Abnormal Condition Diagnosis Device and Method for Mechanical Equipment

Abstract

To provide an abnormality presence diagnosis device and a method for a machine plant, which can be applied even when a frequency band or the like of a vibration waveform is unknown.SOLUTION: An abnormality presence diagnosis device 1 of a machine plant comprises: a vibration waveform collecting part 3 for collecting a vibration waveform for a prescribed period; wavelet conversion means 21 for performing wavelet conversion of the collected vibration waveform, for acquiring a time change of the wavelet coefficient about an entire frequency; wavelet coefficient time waveform acquiring means 23 for, on the basis of the time change of the wavelet coefficient, acquiring a time waveform of the wavelet coefficient in each frequency for every frequency of a prescribed interval; auto correlation function calculating means 25 for calculating an auto correlation function to the time waveform of the wavelet coefficient of each frequency; correlation degree peak extraction means 27 for extracting the optional number of peak values of the correlation degree in the auto correlation function, about each frequency; and determining means 29 for determining presence of occurrence of an abnormal period vibration, on the basis of a relation between, the peak values extracted for each frequency and a time.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an apparatus and method for diagnosing the presence or absence of abnormalities in rotating machinery equipment that performs low-speed rotation, such as a sludge scraping machine used in, for example, sewage treatment plants.

Background Art

[0002] As shown in FIG. 9, the sludge scraping machine 41 is composed of a drive device 43, an endless chain 45, sprockets 47, flights 49, etc. Flights 49 are attached to the endless chain 45 at regular intervals, and this is driven via drive shafts and driven shaft sprockets to move the flights 49 in contact with the rail surface of the sedimentation tank 51, continuously scraping the sludge deposited in the sedimentation tank 51 into the sludge hopper.

[0003] In the sludge scraping machine 41 shown in FIG. 9, sprockets 47 are provided at five locations, namely, a drive device sprocket 53, a drive shaft drive sprocket 54 (coaxial with the driven shaft driven sprocket 55 and having an outer diameter slightly larger), and driven shaft driven sprockets 55, 57, 59, 61.

[0004] Examples of abnormal phenomena in such a sludge scraping machine 41 include elongation of the endless chain 45, wear of the sprockets 47 and flights 49, etc.

[0005] However, the rotational speed of the drive device 43 is very low, for example, about 0.4 rpm, and the traveling speed of the flights 49 is very slow, for example, 0.3 m / min, to scrape the sludge. Also, since most parts of the device are in the water part, it is difficult to detect abnormalities with the five senses from outside the sedimentation tank 51.

[0006] Therefore, as a method for diagnosing abnormalities in such a sludge scraping machine, for example, in Patent Document 1, a method of attaching a vibration sensor to a drive device installed outside the sedimentation tank and performing abnormality diagnosis based on the vibration waveform obtained from the vibration sensor is disclosed. This is based on the idea that the sprocket, flights, and endless chain are all in contact and connected, and abnormal vibrations generated in any of the devices are transmitted to the ground drive device via the endless chain.

[0007] The technique disclosed in Patent Document 1 is a useful technique for abnormal diagnosis of equipment when the equipment to be diagnosed is specified.

[0008] However, existing sludge scraping machines are used in many sewage treatment facilities, and the scale and structure of the facilities (for example, two-story structure, three-story structure, etc.) vary, and naturally the sizes and shapes of the sprockets, flights, and endless chains used there also vary. Therefore, even if an abnormality occurs in the sprocket in one piece of equipment and another, the frequency band and period of the vibration waveform detected by the vibration sensor attached to the drive device are completely different. For this reason, it is difficult to determine whether or not a failure has occurred in the equipment itself, and it is difficult to handle this with the technique disclosed in Patent Document 1.

[0009] In such a case, it is useful to capture the vibration waveform over time to determine the presence or absence of an abnormality. For example, it is conceivable to use wavelet transform disclosed in Patent Documents 2 and 3. Wavelet transform is one of the frequency analysis methods, and it analyzes what frequencies are occurring with what amplitudes over time.

