KR102199104B1 - Apparatus and method for detecting defects in rotating machines - Google Patents

Abstract

According to an embodiment of the present invention, in a method for detecting a defect in a rotating machine, performed by a rotating machine defect inspection apparatus, (a) a plurality of measurements measured every preset time unit from a vibration sensor coupled to the rotating machine Receiving a time-series vibration value; (b) calculating a predictive maintenance value based on a preset predictive maintenance value calculation formula through frequency analysis of a plurality of time-series vibration values; And (c) if the predictive maintenance value is greater than or equal to a preset value, outputting a warning notifying that the possibility of a defect of the rotating machine is high, wherein the time-series vibration value relates to a continuous vibration value measured for a specific time.

Description

Apparatus and method for detecting defects in rotating machinery {APPARATUS AND METHOD FOR DETECTING DEFECTS IN ROTATING MACHINES}

The present invention relates to an apparatus and method for detecting a defect in a rotating machinery based on a value measured by a sensor attached to a rotating machinery.

In order to detect or predict a failure of a specific part of a rotating machine, a difference between a normal state and a damaged state may be detected by measuring a physical quantity related to the failure characteristic of the corresponding part, and the severity of damage of the part may be calculated.

This has been established through a lot of experience and theories regarding defects and vibration/noise in rotating machines. In particular, high-frequency vibration/noise can be usefully used for detecting lubrication problems, wear, fluid flow, cavitation, damage to bearings or gears, and the like.

However, managing a facility with only the magnitude (amplitude) of vibration, which is simply a physical value, varies considerably depending on the type of machine, its components, design requirements, and manufacturing and operating conditions. Therefore, there is a disadvantage in that it is difficult to determine the severity of the defect (damage) only by the corresponding value when calculating only the physical quantity related to the defect (damage) measured according to the type of rotating machine or the operating method of the facility.

The present invention is to solve the problems of the prior art described above, and nondimensionalize various physical quantities measured from a rotating machine, and compare a reference value calculated as a nondimensional physical quantity with a nondimensionalized physical quantity at a specific point in time to It aims to detect or predict damage that may occur.

However, the technical problem to be achieved by the present embodiment is not limited to the technical problem as described above, and other technical problems may exist.

As a technical means for achieving the above technical problem, according to an embodiment of the present invention, in a method for detecting a defect in a rotating machine, performed by a rotating machine defect inspection apparatus, (a) combined with a rotating machine Receiving a plurality of time-series vibration values measured for each preset time unit from the vibration sensor; (b) calculating a predictive maintenance value based on a preset predictive maintenance value calculation formula through frequency analysis of a plurality of time-series vibration values; And (c) if the predictive maintenance value is greater than or equal to a preset value, outputting a warning notifying that the possibility of a defect of the rotating machine is high, and the time-series vibration value may be related to a continuous vibration value measured for a specific time. have.

In step (b), each time-series vibration value is converted into a frequency domain, the peak value is extracted from the converted data, and the peak value is extracted for each time-series vibration value, and the average value of the plurality of peak values and After calculating a reference value by applying the standard deviation of a plurality of peak values to Equation 1 below, it can be applied to a formula for calculating the predictive maintenance value.

In addition, the formula for calculating the predictive maintenance value is the first predictive maintenance value calculation formula for calculating the first predictive maintenance value corresponding to the operating state of the rotating machine at a specific point in time, and corresponding to the change in the operating state of the rotating machine within a preset period. It may include a second predictive maintenance value calculation formula for calculating the second predictive maintenance value.

In addition, the first predictive maintenance value may be calculated by applying the reference value and the peak value corresponding to the vibration value calculated from the vibration sensor at a specific time point to the first predictive maintenance value calculation formula corresponding to Equation 2 below.

In addition, by applying the amount of change of the plurality of peak values measured for each preset time unit, the average reference value of the plurality of peak values, and the preset warning value to the second predictive maintenance value calculation formula corresponding to Equation 3 below, The maintenance value is calculated, but the warning value may mean a value for calculating the predictive maintenance value corresponding to the type of rotating machine.

In addition, the warning value is composed of a first warning value and a second warning value, but the first warning value corresponds to a value to calculate a level of warning indicating attention or monitoring of rotating machinery, and the second warning value is rotated. It can respond to a value to produce a level warning to stop the machine when the machine reaches a hazardous value.

