Method and apparatus for detecting signs of sliding bearing seizure.

Abstract

To provide a detection method of sliding bearing seizure signs and a detection device of sliding bearing seizure signs, that enable detection of seizure signs of a sliding bearing.SOLUTION: A detection method of sliding bearing seizure signs includes: a data acquisition step S1 of acquiring oscillation data in time series that has detected oscillation generated in a slide bearing; an analysis step S3 of analyzing the oscillation data acquired in the data acquisition step S1 by using an analysis method of analyzing time and frequencies at the same time; a graph creation step S4 of creating a graph of a coordinate system comprising a horizontal axis for frequency, and a vertical axis for spectrum strength*frequency obtained by multiplying a spectrum strength by a frequency or a weighted frequency, on the basis of an analysis result analyzed in the analysis step S3; and a determination step S5 of determining presence / absence of seizure signs of a sliding bearing by determining whether or not a maximum point is included in a predetermined frequency range in the graph created in the graph creation step S4.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present invention relates to a method and an apparatus for detecting a seizure prediction of a sliding bearing, which can detect a seizure prediction of the sliding bearing.

Background Art

[0002] In various types of devices involving rotational motion, reciprocating motion, etc., bearings are used to support the shaft in order to move the shaft smoothly with respect to the rotational motion, reciprocating motion, etc. When an abnormality occurs in this bearing, smooth motion is inhibited, and there is a risk of causing a failure of the device. Therefore, it is desired to detect the state of the bearing. The bearing is generally classified into a rolling bearing and a sliding bearing. The rolling bearing is a device that supports a load by placing rolling elements such as balls and rollers between two members (shaft and raceway ring). The sliding bearing is a device in which the shaft and the bearing surface are in direct contact, and the movement of the shaft is supported by the surface. A technique for detecting the state of the rolling bearing is disclosed in, for example, Patent Document 1.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, the technology for detecting the state of the rolling bearing has advanced in the research and understanding of its damage mechanism, and there are various existing technologies such as Patent Document 1. However, the damage mechanism in the sliding bearing has not been elucidated, and the detection of the state of the sliding bearing is desired.

[0005] The present invention has been made in view of the above circumstances, and an object thereof is to provide a method and an apparatus for detecting a seizure prediction of a sliding bearing, which can detect a seizure prediction of the sliding bearing. [Means for solving the problem]

[0006] As a result of various studies, the inventors have found that the above objective can be achieved by the present invention as described below. That is, a sliding bearing seizure prediction method according to one aspect of the present invention comprises: a data acquisition step of acquiring time-series vibration data of vibrations occurring in a sliding bearing; an analysis step of analyzing the vibration data acquired in the data acquisition step using an analysis method that simultaneously analyzes time and frequency; a graph generation step of generating a graph of a coordinate system having a horizontal axis of frequency and a vertical axis of spectral intensity × frequency obtained by multiplying spectral intensity by frequency or a weighted frequency, based on the analysis results analyzed in the analysis step; and a determination step of determining whether or not there is a sign of seizure in the sliding bearing by determining whether or not there is a maximum point in a predetermined frequency range in the graph generated in the graph generation step.

[0007] According to this, a sliding bearing seizure prediction method can be provided that determines whether or not there is a local maximum value in the graph of frequency and (spectral intensity) × (frequency) in a coordinate system, thereby determining whether or not there are signs of sliding bearing seizure.

[0008] In another embodiment, in the sliding bearing seizure prediction method described above, the analysis method is an analysis method using wavelet transform, and the graph generation step involves, for each of the multiple frequency spectra obtained by each of the multiple wavelets, finding the average value of the spectral intensity at each of the multiple frequencies at a predetermined frequency interval, multiplying the found average value by the frequency or a weighted version of the frequency, and plotting the multiplication result obtained by multiplying the frequency in the coordinate system to generate the graph.

[0009] According to this, the analysis method can be provided as a method for detecting signs of sliding bearing seizure using wavelet transform.

[0010] In another embodiment, in the sliding bearing seizure prediction method described above, the determination step determines whether or not there is a local maximum point based on whether or not the sign of the slope of the graph changes.