Prior Art Documents

Patent Documents

[0010]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0011] However, the diagnostic method disclosed in Patent Document 2 diagnoses the remaining life of a bearing by having a diagnostician examine and judge the time-frequency distribution of a wavelet, which is subject to the skills and knowledge of the diagnostician and may lack objectivity.

[0012] Also, in the diagnostic apparatus disclosed in Patent Document 3, although amplitude values in a plurality of frequency bands are extracted using wavelet transform, it is premised that the processing is performed by focusing on specific frequencies caused by the known structure and rotation speed of the bearing. Therefore, it is difficult to diagnose the presence or absence of abnormalities in equipment such as the sludge scooping machine targeted by the present invention, where the equipment scale and structure vary, and the frequency band of the vibration waveform to be targeted is unknown.

[0013] Note that the above problems are common to equipment with unknown frequency bands, etc., and the device to be diagnosed is not limited to the exemplified sludge scooping machine.

[0014] The present invention has been made to solve such problems, and an object thereof is to provide an apparatus and method for diagnosing the presence or absence of abnormalities in mechanical equipment that can be applied even when the frequency band of a vibration waveform is unknown.

Means for Solving the Problems

[0015] (1) The apparatus for diagnosing the presence or absence of abnormalities in mechanical equipment according to the present invention diagnoses the presence or absence of abnormalities in mechanical equipment, and includes a vibration waveform acquisition unit that acquires a vibration waveform for a predetermined time using a vibration sensor installed on the target equipment, and an abnormality presence / absence diagnosis unit that analyzes the vibration waveform data acquired by the vibration waveform acquisition unit to diagnose the presence or absence of abnormalities. The abnormality presence / absence diagnosis unit includes a wavelet transform means that performs wavelet transform on the acquired vibration waveform to obtain the time change of wavelet coefficients for all frequencies, Wavelet coefficient time waveform acquisition means for acquiring the time waveform of the wavelet coefficients at each frequency for each frequency at a predetermined interval from the time change of the wavelet coefficients obtained by wavelet transform; Autocorrelation function calculation means for calculating the autocorrelation function for the time waveforms of the wavelet coefficients at each frequency; Correlation degree peak extraction means for extracting an arbitrary number of peak values of the correlation degree in the autocorrelation function for each of the frequencies; Determination means for determining the presence or absence of abnormal periodic vibration based on the relationship between the peak values and time extracted for each frequency; characterized by comprising the above.

[0016] (2) Further, in the above (1), the determination means creates a distribution diagram showing the relationship between the extracted peak values and time, calculates the centroid of the distribution diagram, compares it with the centroid in the normal state obtained in advance, and determines the presence or absence of abnormality based on the degree of deviation between the two.

[0017] (3) The method for diagnosing the presence or absence of abnormality in mechanical equipment according to the present invention is a method for diagnosing the presence or absence of abnormality in mechanical equipment, a vibration waveform acquisition step of acquiring a vibration waveform for a predetermined time using a vibration sensor installed in the target equipment; a wavelet transform step of wavelet-transforming the acquired vibration waveform to obtain the time change of wavelet coefficients for all frequencies; a wavelet coefficient time waveform acquisition step of acquiring the time waveform of the wavelet coefficients at each frequency for each frequency at a predetermined interval from the time change of the wavelet coefficients obtained by wavelet transform; an autocorrelation function calculation step of calculating the autocorrelation function for the time waveforms of the wavelet coefficients at each frequency; a correlation degree peak extraction step of extracting an arbitrary number of peak values of the correlation degree in the autocorrelation function for each of the frequencies; a determination step of determining the presence or absence of abnormal periodic vibration based on the relationship between the peak values and time extracted for each frequency; It is characterized by comprising

[0018] (4) Further, in the case of the one described in (3) above, the determination step creates a distribution diagram showing the relationship between the extracted peak value and time, calculates the centroid of the distribution diagram, compares it with the centroid at normal times obtained in advance, and determines the presence or absence of an abnormality based on the degree of deviation between the two.

Advantages of the Invention

[0019] In the present invention, the time waveforms of the wavelet coefficients are acquired for all frequencies, and the presence or absence of abnormal periodic vibrations is determined based on the time waveforms of the wavelet coefficients. Therefore, it is possible to diagnose the presence or absence of abnormalities in mechanical equipment with unknown frequency bands of vibration waveforms.

