JP7356956B2 - Abnormality sign diagnostic device and its diagnostic method - Google Patents

Description

The present invention relates to an abnormality symptom diagnostic device for a valve sliding part and a diagnostic method thereof.

A valve that has the function of closing the flow of fluid uses a driving part to close the flow path between the valve body and the valve seat, thereby stopping the flow of fluid and preventing fluid from leaking from the valve.

For example, in an electric valve, there is a worm gear that changes the direction of the motor's rotational driving force by 90 degrees, a threaded part of the valve stem that converts the rotational driving force into linear driving force, and a gland that prevents fluid leakage from the shaft surface that moves linearly. Elements such as the packing part and the seat part, which is the contact surface between the valve body and the valve seat, work together to stop the flow of fluid, but sliding occurs in each element. In these sliding parts, deterioration of the sliding parts occurs due to friction, increasing the risk of leakage from the seat part and the loss of driving force.
In addition, in an air-operated valve, sliding deterioration occurs in the piston seal portion instead of the worm gear.

The sliding parts mentioned above will deteriorate depending on the number of times they are operated, so by disassembling and inspecting the valves, you can identify valves where sliding deterioration is progressing, replace parts, and perform maintenance to prevent valve malfunctions. There is. However, disassembly and inspection requires a lot of effort and cost.

Even in nuclear power generation facilities, valves are overhauled during periodic inspections to prevent accidents, but overhauling large-diameter valves requires a crane, which requires a huge amount of labor and cost. . In addition, the amount of radiation exposure may be high when overhauling and inspecting valves installed in systems with high radiation levels.

For this reason, a device that diagnoses the operability of the valve by measuring the current value of the motor in the drive section (Patent Document 1) and a device that uses an acoustic sensor to diagnose damage and fatigue of the valve stem due to fluid force (Patent Document 2) have been developed. This reduces the man-hours and frequency of periodic inspections.

JP2006-083928A Japanese Patent Application Publication No. 2014-521045

In the above-mentioned prior art, a system for detecting abnormality or monitoring the condition of a valve using an acoustic sensor utilizes acoustic emissions caused by vibration, cracks, or deformation caused by the action of fluid in a flow control valve, and detects deviation from the normal state or fatigue. Diagnose valve failure by detection. Therefore, although it is possible to diagnose the occurrence of structural defects in the flow control function and valves, diagnosis of valves (functions that close fluid) is not complete.

An object of the present invention is to enable highly accurate deterioration sign diagnosis of a valve's dynamic function and fluid closing function without disassembling the valve.

In order to solve the above problems, the abnormality sign diagnosis device of the present invention includes a signal processing section that processes a signal of at least one acoustic sensor that detects the sliding sound of the contact surface of the valve body and the valve seat when the valve seat is closed ; an analysis unit that determines the state of leakage, surface roughness, or friction coefficient of the sliding part that generates the sliding sound from the spectral intensity of the sliding sound that is processed by the signal processing unit and detected by the acoustic sensor; Maintenance, including valve disassembly, by determining signs of deterioration, including whether or not the valve's fluid-closing function and dynamic function are normal, based on the amount of leakage, surface roughness, or friction coefficient of the sliding part determined by the section. a determination unit that predicts the timing, and the analysis unit determines, for each of the sliding parts, the correlation between the spectral intensity of the actually measured sliding sound and the number of times the sliding part operates, and the amount of leakage and surface roughness that were actually measured. The correlation between the measured friction coefficient and the number of operations, and the correlation between the actually measured surface roughness and the number of operations are memorized in advance, and the leakage amount and surface roughness of the sliding part are determined from the spectral intensity of the sliding sound. The state of friction coefficient or friction coefficient can now be determined .

Further, the diagnosis method of the abnormality sign diagnosis device for diagnosing the state of the sliding part of the valve according to the present invention includes a signal from at least one acoustic sensor that detects the sliding sound of the contact surface between the valve body and the valve seat when the valve body and the valve seat are closed. For each processing step and the sliding parts of the valve body and valve seat, the correlation between the spectral intensity of the measured sliding sound and the number of operations of the sliding part, the correlation between the measured leakage amount and the surface roughness, and the actual measurement The correlation between the measured friction coefficient and the number of operations is stored in advance, and the correlation between the measured surface roughness and the number of operations is stored in advance, and from the spectral intensity of the processed sliding sound, the leakage amount of the sliding part that generates the sliding sound, The step of determining the state of the surface roughness or friction coefficient, and the deterioration including whether the valve's fluid closing function and dynamic function are normal or not from the determined leakage amount, surface roughness, or friction coefficient of the sliding part. The present invention includes a step of predicting the timing of maintenance including disassembly of the valve by determining the sign of the problem.

According to the present invention, it is possible to diagnose signs of deterioration of the valve's dynamic function and fluid-closing function with high accuracy without disassembling the valve. The accuracy of maintenance can be improved.

FIG. 3 is a diagram showing a cross section of an electric gate valve and an installation position of an acoustic sensor. FIG. 1 is a diagram showing the configuration of an abnormality sign diagnosis device according to an embodiment. It is a figure which shows an example of the detection signal of an acoustic sensor. FIG. 3 is a diagram showing a processing flow of a waveform extraction/division section. FIG. 3 is a diagram showing the correlation between the spectral intensity of sliding sound and the number of times the sliding portion is operated. It is a figure showing the correlation between surface roughness and number of operations. It is a figure showing the correlation between a friction coefficient and the number of operations. FIG. 3 is a diagram showing the correlation between leakage amount and surface roughness. It is a figure which shows the attachment method of the acoustic sensor which diagnoses with the heat insulating material attached.