In addition, the warning includes at least one warning corresponding to the state of the rotating machine, but in step (c), if the predictive maintenance value is included in the preset section, a primary warning indicating caution or monitoring of the rotating machine is output, and If the predictive maintenance value is greater than or equal to a preset value, the rotating machine may output a second warning to stop the equipment by reaching a dangerous value.

In addition, a rotating machine defect inspection apparatus for detecting a defect in a rotating machine, comprising: a vibration sensor coupled to the rotating machine and measuring a vibration value in a predetermined time unit; And a plurality of time-series vibration values measured for each preset time unit from a vibration sensor coupled with a rotating machine, and predictive maintenance based on a preset predictive maintenance value calculation formula through frequency analysis of the plurality of time-series vibration values. If the value is calculated and the predictive maintenance value is more than a preset value, a warning is output indicating that the possibility of defects of the rotating machine is high, and the time-series vibration value is a rotation including an analysis unit on the continuous vibration value measured for a specific time. It may be a machine defect inspection device.

In addition, a notification unit including at least one of a speaker module or a display module may be further included.

In addition, when the notification unit includes a display module, the display module may display at least one of a warning, a vibration value measured in time units, and a predictive maintenance value.

In addition, the apparatus for detecting a defect in the rotating machine may further include receiving an input whether or not an actual defect has occurred in the rotating machine in response to the output warning.

In addition, the rotating machine defect inspection device extracts the number of times the warning has been output but no actual defect has occurred, and if the number of times that the actual defect has not occurred compared to the number of warnings is output exceeds a preset number, the predictive maintenance value is The warning value to be calculated can be corrected downward.

In addition, the actual defect may include at least one of a defect directly occurring in the rotating machine or a defect occurring in a product produced by the rotating machine.

In the present invention, various physical quantities measured from a rotating machine are dimensionless, and damage that may occur to a rotating machine can be detected or predicted by comparing a reference value calculated as a mechanically dimensioned physical quantity with a nondimensionalized physical quantity at a specific time point.

1 is a diagram showing the configuration of an apparatus for detecting a defect in a rotating machine according to an embodiment of the present invention.
2 is a flowchart illustrating a process of detecting a defect in a rotating machine according to an embodiment of the present invention.
3 is a flowchart illustrating a process of calculating a first predictive maintenance value according to an embodiment of the present invention.
4 is a flowchart illustrating a process of calculating a second predictive maintenance value according to an embodiment of the present invention.
5 is a diagram showing an example of misalignment that may occur in a bearing belonging to a conventional rotating machine.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement the present invention. However, the present invention may be implemented in various different forms and is not limited to the embodiments described herein. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and similar reference numerals are assigned to similar parts throughout the specification.

Throughout the specification, when a part is said to be "connected" to another part, this includes not only "directly connected" but also "electrically connected" with another element interposed therebetween. . In addition, when a part "includes" a certain component, it means that other components may be further included rather than excluding other components unless specifically stated to the contrary.

The following examples are detailed descriptions to aid understanding of the present invention, and do not limit the scope of the present invention. Accordingly, the invention of the same scope performing the same function as the present invention will also belong to the scope of the present invention.

Hereinafter, the rotating machine 210 is a machine that rotates around a rotating shaft such as an electric motor or a turbine, or refers to a part of such a machine, and may be, for example, a motor, a bearing, or the like.

1 is a diagram showing the configuration of an apparatus for detecting a defect in a rotating machine according to an embodiment of the present invention.

Referring to FIG. 1, an apparatus 10 for detecting a defect in a rotating machine may include an analysis unit 100 and a vibration sensor 200.

At this time, the vibration sensor 200 and the analysis unit 100 may be configured as a single device, but may be implemented as a separate independent device according to the implementation method or installation environment of the device, and implemented as a separate independent device. In some cases, they can be interconnected through a communication network.

According to an embodiment of the present invention, the apparatus 10 for detecting a defect in a rotating machine uses a vibration sensor 200 attached to the rotating machine 210 to measure a plurality of time-series vibration values measured for each preset time unit. Measure.

