Bearing condition diagnostic device

Abstract

To provide a bearing condition diagnosis device capable of improving the accuracy of bearing diagnosis, and provide a bearing condition diagnosis method.SOLUTION: A bearing condition diagnosis device 10 includes: multiple filtering processing units 31a, 31b, 31c each having a filter 311a, 311b, 311c for limiting the bandwidth of detection signals of a vibration sensor 20a; a comparison processing part 32 that outputs either one of the vibration signals output from the multiple filtering processing units 31a, 31b, 31c; and a condition determination unit 33 that performs a series of vibration analysis processing of the vibration signal output from the comparison processing part 32. The filters included in the multiple filtering processing units have passbands different from each other.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a bearing condition diagnosis device and a bearing condition diagnosis method. [Background technology]

[0002] When an abnormality occurs in a bearing used in a rotating machine such as a gear device that constitutes a bogie of a railway vehicle, a characteristic frequency component indicating the abnormal state appears in the vibration analysis results. Therefore, it is necessary to extract the characteristic frequency component indicating the abnormal state of the bearing as a diagnostic condition. Patent Document 1 describes a method of filtering the signal detected by a sensor to remove frequency components that are weakly related to the bearing abnormality. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 5409878 Summary of the Invention [Problem to be solved by the invention]

[0004] The signal detected by the sensor contains not only frequency components related to the condition of the bearing, but also frequency components due to other components such as the electric motor or axle. The optimal filter characteristics for filtering the detection signal of the sensor vary depending on various conditions such as the influence of such disturbances and the rotation speed. For this reason, in the above-mentioned conventional technology that performs filtering in a specific frequency band, it may be difficult to prevent damage to the bearing.

[0005] The present invention has been made in consideration of the above-mentioned problems, and has an object to provide a bearing condition diagnosis device and a bearing condition diagnosis method that can improve the accuracy of diagnosing a bearing. [Means for solving the problem]

[0006] In order to achieve the above-mentioned object, a bearing condition diagnosis device according to one embodiment of the present invention is a bearing condition diagnosis device that diagnoses the condition of a bearing of a rotating machine, and includes a vibration sensor that detects vibrations of the bearing, and a diagnosis unit that judges the condition of the bearing according to a characteristic frequency component that characteristically appears in a vibration signal according to an abnormal state of the bearing, wherein the diagnosis unit includes a plurality of filtering processing units including filters that band-limit the detection signal of the vibration sensor, a comparison processing unit that outputs one of the vibration signals output from the plurality of filtering processing units, and a condition judgment unit that performs vibration analysis processing of the vibration signal output from the comparison processing unit, and the filters included in the plurality of filtering processing units each have a different passband.

[0007] With the above configuration, it is possible to perform vibration analysis processing using a vibration signal that is expected to provide a more favorable result as a bearing condition diagnosis result, out of the vibration signals output by the multiple filtering processing units, thereby improving the accuracy of bearing diagnosis.

[0008] As a desirable aspect of the bearing condition diagnosis device, it is preferable that the comparison processing section selects a vibration signal to be output to the condition determination section in accordance with peak components detected in the vibration signals output from the plurality of filtering processing sections.

[0009] This makes it possible to select a vibration signal according to the peak component of the vibration signal.

[0010] As a desirable aspect of the bearing condition diagnosis device, it is preferable that the comparison processing unit performs peak detection of the characteristic frequency components for each vibration signal output from the multiple filtering processing units, and selects a vibration signal to be output to the condition determination unit based on the vibration intensity of the peak component that has been peak detected.

[0011] This enables highly accurate condition diagnosis according to the vibration intensity of the peak component of the vibration signal.

[0012] As a desirable aspect of the bearing condition diagnosis device, it is preferable that the comparison processing unit detects peak components of multiple orders of the characteristic frequency component, and selects a vibration signal to be output to the condition determination unit based on the vibration intensities of the peak components of the multiple orders.

[0013] This enables more accurate condition diagnosis according to the vibration strength of peak components of multiple orders.

[0014] In a preferred aspect of the bearing condition diagnosis device, the comparison processing unit compares the vibration intensities of the peak components of the multiple orders with predetermined thresholds, and selects the vibration signal to be output to the condition determination unit depending on whether or not the vibration intensities are equal to or greater than the threshold.

[0015] This makes it possible to select vibration signals based on threshold value judgment.

[0016] A bearing condition diagnosis method according to one embodiment of the present invention is a bearing condition diagnosis method for diagnosing the condition of a bearing of a rotating machine, which involves band-limiting the detection signal of a vibration sensor using a plurality of filters each having a different pass band, and determining the condition of the bearing based on one of the vibration signals obtained from the signals band-limited by the plurality of filters.

