIG. 2 shows the diameter d of the rolling element 2 and the pitch circle diameter D (the diameter of the circle 22A passing through the centers of the rolling elements 23).

[0038] FIG. 3 is a schematic cross-sectional view of the rolling bearing 20 viewed from a direction perpendicular to the shaft 31 of the rotating machine 30. FIG. 3 shows the contact angle α of the rolling bearing 20. The contact angle α is the angle between the direction 25 of the load applied between the rolling surface of the rolling element 23 (the surfaces of the inner ring 21 and the outer ring 22 in contact with the rolling element 23) and the rolling element 23, and the radial direction 26 of the rolling bearing 20 (the direction perpendicular to the shaft 31).

[0039] Next, with reference to FIG. 4, an example of an abnormality detection method (abnormality diagnosis) will be described. FIG. 4 is a flowchart showing an example of the abnormality detection method performed by the abnormality detection device 100 according to Embodiment 1 of the present invention. In this embodiment, as an example of the abnormality detection method, a process of specifying abnormal parts of the rolling bearing 20 is shown.

[0040] In step S1, the abnormality diagnosis unit 10 is activated, and the process of abnormality diagnosis is started.

[0041] In step S2, the data acquisition unit 11 of the abnormality diagnosis unit 10 acquires the waveform in the time domain of the vibration of the rolling bearing 20 (vibration waveform) from the data logger 18. Step S2 constitutes a data acquisition step in the abnormality diagnosis (abnormality detection method) of this embodiment.

[0042] Here, with reference to FIG. 5, the waveform in the time domain of the vibration of the rolling bearing 20 (vibration waveform) acquired in step S2 will be described. FIG. 5 is a diagram showing an example of the waveform in the time domain of the vibration of the rolling bearing 20. As shown in FIG. 5, periodic vibration (change in acceleration) occurs in the rolling bearing 20 in response to the rotation of the rotating machine 30 (rotation of the inner ring 21 of the rolling bearing 20).

[0043] In step S3, the rotation analysis unit 12 of the abnormality diagnosis unit 10 calculates the root mean square (RMS) value etc. of the vibration waveform (vibration waveform) acquired in step S2. Step S3 is executed by the vibration waveform RMS value calculation unit 12a of the rotation analysis unit 12, and constitutes an RMS value calculation step in the abnormality diagnosis (abnormality detection method) of this embodiment.

[0044] In step 4, the rotation analysis unit 12 of the abnormality diagnosis unit 10 estimates whether the rotating machine is rotating or stopped using the effective value obtained in step 3. Specifically, for example, when using the effective value, if the effective value obtained in step 3 is greater than a predetermined threshold value, it is estimated that the rotating machine is rotating, and if the effective value obtained in step 3 is less than the predetermined threshold value, it is estimated that the rotating machine is stopped. When it is estimated that the rotating machine is stopped, for this vibration data, abnormality detection of the bearing is not performed and the process ends. Step S4 constitutes the rotation analysis process in the abnormality diagnosis (abnormality detection method) of this embodiment. When it is estimated that the rotating machine is rotating, the process proceeds to the next step 5.

[0045] In step 5, the rotation speed calculation unit 13 of the abnormality diagnosis unit 10 performs FFT processing on the vibration waveform in the time domain acquired in step 2 and converts it into intensity distribution data (vibration waveform frequency intensity distribution data) in the frequency domain. Step S5 is executed by the vibration waveform frequency intensity distribution data conversion unit 13a of the rotation speed calculation unit 13 and constitutes the vibration waveform frequency intensity distribution data conversion process in the abnormality diagnosis (abnormality detection method) of this embodiment.

[0046] FIG. 6 is a diagram showing an example of frequency (frequency intensity) analysis of the waveform in the time domain of the vibration of the rolling bearing 20. FIG. 6 shows an example of the intensity distribution in the frequency domain obtained in step 5.

[0047] In FIG. 6, it can be seen that the intensity of the vibration becomes large (that is, peaks occur) at several frequencies.

[0048] In step 6, the rotation speed calculation unit 13 of the abnormality diagnosis unit 10 searches for peaks within a predetermined range (search range ΔF) in the intensity distribution data obtained in step 5 and estimates the rotational frequency fs. Step S6 constitutes the rotation speed calculation process in the abnormality diagnosis (abnormality detection method) of this embodiment.