In addition, the apparatus 10 for detecting a defect in a rotating machine calculates a predictive maintenance value based on a preset predictive maintenance value calculation formula through frequency analysis of a plurality of time-series vibration values measured for each preset time unit, It characterized in that it outputs a warning according to the calculated predictive maintenance value.

In this case, the time-series vibration value refers to a value related to a continuous vibration value measured during a specific time. For example, the rotating machine 210 in operation continuously vibrates. The vibration sensor 200 generates a vibration value by continuously measuring such vibrations every preset time unit.

In addition, the analysis unit 100 converts each of the time-series vibration values measured for each preset time unit into a frequency domain, extracts a peak value from the converted data, and extracts a peak value for each time-series vibration value. do.

In this case, as a method of transforming the time-series vibration value into the frequency domain, data may be transformed by applying a Fourier transform or the like.

The predictive maintenance value may be used as an index to check the current state of the rotating machine 210. For example, if the rotating machine 210 is normal, the previously calculated predictive maintenance value and the currently calculated predictive maintenance value have similar values, but if an abnormality occurs in the rotating machine 210, the previous and current predictive maintenance values Will have a difference.

Accordingly, the predictive maintenance value is a measure for determining whether the state of the rotating machine 210 is normal or abnormal, or inferring to what level a defect has occurred if it is abnormal.

In addition, the warning output from the analysis unit 100 may output different alarms when the value of the predictive maintenance value is greater than or equal to a preset section or value.

According to an embodiment of the present invention, the analysis unit 100 is included in the apparatus 10 for detecting a defect in a rotating machine. In addition, the analysis unit 100 controls the entire process of detecting a defect in a rotating machine as a kind of central processing unit. Each step performed by the analysis unit 100 will be described later with reference to FIG. 2.

Here, the analysis unit 100 may include all types of devices capable of processing data, such as a processor. Here, the'processor' may refer to a data processing device embedded in hardware having a circuit physically structured to perform a function represented by a code or instruction included in a program. As an example of a data processing device built into the hardware as described above, a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, and an application-specific integrated (ASIC) circuit) and processing devices such as field programmable gate arrays (FPGAs), but the scope of the present invention is not limited thereto.

The vibration sensor 200 may mean a device or module that is coupled with the rotating machine 210 and measures a vibration value in a preset time unit.

As a further embodiment, the vibration sensor 200 may be implemented with a module that measures the internal temperature of the rotating machine 210 in addition to vibration, and a predictive maintenance calculation value corresponding thereto may be used.

In addition, the rotating machine defect inspection apparatus 10 may include a notification unit 300 including at least one of a speaker module or a display module, if necessary. For example, a separate speaker or display screen may be provided to notify a user of an alarm. In addition, it is possible to directly inform the state of the rotating machine 210 to an external manager in connection with the portable terminal.

If the notification unit 300 includes a display module, the display module displays at least one of a warning, a vibration value measured in time units, and a predictive maintenance value to notify the user.

2 is a flowchart illustrating a process of detecting a defect in a rotating machine according to an embodiment of the present invention.

Referring to FIG. 2, according to an embodiment of the present invention, a plurality of time-series vibration values measured for each preset time unit are received from the vibration sensor 200 (S210).

The analysis unit 100 calculates the predictive maintenance value through the predictive maintenance value calculation formula (S220).

As described above, the analysis unit 100 calculates a peak value for each of a plurality of time series vibration values through frequency analysis of a plurality of time series vibration values received in step S210.

In addition, the predictive maintenance value is calculated based on the calculated peak value and a preset predictive maintenance value calculation formula, and one of two predictive maintenance value calculation formulas may be selected according to the result to be calculated.

First, there may be a formula for calculating a first predictive maintenance value corresponding to a state in which the rotating machine 210 is operated at a specific point in time. For example, when it is necessary to determine the current state of the rotating machine 210 in operation, the first predictive maintenance value is calculated to determine the state of the rotating machine 210.

Another one may be a second predictive maintenance value calculation formula for calculating a second predictive maintenance value corresponding to a change in the operating state of the rotating machine 210 within a preset period. For example, a second predictive maintenance value is calculated to determine how quickly the rotating machine 210 in operation is deteriorated within a preset period. That is, the second predictive maintenance value is calculated by using the rate of change of the peak value for each of a plurality of time-series vibration values with respect to time.