[0017] This allows vibration analysis processing to be performed using the vibration signal that is expected to produce more favorable results as a bearing condition diagnosis result, out of the various vibration signals obtained from signals that have been band-limited by multiple filters, thereby improving the accuracy of bearing diagnosis. Effect of the Invention

[0018] According to the present invention, it is possible to provide a bearing condition diagnosis device and a bearing condition diagnosis method that can improve the accuracy of diagnosing a bearing. [Brief description of the drawings]

[0019] [Figure 1] FIG. 1 is a diagram showing an outline of the configuration of a bogie of a railway vehicle equipped with a bearing condition diagnosis device according to an embodiment, as viewed from the front. [Diagram 2] FIG. 2 is a diagram showing an outline of the configuration of the bogie of the railway vehicle shown in FIG. 1 as viewed from directly below. [Diagram 3] FIG. 3 is a diagram showing the arrangement of the bearing condition diagnosis device according to the embodiment in a railway vehicle. [Figure 4] FIG. 4 is a block diagram showing an example of a bearing condition diagnosis device according to an embodiment. [Diagram 5] FIG. 5 is a diagram illustrating an example of a filter characteristic. [Figure 6] FIG. 6 is a diagram showing an example of a gear device that is a target of bearing diagnosis by the bearing condition diagnosis device according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of vibration characteristics of a bearing. [Figure 8] FIG. 8 is a diagram illustrating an example of a vibration signal after envelope processing and FFT processing. [Figure 9] FIG. 9 is a diagram showing the relationship between the abnormal state of the bearing and the characteristic frequency component. [Figure 10A] FIG. 10A is a diagram showing a schematic diagram of a peak component of a vibration signal after envelope processing and FFT processing of the bearing condition diagnosis device according to the embodiment. [Figure 10B] FIG. 10B is a diagram showing a schematic diagram of a peak component of a vibration signal after envelope processing and FFT processing of the bearing condition diagnosis device according to the embodiment. [Figure 10C] FIG. 10C is a diagram showing a schematic diagram of a peak component of a vibration signal after envelope processing and FFT processing of the bearing condition diagnosis device according to the embodiment. [Figure 11] FIG. 11 is a flowchart showing an example of the bearing condition diagnosis process. [Figure 12] FIG. 12 is a diagram for explaining the peak detection process in the filtering processing section and the comparison and determination process in the comparison processing section. [Figure 13] FIG. 13 is a flowchart showing an example of the comparison and determination process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Hereinafter, a mode for carrying out the invention (hereinafter, referred to as an embodiment) will be described in detail with reference to the drawings. Note that the present invention is not limited to the following embodiment. Furthermore, the components in the following embodiment include those that a person skilled in the art can easily imagine, those that are substantially the same, and those that are within the so-called equivalent range. Furthermore, the components disclosed in the following embodiment can be appropriately combined.

[0021] Fig. 1 is a diagram showing an outline of the configuration of a railcar bogie equipped with a bearing condition diagnosis device according to an embodiment, as viewed from the front. Fig. 2 is a diagram showing an outline of the configuration of the railcar bogie shown in Fig. 1, as viewed from directly below. As shown in Figs. 1 and 2, the railcar bogie mainly includes a gear device (also called a "drive device") 111, an electric motor 113, a bogie frame 115, a joint 117, an axle 119, wheels 121, and bearings 123.

[0022] The gear device 111, which functions as a reducer, has a small gear (also called a "pinion") 131 and a large gear 133 that mesh with each other. The small gear 131 and the large gear 133 are both spur gears. The gear device 111 also has a bearing 135 that rotatably supports the shaft of the small gear 131. This bearing 135 prevents the shaft of the small gear 131 from whirling. The housing of the bearing 135 is fixed to the case of the gear device 111.

[0023] An electric motor (motor) 113, which is a driving source of the railway vehicle, is fixed to a bogie frame 115. A rotating shaft 125 of the electric motor (motor) 113 is connected to the shaft of a small gear 131 of the gear set 111 via a joint 117. Therefore, the rotational force of the electric motor (motor) 113 is transmitted to the small gear 131 of the gear set 111 via the joint 117. A large gear 133 of the gear set 111 is fitted coaxially with an axle 119. In addition, wheels 121 are also fitted to the axle 119. Therefore, the rotational force from the electric motor (motor) 113 transmitted to the small gear 131 of the gear set 111 is transmitted to the wheels 121 via the large gear 133 and the axle 119.

[0024] The bearing 123 rotatably supports the axle 119. An axle spring 127 for absorbing vibrations (hereinafter referred to as "running vibrations") that occur when the railway vehicle runs is interposed between the bearing 123 and the bogie frame 115. Even if the axle spring 127 is bent due to running vibrations, causing relative displacement between the bogie frame 115 side and the axle 119 side, the joint 117 bends to absorb this displacement.

[0025] The deflection of the axle spring 127 or the joint 117 due to the running vibrations applies various loads to the bearing 135 supporting the shaft of the pinion 131 of the gear set 111. Furthermore, the running vibrations that occur when the railway vehicle is actually running include various factors such as environmental factors such as wind that the railway vehicle is exposed to and factors due to the condition of the track. If the bearing 135 continues to receive such loads, damage such as creep, seizure, or pocket wear may occur in the bearing 135. Furthermore, if damage occurs in the bearing 135, parameters such as vibration, temperature, torque, displacement, AE (Acoustic Emission), or rotation speed of the shaft of the pinion 131 of the gear set 111 or their changes over time will appear differently from normal. It is desirable to be able to detect the actual state of the damaged state of the bearing 135 before it is broken. For this reason, the bearing condition diagnosis device of this embodiment is mounted on the railway vehicle and detects changes caused by damage to the bearing 135 based on changes in parameters corresponding to the damage to the bearing 135 during the actual running of the railway vehicle.