[0011] According to this, a sliding bearing seizure prediction method can be provided that determines whether or not there is a local maximum point based on whether or not the sign of the slope of the graph changes.

[0012] A sliding bearing seizure prediction device according to one aspect of the present invention comprises: a data acquisition unit that acquires time-series vibration data detecting vibrations occurring in a sliding bearing; an analysis unit that analyzes the vibration data acquired by the data acquisition unit using an analysis method that simultaneously analyzes time and frequency; a graph generation unit that generates a graph of a coordinate system having a horizontal axis of frequency and a vertical axis of spectral intensity × frequency, obtained by multiplying spectral intensity by frequency or a weighted frequency, based on the analysis results analyzed by the analysis unit; and a determination unit that determines whether or not there are signs of seizure in the sliding bearing by determining whether or not there is a maximum point in a predetermined frequency range in the graph generated by the graph generation unit. Preferably, in the sliding bearing seizure prediction device described above, the data acquisition unit is an acceleration sensor or AE (Acoustic Emission) sensor attached to the sliding bearing or a member that propagates vibrations occurring in the sliding bearing.

[0013] According to this, a sliding bearing seizure prediction device can be provided that determines whether or not there is a local maximum value in the graph of frequency and (spectral intensity) × (frequency) in a coordinate system, thereby determining whether or not there are signs of sliding bearing seizure. [Effects of the Invention]

[0014] The sliding bearing seizure prediction method and sliding bearing seizure prediction device according to the present invention can detect signs of sliding bearing seizure. [Brief explanation of the drawing]

[0015] [Figure 1]This is a block diagram showing the configuration of a sliding bearing seizure prediction device in an embodiment. [Figure 2] This is a schematic diagram illustrating the arrangement of the AE sensor (an example of the data acquisition unit 1) in the sliding bearing seizure prediction device. [Figure 3] This is a flowchart showing the operation of the sliding bearing seizure prediction device. [Figure 4] As an example, this graph shows averaged vibration data obtained from a bearing friction test. [Figure 5] Figure 4 shows a graph of spectral intensity against frequency, obtained from the averaged vibration data. [Figure 6] Figure 4 shows a graph of (spectral intensity) × (frequency) against frequency, obtained from the averaged vibration data. [Modes for carrying out the invention]

[0016] Hereinafter, one or more embodiments of the present invention will be described with reference to the drawings. However, the scope of the invention is not limited to the disclosed embodiments. In each figure, components denoted by the same reference numerals are identified as identical components, and their descriptions are omitted where appropriate. In this specification, general reference numerals are used without subscripts, while individual components are indicated by subscripts.

[0017] The anti-friction bearing seizure prediction detection device in the embodiment is a device that detects the prediction of seizure of an anti-friction bearing, and includes a data acquisition unit, an analysis unit, a graph generation unit, and a determination unit. The data acquisition unit acquires time-series vibration data obtained by detecting vibrations generated in the anti-friction bearing. The analysis unit analyzes the vibration data acquired by the data acquisition unit by an analysis method that simultaneously analyzes time and frequency. The graph generation unit generates a graph in a coordinate system having a horizontal axis of frequency and a vertical axis of spectral intensity × frequency obtained by multiplying the spectral intensity by the frequency, based on the analysis result analyzed by the analysis unit. The determination unit determines whether or not there is a seizure prediction of the anti-friction bearing by determining whether or not there is a maximum point in a predetermined frequency range in the graph generated by the graph generation unit. Hereinafter, such an anti-friction bearing seizure prediction detection device and an anti-friction bearing seizure prediction detection method implemented thereon will be described more specifically.

[0018] FIG. 1 is a block diagram showing the configuration of the anti-friction bearing seizure prediction detection device in the embodiment. FIG. 2 is a schematic diagram for explaining the arrangement state of the AE sensor (an example of the data acquisition unit 1) in the anti-friction bearing seizure prediction detection device.

