JP2005196701A - Parameter calculating method, state monitoring method, parameter calculating device, state monitoring device, state monitoring system and computer program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method of easily calculating a parameter indicating a state of a monitored object, a method of monitoring the monitored object by using the parameter, a parameter calculating device, a state monitoring device, a state monitoring system and a computer program. <P>SOLUTION: The waveform data indicating the state of the monitored object includes continuous large peaks having a large peak value over a specific range, in a case when the abnormality occurs on the monitored object such as a rolling bearing. The waveform data indicating the state of the monitored object is measured by a sensor 31, and then acquired by a data acquiring device 32, and the state monitoring device 1 calculates the parameter indicating the state of the monitored object on the basis of a total value m of the number of continuous peaks having the peak value within the specific range and the number of continuous large peaks, a ratio p of the maximum peak value and the peak value within the specific range, and a value of q based on the number of the continuous large peaks 2q. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a technique for monitoring various facilities such as a periodic moving body, and implements a method of calculating parameters of waveform data including a plurality of peaks, a parameter calculation device used in the method, and a computer as a parameter calculation device. Computer program, status monitoring method for monitoring the status of a monitoring target such as equipment in a factory using calculated parameters, status monitoring system, status monitoring device, and computer program for realizing a computer as a status monitoring device About.

  As a method of maintaining various facilities such as production facilities used in factories or public facilities used in public facilities in order to prevent the occurrence of failures, a method of regularly checking and maintaining facilities (TBM: Time Based preventive) Maintenance) has been used in the past. In addition to the TBM, there is known a method (CBM: Condition Based preventive Maintenance) that constantly monitors the state of the equipment and performs maintenance when the state of the equipment has deteriorated to a predetermined standard. CBM has advantages such as being able to avoid initial failures after maintenance due to TBM, and being easier to control maintenance costs than TBM. Yes.

  In order to realize the CBM, a state monitoring method that acquires data such as the number of operations, temperature, or vibration indicating the state of the equipment, monitors the state of the equipment, and catches an abnormality sign as soon as possible. It is desirable to establish Since the data indicating the state of the facility differs depending on the field of the facility, various state monitoring methods have been proposed conventionally. For example, in the field of vibrating bodies, a method using an RMS (Root Mean Square) value that is a square root of a value obtained by squaring the amplitude of vibration is known. In the state monitoring method using the RMS value, the vibration amplitude is acquired as data, the RMS value that is the square root of the squared average of the acquired amplitude is calculated, and the calculated RMS value becomes larger than the reference Furthermore, since the amplitude of vibration is large, it can be determined that the equipment is in an abnormal state.

  However, since the RMS value is an unnormalized index, it varies depending on the difference in conditions such as the size of the part or the frequency, and therefore it is necessary to set a standard for each piece of equipment. For this reason, a normalized index that can be used universally is required. As an example of such an index, bicoherence in which the correlation between frequency components of acquired data is normalized is known.

When continuous data having a basic period T such as a vibration signal is a function x (t) of time t and two arbitrary frequencies f ₁ and f ₂ included in the data are used, bicoherence Bic _xxx (f ₁ , F ₂ ) is defined by the following equation.

The bicoherence is a value obtained by normalizing the bispectrum indicating the relationship between the frequencies with the power spectrum, and indicates the magnitude of the relationship between the frequencies. The value of bicoherence is
0 <Bic _xxx (f ₁ , f ₂ ) <1
When the relationship between the frequencies f ₁ and f ₂ is large, the value approaches 1 and when the relationship is small, the value approaches 0. In Patent Document 1 and Patent Document 2, the value of bicoherence is close to 1 while the equipment is normal, and the value of bicoherence approaches 0 when an abnormality occurs in the equipment. It is disclosed that an abnormality of the facility can be detected with high sensitivity by examining how close to 0 it is.
Another example of an index that can be used for monitoring the state of equipment is kurtosis (Kurtosis) that is used for detecting abnormality of a vibrating body. The kurtosis is a value obtained by normalizing the fourth-order central moment of the probability density function in the stochastic process. The function x (t) at time t is a stationary probability process of the average value bar x, and the probability density function is p (x). At this time, the average value x of x and the variance σ ² are expressed by the following equations.

  The kurtosis KT, which is a value obtained by normalizing the fourth-order central moment MT (4) of the data x and the fourth-order central moment MT (4) by the fourth power of the standard deviation (square of variance), is defined by the following equation. Is done.

Since the data obtained from the monitoring target is discrete data, the kurtosis needs to be described in a discrete time system. Assuming that the sampling interval is Δt, the data x _k obtained from the monitoring target is expressed as follows.
x _k = x (kΔt) k = 1, 2,...
When M pieces of data x _k are obtained from the monitoring target, the average value bar x, variance σ ² , fourth-order central moment MT (4), and kurtosis KT of the data are expressed by the following equations.