[0049] In step 7, the drive analysis unit 14 of the abnormality diagnosis unit 10 performs envelope processing on the vibration waveform acquired in step S2 to obtain the envelope (vibration waveform envelope) of the waveform of the vibration in the time domain. That is, the abnormality diagnosis unit 10 obtains a curve (envelope, vibration waveform envelope) that traces the contour of the waveform (vibration waveform) of the vibration in the time domain. Step S7 is executed by the vibration waveform envelope processing unit 14a of the drive analysis unit 14, and in the abnormality diagnosis (abnormality detection method) of this embodiment, it constitutes a vibration waveform envelope processing step for obtaining the envelope (vibration waveform envelope) of the vibration waveform.

[0050] Here, referring to FIG. 7, the envelope (vibration waveform envelope or envelope waveform) of the waveform of the vibration in the time domain of the rolling bearing 20 obtained in step S7 will be described. FIG. 7 is a diagram showing an example of the envelope (envelope waveform) of the waveform of the vibration in the time domain of the rolling bearing 20.

[0051] The envelope indicates the envelope of the waveform of the periodic vibration (change in acceleration) generated in the rolling bearing 20, and as shown in FIG. 7, it becomes a waveform (envelope waveform, vibration waveform envelope) that changes periodically.

[0052] Furthermore, in step 7, the drive analysis unit 14 of the abnormality diagnosis unit 10 performs fast Fourier transform processing (FFT processing) on the obtained vibration waveform envelope in the time domain to convert the vibration waveform envelope in the time domain into intensity distribution data (vibration waveform envelope frequency intensity distribution data) in the frequency domain. Step S7 is executed by the vibration waveform envelope frequency intensity distribution data conversion unit 14b of the drive analysis unit 14, and in the abnormality diagnosis (abnormality detection method) of this embodiment, it constitutes the above-described vibration waveform envelope generation step and also constitutes a vibration waveform envelope frequency intensity distribution data conversion step for converting the vibration waveform envelope into intensity distribution data (vibration waveform envelope frequency intensity distribution data) in the frequency domain.

[0053] In step S8, the drive analysis unit 14 of the abnormality diagnosis unit 10 reads the intensity A at one or more frequencies proportional to the rotational speed of the rotating machine 30 from the vibration waveform envelope frequency intensity distribution data using the vibration waveform envelope frequency intensity distribution data obtained in step S7. Specifically, the drive analysis unit 14 reads the intensity A at one or more frequencies that are an integer (m) multiple of the rotational frequency fs estimated in step S6. Step S8 is executed by the frequency intensity determination unit 14c of the drive analysis unit 14, and constitutes a frequency intensity determination step for determining the intensity A at a frequency that is an integer (m) multiple of the rotational frequency fs in the abnormality diagnosis (abnormality detection method) of this embodiment.

[0054] FIG. 8 is a diagram showing an example of frequency (frequency intensity) analysis of the envelope waveform in the time domain of the vibration of the rolling bearing 20. FIG. 8 shows an example of frequency (frequency intensity) analysis of the envelope waveform in the time domain of the vibration obtained in step S7.

[0055] As shown in FIG. 8, in the frequency (intensity) analysis of the envelope (envelope waveform, vibration waveform envelope) of the vibration waveform in the time domain, it can be seen that the intensity of the vibration is large (that is, a peak occurs) at some frequencies.

[0056] In step S9, the drive analysis unit 14 of the abnormality diagnosis unit 10 determines whether the rotating machine 30 is in operation or idling based on the intensity A at a frequency proportional to the rotational speed of the rotating machine 30 and a predetermined threshold value. Specifically, the drive analysis unit 14 determines whether the rotating machine 30 is in operation or idling based on the intensity (frequency intensity) A at one or more frequencies that are an integer (m) multiple of the rotational frequency fs obtained in step S8 and a predetermined threshold value. When the intensity A at a frequency that is an integer (m) multiple of the rotational frequency fs is greater than the threshold value, it is determined that the machine is in operation. Also, when the intensity A at a frequency that is an integer (m) multiple of the rotational frequency fs is less than the threshold value, it is determined that the machine is idling. Step S9 constitutes a drive analysis step for estimating whether the rotating machine 30 is in operation or idling from the frequency intensity A at one or more frequencies proportional to the rotational speed of the rotating machine 30 in the abnormality diagnosis (abnormality detection method) of this embodiment.

[0057] In the frequency intensity determination step of step S8, the meshing frequency of the gear may be used as a frequency that is an integer (m) multiple of the rotational frequency fs, and the intensity (meshing frequency intensity) A at this meshing frequency of the gear may be obtained. In this case, S8 can be called the meshing frequency intensity determination step. Also, in the drive analysis step of step 9, it is preferable to determine whether the rotating machine 30 is in operation or idling based on the meshing frequency intensity A. As described above, one or more of the meshing frequencies of the primary and higher-order gears can be used as one or more meshing frequencies.