[0019] The anti-friction bearing seizure prediction detection device 1000 in the embodiment, for example, as shown in FIG. 1, includes a data acquisition unit 1, a preprocessing unit 2, a control processing unit 3, an input unit 4, an output unit 5, an interface unit (IF unit) 6, and a storage unit 7.

[0020] The data acquisition unit 1 is a device that acquires time-series vibration data detected by vibrations occurring in a sliding bearing. The data acquisition unit 1 uses sensors such as acceleration sensors and AE (Acoustic Emission) sensors to detect signs of seizure in near real time, and the appropriate sensor is used depending on the frequency of the vibration of the object to be detected. These acceleration sensors and AE sensors are attached to the sliding bearing or to a member that transmits vibrations generated in the sliding bearing, and acquire the time-series vibration data by detecting the vibrations at predetermined sampling intervals. For example, as shown in Figure 2, an AE sensor 1, which is an example of the data acquisition unit 1, is installed in a mechanical equipment Ob (e.g., its mechanism or housing) that has a rotating shaft AX supported by the sliding bearing BR to be detected. As a result, vibrations of the sliding bearing BR that occur in the sliding bearing BR and are transmitted to the mechanical equipment Ob are detected by the AE sensor 1. In this embodiment, the AE sensor 1 is connected to the control processing unit 3 via the pre-processing unit 2, and its output is output to the pre-processing unit 2.

[0021] Note that the data acquisition unit 1 is not limited to the above-described acceleration sensor and AE sensor, and may be other devices. For example, the data acquisition unit 1 is an interface circuit that inputs and outputs data to and from an external device. The external device is a storage medium such as a USB (Universal Serial Bus) memory and an SD card (registered trademark) that stores the vibration data. Alternatively, the external device is a drive device that reads data from a recording medium such as a CD-ROM (Compact Disc Read Only Memory), CD-R (Compact Disc Recordable), DVD-ROM (Digital Versatile Disc Read Only Memory), and DVD-R (Digital Versatile Disc Recordable) that records the vibration data. The interface circuit as the data acquisition unit 1 may be connected to the external device by wire or wirelessly. Alternatively, the data acquisition unit 1 is, for example, a communication interface circuit that transmits and receives communication signals to and from an external device, and the external device is connected to the communication interface circuit via a network (WAN (Wide Area Network, including a public communication network)) or LAN (Local Area Network), and is a server device that manages the vibration data. Such a data acquisition unit 1 may be connected to the control processing unit 3 without passing through the preprocessing unit 2. In such a data acquisition unit 1, after the generation of the vibration data, the detection target can be verified. Here, when the data acquisition unit 1 is an interface circuit or a communication interface circuit, the data acquisition unit 1 may be used in combination with the IF unit 6 (that is, the IF unit 6 may be used as the data acquisition unit 1).

[0022] The preprocessing unit 2 is a device that is connected to the control processing unit 3 and performs predetermined signal processing on the output of the data acquisition unit 1. For example, the preprocessing unit 2 is an amplifier that amplifies the output of the data acquisition unit 1 at a predetermined amplification factor, or an AD converter that converts the output of the data acquisition unit 1 amplified by the amplifier from an analog signal to a digital signal. The preprocessing unit 2 outputs the output of the data acquisition unit 1 subjected to the predetermined signal processing to the control processing unit 3.

[0023] In the example shown in Figure 1, the pre-processing unit 2 is configured with hardware, but it may be configured with software, in which case the pre-processing unit 2 may be functionally configured with the control processing unit 3.

[0024] The input unit 4 is connected to the control processing unit 3 and is a device that inputs various commands, such as a command to instruct the start of detection, and various data necessary for operating the sliding bearing seizure prediction detection device 1000, such as the name of the machinery or equipment to be detected. For example, it may be a keyboard, a mouse, or multiple input switches assigned to predetermined functions. The output unit 5 is connected to the control processing unit 3 and is a device that outputs commands and data input from the input unit 4, as well as the determination results determined as described later, in accordance with the control processing unit 3. For example, it may be a display device such as a CRT display, a liquid crystal display, or an organic EL display, or a printing device such as a printer.