A process of calculating the first predictive maintenance value or the second predictive maintenance value will be described with reference to FIGS. 3 and 4 to be described later.

It is determined whether the predictive maintenance value calculated in step S220 is equal to or greater than a preset value (S230).

If the calculated predictive maintenance value is less than or equal to a preset value, the analysis unit 100 determines that the rotating machine 210 is safe and then performs the operation of step S210 again.

However, if it has more than a preset value, step S240 is executed.

In this case, the preset value may be a value that serves as a reference for determining the presence or absence of the rotating machine 210 according to the predictive maintenance value.

For example, if the primary warning value is defined as 2.5 and the calculated predictive maintenance value is 1.37, the state of the rotating machine 210 can be determined to be good. Conversely, if the predictive maintenance value is 2.9, there is an abnormality. It is judged to be.

If the predictive maintenance value calculated in step S220 is greater than or equal to a preset value, a warning indicating that the possibility of a defect of the rotating machine 210 is high is calculated (S240).

The warning output in step S240 may be composed of at least one or more types so as to respond to various states that the rotating machine 210 may have.

To explain this in detail, if the predictive maintenance value is included in the preset section, the state of the rotating machine 210 outputs a primary warning indicating caution or monitoring, and if the predictive maintenance value is more than a preset value, the rotating machine ( 210) will output a second warning to stop the equipment when it reaches the dangerous level.

For example, if the predictive maintenance value is between 2.5 and 4, the analysis unit 100 outputs a first warning, and if the predictive maintenance value is 4 or more, the analysis unit 100 performs a second warning. It becomes.

At this time, the predictive maintenance value and the preset value of the preset section set to perform the first and second warnings are values used in the predictive maintenance value calculation formula used to calculate the predictive maintenance value or the rotating machine 210 It depends on the type of ).

In addition, as a further embodiment, in response to the output warning, the analysis unit 100 receives input from the manager of the rotating machine 210 whether or not a defect has actually occurred in the rotating machine.

Thereafter, the analysis unit 100 extracts the number of times a warning has been output, but no actual defect has occurred. If, compared to the number of times the warning is output, the number of times that a defect does not actually occur in the rotating machine 210 exceeds a preset number, the warning value for calculating the predictive maintenance value is corrected to be adjusted downward.

The reason for performing these tasks may vary depending on the type of rotating machine 210 and the operating environment, etc., and the analysis unit 100 calculates the predictive maintenance value based on the information received from the manager. This is because accuracy can be improved.

In addition, as another additional embodiment, the reference of the actual defect may be regarded as at least one of a defect occurring in the rotating machine 210 itself or in a secondary product produced by the rotating machine 210.

In addition, the analysis unit 100 generates log data that records the state change of the rotating machine 210 operated within a preset period based on the feedback data, and provides it to the administrator along with a warning output in step S240. I can.

3 is a flowchart illustrating a process of calculating a first predictive maintenance value according to an embodiment of the present invention.

Referring to FIG. 3, in order to calculate the first predictive maintenance value, a reference value is calculated (S310).

In this case, the calculated reference value may be used to compare a peak value for the currently extracted vibration value as a comparison value serving as a reference for applying predictive maintenance.

Accordingly, the reference value is calculated by applying the average value of the plurality of peak values and the standard deviation of the plurality of peak values to Equation 1 below.

[Equation 1]

At this time, the average and standard deviation of the plurality of peak values are calculated based on the formula of the average value and standard deviation in the conventional formula.

The reference value calculated in step S310 and the peak value corresponding to the vibration value calculated from the vibration sensor at a specific time point are applied to the first predictive maintenance calculation formula (S320), and a first predictive maintenance value is calculated (S330).

The first predictive maintenance value is calculated by applying a reference value and a peak value corresponding to the vibration value calculated from the vibration sensor 200 at a specific time point to a first predictive maintenance value calculation formula corresponding to Equation 2 below.

[Equation 2]

Thereafter, the state of the rotating machine 210 is determined in step S230 by using the calculated first predictive maintenance value.

4 is a flowchart illustrating a process of calculating a second predictive maintenance value according to an embodiment of the present invention.

Referring to FIG. 4, in order to calculate the second predictive maintenance value, a preset warning value is set (S410).