[0026] A rotating machine for which the bearing condition diagnosis device according to this embodiment performs bearing condition diagnosis is, for example, a gear unit used in the bogie of a railway vehicle as described above. Hereinafter, the bearing condition diagnosis device according to this embodiment will be described with reference to Figs. 3 and 4.

[0027] Fig. 3 is a diagram showing the arrangement of a bearing condition diagnosis device according to an embodiment within a railway vehicle. As shown in Fig. 3, the bearing condition diagnosis device 10 according to this embodiment is provided within the body of a railway vehicle including, for example, the bogie described above. The bearing condition diagnosis device 10 includes a detection unit 20 that detects at least one of parameters such as vibration, temperature, torque, displacement, AE (Acoustic Emission), and rotation speed, and a diagnosis unit 30 to which the measurement value of the detection unit 20 is input.

[0028] The detection unit 20 is provided near the bearing 135 of the gear device 111. The detection unit 20 may include a microphone (not shown) that collects sounds near the bearing 135 of the gear device 111. The means for transmitting information from the detection unit 20 to the bearing condition diagnosis device 10 may be wired communication or wireless communication.

[0029] Fig. 4 is a block diagram showing an example of a bearing condition diagnosis device according to an embodiment. In the example shown in Fig. 4, the bearing condition diagnosis device 10 includes a detection unit 20 and a diagnosis unit 30. The detection unit 20 includes a vibration sensor 20a and a shaft speed sensor 20b. The diagnosis unit 30 includes a plurality of filtering processing units 31a, 31b, 31c, a comparison processing unit 32, and a state determination unit 33.

[0030] The filtering processing unit 31a includes a filter 311a, an envelope processing unit 312a, and a peak detection unit 313a. The filtering processing unit 31b includes a filter 311b, an envelope processing unit 312b, and a peak detection unit 313b. The filtering processing unit 31c includes a filter 311c, an envelope processing unit 312c, and a peak detection unit 313c.

[0031] The vibration sensor 20a is used to detect vibrations corresponding to an abnormal state in the bearing. A detection signal of the vibration sensor 20a is input to the filters 311a, 311b and 311c of the filtering processing units 31a, 31b and 31c.

[0032] The shaft speed sensor 20b is used to detect the shaft speed of the bearing. A detection signal of the shaft speed sensor 20b is input to a peak detection section 313a, a peak detection section 313b, and a peak detection section 313c of the filtering processing sections 31a, 31b, and 31c.

[0033] In Fig. 4, a configuration including three filtering processing units 31a, 31b, and 31c is illustrated, but the present disclosure is not limited by the number of filtering processing units 31a, 31b, and 31c. For example, a configuration including two filtering processing units or a configuration including four or more filtering processing units may be used. In the following description, a configuration including three filtering processing units 31a, 31b, and 31c shown in Fig. 4 will be described.

[0034] In the filtering processing units 31a, 31b, and 31c, only the filter characteristics of the filters 311a, 311b, and 311c are different from each other.

[0035] Fig. 5 is a diagram showing an example of filter characteristics. As shown in Fig. 5, filters 311a, 311b, and 311c are bandpass filters (hereinafter also referred to as "BPFs") that pass different bands. Note that filters 311a, 311b, and 311c are not limited to BPFs, and for example, filter 311a may be a lowpass filter (LPF) and filter 311c may be a highpass filter (HPF).

[0036] In the following description, filtering processing units 31a, 31b, and 31c may be referred to as "filtering processing units 31." Filters 311a, 311b, and 311c may be referred to as "filters 311." Envelope processing units 312a, 312b, and 312c may be referred to as "envelope processing units 312." Peak detection units 313a, 313b, and 313c may be referred to as "peak detection units 313."

[0037] The filter 311 filters the detection signal of the vibration sensor 20a to remove components other than those in a predetermined band.

[0038] The envelope processing unit 312 performs envelope processing (envelope detection processing) on ​​the signal band-limited by the filter 311 .

[0039] The peak detection unit 313 includes an FFT calculation unit that performs Fast Fourier Transform (FFT) processing of the signal envelope-processed by the envelope processing unit 312, and detects the peak component of the vibration signal after FFT processing when the shaft speed detected by the shaft speed sensor 20b is within a diagnosable range in the downstream diagnosis unit 30.