[0025] Furthermore, a so-called touch panel may be configured from the input unit 4 and the output unit 5. In the case of this touch panel configuration, the input unit 4 is a position input device that detects and inputs the operating position, such as a resistive touch device or a capacitive touch device, and the output unit 5 is a display device. In this touch panel, the position input device is provided on the display surface of the display device, and one or more candidate input contents that can be input to the display device are displayed. When the user touches the display position that displays the input content they want to input, the position input device detects that position, and the display content displayed at the detected position is input to the sliding bearing seizure prediction detection device 1000 as the user's operation input. With such a touch panel, the user can easily understand the input operation intuitively, thus providing a sliding bearing seizure prediction detection device 1000 that is easy for the user to use.

[0026] The IF unit 6 is connected to the control processing unit 3 and is a circuit that performs data input and output with external devices according to the control of the control processing unit 3. Examples include an RS-232C serial communication interface circuit, an interface circuit using the Bluetooth® standard, an infrared communication interface circuit such as the IrDA (Infrared Data Association) standard, and an interface circuit using the USB (Universal Serial Bus) standard. The IF unit 6 is also a circuit that communicates with external devices and may be, for example, a data communication card or a communication interface circuit that conforms to the IEEE 802.11 standard.

[0027] The memory unit 7 is connected to the control processing unit 3 and is a circuit that stores various predetermined programs and various predetermined data in accordance with the control of the control processing unit 3. The various predetermined programs include, for example, a control processing program, and the control processing program includes a control program, an analysis program, a graph generation program, and a determination program. The control program is a program that controls each of the parts 1, 2, 4 to 7 of the sliding bearing seizure prediction detection device 1000 according to the function of each part. The analysis program is a program that analyzes the vibration data acquired by the data acquisition unit 1 using an analysis method that simultaneously analyzes time and frequency. The graph generation program is a program that generates a graph of a coordinate system having frequency on the horizontal axis and spectral intensity × frequency on the vertical axis, which is spectral intensity multiplied by frequency, based on the analysis results analyzed by the analysis program. The determination program is a program that determines whether or not there is a seizure precursor of the sliding bearing by determining whether or not there is a maximum point in a predetermined frequency range in the graph generated by the graph generation program. The various predetermined data include, for example, vibration data acquired by the data acquisition unit 1, and other data necessary for executing each of these programs. Such a storage unit 7 may include, for example, a non-volatile memory element such as ROM (Read Only Memory) or a rewritable non-volatile memory element such as EEPROM (Electrically Erasable Programmable Read Only Memory). The storage unit 7 also includes RAM (Random Access Memory), which serves as the working memory of the control processing unit 3, storing data generated during the execution of the predetermined program. The storage unit 7 may also include a hard disk drive capable of storing large amounts of data.

[0028] The control processing unit 3 is a circuit for detecting signs of sliding bearing seizure by controlling each of the parts 1, 2, 4-7 of the sliding bearing seizure prediction device 1000 according to the function of each part. The control processing unit 3 is configured, for example, with a CPU (Central Processing Unit) and its peripheral circuits. When the control processing program is executed, the control unit 31, analysis unit 32, graph generation unit 33, and determination unit 34 are functionally configured in the control processing unit 3.

[0029] The control unit 31 controls each of the parts 1, 2, 4-7 of the sliding bearing seizure prediction detection device 1000 according to the function of each part, and is in charge of controlling the entire sliding bearing seizure prediction detection device 1000.

[0030] The analysis unit 32 analyzes the vibration data acquired by the data acquisition unit 1 using an analysis method that simultaneously analyzes time and frequency. The analysis method is, for example, an analysis method using wavelet transform. The wavelet transform is an analysis technique that simultaneously obtains time information and frequency information included in the object of analysis, and obtains the frequency spectrum of the object of analysis for each of a plurality of different wavelets. More specifically, the analysis unit 32 obtains averaged vibration data by calculating the moving average of the vibration data acquired by the data acquisition unit 1, which has been preprocessed by the preprocessing unit 2, and then performs a wavelet transform on the obtained averaged vibration data. This obtains the frequency spectrum of the averaged vibration data for each of the plurality of wavelets. In the moving average, the moving average value (moving average amplitude value) at the current sampling timing is obtained by dividing the sum of each amplitude value at each sampling timing for vibration data within a predetermined time period prior to the current sampling timing by the number of samples. By performing a moving average, the signal-to-noise ratio of the averaged vibration data can be improved. The predetermined time is, for example, appropriately determined in advance from a plurality of samples.