When the data distribution follows a normal distribution, KT = 3.0 by the definition of the normal distribution. When the kurtosis value is larger than 3.0, the data distribution is sharper than the normal distribution. When the kurtosis value is smaller than 3.0, the data distribution is larger than the normal distribution. It will be gentle. When an abnormality occurs in the equipment and data having a large amplitude is mixed in the data, the sharpness of the data distribution becomes gentler than the normal distribution, so the kurtosis becomes larger than 3.0. Therefore, it is possible to detect an abnormality in the facility by calculating the kurtosis from the data obtained from the monitoring target.
Japanese Examined Patent Publication No. 62-60011 Japanese Patent Publication No. 64-4611

  Generally, when CBM is performed in a factory, each facility is equipped with a sensor, and data acquired by the sensor is stored online by a computing device such as a personal computer (PC), microcomputer, minicomputer, or process computer. The processed data is processed and the equipment is monitored. Since the behavior of equipment in the factory is high-speed, when a computing device with low computing capacity is used, the speed at which data is accumulated is faster than the processing speed of the computing device. For this reason, there is a possibility that the detection of the equipment abnormality will be failed, or the equipment abnormality detection delay may occur even if it is not a failure. Moreover, since the formula for calculating the kurtosis is complicated, there is a problem that it is difficult for an operator who has seen the data to calculate the kurtosis from the data and grasp the state of the equipment.

  The present invention has been made in view of such circumstances, and an object of the present invention is to obtain a plurality of waveform data from a plurality of accumulated data indicating the state of a monitoring target such as vibration waveform data. Parameter calculation method capable of easily calculating a parameter that is a kurtosis of data, or a parameter that is a ratio between a kurtosis and a kurtosis when a monitoring target is normal, a parameter calculation device used for the calculation, and a computer Is to provide a computer program for realizing the above as the parameter calculation apparatus.

  Furthermore, another object of the present invention is to provide a state monitoring method, a state monitoring device, a state monitoring system, and a computer that can reliably detect a change in the state of a monitoring target using a simply calculated parameter. The object is to provide a computer program for realizing the state monitoring apparatus.

According to a first aspect of the present invention, there is provided a parameter calculation method using a calculation device including a reception unit that receives information, a storage unit that stores information, and a calculation unit, and a plurality of continuous peaks having peak values within a predetermined range, A method of calculating a parameter of waveform data alternately including a plurality of continuous large peaks whose values increase and decrease sequentially outside the predetermined range, wherein the plurality of waveform data with respect to peak values within the predetermined range are calculated. The reception unit receives a ratio p (p is a real number greater than or equal to 1) of the maximum peak values of the large peaks, and sets the value of q where the number of the continuous large peaks is 2q (q is a natural number). And the total value m (m is a natural number) of the number of continuous peaks and the number of continuous large peaks is stored in the storage unit, and the standard value K of the parameter of the waveform data is stored. Storing _N in the storage unit, a parameter KT of the waveform data, the following arithmetic expression

The calculation is performed by the calculation unit based on the above.

  A parameter calculation method according to a second aspect of the present invention is directed to a calculation device including a reception unit that receives information, a storage unit that stores information, and a calculation unit, and a plurality of continuous peaks having peak values within a predetermined range, A method of calculating a parameter of waveform data alternately including a plurality of continuous large peaks whose values increase and decrease sequentially outside the predetermined range, wherein the plurality of waveform data with respect to peak values within the predetermined range are calculated. The reception unit receives a ratio p (p is a real number greater than or equal to 1) of the maximum peak values of the large peaks, and sets the value of q where the number of the continuous large peaks is 2q (q is a natural number). And the total value m (m is a natural number) of the number of the plurality of continuous peaks and the number of the plurality of continuous large peaks is stored in the storage unit, and the parameter Fa of the waveform data is set as follows: Arithmetic expression

The calculation is performed by the calculation unit based on the above.

  According to a third aspect of the present invention, there is provided a state monitoring method using a calculation device including a reception unit that receives information, a storage unit that stores information, and a calculation unit, and a plurality of continuous peaks having peak values within a predetermined range, A method for monitoring the state of the monitoring target based on waveform data indicating the state of the monitoring target, alternately including a plurality of continuous large peaks whose values increase and decrease sequentially outside the predetermined range, For the waveform data, a ratio p (p is a real number of 1 or more) of the maximum peak values of the plurality of large peaks to a peak value within the predetermined range is received by the reception unit, and the number of the continuous large peaks is 2q. (Q is a natural number) The value of q is accepted by the accepting unit, and a total value m (m is a natural number) of the number of consecutive peaks and the number of consecutive large peaks is determined by the storage unit. Remember, said The parameter Fa in the form data, the following arithmetic expression

And the state of the monitoring target is determined by the calculation unit according to the value of the parameter Fa calculated by the calculation unit.

  A parameter calculation apparatus according to a fourth aspect of the invention alternately includes a plurality of continuous peaks having a peak value within a predetermined range and a plurality of continuous large peaks in which the peak value sequentially increases and decreases outside the predetermined range. A device for calculating a parameter of waveform data, the means for receiving a ratio p (p is a real number of 1 or more) of the maximum peak values of the plurality of large peaks to the peak value within the predetermined range for the waveform data; A means for accepting a q value where the number of consecutive large peaks is 2q (q is a natural number), and a total value m (m of the number of consecutive peaks and the number of consecutive large peaks Is a natural number) and the parameter Fa of the waveform data is expressed as

And means for calculating based on the above.

  The state monitoring apparatus according to the fifth aspect of the invention alternately includes a plurality of continuous peaks having a peak value within a predetermined range and a plurality of continuous large peaks in which the peak value sequentially increases and decreases outside the predetermined range. A device for monitoring the state of the monitoring target based on waveform data indicating the state of the monitoring target, wherein the ratio p of the maximum peak value of the plurality of large peaks to the peak value within the predetermined range for the waveform data Means for accepting (p is a real number equal to or greater than 1), means for accepting a q value where the number of consecutive large peaks is 2q (q is a natural number), and the number of consecutive peaks and the continuous Means for storing the total value m (m is a natural number) of the number of large peaks, and the parameter Fa of the waveform data,

And means for determining the state of the monitoring target according to the value of the parameter KT calculated by the means.