Embodiments of the present invention will be described in detail below with reference to the drawings.
The abnormality sign diagnosis device of the embodiment performs valve opening/closing operations on each of the sliding parts of the electric gate valve, such as the worm gear, the threaded part of the valve stem, the contact surface between the valve stem and the gland packing, and the contact surface between the valve body and the valve seat. Acoustic emissions (elastic waves) generated by the sliding sound are measured by an acoustic sensor as sliding sound.Based on the measured sliding sound, the surface roughness, friction coefficient, and leakage amount of the sliding part are obtained, and abnormality signs and maintenance can be detected. Diagnose the timing and condition of the sliding parts, including defects. Since the fluid flow sound becomes the noise of the sliding sound to be measured, the abnormality sign diagnostic device of the embodiment performs the diagnosis while the equipment in which the electric gate valve is installed is stopped.

In explaining the abnormality sign diagnosis device according to the embodiment, first, the installation of an acoustic sensor on an electric gate valve to be diagnosed will be explained with reference to FIG.
FIG. 1 is a diagram showing a cross section of an electric gate valve 1 (hereinafter referred to as valve 1) and the installation position of an acoustic sensor 2.

Sliding occurs on the worm gear 24 of the valve 1, the valve stem threaded portion 25, the contact surface between the valve stem 4 and the gland packing 28, and the contact surface between the valve body 29 and the valve seat 30 when the valve 1 is opened and closed. For this reason, the acoustic sensors 2 are respectively installed directly or via the listening needle bar 31 at positions on the outer surface of the valve 1 that are the propagation path of the direct wave of the sliding sound near these sliding parts.

Further, when opening and closing the valve 1, the acoustic sensor 2 is installed on the motor 22 or the manual handle 23, which is the drive section 5 of the valve 1, to determine the drive state of the drive section 5. It is also used to reduce noise during detection by other acoustic sensors 2.

When the acoustic sensor 2 is installed directly near the sliding part, the couplant 21 is interposed as an acoustic transmission medium between the acoustic sensor 2 and the detection surface.
When installing the acoustic sensor 2 via the listening needle bar 31, the couplant 21 is interposed between the acoustic sensor 2 and the listening needle bar 31.
The material of the couplant 21 is grease, wax, adhesive, or the like.

The acoustic sensor 2 or the acoustic sensor 2 and listening needle bar 31 are fixed by hand-held pressing or by using a fixing jig. This fixture may be attached to the detection surface by a magnet, adhesive, welding, or solder, or may be fixed by an arm or tripod set on the ground. Since the propagation of sound is reduced through the air phase, it is preferable to attach the acoustic sensor 2 to the same component.

As the acoustic sensor 2, an acoustic emission sensor, a piezoelectric sensor, or an ultrasonic sensor can be applied.

Next, the configuration of the abnormality sign diagnosis device 20 according to the embodiment will be explained with reference to FIG. 2.
The abnormality sign diagnosis device 20 in FIG. 2 detects the sliding sound of the worm gear 24 with an acoustic sensor 2a provided in the drive unit 5 of the valve 1 in FIG. The sliding sound of the surface is detected, and the sliding sound of the contact surface between the valve body 29 and the valve seat 30 is detected by the acoustic sensor 2c, and an abnormality sign diagnosis is performed. It goes without saying that the abnormality sign diagnosis device 20 is not limited to the acoustic sensors 2a, 2b, and 2c shown in FIG. 2, and can perform abnormality sign diagnosis using sliding sounds detected by the acoustic sensors 2 installed at other locations.

The abnormality sign diagnosis device 20 includes a signal processing unit 9 that processes a signal of sliding sound detected by at least one acoustic sensor 2, and a sliding sound detection unit 9 based on the sliding sound of the sliding part acquired by the signal processing unit 9. Based on the analysis results obtained from the analysis section 11, the analysis section 11 calculates the leakage amount value, surface roughness value, friction coefficient value, etc. , and a determination unit 16 that performs abnormality determination.

The signal processing unit 9 is a processing unit that processes each detection signal of the acoustic sensor 2 connected to the abnormality sign diagnosis device 20, and includes an amplification unit 6 that amplifies the detection signal to a predetermined level and a frequency of 10 to 100 kHz or less. It is comprised of a high-pass filter 7 that cuts out elastic waves in the region, and an A/D converter 8 that converts the detection signal of the acoustic sensor 2 into digital data.
Further, the signal processing section 9 may further include a low-pass filter or an envelope detection circuit to enhance signal discrimination of elastic waves.

The analysis unit 11 is a sliding sound waveform extraction/splitting unit that extracts or separates the waveform of the sliding sound of a predetermined sliding part from the sliding sound detected by at least one acoustic sensor 2 processed by the signal processing unit 9. 10, a spectrum analysis section 15 that analyzes the waveform of the sliding sound in the frequency domain, and a leakage amount value, surface roughness value, and friction coefficient value from the time change of the spectral intensity of the sliding sound obtained by the spectrum analysis section 15. It has a leak amount value acquisition section 12, a surface roughness value acquisition section 13, and a friction coefficient value acquisition section 14, which obtain each of the following.
The details of the sliding sound waveform extraction and division section 10, the leakage value acquisition section 12, the surface roughness value acquisition section 13, and the friction coefficient value acquisition section 14 will be described later.

The determination unit 16 determines the friction loss based on the leakage amount, surface roughness, friction coefficient, and rate of change of these acquired by the leakage value acquisition unit 12, surface roughness value acquisition unit 13, and friction coefficient value acquisition unit 14. The partitioning capacity is estimated by combining the abnormality determination unit 19 that determines abnormalities including increases, the number of operations until leakage estimating unit 17 that estimates the number of operations until leakage occurs, and the operating force information of the drive unit. It has a partitioning capacity estimating unit 18.