[0031] The graph generation unit 33 generates a graph of a coordinate system having frequency on the horizontal axis and spectral intensity × frequency on the vertical axis, which is obtained by multiplying spectral intensity by frequency, based on the analysis results analyzed by the analysis unit 32. More specifically, the graph generation unit 33 calculates the average value of the spectral intensity at each of several frequencies at a predetermined frequency interval for each frequency spectrum obtained by each wavelet, multiplies the calculated average value by the frequency, and creates the graph by plotting the multiplication result of the frequency multiplied in the coordinate system. The predetermined frequency interval is determined appropriately in advance from, for example, several samples.

[0032] The determination unit 34 determines whether or not there are signs of seizure in the sliding bearing by determining whether or not there is a local maximum in a predetermined frequency range in the graph generated by the graph generation unit 33. More specifically, the determination unit 34 determines whether or not there is a local maximum by determining whether or not the sign of the slope of the graph changes. At a local maximum, the slope of the graph becomes 0, so the presence or absence of a local maximum can be determined by whether or not there is a change in sign. For example, if the frequency is i, the frequency i+1 is a small frequency interval d away from frequency i, and the graph is G, the determination unit 34 calculates the slope GR(i+1)=(G(i+1)-G(i)) / d at frequency i+1 in the predetermined frequency range fs~fe (fs≦i≦fe), determines whether or not there is a change in sign, and determines whether or not there is a local maximum. The predetermined frequency range is determined appropriately in advance from a plurality of samples, for example.

[0033] The control unit 31 outputs the determination result determined by the determination unit 34 from the output unit 5.

[0034] These control processing unit 3, input unit 4, output unit 5, IF unit 6, and storage unit 7 can be configured by a computer, such as a desktop, notebook, or tablet computer. Furthermore, if the data acquisition unit 1 is an interface circuit or communication interface circuit, the IF unit 6 can be used in conjunction with the data acquisition unit 1. Therefore, the sliding bearing seizure prediction device 1000, including the data acquisition unit 1, can be configured by a computer.

[0035] Next, the operation of this embodiment will be described. Figure 3 is a flowchart showing the operation of the sliding bearing seizure prediction device.

[0036] When the sliding bearing seizure prediction device 1000 with this configuration is powered on, it performs the necessary initialization of each part and starts operating. The control processing unit 3 is functionally configured with a control unit 31, an analysis unit 32, a graph generation unit 33, and a determination unit 34 through the execution of its control processing program.

[0037] When the sliding bearing seizure prediction detection device 1000 receives a command from the input unit 4 to instruct the start of prediction detection, it repeatedly executes each of the processes S1 to S6 shown in Figure 3 at a predetermined sampling interval until it receives a command from the input unit 4 to instruct the end of prediction detection. Here, since vibration data for prediction detection cannot be generated until the detection result of the AE sensor 1, which is an example of the data acquisition unit 1, is acquired for the predetermined time, the process of acquiring the detection result of the AE sensor 1 and storing it in the storage unit 7 is repeatedly executed at the predetermined sampling interval from the start of prediction detection until the predetermined time has elapsed.

[0038] After the predetermined time has elapsed since the start of the predictive detection, and it is time for sampling, the processing for the current sampling timing is started. In Figure 3, first, the sliding bearing seizure predictive detection device 1000 acquires data from the AE sensor 1 by the control unit 31 of the control processing unit 3 (S1, data acquisition step), the acquired data is preprocessed by the preprocessing unit 2, and the preprocessed data from the data acquisition unit 1 is stored in the storage unit 7 in association with the current sampling timing (S2, preprocessing step).