  The state monitoring system according to the sixth aspect of the invention alternately includes a plurality of continuous peaks having a peak value within a predetermined range and a plurality of continuous large peaks in which the peak value sequentially increases and decreases outside the predetermined range. A system for monitoring the state of the monitoring target based on waveform data indicating the state of the monitoring target, the data acquisition device acquiring the waveform data, and the waveform data acquired by the means within the predetermined range Means for determining the ratio p (p is a real number of 1 or more) of the maximum peak values of the plurality of large peaks to the peak value of q, and the value of q where the number of the continuous large peaks is 2q (q is a natural number) And the waveform data serving as a criterion for determining the state of the monitoring target using a total value m (m is a natural number) of the number of the plurality of continuous peaks and the number of the plurality of continuous large peaks. The parameter Fa, the following arithmetic expression

And a calculation device for calculating based on the above.

  According to a seventh aspect of the present invention, there is provided a computer program in which a plurality of continuous peaks having a peak value within a predetermined range and a plurality of continuous large peaks whose peak values sequentially increase and decrease outside the predetermined range are alternately displayed on the computer. A computer program for calculating parameters of the waveform data included in the computer, wherein the computer has a ratio p (p is a real number greater than or equal to 1) of the maximum peak values of the plurality of large peaks to the peak values within the predetermined range in the waveform data. , The value of q where the number of the continuous large peaks is 2q (q is a natural number), and the total value m (m is the number of the continuous multiple peaks and the number of the continuous large peaks) Natural number), and the computer calculates the parameter Fa of the waveform data as follows:

And a procedure for calculating based on the above.

  A computer program according to an eighth aspect of the present invention alternately includes a plurality of peaks having peak values within a predetermined range and a plurality of continuous large peaks in which the peak values sequentially increase and decrease outside the predetermined range. A computer program for monitoring the state of the monitoring target based on waveform data indicating the state of the monitoring target, wherein the computer has a maximum peak of the plurality of large peaks with respect to a peak value within the predetermined range in the waveform data. A ratio of values p (p is a real number equal to or greater than 1), a value of q in which the number of consecutive large peaks is 2q (q is a natural number), and the number of consecutive peaks and the plurality of consecutive peaks The procedure for reading the total value m (m is a natural number) with the number of large peaks, and the computer is used to calculate the parameter Fa of the waveform data as follows:

And a procedure for causing the computer to determine the state of the monitoring target according to the calculated value of the parameter Fa.

  The inventor of the present application pays attention to the behavior of the waveform data recognized at the time of initial abnormality with respect to the waveform data obtained from the periodic moving body, and the parameters that are the kurtosis of a plurality of data constituting the waveform data, or the kurtosis and the monitoring target are normal. We devised a simple method to calculate the parameter, which is the ratio with the kurtosis of time.

  Consider data obtained from a periodic motion body such as rolling bearing vibration as data obtained from a monitoring target. FIG. 6 is a schematic characteristic diagram illustrating an example of waveform data indicating vibration obtained from a periodic moving body. The horizontal axis indicates time, and the vertical axis indicates vibration. In the case of a rolling bearing, a large vibration is generated each time the rolling element comes into contact with the shaft. When this vibration is measured by a sensor, waveform data in which the displacement of the vibration forms a peak every time the rolling element contacts the shaft as shown in FIG. 6A is obtained. When an initial abnormality such as a scratch occurs on the rolling element of the rolling bearing, the periodic moving body generates a larger vibration than normal. For example, in a rolling bearing composed of m (m is a natural number) rolling elements, when one rolling element is damaged, when the damaged rolling element contacts the shaft, the other rolling element contacts the shaft. Larger vibrations are generated as compared to cases. In addition, along with the occurrence of this large vibration, a foreshock vibration that gradually increases from normal vibration and an aftershock vibration that gradually decreases from large vibration are often derived forward and backward. Accordingly, from the monitoring target at the time of initial abnormality, as shown in FIG. 6B, the peak size increases in order from the normal size and decreases from the maximum size to the normal size in order. Waveform data in which continuous peaks occur periodically can be obtained.

Here, 2q (q is a natural number) of peaks whose peak sizes are larger than normal (hereinafter referred to as large peaks) are generated periodically and q in the first half of the continuous large peaks. It is assumed that the size of the large peaks and the size of the q large peaks in the latter half are symmetrical. Further, the ratio between the size of a large peak (hereinafter referred to as the maximum peak) having the maximum peak size and the peak size at normal time is defined as p: 1. Furthermore, for the latter half of the continuous large peak, the magnitude of the large peak attenuates in proportion to the number when each large peak is numbered with the largest peak as the first, and the magnitude of the (q + 1) th peak It is assumed that the size is the size of a normal peak. At this time, the ratio r of the size of each large peak to the size of the peak at the normal time is expressed by the following equation (1).
r = p− (i−1) (p−1) / q i = 1, 2,..., q (1)

  In a rolling bearing composed of m rolling elements, the same rolling element contacts the shaft once every m times and generates the same vibration. Therefore, the waveform data at the time of initial abnormality includes a group of continuous large peaks. It is included for every m peaks. Therefore, waveform data including two large peaks each having i = 1 to q in equation (1) for every m peaks is obtained from the monitoring target at the time of initial abnormality. At this time, the total value of the number of continuous peaks whose peak size is normal and the number of continuous large peaks is m.