Brief Description of the Drawings

[0020]

Figure 1

Figure 2

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Figure 8

Figure 9

Embodiment for Carrying Out the Invention

[0021] A presence / absence diagnosis device for mechanical equipment according to an embodiment of the present invention (hereinafter simply referred to as the "presence / absence diagnosis device") will be described by taking a sludge scooping machine as an example of the mechanical equipment to be diagnosed. First, the presence / absence diagnosis device will be outlined based on FIG. 1, and then the presence / absence diagnosis method will be described. In FIG. 1, the reference numerals indicating each part of the sludge scooping machine 41 are the same as those in FIG. 9.

[0022] As shown in FIG. 1, the presence / absence diagnosis device 1 for mechanical equipment according to the present embodiment includes a vibration waveform acquisition unit 3 and a presence / absence diagnosis unit 7 that is connected to the vibration waveform acquisition unit 3 by a cable 5 and analyzes the vibration waveform data acquired by the vibration waveform acquisition unit 3 to diagnose the presence or absence of abnormalities. Each component will be described in detail.

[0023] <Vibration waveform acquisition unit> As shown in FIG. 1, the vibration waveform acquisition unit 3 includes a vibration sensor 13 composed of a piezoelectric element 9 and a magnet 11 for installing the piezoelectric element 9 at a measurement location, an amplifier 15 for amplifying the signal output from the vibration sensor 13, and an A / D converter 17 for converting the analog data of the vibration amplified by the amplifier 15 into a digital signal. The vibration sensor 13 is attached near the output shaft of the driving device 43 (the position indicated by P in the figure) by the magnet 11. The vibration waveform data acquired by the vibration waveform acquisition unit 3 is transmitted to the presence / absence diagnosis unit 7 via the cable 5.

[0024] In the present embodiment, an example of using one vibration waveform acquisition unit 3 is shown, but in the present invention, the number of vibration waveform acquisition units 3 is not limited to one, and the use of a plurality of them is not excluded.

[0025] <Presence / absence diagnosis unit> The abnormality presence / absence diagnosis unit 7 is composed of a device and a program installed in a PC (Personal Computer). As shown in FIG. 1, the abnormality presence / absence diagnosis unit 7 includes a vibration waveform data storage means 19, a wavelet transform means 21, a wavelet coefficient time waveform acquisition means 23, an autocorrelation function calculation means 25, a correlation degree peak extraction means 27, a determination means 29, an input means 31 such as a keyboard, and a display means 33 such as a monitor. The vibration waveform data storage means 19, the wavelet transform means 21, the wavelet coefficient time waveform acquisition means 23, the autocorrelation function calculation means 25, the correlation degree peak extraction means 27, and the determination means 29 are realized by the CPU installed in the PC executing a program. Hereinafter, each means will be described in detail.

[0026] 《Vibration waveform data storage means》 The vibration waveform data storage means 19 inputs the signal transmitted from the vibration sensor unit, performs appropriate filter processing on the vibration waveform data, and stores it.

[0027] 《Wavelet transform means》 The wavelet transform means 21 performs wavelet transform on the collected vibration waveform to obtain the time change of the wavelet coefficients for all frequencies. Wavelet transform is one of the frequency analysis methods and is used to see what frequencies are generated with what amplitudes over time. Here, in the present invention, the reason for using wavelet transform will be described by taking the sludge scraping machine diagnosed in this embodiment as an example. Since the sludge scraping machine is designed based on the sludge treatment amount, there are those with different specifications for each facility, such as the facility scale, the structure of the facility, the types of mechanical parts used, and the part dimensions. Therefore, if the specifications are different, the vibration frequencies generated during abnormalities will also be different.