More specifically, the abnormality sign diagnosis device 20 includes an analog signal circuit such as an amplifier and an A/D converter, a microcomputer circuit that performs acoustic analysis and judgment using a built-in program, and a display device that displays analysis results and judgment results. Consists of etc.

When inspecting the valve 1, the abnormality sign diagnosis device 20 drives the drive unit 5 of the valve 1 and moves the valve rod 4 up and down or rotates the worm gear 24 of the valve 1, with the flow of working fluid stopped. It slides on the contact surface between the valve stem 4 and the gland packing 28, and the contact surface between the valve body 29 and the valve seat 30. The abnormality sign diagnosis device 20 detects sliding sounds using acoustic sensors 2a, 2b, and 2c, and determines abnormality, estimates partitioning capacity, and estimates the number of operations until leakage occurs.

Next, details of the processing of the sliding sound waveform extraction/dividing section 10 will be explained with reference to FIG.
FIG. 3 is a diagram showing an example of temporal changes in detection signals of the acoustic sensors 2a, 2b, and 2c.
The acoustic sensor 2a detects the sliding sound of the worm gear 24, the acoustic sensor 2b detects the sliding sound of the contact surface between the valve stem 4 and the gland packing 28, and the acoustic sensor 2c detects the sliding sound of the contact surface between the valve body 29 and the valve seat 30. The sliding sounds of each are detected in synchronization.

The signal waveforms of the sliding sound of the worm gear 24, the sliding sound of the contact surface between the valve stem 4 and the gland packing 28, and the sliding sound of the contact surface between the valve body 29 and the valve seat 30 have unique characteristics. For example, in the worm gear 24 detected by the acoustic sensor 2a, sliding noise is generated by hammering, and has a sudden signal waveform that rises sharply and then decays. The sliding sound of the contact surface between the valve body 29 and the valve seat 30 when the valve is closed, which is detected by the acoustic sensor 2c, also has a sudden signal waveform due to hammering. Furthermore, the sliding sound between the valve stem 4 and the gland packing 28 detected by the acoustic sensor 2b has a continuous signal waveform that is constantly generated during driving.

In addition, signals of driving motor sound or air sound, signals of sliding noise generated in gears and threads that occur at regular intervals, shock pulses due to wobbling of the valve body just before closing, and the valve body and valve at the time of closing. The sliding sound signals generated in the seat part of the seat and other abnormal pulses caused by jamming, etc., each have their own unique sound patterns. Therefore, the sliding portion can be identified by classifying the sliding sound patterns.

By the way, the sliding sound propagates not only to the installed acoustic sensor 2 but also to other acoustic sensors 2. Therefore, the acoustic sensor 2 detects not only the target sliding sound but also other sliding sounds. For example, the acoustic sensor 2c detects not only the sliding sound of the contact surface between the valve body 29 and the valve seat 30, but also the sliding sound of the contact surface of the valve stem 4 and the gland packing 28, and the sliding sound of the worm gear 24. . In other words, the sliding sound of the contact surface between the valve stem 4 and the gland packing 28 and the sliding sound of the worm gear 24 are superimposed on the detection signal of the acoustic sensor 2c.

The sliding sound of the contact surface between the valve body 29 and the valve seat 30 detected by the acoustic sensor 2c has a sudden signal waveform, and the amplitude period of the signal is short. On the other hand, the sliding sound of the contact surface between the valve stem 4 and the gland packing 28 and the sliding sound of the worm gear 24 detected by the acoustic sensor 2c are signals after a predetermined delay time, and the propagation As a result, the signal is attenuated. Note that FIG. 3 is a diagram for explaining propagation signals of the acoustic sensor 2c, and does not explain all propagation signals.

Therefore, in the signal detected by the acoustic sensor 2c, a predetermined period of the signal waveform with a relatively large amplitude is identified as the sliding sound of the contact surface between the valve stem 4 and the gland packing 28, and signals in other periods are noise. Remove as.

More specifically, the sliding sound of the contact surface between the valve body 29 and the valve seat 30 when they are closed changes depending on the installation state of the valve.
When the valve stem is in an upright position, the valve body is aligned by its own weight, so the sliding that occurs when the valve body is seated occurs multiple times due to alignment, and then the entire seat surface It slides into place and closes the flow path. For this reason, it is preferable to pay attention to the sliding noise caused by the final seating.

In addition, in a valve that is not in an upright position, the valve body and valve seat are not aligned and a sliding sound is generated due to the uneven seating surface, and the first half of the acquired sliding sound is due to the sliding of the uneven seating surface. Yes, the latter half involves sliding of the entire seat part. Therefore, it is preferable to focus on the latter half of the sliding sound and diagnose the potential for leakage.

FIG. 4 is a diagram showing a processing flow of the waveform extracting/dividing section 10 that performs the above processing.
In step S41, the waveform extracting/dividing unit 10 repeats steps S42 to S46 for each signal of the acoustic sensor 2 digitally converted by the A/D converter 8.

In step S42, the waveform extracting/dividing unit 10 determines whether the signal waveform is a sudden type waveform pattern, and if it is a sudden type, the process proceeds to step S43 (Yes in S42), and the signal waveform is a sudden type such as a continuous type. If the waveform patterns are different, the process advances to step S44 (No in S42).

In step S43, the waveform extracting/dividing unit 10 extracts a signal waveform of a predetermined period with large amplitude from the signal of the acoustic sensor 2, and specifies it as a signal waveform of sliding sound (S45). Then, the process advances to step S46.