[0040] The comparison processing unit 32 performs a comparison and judgment process according to the output of the filtering processing unit 31. Specifically, the comparison processing unit 32 compares the peak components of the vibration signals after FFT processing output from the filtering processing units 31a, 31b, and 31c with a predetermined threshold value, and outputs the vibration signals that satisfy predetermined judgment conditions to the downstream state judgment unit 33. A specific example of the comparison and judgment process in the comparison processing unit 32 will be described in detail in the bearing state diagnosis process (see FIG. 11) and the comparison and judgment process (see FIG. 13) according to the embodiment described later.

[0041] The condition determination unit 33 analyzes the vibration signal output from the comparison processing unit 32 (hereinafter also referred to as "vibration analysis processing") to determine the condition of the bearing, and outputs a bearing condition diagnosis result. In the vibration analysis processing, "ACOUS NAVI" (registered trademark), which was developed as a bearing abnormal sound digital analysis system, may be used. The present disclosure is not limited by the vibration analysis method used in the condition determination unit 33. In addition, the present disclosure is not limited by the bearing condition determination method used in the condition determination unit 33.

[0042] Fig. 6 is a diagram showing an example of a gear device that is a target of bearing diagnosis by the bearing condition diagnosis device according to the embodiment. In the example shown in Fig. 6, an electric motor 113a and a pump 140, which is a rotating mechanical equipment, are assembled to a gear device (also called a "drive device") 111a.

[0043] The gear device 111a functions as a reducer and has a small gear (also called a "pinion") 131a and a large gear 133a that mesh with each other. The small gear 131a and the large gear 133a are spur gears. The gear device 111a has two bearings 135a and 135b that rotatably support the shaft of the small gear 131a, and two bearings 141a and 141b that rotatably support the shaft of the large gear 133a. The bearings 135a and 135b are arranged on both sides of the small gear 131a so as to sandwich it, and the bearings 141a and 141b are arranged on both sides of the large gear 133a so as to sandwich it. In this embodiment, as an example, an example is shown in which the bearing 135a of the gear device 111a is the target of state determination.

[0044] The housings (not shown) of the bearings 135a, 135b and the bearings 141a, 141b are fixed to the case of the gear set 111a. The bearings 135a, 135b prevent the shaft of the pinion 131a from whirling, and the bearings 141a, 141b prevent the shaft of the gear set 133a from whirling. The rotational force of the electric motor (motor) 113a is transmitted to the pinion 131a of the gear set 111a. The rotational force from the electric motor (motor) 113a transmitted to the pinion 131a is transmitted to the pump 140 via the gear set 133a. Therefore, the pump 140 operates when the electric motor (motor) 113a operates.

[0045] The vibration sensor 20a is provided on the bearing 135a of the gear device 111a. In the example shown in Fig. 6, the vibration sensor 20a is not provided on the bearings 135b, 141a, and 141b, but when the bearings 135b, 141a, and 141b are to be subjected to state determination, the vibration sensor 20a may be provided on each of the bearings 135b, 141a, and 141b.

[0046] 6, the shaft speed sensor 20b is provided on the electric motor 113a, but the mounting position of the shaft speed sensor 20b is not limited to this. When the bearings 141a and 141b are to be subjected to the state determination, the shaft speed sensor 20b may be provided on the pump 140.

[0047] Fig. 7 is a diagram showing a schematic example of the vibration characteristics of a bearing. In the example shown in Fig. 7, the horizontal axis indicates frequency [Hz], and the vertical axis indicates vibration intensity (vibration strength) [dB]. The solid line in Fig. 7 shows the waveform when the bearing is normal, and the dashed line in Fig. 7 shows the waveform when the bearing is damaged.

[0048] Fig. 8 is a diagram showing an example of a vibration signal after envelope processing and FFT processing, in which the horizontal axis indicates frequency [Hz] and the vertical axis indicates vibration intensity (vibration strength) [dB].

[0049] The example shown in Fig. 7 shows early signs of bearing damage caused by bearing seizure, etc. As shown in Fig. 7, a large difference in vibration intensity occurs in a specific frequency band fw, which is an early sign of bearing damage caused by bearing seizure, etc.

[0050] By performing FFT processing on the vibration signal after envelope processing shown in FIG. 8, it is possible to detect multiple characteristic frequency components (peak components) corresponding to abnormal conditions of the bearing (see FIG. 8).

[0051] Fig. 9 is a diagram showing the relationship between abnormal states of bearings and characteristic frequency components. As shown in Fig. 9, as characteristic frequency components according to abnormal states of bearings, for example, the characteristic frequency component [Hz] of vibration caused by damage to the inner ring is expressed by the formula shown as Zfi, the characteristic frequency component [Hz] of vibration caused by damage to the outer ring is expressed by the formula shown as Zfc, and the characteristic frequency component [Hz] of vibration caused by damage to rollers (rolling elements) is expressed by the formula shown as 2fb. In the example shown in Fig. 9, Z indicates the number of rollers (rolling elements), fr indicates the inner ring rotation speed [Hz], fb indicates the rolling element rotation speed [Hz], dm indicates the pitch circle diameter, Da indicates the rolling element diameter, and α indicates the contact angle.