The preset warning value means a value for calculating the predictive maintenance value corresponding to the type of the rotating machine 210.

Accordingly, when the state of the rotating machine 210 is divided into at least one state such as a caution step and a risk step according to the second predictive maintenance value, at least one warning value may also exist.

In the present invention, the warning value is divided into a first warning value and a second warning value.

In this case, the first warning value may be used to calculate a value for calculating a level warning indicating attention or monitoring of the rotating machine 210. In addition, the secondary warning value corresponds to a value for calculating a warning level for stopping the equipment when the rotating machine 210 reaches a dangerous value.

Accordingly, the user sets a warning value to check whether the state of the rotating machine 210 deteriorates at a certain speed compared to the time of the rotating machine 210.

The amount of change of the plurality of peak values and the average reference value of the plurality of peak values are calculated (S420).

In order to perform step S420, the analysis unit 100 calculates peak values for a plurality of vibration values measured for each preset time unit. In addition, the analysis unit 100 calculates a change amount and an average reference value by using the calculated peak values.

Each value is applied to the second predictive maintenance calculation formula (S430), and a second predictive maintenance value is calculated (S440).

In order to calculate the second predictive maintenance value, the analysis unit 100 calculates the amount of change of the plurality of peak values calculated in step S420, the average reference value of the plurality of peak values, and the warning value calculated in step S410 by the following equation: It is applied to the formula for calculating the second predictive maintenance value corresponding to 3.

[Equation 3]

The value of 0.3 used in Equation 3 may be a number representing 3 months.

Typically, when the peak value of the rotating machine 210 rises by about 30% per month, it is determined that an abnormal symptom of the rotating machine 210 has occurred, so the unit of 0.3 has been used, but the rotating machine 210 The number may vary depending on the type or characteristics of the operating part.

For example, if the data (amplitude size/Hz) collected from the rotating machine 210 is calculated as shown in Table 1 below, the analysis unit 100 applies the equations 1 to 3 to calculate the predictive maintenance value. do.

Cycle(s) 0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 Peak value 4.5 3.9 3.3 4.1 4.3 3.6 4.4 4.2 3.5 4.0

A process for the analysis unit 100 to determine the current state of the rotating machine 210 through the example data shown in Table 1 and the formula for calculating the first predictive maintenance value may proceed as follows.

The analysis unit 100 calculates an average value of 4.0 and a standard deviation of 0.3815 based on the data in Table 1, and calculates a reference value of 4.7431 based on this. At this time, the reference value is a value calculated through Equation 1 based on data collected during a period of about one week to one month.

If the peak value calculated based on the vibration value extracted at a specific time period is 6.5, the analysis unit 100 calculates a predictive maintenance value of 1.370 using the first predictive maintenance value calculation formula and the peak value 6.5. do.

Thereafter, when the preset warning value is 2.5, the analysis unit 100 may confirm that the first predictive maintenance value 1.370 is a safe state of the rotating machine 210.

In addition, through the example data presented in Table 1 and the formula for calculating the second predictive maintenance value, the analysis unit 100 may determine the degree of occurrence of the defect of the rotating machine 210 through the following method.

If the analysis unit 100 may calculate a change amount of the peak value as 1.4 based on a time of one day, the average reference value for one month may be 4.0 based on the data in Table 1 above.

At this time, when the preset first warning value is 2.5, the analysis unit calculates a second predictive maintenance value of 2.9166 by substituting the change rate of 0.35 and the previously calculated average reference value into the second predictive maintenance value calculation formula.

Thereafter, the analysis unit 100 calculates a warning because the second predictive maintenance value is higher than the first warning value of 2.5, and informs the manager that a defect may occur in the rotating machine 210.

5 is a diagram showing an example of misalignment that may occur in a bearing belonging to a rotating machine 210 used in the related art.

Referring to Fig. 5, Fig. (a) is an image of bearing misalignment, and Fig. (b) is a graph showing an example of a spectrum in a typical frequency domain caused by bearing misalignment.

The bearing misalignment, which can be seen in Fig. (a), occurs when the bearing is not installed on the same plane. That is, due to thermal growth or soft foot, the machine deviates at an angle with respect to the axis where the torsion occurs.