When the waveform data based on the above model is sampled at a period based on the sampling theorem, a data string {x _k } having the vibration value as the value of the data x _k is obtained. The average value of this data string {x _k } is zero. The data string {x _k } obtained from the monitoring target at the time of initial abnormality includes data corresponding to each of the large peaks where i = 1 to q in the equation (1) at a rate of 2 / m. .

The variance, quartic center moment and kurtosis of the data string {x _k : k = 1, 2,..., N} when N pieces of data are obtained from the normal monitoring target are represented by σ _N ² and MT, respectively. _{Let N} (4) and KT _N. In addition, the dispersion, the fourth-order central moment, and the kurtosis of the data string obtained from the monitoring target at the time of the initial abnormality are set as σ ² , MT (4), and KT, respectively. When the average is set as bar x and the value of the data corresponding to the portion other than the large peak is set as x _k ′, the variance σ ² at the time of initial abnormality can be expressed by the following equation (2).

Since the average value bar x is 0, the expression (2) can be further expressed by the following expression (3).

Similarly, the fourth-order central moment MT (4) at the time of initial abnormality can be expressed by the following equation (4).

It should be noted that the following formula was used to obtain the equations (3) and (4).

From the equations (3) and (4), the kurtosis KT at the time of initial abnormality can be expressed by the following equation (5).

By using the equation (5), the kurtosis KT at the time of initial abnormality can be obtained from the values of m, p, and q and the value of the kurtosis KT _N when the monitoring target is normal. Further, when the data string obtained from the monitoring target at the normal time follows a normal distribution, the kurtosis KT at the time of initial abnormality can be obtained from the value 3.0 of the kurtosis KT _N at the normal time using the equation (5). it can. Further, the ratio Fa = KT / KT _N between the kurtosis KT of the data string obtained from the monitoring target and the kurtosis KT _N at the normal time can be expressed by the following equation (6).

The parameter Fa, which is the ratio between the kurtosis KT of the data string obtained from the monitoring target and the kurtosis KT _N in the normal state, can be easily obtained from the equation (6).

As described above, when waveform data indicating the state of the monitoring target such as vibration data is obtained, parameters KT and Fa for the waveform data are calculated by knowing the values of m, p, and q. Can do. In many cases, since it is difficult to obtain continuous waveform data, a data string {x obtained by measuring the state of a monitored object such as a vibration value at a predetermined period such as a period based on the sampling theorem Waveform data can be constructed with _k }, and the parameter KT and the parameter Fa, which are the kurtosis of the data string, can be calculated. When the monitoring target is normal and the data string {x _k } follows a normal distribution, the parameter KT is 3.0 and the parameter Fa is 1.0. By calculating the parameter KT or the parameter Fa for the data obtained from the monitoring target and comparing the value with the normal value, the state of the monitoring target can be known.

  FIG. 7 shows the result of calculating the parameter KT in the case of p = 1, 2,..., 6 and q = 1, 2,. It is a chart. FIG. 8 is a chart showing the result of calculating the parameter Fa in the same case by the equation (6). When the equipment to be monitored is a rolling bearing with 12 rolling elements, a group of continuous large peaks occurs every 12 vibrations. When p = 1, the maximum peak has not occurred, and therefore the monitoring target is normal. Further, when q = 1, only the maximum peak is generated. As p increases, the parameter KT and the parameter Fa also increase and deviate from the standard values. As q increases, the kurtosis KT and the parameter Fa decrease and conversely approach the standard values. By the way, when the abnormality of the monitoring target is small, such as when the wound generated on the rolling element is small, p and q are small, and the abnormality of the monitoring target is larger, such as when the scratch generated on the rolling element is enlarged. It is inferred that p and q become larger when the scale is reached. By observing a change in the value of the parameter KT or the parameter Fa, the degree of abnormality of the monitoring target can be determined.

  In the first invention, the parameter KT, which is the kurtosis of a plurality of data constituting the waveform data indicating the state of the monitored object, is simply calculated by using the equation (5).

  In the second, fourth and seventh inventions, the parameter Fa, which is the ratio between the kurtosis of a plurality of data constituting the waveform data indicating the state of the monitoring target and the kurtosis when the monitoring target is normal, is ( 5) It is simply calculated by using the equation.

  In the third, fifth, sixth, and eighth inventions, the state of the monitoring target is determined at high speed based on the parameter values that are simply calculated.

  According to the first invention, the parameter KT, which is the kurtosis of a plurality of data constituting the waveform data indicating the state of the monitored object, is sufficiently faster than the speed at which data is accumulated by using a simple formula. Can be calculated.

  According to the second, fourth and seventh inventions, the parameter Fa, which is the ratio between the kurtosis of a plurality of data constituting the waveform data indicating the state of the monitoring target and the kurtosis when the monitoring target is normal, By using a simple formula, the calculation can be made sufficiently faster than the speed at which data is accumulated. In particular, the parameters can be calculated using a simple calculation device such as a calculator.

  According to the third, fifth, sixth, and eighth inventions, the state of the monitoring target can be determined at high speed by determining the state of the monitoring target using the simply calculated parameters. . In addition, the present invention has an excellent effect, such as being able to perform state monitoring that immediately responds to the state of the facility without causing failure or delay in the detection of the abnormality of the facility.