[0058] In step S10, when the drive determination of the rotating machine obtained in step S9 indicates that the rotating machine is in operation, the abnormality specifying unit 15 of the abnormality diagnosis unit 10 performs abnormality detection (specification of abnormal components) of the rolling bearing 20 for operation. Step S10 constitutes an abnormality specifying step for specifying abnormal components of the rolling bearing 20 by using the drive analysis result in the drive analysis step S9 and the characteristic frequency indicating the cause of vibration of the rolling bearing 20 in the abnormality diagnosis (abnormality detection method) of this embodiment. In this case, the rotational frequency fs of the rotating machine 30 calculated in step S6 is taken into consideration.

[0059] In step S11, when the drive determination of the rotating machine obtained in step S9 indicates that the rotating machine is idling, the abnormality specifying unit 15 of the abnormality diagnosis unit 10 performs abnormality detection (specification of abnormal components) of the rolling bearing 20 for idling. Step S11 constitutes an abnormality specifying step for specifying abnormal components of the rolling bearing 20 by using the drive analysis result in the drive analysis step S9 and the characteristic frequency indicating the cause of vibration of the rolling bearing 20 in the abnormality diagnosis (abnormality detection method) of this embodiment. In this case, the rotational frequency fs of the rotating machine 30 calculated in step S6 is taken into consideration.

[0060] In steps S10 and 11, the abnormality specifying unit 15 of the abnormality diagnosis unit 10 specifies abnormal components of the rolling bearing 20 by using the rotational frequency fs of the rotating machine 30 calculated in step S6 and the characteristic frequency of the rolling bearing 20. That is, in steps S10 and 11, abnormality detection (bearing abnormality detection) is performed on the rolling bearing 20.

[0061] In step S12, the abnormality diagnosis unit 10 finishes the abnormality diagnosis of the rolling bearing 20 and stops.

[0062] The characteristic frequency of the rolling bearing 20 is a value determined from the geometric dimensions of the rolling bearing 20 and the rotational speed (rotational frequency) of the rotating machine 30, and is a frequency indicating the cause of abnormal vibration of the rolling bearing 20. The characteristic frequency is, for example, the revolution frequency (rotational frequency of the cage 24) FTF of the rolling element 23, the rotation frequency BSF of the rolling element 23, the outer ring rolling element passing frequency (the frequency at which the rolling element 23 passes a point on the outer ring 22) BPFO, and the inner ring rolling element passing frequency (the frequency at which the rolling element 23 passes a point on the inner ring 21) BPFI. These characteristic frequencies can be calculated by the following formulas. · Revolution frequency FTF of the rolling element 23 FTF = 1 / 2 * (1 - d / D * cosα) * fs (1) · Rotation frequency BSF of the rolling element 23 BSF = D / (2d) * [1 - (d / D * cosα) 2 * fs (2) · Outer ring rolling element passing frequency BPFO BPFO = z / 2 * (1 - d / D * cosα) * fs (3) · Inner ring rolling element passing frequency BPFI BPFI = z / 2 * (1 + d / D * cosα) * fs (4) However, in formulas (1) to (4), d is the diameter (mm) of the rolling element 23, D is the pitch circle diameter (mm) α is the contact angle (radian) of the rolling bearing 20 z is the number of rolling elements 23 fs is the rotational frequency (Hz) of the rotating machine 30 is.

[0063] If there are abnormalities such as damage or defects in the rolling bearing 20, peaks will occur at the characteristic frequencies related to the abnormal part and the frequencies of its harmonics in the waveform of the frequency domain of the vibration of the rolling bearing 20. That is, when there is an abnormality in the cage 24, peaks will occur at the frequency represented by Equation (1) and the frequencies of its harmonics. When there is an abnormality in the rolling element 23, peaks will occur at twice the frequency of Equation (2) and the frequencies of its harmonics. When there is an abnormality in the outer ring 22, peaks will occur at the frequency of Equation (3) and the frequencies of its harmonics. When there is an abnormality in the inner ring 21, peaks will occur at the frequency of Equation (4) and the frequencies of its harmonics.

[0064] In the abnormality detection (identification of abnormal parts) in Step 10 and Step 11, a threshold value for detecting each peak is set, and it is determined that an abnormality has occurred in the part corresponding to the characteristic frequency of the peak exceeding the threshold value. In this case, the threshold value may be set for each characteristic frequency. Also, since the magnitude of each peak is different during driving and idling, it is preferable to change the value of the threshold depending on whether it is during driving or idling. That is, in the abnormality detection (identification of abnormal parts) for driving in Step 10 and the abnormality detection (identification of abnormal parts) for idling in Step 11, the value of the threshold for detecting each peak is changed. Usually, the peak value is larger during driving than during idling. Therefore, it is preferable to make the threshold value in the abnormality detection (identification of abnormal parts) during driving larger than the threshold value in the abnormality detection (identification of abnormal parts) during idling.