[0052] As described above, various abnormal conditions of the bearing can be determined by performing vibration analysis including FFT after filtering and envelope processing on the detection signal of the vibration sensor 20a. Note that, in order to detect abnormal conditions of the bearing with high accuracy, it is necessary to perform vibration analysis processing while excluding disturbances caused by vibrations generated by meshing between the pinion 131a and the gear 133a shown in FIG. 6.

[0053] FIG. 10A is a diagram showing a peak component of a vibration signal after envelope processing and FFT processing of the bearing condition diagnosis device according to the embodiment. FIG. 10B is a diagram showing a peak component of a vibration signal after envelope processing and FFT processing of the bearing condition diagnosis device according to the embodiment. FIG. 10C is a diagram showing a peak component of a vibration signal after envelope processing and FFT processing of the bearing condition diagnosis device according to the embodiment. In FIG. 10A, FIG. 10B, and FIG. 10C, the horizontal axis indicates frequency [Hz], and the vertical axis indicates vibration intensity (vibration intensity) [dB]. Also, f1 indicates the first-order component of the characteristic frequency, f2 indicates the second-order component of the characteristic frequency, f3 indicates the third-order component of the characteristic frequency, and f4 indicates the fourth-order component of the characteristic frequency.

[0054] Fig. 10A shows the peak components of the vibration signal output from the filtering processing unit 31a shown in Fig. 4. Fig. 10B shows the peak components of the vibration signal output from the filtering processing unit 31b shown in Fig. 4. Fig. 10C shows the peak components of the vibration signal output from the filtering processing unit 31c shown in Fig. 4.

[0055] In this embodiment, as described above, the filter characteristics of the filters 311a, 311b, and 311c are made different (see FIG. 5). This causes the vibration strength of the peak components appearing in the vibration signal to differ, as shown in FIG. 10A, FIG. 10B, and FIG. 10C. Specifically, for example, as shown in FIG. 10B, the vibration strength of the peak component of the vibration signal output from the filtering processing unit 31b is the largest, and for example, as shown in FIG. 10C, the vibration strength of the peak component of the vibration signal output from the filtering processing unit 31c is the smallest.

[0056] As shown in Figure 10B, when the vibration intensity of the peak component of the vibration signal is the greatest, that is, in the examples shown in Figures 10A, 10B and 10C, the accuracy of the bearing condition diagnosis results can be improved by performing vibration analysis processing on the vibration signal output from the filtering processing unit 31b shown in Figure 10B in the downstream condition determination unit 33.

[0057] In the present disclosure, an example is described in which four peak components from the first to fourth order components of a feature frequency are detected, but the order of the feature frequency for detecting the peak components is not limited to this. For example, a mode in which peak components up to the third order components of a feature frequency are detected, or a mode in which peak components up to the fifth order components and higher of a feature frequency are detected may be used.

[0058] A specific example of a bearing condition diagnosis procedure in the bearing condition diagnosis device 10 according to this embodiment will now be described.

[0059] Fig. 11 is a flowchart showing an example of a bearing condition diagnosis process. Fig. 12 is a diagram for explaining the peak detection process in the filtering processing section and the comparison and judgment process in the comparison processing section. In the explanation of the bearing condition diagnosis process shown below, the peak detection process in the peak detection section 313 of the filtering processing section 31 and the comparison and judgment process in the comparison processing section 32 will be explained in detail.

[0060] As a precondition for the bearing condition diagnosis process shown in FIG. 11, for example, it is assumed that a railway vehicle (see FIG. 1) equipped with the bearing condition diagnosis device 10 according to the embodiment is in motion.

[0061] When a railway vehicle equipped with the bearing condition diagnosis device 10 according to the embodiment is traveling, the filtering processing units 31a, 31b, and 31c each perform filtering processing, envelope processing (envelope detection processing), and FFT processing of the detection signal of the vibration sensor 20a, respectively.

[0062] The filtering processing unit 31 determines whether or not the shaft speed detected by the shaft speed sensor 20b is within a diagnosable range in the downstream diagnosis unit 30 (step S101). If the shaft speed detected by the shaft speed sensor 20b is outside the diagnosable range (step S101; No), the filtering processing unit 31 repeats the process of step S101 until the shaft speed detected by the shaft speed sensor 20b is within the diagnosable range (step S101; Yes).

[0063] When the shaft speed detected by the shaft speed sensor 20b is within the diagnosable range (step S101; Yes), the filtering processing unit 31 detects peak components of the vibration signal after FFT processing (step S102). Hereinafter, the peak detection processing in the filtering processing unit 31 of the bearing condition diagnosis device 10 according to the embodiment will be described with reference to FIG.

[0064] Specifically, the peak detection unit 313 of the filtering processing unit 31 detects peak vibration intensity V1, peak vibration intensity V2, peak vibration intensity V3, and peak vibration intensity V4 in a first frequency bandwidth bw1, a second frequency bandwidth bw2, a third frequency bandwidth bw3, and a fourth frequency bandwidth bw4 within a predetermined range (for example, a 10 Hz width centered on the frequency of the characteristic frequency component) that includes each of the first-order component f1, second-order component f2, third-order component f3, and fourth-order component f4 of the characteristic frequency, as shown in Figure 12.