Hereinafter, the present invention will be specifically described with reference to the drawings showing embodiments thereof.
(Embodiment 1)
FIG. 1 is a block diagram showing a configuration of a state monitoring system according to Embodiment 1 of the present invention. The state monitoring system of the present invention is operated to monitor the state of equipment (monitoring target) that is a periodic moving body such as a rolling bearing or a gear, and to issue an alarm when an abnormality occurs in the equipment. The equipment is provided with a sensor 31 that measures data indicating the state of the equipment such as vibration displacement. The sensor 31 is connected to the data acquisition device 32 and is configured to input measurement data to the data acquisition device 32. The data acquisition device 32 has a function of sampling measurement data input from the sensor 31 at a predetermined cycle and acquiring waveform data composed of a plurality of sampled data. The data acquisition device 32 is connected to the communication network N, and the communication network N is connected to the state monitoring device 1 of the present invention. The data acquisition device 32 is configured to transmit the acquired data to the state monitoring device 1 via the communication network N.

  The state monitoring device 1 also has a function as a parameter calculation device of the present invention, and is configured using a computer. The state monitoring device 1 includes a CPU (arithmetic unit) 11 that performs computation, a RAM (storage unit) 12 that stores temporary information generated along with the computation, an external storage device 13 such as a CD-ROM drive, And an internal storage device 14 such as a hard disk. The CPU 11 reads the computer program 20 of the present invention from the recording medium 2 such as a CD-ROM by the external storage device 13 and stores the read computer program 20 in the internal storage device 14. The computer program 20 is loaded from the internal storage device 14 to the RAM 12 as necessary, and the CPU 11 executes processing necessary for the state monitoring device 1 based on the loaded computer program 20.

  The CPU 11 is connected to an input unit 15 (accepting unit) connected to the communication network N. The CPU 11 receives the data transmitted from the data acquisition device 32 via the communication network N at the input unit 15. The CPU 11 is connected to an output unit 16 that outputs information to the outside.

Further, the internal storage device 14 stores a standard value KT _N of a parameter that is a kurtosis of a plurality of data constituting the waveform data acquired by the data acquisition device 32 when the monitoring target is normal.

  Furthermore, the state monitoring system according to the present embodiment includes an alarm device 4. The alarm device 4 includes any one or a combination thereof such as a buzzer, a lamp, or a display unit that displays the content of the alarm, and is connected to the output unit 16 of the state monitoring device 1. The CPU 11 transmits information indicating an abnormality of the facility from the output unit 16 to the alarm device 4, and the alarm device 4 notifies the abnormality of the facility according to the information received from the state monitoring device 1.

  The computer program 20 may be loaded from the external server device (not shown) connected to the communication network N to the state monitoring device 1 according to the present invention and stored in the internal storage device 14.

  FIG. 2 is a flowchart showing operations performed by the state monitoring system according to Embodiment 1 of the present invention. The sensor 31 measures data such as vibration associated with the operation of the equipment, and the data acquisition device 32 samples the measurement data input from the sensor 31 at a predetermined cycle (S101), and obtains a plurality of results obtained as a result of the sampling. The waveform data as shown in FIG. Next, the data acquisition device 32 determines whether or not waveform data having a predetermined length including sufficient data for calculating a parameter has been acquired (S102), and acquires waveform data having a predetermined length. If not (S102: NO), the process returns to step S101 to continue sampling. When waveform data having a predetermined length has been acquired (S102: YES), the data acquisition device 32 has a period in which continuous large peaks having a peak value greater than a predetermined range are included in the acquired waveform data. It is determined whether or not it is included (S103). When the large peak is not included in the waveform data (S103: NO), the data acquisition device 32 finishes the process assuming that the equipment is normal.

  When continuous large peaks as shown in FIG. 6B are included in the waveform data (S103: YES), the data acquisition device 32 determines the number of continuous peaks whose peak values are within a predetermined range. And the number of consecutive large peaks m, the ratio p of the peak value of the maximum peak to the peak value within a predetermined range, and the q value where the number of consecutive large peaks is 2q Then, the measured m, p, and q are transmitted to the state monitoring apparatus 1 via the communication network N (S105).

The state monitoring device 1 receives m, p, and q at the input unit 15 (S106), and the CPU 11 stores the standard values KT _{N of the} parameters stored in the internal storage device 14 in accordance with the computer program 20 loaded into the RAM 12. Is read into the RAM 14 (S107). Next, the CPU 11 calculates a parameter KT obtained by substituting m, p, q, and KT _N into the equation (5) according to the computer program 20 loaded in the RAM 12 (S108). Next, in accordance with the computer program 20 loaded in the RAM 12, the CPU 11 determines whether or not the calculated value of the parameter KT is a value included in a predetermined range in which it is recognized that the monitoring target is abnormal (S109).

  When the value of the parameter KT is not included in the predetermined range (S109: NO), the CPU 11 determines that the monitored facility is normal according to the computer program 20 loaded in the RAM 12, and ends the process. When the value of the parameter KT is included in the predetermined range (S109: YES), the CPU 11 determines that the equipment to be monitored is abnormal according to the computer program 20 loaded in the RAM 12, and according to the value of the parameter KT. Abnormality information indicating the degree of abnormality is transmitted from the output unit 16 to the alarm device 4 (S110), and the process ends. In accordance with the abnormality information received from the state monitoring device 1, the alarm device 4 notifies an abnormality of the facility by sounding a buzzer, turning on a lamp, displaying the content of the abnormality information on the display unit, or the like. As a result, measures such as manually stopping the equipment determined to be abnormal are taken.