[0039] Next, the sliding bearing seizure prediction device 1000 obtains averaged vibration data using the analysis unit 32 of the control processing unit 3, and then performs a wavelet transform on the obtained averaged vibration data (S3, analysis step).

[0040] Next, the sliding bearing seizure prediction detection device 1000 uses the graph generation unit 33 of the control processing unit 3 to generate a graph of (spectral intensity) × (frequency) against frequency based on the analysis results analyzed by the analysis unit 32 in process S3, and stores the generated graph in the storage unit 7 (S4, graph generation process).

[0041] Next, the sliding bearing seizure prediction detection device 1000 determines whether or not there are signs of seizure in the sliding bearing BR by determining, using the determination unit 34 of the control processing unit 3, whether or not there is a maximum point in a predetermined frequency range in the graph generated by the graph generation unit 33 in process S4, and stores the determination result in the storage unit 7 (S5, determination step).

[0042] Then, the sliding bearing seizure prediction detection device 1000 outputs the determination result determined by the determination unit 34 in process S5 to the output unit 5 via the control unit 31 of the control processing unit 3, and terminates this process at the current sampling timing. If necessary, the determination result may also be output to an external device from the IF unit 6.

[0043] Regarding the detection of signs of seizure, the sliding bearing seizure prediction device 1000 operates as follows.

[0044] As a specific example, we will explain the results obtained using the sliding bearing seizure prediction device 1000 when a load is applied to a sliding bearing BR and rotated using a bearing friction testing device.

[0045] Figure 4 is a graph showing averaged vibration data from a bearing friction test as an example. The horizontal axis of Figure 4 represents the elapsed time [minutes] from the start of the test, and the vertical axis represents the amplitude value (output voltage value [V] of AE sensor 1). Figure 5 is a graph of spectral intensity against frequency (fa graph) obtained from the averaged vibration data shown in Figure 4. Figure 5A shows the fa graphs for sample points M1 to M4 shown in Figure 4, and Figure 5B shows the fa graphs for sample points M5 to M8 shown in Figure 4. Figure 6 is a graph of (spectral intensity) × (frequency) against frequency (f-af graph) obtained from the averaged vibration data shown in Figure 4. Figure 6A shows the f-af graphs for sample points M1 to M4 shown in Figure 4, and Figure 6B shows the f-af graphs for sample points M5 to M8 shown in Figure 4.

[0046] A bearing friction test was conducted on a φ10 sliding bearing under continuous operation at a rotational speed of 1200 [rpm] and a load of 31.4 [N]. As shown in Figure 4, the averaged vibration data showed that the amplitude did not swing to its maximum extent for approximately 20 minutes from the start of rotation, but after approximately 20 minutes, the amplitude sometimes swinged to its maximum extent, and seizure occurred approximately 25 minutes after the start of rotation, causing the rotation to stop.

[0047] Therefore, within the range from the start of rotation to 20 minutes, four first to fourth sample points M1 to M4 were set at arbitrary times, and within the range from 20 minutes onward, four fifth to eighth sample points were set at arbitrary times. For each of the first to eighth sample points M1 to M8, 1 ms of averaged vibration data was extracted and analyzed.

[0048] In the above analysis, a wavelet transform was first performed on each of the averaged vibration data from the first to the eighth sample points M1 to M8. For each of the multiple frequencies at predetermined frequency intervals, the average value of the spectral intensity at that frequency was obtained, and a graph of spectral intensity against frequency (fa graph) was generated. The results are shown in Figure 5. In Figure 5A, the fa graph for the first sample point M1 is shown as a solid line, the fa graph for the second sample point M2 is shown as a short dashed line (···), the fa graph for the third sample point M3 is shown as a dashed line, and the fa graph for the fourth sample point M4 is shown as a long dashed line (- - -). In Figure 5B, the fa graph for the fifth sample point M5 is shown as a solid line, the fa graph for the sixth sample point M6 is shown as a short dashed line (···), the fa graph for the seventh sample point M7 is shown as a dashed line, and the fa graph for the eighth sample point M8 is shown as a long dashed line (- - -). Comparing the first to fourth f-a graphs shown in Figure 5A with the fifth to eighth f-a graphs shown in Figure 5B, although the magnitude of the peak values ​​differs, all fa graphs have profiles with multiple peaks on the low-frequency side (three peaks in the range of 0 to 500 kHz in the example shown in Figure 5), making it difficult to significantly differentiate the first to fourth f-a graphs shown in Figure 5A with the fifth to eighth f-a graphs shown in Figure 5B. This is because AE attenuates more easily and the signal weakens as the frequency increases, and it is presumed that warning signs before burnout do not appear in the fa graphs.