[0028] The vibration frequency that occurs during this abnormality can be identified by conducting abnormal simulation tests, vibration excitation tests, etc. for each individual sludge scraper. However, it is not practical to conduct simulation tests, etc. on sludge scrapers during operation because it takes a significant amount of effort and time. However, even if the specifications of the sludge scraper are different, when abnormalities such as wear occur in the sprockets, etc. inside the scraper tank, it is a fact that periodic vibrations in a certain frequency band will appear, and only the frequency band that occurs during abnormalities differs depending on the specifications of the scraper. Therefore, by using wavelet transform, it is possible to calculate for all frequencies within the frequency range to be analyzed whether there are periodic abnormal vibrations for each frequency, so that information on whether there are periodic vibrations can be obtained. For this reason, even if the frequency band during abnormalities is unknown, it is possible to determine the presence or absence of abnormalities without identifying it.

[0029] 《Wavelet Coefficient Time Waveform Acquisition Means》 The wavelet coefficient time waveform acquisition means 23 acquires the time waveform of the wavelet coefficients at each frequency for each frequency at predetermined intervals of the time change of the wavelet coefficients obtained by wavelet transform.

[0030] 《Autocorrelation Function Calculation Means》 The autocorrelation function calculation means 25 calculates the autocorrelation function for the time waveform of the wavelet coefficients of each frequency.

[0031] 《Correlation Degree Peak Extraction Means》 The correlation degree peak extraction means 27 extracts the peak values of the correlation degree in the autocorrelation function for an arbitrary number of points for each of the above frequencies.

[0032] 《Judgment Means》 The judgment means 29 determines the presence or absence of the occurrence of abnormal periodic vibrations based on the relationship between the peak values extracted for each frequency and time. A specific example of the judgment method by the judgment means 29 will be shown in the explanation of the abnormality presence / absence diagnosis method described later.

[0033] [Diagnosis Method for Abnormality of Machinery Equipment] Next, a method for diagnosing the presence or absence of abnormalities in machinery equipment using the above-described abnormality diagnosis device 1 (hereinafter simply referred to as the "abnormality diagnosis method") will be described. As shown in FIG. 2, the abnormality diagnosis method according to the present embodiment includes a vibration waveform acquisition step, a wavelet transform step, a wavelet coefficient time waveform acquisition step, an autocorrelation function calculation step, a correlation degree peak extraction step, and a determination step. Hereinafter, each step will be described.

[0034] [Vibration Waveform Acquisition Step] The vibration waveform acquisition step is a step of acquiring an acceleration waveform for a predetermined time using the vibration sensor 13 installed in the target equipment. Specifically, the piezoelectric element 9 of the vibration waveform acquisition unit 3 shown in FIG. 1 is attached to the drive device 43 of the sludge scooping machine 41 with a magnet 11 to acquire a vibration waveform. Note that the acquisition time of the vibration waveform data is set to a predetermined time set in advance. FIG. 3 shows an acceleration waveform when acceleration is acquired as vibration data for an acquisition time of 300 seconds.

[0035] [Wavelet Transform Step] The wavelet transform step is a step of obtaining the time change of the wavelet coefficients for all frequencies by wavelet-transforming the acquired vibration waveform by the wavelet transform means 21.

[0036] FIG. 4 shows an example of the time change of the wavelet coefficients, where the vertical axis represents frequency (Hz), the horizontal axis represents time (sec), and the gray scale represents the intensity of the wavelet coefficients. FIG. 4(a) shows the case where wear has occurred on the sprocket 47, and FIG. 4(b) shows the normal state.

[0037] [Wavelet Coefficient Time Waveform Acquisition Step] The wavelet coefficient time waveform acquisition step is a step performed by the wavelet coefficient time waveform acquisition means 23, and acquires the time waveforms of the wavelet coefficients at each frequency for each frequency at a predetermined interval from the time change of the wavelet coefficients obtained by wavelet transform. For example, the entire frequency band is divided into a predetermined number (for example, 128 divisions), and the time waveforms of the wavelet coefficients are acquired for each of the divided frequency bands (for example, every 14 Hz).

[0038] Figure 5 shows the time waveform graphs of the wavelet coefficients at each frequency (a, b, c, ··· z frequencies) acquired by the wavelet coefficient time waveform acquisition means 23, where the vertical axis is the amplitude (m / s 2 ), and the horizontal axis (sec) represents time.