  In the above process, the process of determining the state of the monitoring target based on whether or not the value of the parameter KT is included in the predetermined range is not limited to this, and the calculated value of the parameter KT is not limited to this. The internal storage device 14 may store the process and determine the state of the monitoring target based on the transition of the parameter KT value.

As described above in detail, in the present embodiment, waveform data indicating the state of equipment such as vibration in a rolling bearing is acquired, and continuous large peaks having a large peak value outside a predetermined range are periodically waveform data. , The total value m of the number of consecutive peaks whose peak value is within a predetermined range and the number of consecutive large peaks, the ratio p between the maximum peak value and the peak value within a predetermined range, continuous The parameter KT, which is the kurtosis of a plurality of data constituting the waveform data, is calculated using the q value where the number of large peaks is 2q and the standard value KT _N of the parameter. By simply calculating the parameter KT using Equation (5), which is simpler than the conventional equation for calculating kurtosis, the parameter KT is calculated sufficiently faster than the data accumulation rate. Is possible. Therefore, it is possible to perform state monitoring that immediately responds to the state of the facility without causing failure or delay in the facility abnormality detection.

  In addition, in this Embodiment, although the form which monitors the state of an installation based on the data which the one sensor 31 measured is shown, it is not restricted to this, Each of one installation or several installations Are provided with a plurality of sensors 31, and each of the plurality of sensors 31 is connected to the state monitoring device 1 via the data acquisition device 32 and the communication network N, and one or a plurality of the sensors 31 are determined based on the respective data measured by the respective sensors 31. It is good also as a form which monitors the state of these facilities.

  In the present embodiment, the waveform data is acquired by accumulating the data acquired by the data acquisition device 32. However, the data acquisition device 32 does not accumulate the acquired data, but the state monitoring device 1 The state monitoring apparatus 1 may acquire the waveform data. In the present embodiment, the alarm device 4 is connected to the output unit 16 of the state monitoring device 1. However, when the state monitoring device 1 determines that the facility is abnormal, the facility is provided. It is good also as a form which connected the control apparatus which controls facilities, such as stopping. Further, the data acquisition device 32 shows a form in which discrete data obtained by sampling is acquired to acquire waveform data. However, the present invention is not limited to this, and the data acquisition device 32 continuously takes in data measured by the sensor 31. The continuous waveform data may be acquired.

  In the present embodiment, a mode is shown in which the total value m of the number of continuous peaks whose peak value is within a predetermined range and the number of continuous large peaks is measured. However, the present invention is not limited to this. The state monitoring device 1 may store the value of m in advance. When the monitoring target is a rolling bearing and a single rolling element is scratched, the rolling element having a scratch among the plurality of rolling elements causes a large peak, so the number of rolling elements that the rolling bearing has is It becomes the value of m. When the monitoring target is a gear and one tooth is damaged, the number of gear teeth is m. As described above, since the value of m at the time of the initial failure is often determined in advance, the present invention can be realized even when the state monitoring device 1 stores the value of m in advance.

  Furthermore, in the present embodiment, a form for monitoring equipment in a factory is shown, but the present invention is not limited to this, and a form for monitoring the state of other general devices may be used. It may be in the form of monitoring phenomena such as fluctuations in sales data or stock prices.

(Embodiment 2)
FIG. 3 is a block diagram showing the configuration of the state monitoring system according to Embodiment 2 of the present invention. In the second embodiment, the state of the monitoring target is monitored using the parameter Fa, which is the ratio of the kurtosis of the plurality of data constituting the waveform data indicating the state of the monitoring target and the kurtosis at the normal time. The internal storage device 14 corresponds to a known value such as the number of rolling elements or the number of gears of a rolling bearing, and the number of continuous peaks whose waveform values are within a predetermined range and the number of continuous peaks. The total value m with the number is stored. Other configurations of the state monitoring system are the same as those in the first embodiment, and the corresponding parts are denoted by the same reference numerals and description thereof is omitted.

  FIG. 4 is a flowchart showing operations performed by the state monitoring system according to Embodiment 2 of the present invention. The sensor 31 measures data such as vibration associated with the operation of the equipment to be monitored, and the data acquisition device 32 samples the measurement data input from the sensor 31 at a predetermined cycle (S201), and the result of the sampling. Waveform data including a plurality of obtained data is acquired. Next, the data acquisition device 32 determines whether or not waveform data having a predetermined length has been acquired (S202). If waveform data having a predetermined length has not been acquired (S202: NO), step S202 is performed. The processing is returned to S201 and sampling is continued. When waveform data having a predetermined length is acquired (S202: YES), the data acquisition device 32 includes a large peak having a peak value greater than a predetermined range in the acquired waveform data. It is determined whether or not (S203). If the large peak is not included in the waveform data (S203: NO), the data acquisition device 32 terminates the process assuming that the equipment is normal. When the continuous large peak is included in the waveform data (S203: YES), the data acquisition device 32 determines the ratio p of the peak value of the maximum peak to the peak value within the predetermined range and the number of continuous large peaks. The value of q is set to 2q (S204), and the measured p and q are transmitted to the state monitoring apparatus 1 via the communication network N (S205).