In step S44, the waveform extracting/dividing unit 10 extracts the signal waveform of the entire period from the signal of the acoustic sensor 2, and specifies it as the signal waveform of sliding sound (S45). Then, the process advances to step S46.

In step S46, the waveform extracting/dividing unit 10 repeats steps S42 to S45 for the number of acoustic sensors 2.

Hereinafter, the processing contents of the leak amount value acquisition section 12, the surface roughness value acquisition section 13, and the friction coefficient value acquisition section 14 in the analysis section 11 of the abnormality sign diagnosis device 20 of the embodiment will be explained.

FIG. 5 shows the spectral intensities of the sliding sounds of the worm gear 24, the sliding sound of the contact surface between the valve stem 4 and the gland packing 28, and the sliding sound of the contact surface of the valve body 29 and the valve seat 30. FIG. 4 is a diagram showing the correlation between the number of operations of the sliding part and the number of operations of the sliding part.

The analysis unit 11 of the abnormality sign diagnosis device 20 stores in advance the correlation between the actually measured spectrum intensity of the sliding sound and the number of operations of the sliding portion for each sliding portion.
Then, the analysis unit 11 calculates the spectrum of the sliding sound shown in FIG. From the correlation between the strength and the number of times the sliding part operates, the number of times the sliding part is operated is determined, taking into account the deterioration due to the operation of the sliding part from the beginning of use when the sliding sound is detected, and the deterioration due to fluid force, age, etc. Here, it should be noted that the number of actuations determined from the spectral intensity of the detected sliding sound does not match the actual number of actuations, and that deterioration corresponding to the determined number of actuations is detected.

FIG. 6A is a diagram showing the correlation between the surface roughness and the number of operations in each of the sliding parts.
The analysis unit 11 stores in advance the correlation between the actually measured surface roughness and the number of operations.

The surface roughness value acquisition unit 13 acquires the surface roughness value of the sliding portion that generates the sliding sound based on the sliding sound detected by the acoustic sensor 2.
Specifically, as described above, the surface roughness value acquisition unit 13 first calculates the number of times the sliding part has operated since the start of use, based on the spectral intensity of the sliding sound detected by the acoustic sensor 2. (Figure 5). Then, from the correlation between the surface roughness and the number of operations shown in FIG. 6A, the surface roughness value of the sliding portion corresponding to the number of operations is obtained.

FIG. 6B is a diagram showing the correlation between the friction coefficient and the number of operations in each of the sliding parts.
The analysis unit 11 stores in advance the correlation between the actually measured coefficient of friction and the number of operations.

Based on the sliding sound detected by the acoustic sensor 2, the friction coefficient value acquisition unit 14 acquires the friction coefficient value of the sliding part that emits the sliding sound.
Specifically, as described above, the friction coefficient value acquisition unit 14 first calculates the number of times the sliding part has operated since the start of use, based on the spectral intensity of the sliding sound detected by the acoustic sensor 2 ( Figure 5). Then, from the correlation between the friction coefficient and the number of operations shown in FIG. 6B, the friction coefficient value of the sliding portion corresponding to the number of operations is obtained.

FIG. 6C is a diagram showing the correlation between leakage amount and surface roughness in a sliding portion that seals fluid.
The analysis unit 11 stores in advance the correlation between the measured leakage amount and the surface roughness.

The leakage amount value acquisition unit 12 acquires the leakage amount of the sliding portion that emits the sliding sound based on the sliding sound detected by the acoustic sensor 2.
Specifically, as described above, the leakage value acquisition unit 12 first calculates the number of times the sliding part has operated since the start of use, based on the spectral intensity of the sliding sound detected by the acoustic sensor 2. Figure 5). Then, from the correlation between the surface roughness and the number of operations shown in FIG. 6A, the surface roughness value of the sliding portion corresponding to the number of operations is obtained. Thereafter, from the correlation between the leakage amount and the surface roughness shown in FIG. 6C, a leakage amount value corresponding to the obtained surface roughness value of the sliding portion is obtained.

Next, the number of operations estimating section 17 until leakage occurs in the determining section 16, the partitioning capacity estimating section 18, and the abnormality determining section 19 will be explained in detail. With this configuration, the abnormality sign diagnosis device 20 of the embodiment predicts the maintenance timing including disassembly of the valve 1, or predicts deterioration including whether or not the fluid closing function and dynamic function of the valve 1 are normal. Determine the signs.

The number of operations until leakage estimating section 17 estimates the number of operations until leakage occurs from the leakage amount acquired by the leakage amount value acquisition section 12, the correlation between the leakage amount and surface roughness, and the correlation between surface roughness and the number of operations. Estimate the number of activations.

Specifically, when the leakage amount acquired by the leakage amount value acquisition section 12 is "0", that is, when there is no leakage, the operation number estimation unit 17 until the leakage occurs calculates the correlation between the leakage amount and the surface roughness. (FIG. 6C), find the surface roughness of the sliding part where the amount of leakage is greater than "0". Then, from the correlation between the surface roughness and the number of operations (FIG. 6A), the number of operations corresponding to the surface roughness of the sliding portion at which the amount of leakage is greater than "0" is determined. The number of operations until a leak occurs estimating section 17 subtracts the number of operations when the sliding sound is detected from the number of operations determined above to obtain the number of operations until a leak occurs.

The number of operations until a leak occurs estimating unit 17 predicts the timing when the calculated number of operations until a leak occurs reaches a predetermined number of operations as a maintenance period including disassembly of the valve 1.