[0049] Therefore, in order to emphasize the signal which weakens as the frequency increases, the inventor further multiplied the average value of the spectral intensity at the frequency in question by that frequency to generate a graph of (spectral intensity) × (frequency) as a function of frequency (f-af graph). The result is shown in Figure 6. Similar to Figure 5, in Figure 6A, the f-af graph for the first sample point M1 is shown as a solid line, the f-af graph for the second sample point M2 is shown as a short dashed line (···), the f-af graph for the third sample point M3 is shown as a dotted line, and the f-af graph for the fourth sample point M4 is shown as a long dashed line (- - -). In Figure 6B, the f-af graph for the 5th sample point M5 is shown as a solid line, the f-af graph for the 6th sample point M6 is shown as a short dashed line (···), the f-af graph for the 7th sample point M7 is shown as a dotted line, and the f-af graph for the 8th sample point M8 is shown as a long dashed line (- - -). Comparing the 1st to 4th f-af graphs shown in Figure 6A with the 5th to 8th f-af graphs shown in Figure 6B, the 5th to 8th f-af graphs shown in Figure 6B show a peak at approximately 800 kHz in the frequency band from approximately 750 kHz to approximately 1250 kHz, compared to the 1st to 4th f-af graphs shown in Figure 6A. Therefore, by monitoring the presence or absence of a maximum value in the frequency band from approximately 750 kHz to 1250 kHz, it is possible to determine whether there are any signs of seizure, which are events that occur before seizure occurs when the rotation state changes from the normal rotation state to a different rotation state.

[0050] As described above, the sliding bearing seizure prediction device 1000 and the sliding bearing seizure prediction method implemented therein in the embodiment can determine whether or not there is a local maximum value in the graph of frequency and (spectral intensity) × (frequency) coordinate system.

[0051] According to this embodiment, a sliding bearing seizure prediction device 1000 and a sliding bearing seizure prediction method using wavelet transform can be provided as the analysis method.

[0052] According to the present embodiment, it is possible to provide a sliding bearing seizure prediction detection device 1000 and a sliding bearing seizure prediction detection method for determining whether or not the maximum point is present depending on whether or not the sign of the slope of the graph changes.

[0053] In the above-described embodiment, the graph generation unit 33 generates a graph by multiplying the spectral intensity by the frequency. However, the spectral intensity may be multiplied by the weighted frequency. In this case, the graph generation unit generates a graph in a coordinate system including the horizontal axis of the frequency and the vertical axis of the spectral intensity × frequency obtained by multiplying the spectral intensity by the weighted frequency, based on the analysis result analyzed by the analysis unit 32. More specifically, the graph generation unit obtains the average value of the spectral intensity at each of a plurality of frequencies at a predetermined frequency interval in a plurality of frequency spectra obtained by each of a plurality of wavelets, multiplies the obtained average value by the frequency weighted thereby, and plots the multiplication result multiplied by the frequency in the coordinate system to generate the graph. The weight W is appropriately set in advance from a plurality of samples, for example, according to the frequency f, and is stored in the storage unit 7 in advance. The weight W is preferably adopted, for example, such that W = Kf0 (K is a constant (K > 0), f1 ≦ f0 ≦ f2)) in a frequency band f1 to f2 (f1 < f2) determined in advance in accordance with an empirical rule. Alternatively, for example, the weight W is set such that the weight W(ft) of the target frequency ft is larger than other weights W. Thereby, a graph emphasizing the target frequency ft can be generated, the target frequency ft is scanned, the seizure of the sliding bearing is evaluated, and the presence or absence of the seizure of the sliding bearing can be preferably determined by determining the target frequency ft based on the evaluation result.