  The state monitoring device 1 receives p and q at the input unit 15 (S206). The CPU 11 reads the value of m stored in the internal storage device 14 according to the computer program 20 loaded in the RAM 12 (S207), and calculates the parameter Fa obtained by substituting m, p, and q into equation (6). (S208). In addition, CPU11 may perform the process which receives the value of m from the outside. Next, the CPU 11 determines whether or not the value of the calculated parameter Fa is a value included in a predetermined range in which it is recognized that the monitoring target is abnormal according to the computer program 20 loaded in the RAM 12 (S209).

  When the value of the parameter Fa is not included in the predetermined range (S209: NO), the CPU 11 determines that the monitored facility is normal according to the computer program 20 loaded in the RAM 12, and ends the process. When the value of the parameter Fa is included in the predetermined range (S209: YES), the CPU 11 determines that the equipment to be monitored is abnormal according to the computer program 20 loaded in the RAM 12, and according to the value of the parameter Fa Abnormality information indicating the degree of abnormality is transmitted from the output unit 16 to the alarm device 4 (S210), and the process ends. The alarm device 4 notifies the equipment abnormality in accordance with the abnormality information received from the state monitoring device 1.

  In the above process, the process of determining the state of the monitoring target based on whether or not the value of the parameter Fa is included in the predetermined range is performed. However, the present invention is not limited to this, and the calculated value of the parameter Fa is used. The internal storage device 14 may store the process and determine the state of the monitoring target based on the transition of the value of the parameter Fa.

  As described above in detail, in the present embodiment, waveform data indicating the state of equipment such as vibration in a rolling bearing is acquired, and continuous large peaks having a large peak value outside a predetermined range are periodically waveform data. , The predetermined value m such as the number of rolling elements, the ratio p between the maximum peak value and the peak value within a predetermined range, and the q value where the number of continuous large peaks is 2q. The parameter Fa, which is the ratio between the kurtosis of a plurality of data constituting the waveform data and the kurtosis when the equipment is normal, is calculated. By simply calculating the parameter Fa using the simple expression (6), the parameter Fa can be calculated sufficiently faster than the speed at which data is accumulated. Therefore, it is possible to perform state monitoring that immediately responds to the state of the facility without causing failure or delay in the facility abnormality detection.

(Embodiment 3)
FIG. 5 is a conceptual diagram showing a state monitoring method according to Embodiment 3 of the present invention. In the present embodiment, the state of the facility can be grasped using a simple calculation device such as a calculator. A data display device 51 such as an oscilloscope is connected to the sensor 31 provided in the facility, and the data display device 51 displays the waveform data measured by the sensor 31. The waveform data as shown in FIG. 6 is displayed on the data display device 51. When an abnormality occurs in the equipment, the waveform data including continuous large peaks as shown in FIG. 6B is displayed. An operator such as an operator of the facility can calculate from the data displayed on the data display device 51 a ratio p between the maximum peak value and a peak value within a predetermined range at normal time, and q where the number of continuous large peaks is 2q. The rolling element of a rolling bearing which is a total value m of p and q and the number of continuous peaks whose peak value is within a predetermined range and the number of continuous large peaks to the calculation device 52 such as a calculator. And a known number m, such as the number of, is input, and the parameter Fa is calculated by the calculation device 52 using equation (6). Since the parameter Fa can be easily calculated by using the simple expression (6), the operator who directly observes the waveform data measured by the sensor 31 is in a site where the equipment is operating. You can easily grasp the state of the equipment.

It is a block diagram which shows the structure of the state monitoring system which concerns on Embodiment 1 of this invention. It is a flowchart which shows the operation | movement which the state monitoring system which concerns on Embodiment 1 of this invention performs. It is a block diagram which shows the structure of the state monitoring system in Embodiment 2 of this invention. It is a flowchart which shows the operation | movement which the state monitoring system which concerns on Embodiment 2 of this invention performs. It is a conceptual diagram which shows the state monitoring method in Embodiment 3 of this invention. It is a typical characteristic figure which shows the example of the waveform data which shows the vibration obtained from a periodic motion body. FIG. 6 is a chart showing the result of calculating the parameter KT in the case of p = 1, 2,..., 6 and q = 1, 2,. 6 is a chart showing the result of calculating the parameter Fa by the equation (6) in the case of p = 1, 2,..., 6 and q = 1, 2,.

Explanation of symbols

1 Condition monitoring device (parameter calculation device)
11 CPU
2 Recording medium 20 Computer program 31 Sensor 32 Data acquisition device N Communication network

Claims (8)

Using a calculation device including a reception unit that receives information, a storage unit that stores information, and a calculation unit, a plurality of consecutive peaks having a peak value within a predetermined range, and the peak value sequentially increase outside the predetermined range. A method for calculating parameters of waveform data including alternately a plurality of continuous large peaks that decrease in number,
With respect to the waveform data, a ratio p (p is a real number of 1 or more) of a maximum peak value of the plurality of large peaks with respect to a peak value within the predetermined range is received by the reception unit,
The reception unit receives a value of q in which the number of consecutive large peaks is 2q (q is a natural number),
A total value m (m is a natural number) of the number of the plurality of continuous peaks and the number of the plurality of continuous large peaks is stored in the storage unit;
Storing the standard value KT _N of the parameter of the waveform data in the storage unit;
The parameter KT of the waveform data is expressed as