In addition, when the leakage amount acquired by the leakage amount value acquisition section 12 is "0", the operation number estimating section 17 until the occurrence of leakage determines that the function of closing the fluid of the valve is "normal", and the leakage amount is determined to be "normal". is larger than "0", it is determined that the function of the valve to close the fluid is "not normal (abnormal)".
In addition, when the calculated number of operations until leakage occurs is smaller than a predetermined number of operations, the unit 17 determines that there is a sign of deterioration regarding the valve's fluid closing function.

The gate capacity estimating unit 18 estimates the gate capacity for closing and opening the valve body of the gate valve by combining the operating force information of the drive unit.

Specifically, the partitioning capacity estimation unit 18 measures the drive current of the drive unit 5 of the valve 1 in synchronization with the detection of the sliding sound, and calculates the rate of change in the drive force. Then, the rate of change in the friction coefficient value of each sliding portion acquired by the friction coefficient value acquisition unit 14 is determined. The partitioning capacity estimation unit 18 compares the rate of change in the driving force with the rate of change in the friction coefficient value of each sliding part, and determines whether the rate of change in the friction coefficient value increases when the rate of change in the driving force increases. It is predicted that the sliding part that has become damaged is the cause of the increase in driving force.
Furthermore, the partitioning capacity estimating unit 18 calculates the number of operations to reach the maximum driving force of the drive unit 5 from the correlation between the driving force and the number of operations, and predicts the number of operations at the limit of the valve partitioning ability. As the number of operations at this time, the number of operations determined from the correlation between the spectral intensity of the actually measured sliding sound and the number of operations of the sliding part is applied.

The abnormality determination unit 19 determines an abnormality including an increase in friction loss based on the friction coefficients acquired by the friction coefficient value acquisition unit 14 and their rate of change, or the abnormality determination unit 19 determines an abnormality including an increase in friction loss. Based on the surface roughness and the rate of change thereof, an abnormality including an increase in surface roughness loss is determined.

Specifically, the abnormality determination unit 19 acquires the friction coefficient value of each sliding portion obtained from the sliding sound acquired by the friction coefficient value acquisition unit 14. Furthermore, the surface roughness of each sliding portion obtained from the sliding sound obtained by the surface roughness value obtaining section 13 is obtained.
Then, the abnormality determination unit 19 calculates the obtained amount of change in the coefficient of friction and the amount of change in surface roughness of each sliding portion.

The abnormality determination unit 19 calculates the number of operations at which the friction coefficient reaches the maximum allowable value from the correlation between the friction coefficient and the number of operations at each sliding part, and determines the predetermined number of operations at which the maximum value of the friction coefficient is reached at the valve 1. Anticipate the timing of maintenance, including disassembly. Then, the earliest timing for each sliding part is set as the maintenance time. Furthermore, the sliding portion having the largest value among the amount of change in the coefficient of friction of each sliding portion is determined to be the sliding portion where an abnormality is predicted.

In addition, the abnormality determination unit 19 determines the number of operations at which the surface roughness reaches the maximum allowable value from the correlation between the surface roughness of each sliding part and the number of operations, and determines a predetermined number of operations at which the maximum surface roughness is reached. The value is predicted to be the timing of maintenance including disassembly of valve 1. Then, the earliest timing for each sliding part is set as the maintenance time. Further, the sliding portion having the maximum value among the amount of change in surface roughness of each sliding portion is determined as the sliding portion where an abnormality is predicted.

Furthermore, when the friction coefficient acquired by the friction coefficient value acquisition unit 14 is larger than the normal friction coefficient, the abnormality determination unit 19 has a function and a dynamic function to close the fluid of the valve according to the amount of change in the friction coefficient. The abnormality determining unit 19 determines whether the friction coefficient is normal or not, and determines that there is a sign of deterioration of the sliding portion when the amount of change in the friction coefficient is larger than a predetermined value.

The abnormality determination unit 19 also has a function of closing the fluid in the valve according to the amount of change in surface roughness when the surface roughness acquired by the surface roughness value acquisition unit 13 is larger than the normal surface roughness. In addition to determining whether the dynamic function is normal or not, the abnormality determination unit 19 determines that there is a sign of deterioration of the sliding portion when the amount of change in surface roughness is larger than a predetermined value.

The abnormality determination unit 19 uses at least the friction coefficient value acquisition unit 14 when determining whether or not the fluid closing function and dynamic function of the valve are normal or when determining a sign of deterioration of the sliding part. The determination may be made based on either the acquired friction coefficient or the surface roughness acquired by the surface roughness value acquisition unit 13.

In the abnormality sign diagnosis device 20 of the embodiment described above, each of the sliding sounds of a plurality of sliding parts is detected by a plurality of acoustic sensors 2, and the sliding sounds other than the target sliding sound detected by the acoustic sensor 2 are detected. It has been explained that the moving sound is removed by the waveform extracting/dividing unit 10 and a deterioration sign diagnosis is performed.

However, instead of this, the waveform extracting/dividing unit 10 may analyze the pattern of the sliding sound detected by the acoustic sensor 2 and classify it into each sliding part. At this time, since the sliding sound propagates and attenuates, an acoustic sensor is placed near the sliding part where signs of deterioration are to be diagnosed.

Specifically, as shown in FIG. 1, an acoustic sensor 2 is provided to detect the sliding sound of the valve stem 4 and the gland packing 28, and the waveform extraction/splitting unit 10 extracts the sliding sound, and as a propagation signal, the acoustic sensor 2 detects the sliding sound of the valve stem 4 and the gland packing 28. The acoustic sensor 2 detects the sliding sound of the motor 22 which is the driving part 5, the worm gear 24 of the valve 1, the valve stem screw part 25, and the sliding surfaces of the valve body 29 and the valve seat 30, and the waveform extraction/dividing part 10 Each sliding sound is divided according to the waveform pattern.