[0039] <Autocorrelation function calculation step> The autocorrelation function calculation step is a step of calculating the autocorrelation function for the time waveforms of the wavelet coefficients at each frequency by the autocorrelation function calculation means 25. Figure 6 shows the autocorrelation function, where the vertical axis represents the degree of autocorrelation and the horizontal axis represents time (sec). By calculating the autocorrelation function, the periodicity of the intensity of the wavelet coefficients can be extracted.

[0040] <Correlation degree peak extraction step> The correlation degree peak extraction step is a step of extracting, for each of the above frequencies, an arbitrary number of peak values of the correlation degree in the autocorrelation function by the correlation degree peak extraction means 27. For example, in the example shown in Figure 6, an example of extracting 4 points (indicated by the dotted circles in the figure) from the larger peak values in the case of z Hz is shown.

[0041] <Judgment step> The judgment step is a step of judging the presence or absence of abnormal periodic vibration generation based on the relationship between the peak values and time extracted for each frequency by the judgment means 29. As an example of a specific determination method, a distribution diagram showing the relationship between the extracted peak value (correlation degree) and time is created, the centroid of the distribution diagram is calculated, compared with the centroid under normal conditions obtained in advance, and the presence or absence of abnormality is determined based on the deviation degree between the two. Here, the reason for determining the presence or absence of abnormality based on the centroid of the distribution diagram will be explained. The reason for calculating the centroid from the distribution diagram is that when wear occurs on the sprocket of the sludge scraping machine, due to the number of worn teeth, the shape of the wear, and the degree of wear, there will be a subtle difference in the meshing period between the sprocket and the chain, so there will also be a certain degree of variation in the distribution of peak values. However, even if there is a variation in the meshing period, if there is an abnormality, peaks that do not appear under normal conditions will appear periodically in the autocorrelation function graph, so the centroid of the distribution diagram will deviate from that under normal conditions. By utilizing such facts, the determination of the presence or absence of abnormality can be simplified.

[0042] Figure 7 shows a distribution diagram in a state where an abnormality has occurred in the sprocket 47 shown in Figure 4(a), and the ● in the figure indicates the centroid of the distribution diagram. Figure 8 is a plot of the centroid of the distribution diagram obtained under normal conditions shown in Figure 4(b) on the distribution diagram shown in Figure 7.

[0043] As shown in Figure 8, it can be seen that the centroid at the time of abnormality is greatly deviated from the centroid under normal conditions. Then, a threshold value for determining whether it is abnormal regardless of the degree of this deviation is set in advance, and if it exceeds this threshold value, it is determined to be abnormal.

[0044] Here, the specific idea for setting the threshold value will be explained. When the sprocket wears out, periodic vibrations occur, and periodic and continuous peak components also appear in the autocorrelation function graph. The occurrence period of this peak component is based on the meshing period (unit: seconds) of the sprocket and the chain that can be calculated, and peak components appear repeatedly up to an integer multiple (about 5 times) of the basic period in the autocorrelation function graph. Therefore, it was confirmed that it is possible to determine the presence or absence of abnormality by analyzing up to the 5-fold component of the basic period, so the centroid of a plurality of peak components up to the 5-fold component was taken. As a result, since it can be determined that the centroid point that deviates approximately 3 times from the meshing period of the sprocket and the chain is in an abnormal state, the approximate 3-fold value of the meshing period of the sprocket and the chain may be used as the threshold value.

[0045] In the example shown in FIG. 8, focusing on the time axis, the centroid during normal operation is 32 [sec], while the threshold value is set to 80 [sec], which is about 3 times that during normal operation. On the other hand, the centroid during abnormal operation is 110 [sec], which greatly exceeds the threshold value of 80 [sec], so it is determined that an abnormality has occurred.

[0046] As described above, in the present invention, the time waveform of the wavelet coefficient is acquired for all frequencies, and the presence or absence of abnormal periodic vibration is determined based on the time waveform of the wavelet coefficient. Therefore, it is possible to diagnose the presence or absence of abnormality in mechanical equipment with an unknown frequency band of the vibration waveform.

[0047] In the above description, as a method for determining the presence or absence of abnormal periodic vibration based on the relationship between the peak value extracted for each frequency and time, the centroid of the distribution diagram showing the relationship between the extracted peak value and time is calculated, compared with the centroid during normal operation obtained in advance, and the presence or absence of abnormality is determined based on the degree of deviation between the two. However, the determination of the presence or absence of abnormal periodic vibration in the present invention is not limited to this, and it may be performed by the mode value or the median value for the distribution showing the relationship between the extracted peak value and time.