The parameter calculation method is characterized in that the calculation unit calculates based on the above. Using a calculation device including a reception unit that receives information, a storage unit that stores information, and a calculation unit, a plurality of consecutive peaks having a peak value within a predetermined range, and the peak value sequentially increase outside the predetermined range. A method for calculating parameters of waveform data including alternately a plurality of continuous large peaks that decrease in number,
With respect to the waveform data, a ratio p (p is a real number of 1 or more) of a maximum peak value of the plurality of large peaks with respect to a peak value within the predetermined range is received by the reception unit,
The reception unit receives a value of q in which the number of consecutive large peaks is 2q (q is a natural number),
A total value m (m is a natural number) of the number of the plurality of continuous peaks and the number of the plurality of continuous large peaks is stored in the storage unit;
The parameter Fa of the waveform data is expressed as

The parameter calculation method is characterized in that the calculation unit calculates based on the above. Using a calculation device including a reception unit that receives information, a storage unit that stores information, and a calculation unit, a plurality of consecutive peaks having a peak value within a predetermined range, and the peak value sequentially increase outside the predetermined range. A method of monitoring the state of the monitoring target based on waveform data indicating the state of the monitoring target, alternately including a plurality of continuous large peaks that decrease in number,
With respect to the waveform data, a ratio p (p is a real number of 1 or more) of a maximum peak value of the plurality of large peaks with respect to a peak value within the predetermined range is received by the reception unit,
The reception unit receives a value of q in which the number of consecutive large peaks is 2q (q is a natural number),
A total value m (m is a natural number) of the number of the plurality of continuous peaks and the number of the plurality of continuous large peaks is stored in the storage unit;
The parameter Fa of the waveform data is expressed as

Based on the calculation unit,
A state monitoring method, wherein the state of the monitoring target is determined by the arithmetic unit according to the value of the parameter Fa calculated by the arithmetic unit. An apparatus for calculating parameters of waveform data alternately including a plurality of continuous peaks having a peak value within a predetermined range and a plurality of continuous large peaks whose peak values sequentially increase and decrease outside the predetermined range. And
Means for receiving a ratio p (p is a real number of 1 or more) of the maximum peak values of the plurality of large peaks to the peak value within the predetermined range for the waveform data;
Means for accepting a value of q in which the number of consecutive large peaks is 2q (q is a natural number);
Means for storing a total value m (where m is a natural number) of the number of consecutive peaks and the number of consecutive large peaks;
The parameter Fa of the waveform data is expressed as

And a parameter calculating device. Waveform data indicating the state of the monitoring target, alternately including a plurality of continuous peaks having a peak value within a predetermined range and a plurality of continuous large peaks whose peak values sequentially increase and decrease outside the predetermined range An apparatus for monitoring the status of the monitoring target based on:
Means for receiving a ratio p (p is a real number of 1 or more) of the maximum peak values of the plurality of large peaks to the peak value within the predetermined range for the waveform data;
Means for accepting a value of q in which the number of consecutive large peaks is 2q (q is a natural number);
Means for storing a total value m (where m is a natural number) of the number of consecutive peaks and the number of consecutive large peaks;
The parameter Fa of the waveform data is expressed as

Means to calculate based on
Means for determining the state of the monitoring target according to the value of the parameter KT calculated by the means. Waveform data indicating the state of the monitoring target, alternately including a plurality of continuous peaks having a peak value within a predetermined range and a plurality of continuous large peaks whose peak values sequentially increase and decrease outside the predetermined range A system for monitoring the status of the monitoring target,
A data acquisition device for acquiring the waveform data;
Means for obtaining a ratio p (p is a real number of 1 or more) of the maximum peak values of the plurality of large peaks to the peak values within the predetermined range for the waveform data acquired by the means;
Means for obtaining a value of q in which the number of the continuous large peaks is 2q (q is a natural number);
By using a total value m (m is a natural number) of the number of the plurality of continuous peaks and the number of the plurality of continuous large peaks, the parameter Fa of the waveform data serving as a determination criterion for the state of the monitoring target is determined. The following formula

A state monitoring system comprising: a computing device that calculates based on Let the computer calculate a parameter of waveform data that alternately includes a plurality of continuous peaks having peak values within a predetermined range and a plurality of continuous large peaks whose peak values sequentially increase and decrease outside the predetermined range. A computer program,
A ratio p (p is a real number greater than or equal to 1) of the maximum peak values of the plurality of large peaks to a peak value within the predetermined range in the waveform data, and 2q (q is A step of reading a value of q as a natural number) and a total value m (m is a natural number) of the number of consecutive peaks and the number of consecutive large peaks;
The computer calculates the parameter Fa of the waveform data as

A computer program comprising: a procedure for calculating based on the program. Waveform data indicating the state of the monitoring target, wherein the computer alternately includes a plurality of peaks having peak values within a predetermined range and a plurality of continuous large peaks whose peak values increase and decrease sequentially outside the predetermined range. And a computer program for monitoring the state of the monitoring target,
A ratio p (p is a real number greater than or equal to 1) of the maximum peak values of the plurality of large peaks to a peak value within the predetermined range in the waveform data, and 2q (q is A step of reading a value of q as a natural number) and a total value m (m is a natural number) of the number of consecutive peaks and the number of consecutive large peaks;
The computer calculates the parameter Fa of the waveform data as

The procedure to calculate based on
A computer program comprising: causing a computer to determine the state of the monitoring target according to the calculated value of the parameter Fa.

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