[0065] The comparison processing unit 32 performs a comparison and judgment process according to the output of the filtering processing unit 31 (step S103). Fig. 13 is a flowchart showing an example of the comparison and judgment process.

[0066] As a precondition for the comparison and judgment process shown in FIG. 13, the comparison processing unit 32 is set with a first threshold value Vth1 for the peak vibration intensity V1 of the first-order component f1 of the characteristic frequency as shown in FIG. 12. Also, the comparison processing unit 32 is set with a second threshold value Vth2 for the peak vibration intensity V2 of the second-order component f2 of the characteristic frequency as shown in FIG. 12. Also, the comparison processing unit 32 is set with a third threshold value Vth3 for the peak vibration intensity V3 of the third-order component f3 of the characteristic frequency as shown in FIG. 12. Also, the comparison processing unit 32 is set with a fourth threshold value Vth4 for the peak vibration intensity V4 of the fourth-order component f4 of the characteristic frequency as shown in FIG. Also, the comparison processing unit 32 has a function of counting the number n of filtering processing units 31 that have completed the processes from the following steps S201 to S208, and the value of n is reset (n=0). It is also assumed that a predetermined determination condition is set in the comparison processing unit 32 for determining which vibration signal is to be output from among the vibration signals output from the filtering processing units 31a, 31b, and 31c.

[0067] 12 shows an example in which the first threshold Vth1, the second threshold Vth2, the third threshold Vth3, and the fourth threshold Vth4 are different values, but the present invention is not limited to this. For example, the first threshold Vth1, the second threshold Vth2, the third threshold Vth3, and the fourth threshold Vth4 may be the same value, or more than one of the first threshold Vth1, the second threshold Vth2, the third threshold Vth3, and the fourth threshold Vth4 may be the same value. The first threshold Vth1, the second threshold Vth2, the third threshold Vth3, and the fourth threshold Vth4 may be set so that the bearing condition diagnosis result in the downstream condition determination unit 33 is more favorable, that is, so that a highly accurate condition diagnosis is possible.

[0068] The comparison processing unit 32 first performs threshold determination on the peak vibration intensity V1 of the first-order component f1 of the feature frequency, the peak vibration intensity V2 of the second-order component f2 of the feature frequency, the peak vibration intensity V3 of the third-order component f3 of the feature frequency, and the peak vibration intensity V4 of the fourth-order component f4 of the feature frequency for the vibration signal output from the filtering processing unit 31a.

[0069] Specifically, the comparison processing unit 32 performs a comparison judgment of the peak vibration intensity V1 of the first-order component f1 of the feature frequency against the first threshold value Vth1 (step S201), and holds the comparison judgment result (step S202). The comparison result may be held by the comparison processing unit 32, or may be stored in a storage unit (not shown).

[0070] 12 shows an example in which the peak vibration intensity V1 is equal to or greater than the first threshold value Vth1 (V1≧Vth1). In this case, the comparison processing unit 32 holds the fact that the peak vibration intensity V1 is equal to or greater than the first threshold value Vth1 (V1≧Vth1) as a comparison determination result.

[0071] Next, the comparison processing unit 32 compares the peak vibration intensity V2 of the secondary component f2 of the characteristic frequency with the second threshold value Vth2 (step S203), and holds the comparison result (step S204).

[0072] FIG. 12 shows an example in which the peak vibration intensity V2 is equal to or greater than the second threshold value Vth2 (V2 ≧ Vth2). In this case, the comparison processing unit 32 holds that the peak vibration intensity V2 is equal to or greater than the second threshold value Vth2 (V2 ≧ Vth2) as a comparison determination result.

[0073] Subsequently, the comparison processing unit 32 performs a comparison determination of the peak vibration intensity V3 of the third-order component f3 of the characteristic frequency with respect to the third threshold value Vth3 (step S205), and holds the comparison determination result (step S206).

[0074] FIG. 12 shows an example in which the peak vibration intensity V3 is less than the third threshold value Vth3 (V3 < Vth3). In this case, the comparison processing unit 32 holds that the peak vibration intensity V3 is less than the third threshold value Vth3 (V3 < Vth3) as a comparison determination result.

[0075] Subsequently, the comparison processing unit 32 performs a comparison determination of the peak vibration intensity V4 of the fourth-order component f4 of the characteristic frequency with respect to the fourth threshold value Vth4 (step S207), and holds the comparison determination result (step S208).

[0076] FIG. 12 shows an example in which the peak vibration intensity V4 is less than the fourth threshold value Vth4 (V4 < Vth4). In this case, the comparison processing unit 32 holds that the peak vibration intensity V4 is less than the fourth threshold value Vth4 (V4 < Vth4) as a comparison determination result.

[0077] After step S208, the comparison processing unit 32 counts up (increments) the number n of the filtering processing units 31 for which the processing from step S201 to step S208 has been completed (step S209).