By the way, when the motor 22 is being driven, continuous noise may be generated by the motor and other sliding sounds may be buried. For this reason, it is preferable to operate the valve 1 using the manual handle 23 and detect the sliding sound.

Further, since a pulse is generated due to hammering when the valve 1 starts operating, a closing operation is preferable to an opening operation so as not to be confused with the sliding noise between the valve body 29 and the valve seat 30.
In this case, the sliding noise caused by the initial hammering, the sliding noise of the gland packing 28 that always occurs during driving, the sliding noise of the gear (not shown) of the manual handle 23 that occurs at a constant cycle, the worm gear 24, and Sliding noise generated in the threaded portion of the valve stem 25, impact noise due to wobbling of the valve body 29 just before closing, sliding noise generated between the valve body 29 and the seat portion of the valve seat 30 during closing, and other abnormalities due to jamming, etc. Pulse sounds can be obtained as different patterns of sliding sounds.

The waveform extracting/dividing unit 10 classifies the sliding sound according to the signal pattern of the sliding sound, so that the abnormality sign diagnosis device 20 can detect the states of multiple sliding parts even in a single acoustic sensor 2. can be diagnosed.
Note that it is preferable that the acoustic sensor 2 be installed near the sliding location where diagnosis should be given the highest priority, or near the gland packing 28 where the amplitude of sliding sound is small.

In the above embodiment, the valve 1 having a gate valve has been described, but the valve type is not limited to this, and the same diagnosis can be performed for a globe valve , a butterfly valve, and a ball valve.
In a globe valve , like a gate valve, sliding occurs in the seat portion, which is the contact surface between the valve body and the valve seat, during opening and closing operations. Compared to a gate valve, the operating time and sliding distance are short, but a short sliding sound signal can be obtained after the collision sound occurs. This sliding sound signal makes it possible to diagnose the condition of the seat portion.
In addition, butterfly valves and ball valves close the flow path by rotating the valve stem and valve body, but since sliding occurs similar to the gate valve described above, diagnostics can be made using the sliding sound signal. becomes possible.

Further, in the above embodiment, the case where the valve 1 is driven by an electric valve is explained, but the same diagnosis can be performed for an air-operated valve as well. In the air-operated valve, the sliding movement is caused by the vertical movement of the piston instead of the sliding movement of the drive unit 5 of the electric valve. By acquiring this sliding sound with the acoustic sensor 2, the same diagnosis as in the case of an electric valve is performed.

Next, a case will be described in which the abnormality sign diagnosis device 20 of the embodiment is applied to nuclear power generation equipment.
Valves installed in nuclear power generation equipment are high-temperature valves, valves where fluid is constantly passing through the valve, valves installed in areas with high radiation levels in the atmosphere, and valves installed in areas where there is a lot of noise during operation. There are various types of valves. Therefore, when applying to nuclear power generation equipment, it is preferable to carry out the test in quiet conditions, such as during regular inspections, at room temperature, when the system is stopped, and the radiation dose is not high.

Many of the valves that become hot during operation of nuclear power generation equipment are covered with a heat insulating material 33, making it difficult to install the acoustic sensor 2 near the contact surface between the valve body 29 and the valve seat 30 shown in FIG. . Although the heat insulating material 33 is normally designed to be removable, it is desirable to carry out the diagnosis with the heat insulating material 33 attached because the purpose is to shorten the examination period.

FIG. 7 is a diagram showing a method of attaching the acoustic sensor 2 for diagnosis with the heat insulating material 33 attached.
The heat insulating material 33 of the valve box 3 is provided with a cylindrical access passage that does not affect the heat retention effect. Insert the needle bar 31. Here, it is important that the listening needle bar 31 does not come into contact with internal substances such as cotton of the heat insulating material 33, and other structures may be used as long as this is achieved.

Since the listening needle bar 31 has a structure that does not come into contact with anything other than the acoustic sensor 2 and the valve box 3, the signal generated from the sliding portion of the valve body 29 and the valve seat 30 is not dispersed in the listening needle bar 31. Since no noise is generated in the listening needle bar 31, more accurate diagnosis is possible.
As a result, in nuclear power generation equipment, it is possible to reduce the amount of material, labor, cost, and radiation exposure for disassembling and inspecting valves, and to improve the operating rate by shortening the inspection period.

Further, the present invention is not limited to the above-described embodiments, and includes various modifications. The above embodiments have been described in detail to explain the present invention in an easy-to-understand manner, and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment.

1 Electric gate valve (valve)
2, 2a, 2b, 2c acoustic sensor 6 amplification section (signal processing section)
7 High-pass filter (signal processing section)
8 A/D converter (signal processing section)
9 Signal processing section 10 Sliding sound waveform extraction and division section (analysis section)
11 Analysis section 12 Leakage amount value acquisition section (Analysis section)
13 Surface roughness value acquisition section (analysis section)
14 Friction coefficient value acquisition section (analysis section)
15 Spectrum analysis section (analysis section)
16 Judgment unit 17 Estimation unit of number of operations until leakage occurs (judgment unit)
18 Partitioning capacity estimation section (judgment section)
19 Abnormality judgment section (judgment section)
20 Abnormal sign diagnostic device