[0065] The meshing frequency of the gears is obtained by multiplying the rotational speed of the rotating machine 30 such as a motor by the number of teeth of the gears in the gear device 40 connected to the rotating machine 30, for example, a speed reducer (speed increaser).

[0066] As described above, the abnormality diagnosis unit 10 according to the present embodiment can estimate whether the rotating machine 30, to which the gear device 40 is connected, is in operation or idling while the rotational speed and load of the rotating machine 30 are changing, by using the vibration data of the rolling bearing 20. Therefore, even without receiving a drive control signal from the control device 19 or installing a torque sensor in the rotating machine 30, the abnormality diagnosis unit 10 can estimate whether the rotating machine 30 is in operation or idling, and perform diagnosis based on whether it is in operation or idling. Thus, cost reduction, work efficiency, and diagnostic accuracy can be improved. In addition, diagnosis by retrofitting the device is also possible. While the torque sensor needs to be arranged so as to be sandwiched between two rotating bodies, the vibration detector 17 only needs to be arranged on the surface of the object to be detected, and can be easily arranged on the object to be detected.

[0067] Note that the present invention is not limited to the above-described embodiments, and various modifications are possible. For example, the above embodiments have been described in detail for easy understanding of the present invention, and the present invention is not necessarily limited to the aspect including all the configurations described. Also, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment. In addition, the configuration of another embodiment can be added to the configuration of one embodiment. Further, a part of the configuration of each embodiment can be deleted, or other configurations can be added or replaced.

Explanation of Reference Numerals

[0068] 10…Abnormality diagnosis unit, 11…Data acquisition unit, 12…Rotation analysis unit, 13…Rotational speed calculation unit, 14…Drive analysis unit, 15…Abnormality identification unit, 16…Output unit, 17 Vibration detector, 18…Data logger, 19…Computer,, 20…Rolling bearing, 21…Inner ring, 22…Outer ring, 23…Rolling element, 24…Cage, 25…Direction of load, 26…Radial direction, 30…Rotating machine, 31…Shaft (rotating shaft), 40…Gear device, 100…Abnormality detection device.

Claims

1. An abnormal detection device for a rolling bearing used in a rotating machine that supports a rotating shaft to which a gear device is connected with a rolling bearing, comprising a vibration detector that detects vibration of the rolling bearing, and an abnormality diagnosis unit that diagnoses an abnormality of the rolling bearing based on a vibration waveform in the time domain obtained from the vibration detector. The abnormality diagnosis unit has a drive analysis unit that determines whether the rotating machine is in rotation during driving or in idling rotation during non-driving based on the vibration waveform. According to the determination result of the drive analysis unit, when the rotating machine is in driving, abnormal detection of the rolling bearing for driving is executed, and when the rotating machine is in idling rotation, abnormal detection of the rolling bearing for idling rotation is executed. In the abnormal detection of the rolling bearing for driving, a threshold value for detecting a peak related to an abnormal part that occurs in the waveform in the frequency domain of the vibration of the rolling bearing is made larger than the threshold value for detecting the peak in the abnormal detection of the rolling bearing for idling rotation. An abnormal detection device for a rolling bearing.

2. In the abnormal detection device for a rolling bearing according to claim 1, the drive analysis unit, a vibration waveform envelope processing unit that obtains a vibration waveform envelope that is an envelope of the vibration waveform, a vibration waveform envelope frequency intensity distribution data conversion unit that converts the vibration waveform envelope into vibration waveform envelope frequency intensity distribution data that is intensity distribution data in the frequency domain by fast Fourier transform processing, and a frequency intensity determination unit that obtains frequency intensities at one or more frequencies proportional to the rotational speed of the rotating machine using the vibration waveform envelope frequency intensity distribution data. The drive analysis unit is an abnormal detection device for a rolling bearing that determines whether the rotating machine is in driving or in idling rotation based on the frequency intensity.

3. In the abnormal detection device for a rolling bearing according to claim 1, the drive analysis unit, a vibration waveform envelope processing unit that obtains a vibration waveform envelope that is an envelope of the vibration waveform, A vibration waveform envelope frequency intensity distribution data conversion unit that converts the vibration waveform envelope into vibration waveform envelope frequency intensity distribution data, which is intensity distribution data in the frequency domain by fast Fourier transform processing; A mesh frequency intensity determination unit that obtains a mesh frequency intensity, which is the intensity at the mesh frequency of the gear, using the vibration waveform envelope frequency intensity distribution data; and The drive analysis unit is an abnormal detection device for a rolling bearing that determines whether the rotating machine is in driving or idling based on the mesh frequency intensity.