Explanation of symbols

[0048] 1 Abnormality Presence / Absence Diagnostic Device 3 Vibration Waveform Acquisition Unit 5 Cable 7 Abnormality Presence / Absence Diagnosis Unit 9 Piezoelectric Element 11 Magnet 13 Vibration Sensor 15 Amplifier 17 A / D Converter 19 Vibration Waveform Data Storage Means 21 Wavelet Transform Means 23 Wavelet Coefficient Time Waveform Acquisition Means 25 Autocorrelation Function Calculation Means 27 Correlation Degree Peak Extraction Means 29 Judgment Means 31 Input Means 33 Display Means 41 Sludge Scraper 43 Driving Device 45 Endless Chain 47 Sprockets (53, 54, 55, 57, 59, 61) 49 Flight 51 Sedimentation Tank 53 Driving Device Sprocket 54 Driving Shaft Driving Sprocket 55, 57, 59, 61 Driving Shaft Driven Sprockets

Claims

1. An abnormal condition diagnosis device for mechanical equipment that diagnoses the presence or absence of abnormalities in mechanical equipment, comprising a vibration waveform acquisition unit that acquires a vibration waveform for a predetermined time using a vibration sensor installed on the target equipment, and an abnormal condition diagnosis unit that analyzes the vibration waveform data acquired by the vibration waveform acquisition unit to diagnose the presence or absence of abnormalities, wherein the abnormal condition diagnosis unit comprises a wavelet transform means for performing wavelet transform on the acquired vibration waveform to obtain the time variation of wavelet coefficients for all frequencies, a wavelet coefficient time waveform acquisition means for obtaining the time waveform of wavelet coefficients at each frequency for every frequency at a predetermined interval from the time variation of the wavelet coefficients obtained by the wavelet transform, an autocorrelation function calculation means for calculating the autocorrelation function for the time waveform of wavelet coefficients at each frequency, a correlation degree peak extraction means for extracting an arbitrary number of peak values of the correlation degree in the autocorrelation function for each of the frequencies, and a determination means for determining the presence or absence of abnormal periodic vibration based on the relationship between the peak values extracted for each frequency and time. An abnormal condition diagnosis device for mechanical equipment, characterized by comprising the above.

2. The determination means creates a distribution diagram showing the relationship between the extracted peak values and time, calculates the centroid of the distribution diagram, compares it with the centroid during normal operation obtained in advance, and determines the presence or absence of abnormalities based on the degree of deviation between the two. The abnormal condition diagnosis device for mechanical equipment according to Claim 1, characterized by this.

3. An abnormal condition diagnosis method for mechanical equipment that diagnoses the presence or absence of abnormalities in mechanical equipment, comprising a vibration waveform acquisition step of acquiring a vibration waveform for a predetermined time using a vibration sensor installed on the target equipment, a wavelet transform step of performing wavelet transform on the acquired vibration waveform to obtain the time variation of wavelet coefficients for all frequencies, a wavelet coefficient time waveform acquisition step of obtaining the time waveform of wavelet coefficients at each frequency for every frequency at a predetermined interval from the time variation of the wavelet coefficients obtained by the wavelet transform, an autocorrelation function calculation step of calculating the autocorrelation function for the time waveform of wavelet coefficients at each frequency, a correlation degree peak extraction step of extracting an arbitrary number of peak values of the correlation degree in the autocorrelation function for each of the frequencies, and a determination step of determining the presence or absence of abnormal periodic vibration based on the relationship between the peak values extracted for each frequency and time. An abnormal condition diagnosis method for mechanical equipment, characterized by comprising the above. ​

4. The determination step creates a distribution diagram showing the relationship between the extracted peak values and time, calculates the centroid of the distribution diagram, compares it with the centroid during normal operation obtained in advance, and determines the presence or absence of an abnormality based on the degree of deviation between the two. The method for diagnosing the presence or absence of an abnormality in mechanical equipment according to claim 3, characterized in that.

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