Claims (15)

a signal processing unit that processes a signal from at least one acoustic sensor that detects the sliding sound of the contact surface between the valve body and the valve seat when the valve body is closed ;
an analysis unit that determines the state of the leakage amount, surface roughness, or friction coefficient of the sliding part that generates the sliding sound from the spectral intensity of the sliding sound processed by the signal processing unit and detected by the acoustic sensor;
The valve is disassembled by determining signs of deterioration, including whether or not the valve's fluid-closing function and dynamic function are normal, based on the amount of leakage, surface roughness, or friction coefficient of the sliding part determined by the analysis section. a determination unit that predicts maintenance timing including;
Equipped with
The analysis section calculates, for each sliding part, the correlation between the actually measured spectral intensity of sliding sound and the number of operations of the sliding part, the correlation between the actually measured leakage amount and surface roughness, and the actually measured coefficient of friction and the number of operations. and the correlation between the measured surface roughness and the number of operations are stored in advance, and the state of the leakage amount, surface roughness, or friction coefficient of the sliding part is determined from the spectral intensity of the sliding sound.
An abnormality sign diagnostic device characterized by: The abnormality sign diagnostic device according to claim 1,
The signal processing unit is a sliding part between a valve body and a valve seat of a valve, a sliding part between a gland packing and a valve stem, a sliding part of a threaded part of a valve stem, a sliding part of a worm gear of an electric valve, or An abnormality sign diagnostic device characterized by processing a signal from an acoustic sensor that detects sliding noise generated in a sliding portion of a piston portion of an air-operated valve. The abnormality sign diagnostic device according to claim 2,
In the sliding part between the valve body and the valve seat of the valve in the upright state where the valve stem is upright, the analysis unit detects the last of the sliding sounds that occur multiple times when the valve body is seated. The timing of maintenance is predicted based on the movement noise, and in the sliding part between the valve body and valve seat of the valve that is not upright, the sliding sound of the uneven seating surface in the first half and the sliding sound of the entire seating surface in the second half are detected. An abnormality sign diagnostic device that predicts maintenance timing based on moving sounds. The abnormality sign diagnostic device according to claim 1,
The abnormality sign diagnostic device is characterized in that the signal processing unit processes a signal from an acoustic sensor such as an acoustic emission sensor, a piezoelectric sensor, or an ultrasonic sensor. The abnormality sign diagnostic device according to claim 2,
The signal processing section includes a sliding part between the valve body and the valve seat, a sliding surface between the valve stem and the gland packing, a sliding part of the threaded part of the valve stem, a sliding part of the worm gear of the electric valve, or Processing the signal of an acoustic sensor installed directly or via a listening needle at a position on the outer surface of the valve, which is a propagation path of a direct wave of the sliding sound of the sliding part of the piston part of the air-operated valve. Abnormality predictive diagnostic device. The abnormality sign diagnostic device according to claim 5,
The acoustic sensor is installed by a built-in or removable installation jig or by adhesive or soldering or hand-held;
The removable mounting fixture may be mounted directly to the outer surface of the valve by one or more of magnets, suction cups, vises, bolts, or an arm extending from a tripod or pole or other equipment mounted on the ground. An abnormality sign diagnosis device characterized in that it is installed by. The abnormality sign diagnostic device according to claim 5,
If the valve is covered with a heat insulating material or wall, a cylindrical access passage that can be opened and closed is secured in the heat insulating material or wall, and a cylindrical mounting jig integrated with the listening needle bar is installed there. An abnormality sign diagnostic device, wherein the listening needle bar is inserted so as not to come into contact with anything other than the acoustic sensor and the valve. The abnormality sign diagnostic device according to claim 2,
The analysis section converts the signal of the acoustic sensor during the closing operation of the valve into a hammering sliding sound, a sliding sound of a gland packing sliding part, a motor sound of a drive part, or an air sound, depending on the signal pattern. , sliding sound of gears and threads, impact noise due to wobbling of the valve just before closing, sliding sound of the valve body and valve seat during closing, and other abnormal noises due to jamming etc. An abnormality sign diagnostic device characterized by having an extraction/dividing section. a signal processing unit that processes a signal from at least one acoustic sensor that detects the sliding sound of the contact surface between the valve body and the valve seat when the valve body is closed;
For each sliding part, the correlation between the actually measured spectral intensity of the sliding sound and the number of operations of the sliding part, and the correlation between the actually measured coefficient of friction and the number of operations are stored in advance and processed by the signal processing section. an analysis unit that determines the state of the friction coefficient of the sliding part that generates the sliding sound from the spectral intensity of the sliding sound detected by the acoustic sensor;
When the friction coefficient of the sliding part obtained by the analysis section is larger than the normal friction coefficient, it is determined whether the valve's fluid closing function and dynamic function are normal, depending on the amount of change in the friction coefficient. a determination unit that determines that there is a sign of deterioration of the sliding part when the amount of change in the coefficient of friction is larger than a predetermined value;
An abnormality sign diagnostic device comprising : a signal processing unit that processes a signal from at least one acoustic sensor that detects the sliding sound of the contact surface between the valve body and the valve seat when the valve body is closed;
For each sliding part, the correlation between the actually measured spectral intensity of the sliding sound and the number of operations of the sliding part, and the correlation between the actually measured surface roughness and the number of operations are stored in advance and processed by the signal processing section. an analysis unit that determines the state of surface roughness of the sliding part that generates the sliding sound from the spectral intensity of the sliding sound detected by the acoustic sensor;
When the surface roughness obtained by the analysis section is larger than the normal surface roughness, it is determined whether the valve's fluid closing function and dynamic function are normal according to the amount of change in the surface roughness. and a determination unit that determines that there is a sign of deterioration of the sliding part when the amount of change in the surface roughness value is larger than a predetermined value.