4. In the abnormal detection device for a rolling bearing according to claim 3, The mesh frequency intensity determination unit obtains the mesh frequency intensity at the mesh frequencies of the first-order and higher-order gears as the mesh frequency intensity, The drive analysis unit is an abnormal detection device for a rolling bearing that determines whether the rotating machine is in driving or idling based on the mesh frequency intensity at at least one of the first-order and higher-order mesh frequencies.

5. In the abnormal detection device for a rolling bearing according to claim 3, The mesh frequency intensity determination unit obtains the mesh frequency intensity at the mesh frequencies of the first-order and higher-order gears as the mesh frequency intensity, The drive analysis unit is an abnormal detection device for a rolling bearing that determines whether the rotating machine is in driving or idling based only on the mesh frequency intensity at the higher-order mesh frequency.

6. A rotating machine in which a rotating shaft to which a gear device is connected is supported by a rolling bearing, A rotating machine provided with the abnormal detection device for a rolling bearing according to any one of claims 1 to 5.

7. An abnormal detection method for a rolling bearing used in a rotating machine in which a rotating shaft to which a gear device is connected is supported by a rolling bearing, An abnormality diagnosis step for diagnosing an abnormality of a rolling bearing based on a vibration waveform in the time domain obtained from a vibration detector that detects vibration of the rolling bearing is provided. The abnormality diagnosis step includes a drive analysis step of determining whether the rotating machine is in operation during rotation when driven or in idling rotation during non-driving based on the vibration waveform. According to the determination result of the drive analysis step, when the rotating machine is in operation, an abnormality detection of the rolling bearing for operation is executed, and when the rotating machine is in idling rotation, an abnormality detection of the rolling bearing for idling rotation is executed. In the abnormality detection of the rolling bearing for operation, a threshold value for detecting a peak related to an abnormal part that occurs in the waveform in the frequency domain of the vibration of the rolling bearing is set larger than the threshold value for detecting the peak in the abnormality detection of the rolling bearing for idling rotation.

8. In the method for detecting an abnormality of a rolling bearing according to claim 7, The drive analysis step includes a vibration waveform envelope processing step of obtaining a vibration waveform envelope that is an envelope of the vibration waveform, a vibration waveform envelope frequency intensity distribution data conversion step of converting the vibration waveform envelope into vibration waveform envelope frequency intensity distribution data that is intensity distribution data in the frequency domain by fast Fourier transform processing, a frequency intensity determination step of obtaining frequency intensities at one or more frequencies proportional to the rotational speed of the rotating machine using the vibration waveform envelope frequency intensity distribution data. The drive analysis step is a method for detecting an abnormality of a rolling bearing that determines whether the rotating machine is in operation or in idling rotation based on the frequency intensity.

9. In the method for detecting an abnormality of a rolling bearing according to claim 7, The drive analysis step includes a vibration waveform envelope processing step of obtaining a vibration waveform envelope that is an envelope of the vibration waveform, a vibration waveform envelope frequency intensity distribution data conversion step of converting the vibration waveform envelope into vibration waveform envelope frequency intensity distribution data that is intensity distribution data in the frequency domain by fast Fourier transform processing, A meshing frequency intensity determination step of obtaining a meshing frequency intensity, which is the intensity at the meshing frequency of the gear, using the vibration waveform envelope frequency intensity distribution data, is included. The drive analysis step is an abnormal detection method for a rolling bearing that determines whether the rotating machine is in driving or idling based on the meshing frequency intensity.

10. In the abnormal detection method for a rolling bearing according to Claim 9, In the meshing frequency intensity determination step, as the meshing frequency intensity, the meshing frequency intensities at the meshing frequencies of the primary and higher-order gears are obtained. The drive analysis step is an abnormal detection method for a rolling bearing that determines whether the rotating machine is in driving or idling based on the meshing frequency intensity at at least one of the meshing frequencies of the primary and higher-order meshing frequencies.

11. In the abnormal detection method for a rolling bearing according to Claim 9, In the meshing frequency intensity determination step, as the meshing frequency intensity, the meshing frequency intensities at the meshing frequencies of the primary and higher-order gears are obtained. The drive analysis step is an abnormal detection method for a rolling bearing that determines whether the rotating machine is in driving or idling based only on the meshing frequency intensity at the higher-order meshing frequency.

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