[0078] The comparison processing unit 32 determines whether the comparison determination of all the peak vibration components of the characteristic frequency has been completed. Specifically, the comparison processing unit 32 determines whether the number n of the filtering processing units 31 for which the processing from step S201 to step S208 has been completed is equal to the number N (here, 3) of the filtering processing units 31 provided in the diagnosis unit 30 (n = N) (step S210).

[0079] When the number n of the filtering processing units 31 for which the processing from step S201 to step S208 has been completed is less than N (n < N) (step S210; No), here, for example, when n = 1, the process returns to the process of step S201 described above, and for the vibration signal output from the filtering processing unit 31b, the threshold determination of the peak vibration intensity V1 of the primary component f1 of the characteristic frequency, the peak vibration intensity V2 of the secondary component f2 of the characteristic frequency, the peak vibration intensity V3 of the tertiary component f3 of the characteristic frequency, and the peak vibration intensity V4 of the quaternary component f4 of the characteristic frequency is performed (steps S201 to S208).

[0080] Also, for example, when n = 2, the process returns to the process of step S201 described above, and for the vibration signal output from the filtering processing unit 31c, the threshold determination of the peak vibration intensity V1 of the primary component f1 of the characteristic frequency, the peak vibration intensity V2 of the secondary component f2 of the characteristic frequency, the peak vibration intensity V3 of the tertiary component f3 of the characteristic frequency, and the peak vibration intensity V4 of the quaternary component f4 of the characteristic frequency is performed (steps S201 to S208).

[0081] When the number n of the filtering processing units 31 for which the processing from step S201 to step S208 has been completed becomes N (n = N) (step S210; Yes), the comparison processing unit 32 performs a selection determination process of the vibration signal based on the comparison determination results obtained by repeating steps S201 to S208 for each of the filtering processing units 31a, 31b, and 31c (step S211). In the selection determination process, a predetermined determination condition set in advance is used to determine which vibration signal to output among the vibration signals output from the filtering processing units 31a, 31b, and 31c. Hereinafter, the determination condition in the selection determination process will be described.

[0082] The judgment conditions in the selection judgment process by the comparison processing unit 32 may be, for example, a mode in which a vibration signal is selected in which the peak vibration intensity V1 of the first-order component f1 of the feature frequency, the peak vibration intensity V2 of the second-order component f2 of the feature frequency, the peak vibration intensity V3 of the third-order component f3 of the feature frequency, and the peak vibration intensity V4 of the fourth-order component f4 of the feature frequency are all greater than or equal to the respective threshold values ​​(first threshold Vth1, second threshold Vth2, third threshold Vth3, fourth threshold Vth4).

[0083] In addition, the judgment conditions in the selection judgment process by the comparison processing unit 32 may be, for example, any of the peak vibration intensity V1 of the first-order component f1 of the feature frequency, the peak vibration intensity V2 of the second-order component f2 of the feature frequency, the peak vibration intensity V3 of the third-order component f3 of the feature frequency, and the peak vibration intensity V4 of the fourth-order component f4 of the feature frequency, for example, a vibration signal in which the peak vibration intensity V1 of the first-order component f1 of the feature frequency is greater than or equal to a first threshold value Vth1.

[0084] In addition, the judgment conditions in the selection judgment process by the comparison processing unit 32 may be, for example, a case in which the peak vibration intensity V1 of the first-order component f1 of the feature frequency, the peak vibration intensity V2 of the second-order component f2 of the feature frequency, the peak vibration intensity V3 of the third-order component f3 of the feature frequency, and the peak vibration intensity V4 of the fourth-order component f4 of the feature frequency in multiple vibration signals are all greater than or equal to their respective thresholds (first threshold Vth1, second threshold Vth2, third threshold Vth3, fourth threshold Vth4), and the vibration signal with the largest peak vibration intensity V1 of the first-order component f1 of the feature frequency may be selected.

[0085] In addition, the judgment conditions in the selection judgment process by the comparison processing unit 32 may be, for example, a vibration signal that selects the largest number of peak components, each of which has a peak vibration intensity V1 of the first-order component f1 of the feature frequency, a peak vibration intensity V2 of the second-order component f2 of the feature frequency, a peak vibration intensity V3 of the third-order component f3 of the feature frequency, and a peak vibration intensity V4 of the fourth-order component f4 of the feature frequency that are greater than or equal to the respective threshold values ​​(first threshold Vth1, second threshold Vth2, third threshold Vth3, fourth threshold Vth4).

[0086] Furthermore, a combination of a plurality of the above-mentioned determination conditions may be used.

[0087] The judgment conditions in the selection judgment process by the comparison processing unit 32 are not limited to the above. The judgment conditions in the selection judgment process by the comparison processing unit 32 may be set, similar to the above-mentioned first threshold value Vth1, second threshold value Vth2, third threshold value Vth3, and fourth threshold value Vth4, so long as they are set so that the bearing condition diagnosis result in the subsequent condition determination unit 33 becomes more favorable, that is, so that a highly accurate condition diagnosis is possible.