An abnormality sign diagnostic device characterized by: a signal processing unit that processes a signal from at least one acoustic sensor that detects the sliding sound of the contact surface between the valve body and the valve seat when the valve body is closed;
For each sliding part, the correlation between the actually measured spectral intensity of sliding sound and the number of operations of the sliding part, the correlation between the actually measured coefficient of friction and the number of operations, and the correlation between the actually measured surface roughness and the number of operations were determined in advance. an analysis unit that stores the information and determines the surface roughness or friction coefficient of the sliding portion from the spectral intensity of the sliding sound;
When the friction coefficient of the sliding part obtained by the analysis section is larger than the normal friction coefficient, it is determined whether the valve's fluid closing function and dynamic function are normal, depending on the amount of change in the friction coefficient. In addition, if the amount of change in the coefficient of friction is larger than a predetermined value, it is determined that there is a sign of deterioration of the sliding part, or
When the surface roughness obtained by the analysis section is larger than the normal surface roughness, it is determined whether or not the valve's fluid closing function and dynamic function are normal according to the amount of change in the surface roughness. and a determination unit that determines that there is a sign of deterioration of the sliding part when the amount of change in the surface roughness value is larger than a predetermined value.
An abnormality sign diagnostic device characterized by: a signal processing unit that processes a signal from at least one acoustic sensor that detects the sliding sound of the contact surface between the valve body and the valve seat when the valve body is closed;
For each sliding part, the correlation between the actually measured spectral intensity of sliding sound and the number of operations and/or elapsed time of the sliding part, and the correlation between the actually measured coefficient of friction and the number of operations and/or elapsed time are memorized in advance. an analysis unit that determines the state of the friction coefficient of the sliding part from the spectral intensity of the sliding sound;
From the correlation between the friction coefficient of the sliding part obtained by the analysis section and the number of operations and/or the elapsed time, the number of operations and/or elapsed time at which the friction coefficient reaches the maximum allowable value is determined, and the maximum friction coefficient is determined. and a determination unit that predicts the timing for maintenance including valve disassembly when the number of operations and/or elapsed time reaches a predetermined number of operations and/or elapsed time until the value is reached.
An abnormality sign diagnostic device characterized by: a signal processing unit that processes a signal from at least one acoustic sensor that detects the sliding sound of the contact surface between the valve body and the valve seat when the valve body is closed;
For each sliding part, the correlation between the actually measured spectral intensity of the sliding sound and the number of operations of the sliding part, and the correlation between the actually measured coefficient of friction and the number of operations are memorized in advance, and the spectral intensity of the sliding sound is calculated. an analysis unit that determines the state of the friction coefficient of the sliding part from
In synchronization with the detection of the sliding sound, the drive current of the drive section of the valve is measured, the rate of change in the driving force is determined, and the rate of change in the friction coefficient value of each sliding section acquired by the analysis section is determined, and the drive current of the drive section of the valve is determined. By comparing the rate of change of force and the rate of change of the friction coefficient value of each sliding part, when the rate of change of the driving force increases, the sliding part whose rate of change of the friction coefficient value increases becomes the driving force. In addition to predicting the cause of the decline,
and a determination unit that calculates the number of operations to reach the maximum driving force of the drive unit from the correlation between the driving force and the number of operations, and predicts the number of operations at the limit of the valve gate ability.
An abnormality sign diagnostic device characterized by: a signal processing unit that processes a signal from at least one acoustic sensor that detects the sliding sound of the contact surface between the valve body and the valve seat when the valve body is closed;
For each sliding part, the correlation between the measured spectral intensity of sliding sound and the number of operations and/or elapsed time of the sliding part, the correlation between the measured leakage amount and surface roughness, and the correlation between the actually measured surface roughness and operation. The correlation between the number of times and/or the elapsed time is stored in advance, and the amount of leakage of the sliding part and the surface roughness are determined from the spectral intensity of the sliding sound processed by the signal processing unit and detected by the acoustic sensor. An analysis department that determines the state,
The surface roughness at which leakage occurs is determined from the correlation between the amount of leakage of the sliding part and the surface roughness, and the surface roughness at which leakage occurs is determined from the correlation between the surface roughness and the number of operations and/or the elapsed time. Calculate the number of operations and/or elapsed time, and subtract the number of operations and/or elapsed time when a sliding sound is detected in the absence of leakage from the number of operations and/or elapsed time at which leakage occurs, and calculate the number of operations and/or elapsed time until leakage occurs. A determination unit that calculates the number of operations and/or the elapsed time and predicts the timing at which the calculated number of operations and/or elapsed time until the occurrence of a leak reaches a predetermined number of operations or/and elapsed time as the maintenance period including disassembly of the valve. and
An abnormality sign diagnostic device characterized by: A diagnostic method for an abnormality sign diagnostic device for diagnosing the state of a sliding part of a valve, the method comprising:
processing a signal from at least one acoustic sensor that detects a sliding sound of a contact surface between the valve body and the valve seat when the valve body is closed ;
For each of the sliding parts of the valve body and valve seat, the correlation between the measured sliding sound spectral intensity and the number of times the sliding part operated, the correlation between the measured leakage amount and surface roughness, and the measured friction coefficient and operation. The correlation between the number of operations and the correlation between the measured surface roughness and the number of operations is stored in advance, and the leakage amount, surface roughness, or a step of determining the state of the friction coefficient;
Based on the leakage amount, surface roughness, or coefficient of friction of the sliding parts, we can determine signs of deterioration, including whether the valve's fluid-closing function and dynamic function are normal, and determine when it is time for maintenance, including valve disassembly. a step of predicting;
A diagnostic method characterized by comprising:

Images