[0054] Furthermore, in the above-described embodiment, the presence or absence of signs of sliding bearing seizure was determined, but an index value representing the degree of sliding bearing seizure may also be obtained. In this case, the graph generation unit generates a graph at a preset reference time and a graph at the time of diagnosis, and the sliding bearing seizure prediction detection device 1000, as a sliding bearing diagnostic device, includes an index value calculation unit that, instead of the determination unit, calculates an index value representing the degree of sliding bearing seizure as a diagnostic result based on the difference between the graph at the reference time and the graph at the time of diagnosis generated by the graph generation unit. The difference is obtained, for example, by calculating the difference between the graph at the reference time and the graph at the time of diagnosis for each frequency in the entire frequency range of the wavenumber spectrum, and then calculating the sum of these differences. The larger the difference, the more advanced the sliding bearing seizure is, and the larger the index value. The index value calculation unit stores, for example, such a correspondence between the difference and the index value in advance, and converts the difference into the index value based on the correspondence. The aforementioned reference time is the point in time when seizure has not occurred in the sliding bearing, for example, the time when it is first put into use or when it is put back into use after maintenance. Based on this, the user (operator) can determine the degree of seizure in the sliding bearing by referring to the aforementioned index value.

[0055] To illustrate the present invention, the embodiments have been adequately and fully described above with reference to the drawings. However, those skilled in the art should recognize that it is easy to modify and / or improve upon the embodiments described above. Therefore, unless such modifications or improvements implemented by those skilled in the art fall outside the scope of the claims, such modifications or improvements shall be considered to be included within the scope of the claims. [Explanation of symbols]

[0056] 1000 Predictive detection device for sliding bearing seizure 1. Data acquisition unit 3. Control Processing Unit 6. Interface Section (IF Section) 7 Memory section 31 Control Unit 32 Analysis Department 33 Graph generation unit 34 Judgment section

Claims

1. A data acquisition process that obtains time-series vibration data by detecting vibrations occurring in a sliding bearing, An analysis step is performed in which the vibration data acquired in the data acquisition step is analyzed using an analysis method that simultaneously analyzes time and frequency. A graph generation step generates a graph of a coordinate system having frequency on the horizontal axis and spectral intensity × frequency on the vertical axis, which is obtained by multiplying spectral intensity by frequency or a weighted frequency, based on the analysis results obtained in the above analysis step. The system includes a determination step of determining whether or not there are signs of seizure in the sliding bearing by determining whether or not there is a maximum point in a predetermined frequency range in the graph generated in the graph generation step. A method for detecting signs of sliding bearing seizure.

2. The aforementioned analysis method is an analysis method using wavelet transform, The graph generation step involves, for each of the multiple frequency spectra obtained by each of the multiple wavelets, calculating the average value of the spectral intensity at each of the multiple frequencies at a predetermined frequency interval, multiplying the calculated average value by the frequency or a weighted version of the frequency, and plotting the result of the multiplication by the frequency in the coordinate system to generate the graph. The method for detecting signs of sliding bearing seizure according to claim 1.

3. The determination step determines whether or not the graph has a local maximum point based on whether or not the sign of the slope of the graph changes. The method for detecting signs of sliding bearing seizure according to claim 1.

4. A data acquisition unit that acquires time-series vibration data by detecting vibrations occurring in a sliding bearing, An analysis unit analyzes the vibration data acquired by the data acquisition unit using an analysis method that simultaneously analyzes time and frequency. A graph generation unit generates a graph of a coordinate system having frequency on the horizontal axis and spectral intensity × frequency on the vertical axis, which is obtained by multiplying spectral intensity by frequency or a weighted frequency, based on the analysis results obtained by the aforementioned analysis unit. The system includes a determination unit that determines whether or not there are signs of seizure in the sliding bearing by determining whether or not there is a maximum point in a predetermined frequency range in the graph generated by the graph generation unit. Sliding bearing seizure prediction device.

Images