[0088] Then, the comparison processing unit 32 outputs the vibration signal that satisfies the judgment conditions as described above, for example, the peak vibration intensity V1 of the first-order component f1 of the feature frequency, the peak vibration intensity V2 of the second-order component f2 of the feature frequency, the peak vibration intensity V3 of the third-order component f3 of the feature frequency, and the peak vibration intensity V4 of the fourth-order component f4 of the feature frequency, all of which are greater than or equal to the respective threshold values ​​(first threshold Vth1, second threshold Vth2, third threshold Vth3, fourth threshold Vth4), to the downstream state judgment unit 33 (step S212), and returns to the bearing state diagnosis processing shown in FIG. 11.

[0089] 11, the condition determination unit 33 performs vibration analysis processing of the vibration signal output from the comparison processing unit 32 (step S104). Based on the vibration analysis processing result, the condition determination unit 33 performs bearing condition determination, outputs the bearing condition diagnosis result (step S105), and ends the bearing condition diagnosis processing.

[0090] By the comparison and judgment process (FIG. 13) executed in the above-mentioned bearing condition diagnosis process (FIG. 11), it is possible to diagnose the condition of the bearing using a vibration signal which has a large vibration intensity in the peak component and is expected to produce more favorable results as the bearing condition diagnosis result, thereby improving the accuracy of the bearing condition diagnosis result.

[0091] In this way, according to this embodiment, a bearing condition diagnosis device and a bearing condition diagnosis method are obtained that can improve the accuracy of diagnosing a bearing.

[0092] In the above-described embodiment, an example was shown of detecting the condition of the bearings of the gear device that constitutes the bogie of a railway vehicle, but the scope of application is not limited to this. For example, the present invention can be applied to various types of rotating machinery equipment as long as it has a configuration that includes bearings. [Explanation of symbols]

[0093] 10. Bearing condition diagnostic device 20 Detection unit 20a Vibration Sensor 20b Shaft speed sensor 30 Diagnostic Department 31, 31a, 31b, 31c Filtering processing unit 32 Comparison processing section 33 Status determination unit 311,311a,311b,311c Filters 312, 312a, 312b, 312c Envelope processing section 313, 313a, 313b, 313c Peak detector 111,111a Gearing 113, 113a Electric motor 115 Bogie Frame 117 Joints 119 Axle 121 Wheels 123 Bearings 131,131a Pinion 133,133a Gear 135, 135a, 135b Bearings 140 Pump 141a, 141b Bearings

Claims

1. A bearing condition diagnosis device for diagnosing the condition of a bearing of a rotating machine, comprising: a vibration sensor for detecting vibration of the bearing; a diagnosis unit that judges a state of the bearing in accordance with a characteristic frequency component that appears in a vibration signal in accordance with an abnormal state of the bearing; Equipped with The diagnosis unit includes: a plurality of filtering processing units each including a filter for band-limiting a detection signal of the vibration sensor; a comparison processing unit that outputs any one of the vibration signals output from the plurality of filtering processing units; a state determination unit that performs vibration analysis processing on the vibration signal output from the comparison processing unit; Equipped with The filters included in the plurality of filtering units each have a different passband, The comparison processing unit: selecting a vibration signal to be output to the state determination unit according to peak components detected in the vibration signals output from the plurality of filtering processing units; The comparison processing unit: performing peak detection of the characteristic frequency component for each of the vibration signals output from the plurality of filtering processing units, and selecting a vibration signal to be output to the state determination unit based on the vibration intensity of the peak component that has been detected; The comparison processing unit: detecting peak components of a plurality of orders of the characteristic frequency component, and selecting a vibration signal to be output to the state determination unit based on vibration intensities of the peak components of the plurality of orders; The comparison processing unit: comparing the vibration intensities of the peak components of the plurality of orders with a predetermined threshold, and selecting a vibration signal to be output to the state determination unit depending on whether the vibration intensities are equal to or greater than the threshold; Bearing condition diagnostic device.

2. The comparison processing unit: a threshold is set for each of the vibration intensities of the peak components of the plurality of orders, and a vibration signal in which all of the peak components of the plurality of orders are equal to or greater than the threshold is selected. The bearing condition diagnosis device according to claim 1 .

3. The comparison processing unit: a threshold is set for each of the vibration intensities of the peak components of the plurality of orders, and a vibration signal in which any one or more of the peak components of the plurality of orders is equal to or greater than the threshold is selected. The bearing condition diagnosis device according to claim 1 .

4. The comparison processing unit: a threshold is set for each of the vibration intensities of the peak components of the plurality of orders, and when all of the peak components of the plurality of orders in the plurality of vibration signals are equal to or greater than the threshold, a vibration signal in which the vibration intensity of the peak component of a predetermined order is equal to or greater than the threshold is selected. The bearing condition diagnosis device according to claim 1 .

5. The comparison processing unit: a threshold is set for each of the vibration intensities of the peak components of the plurality of orders, and a vibration signal having the largest number of peak components equal to or greater than the threshold is selected. The bearing condition diagnosis device according to claim 1 .

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