At this time, the bending of the rotating body means a change in metal structure characteristics due to non-uniform thermal conditions, sagging of the rotating body, and rubbing, and the bearings operating in this state have a shorter or abnormal period than usual. Vibration occurs, and the vibration sensor 200 senses it.

At this time, Fig. (b) is a typical spectrum graph caused by bearing misalignment.

Looking at this, the 1x RPM axial component is high, and the 2x and 3x harmonic components are also involved. In addition, it has a high peak at the number of rollers X TS.

The change in the peak value is applied to the predictive maintenance value calculation formula of the present invention, and a defect in the bearing can be found through the calculated result.

In the present invention, a preset vibration value is used to find a defect in the rotating machine 210 such as a bearing. However, as an additional embodiment, in addition to the vibration value, a defect of the rotating machine 210 may be calculated by setting a predictive maintenance value calculation formula using a change value of the noise frequency or temperature generated by the rotating machine 210.

An embodiment of the present invention may also be implemented in the form of a recording medium including instructions executable by a computer, such as a program module executed by a computer. Computer-readable media can be any available media that can be accessed by a computer, and includes both volatile and nonvolatile media, removable and non-removable media. Further, the computer-readable medium may include a computer storage medium. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

Although the methods and systems of the present invention have been described in connection with specific embodiments, some or all of their components or operations may be implemented using a computer system having a general-purpose hardware architecture.

The above description of the present invention is for illustrative purposes only, and those of ordinary skill in the art to which the present invention pertains will be able to understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative and non-limiting in all respects. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as being distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the claims to be described later rather than the detailed description, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. do.

1: Device for detecting defects in rotating machinery
100: analysis unit 200: vibration sensor
210: rotating machine 300: alarm unit

Claims (13)

In a method for detecting a defect in a rotating machine, performed by a rotating machine defect inspection device,
(a) receiving a plurality of time-series vibration values measured for each preset time unit from a vibration sensor coupled to the rotating machine;
(b) calculating a predictive maintenance value based on a preset predictive maintenance value calculation formula through frequency analysis of the plurality of time-series vibration values;
(c) if the predictive maintenance value is greater than or equal to a preset value, outputting a warning notifying that the rotating machine is likely to be defective; And
(d) the apparatus for detecting a defect in the rotating machine, in response to an output warning, receiving input from an administrator whether or not a defect has actually occurred in the rotating machine
The time-series vibration value relates to a continuous vibration value measured during a specific time,
Step (b)
After converting each of the time series vibration values into a frequency domain, a peak value is extracted from the converted data, and a peak value is extracted for each time series vibration value, and an average value of a plurality of peak values and a standard of a plurality of peak values After calculating the reference value by applying the deviation, it is applied to the formula for calculating the predictive maintenance value,
The first predictive maintenance value calculation formula for calculating the first predictive maintenance value corresponding to the operating state of the rotating machine at a specific point in time is the reference value and the peak value corresponding to the vibration value calculated from the vibration sensor at a specific point in time. Is calculated by applying to Equation 2 below,
Step (d)
Extracting the number of times the warning was output from the rotating machine defect inspection device but the actual defect did not occur, and when the number of times that the actual defect did not occur compared to the number of times the warning was output exceeds a preset number, the predictive maintenance The method of detecting a defect in a rotating machine to downwardly correct the preset value for calculating a value.
[Equation 2]

The method of claim 1,
Step (b)
A method of detecting a defect in a rotating machine, calculating a reference value by applying an average value of a plurality of peak values and a standard deviation of a plurality of peak values to Equation 1 below, and then applying it to the predictive maintenance value calculation formula.
[Equation 1]

The method of claim 2,
The formula for calculating the predicted maintenance value is
And a second predictive maintenance value calculation formula for calculating a second predictive maintenance value corresponding to a change in the operating state of the rotating machine within a preset period of time. delete The method of claim 3,
By applying the amount of change of the plurality of peak values measured for each preset time unit, the average reference value of the plurality of peak values, and a preset warning value to the formula for calculating the second predictive value corresponding to Equation 3 below, the second prediction Calculate the conservation value,
The warning value means a value for calculating the predictive maintenance value corresponding to the type of the rotating machine.
[Equation 3]

The method of claim 5,
The warning value is composed of a first warning value and a second warning value,
The primary warning value corresponds to a value for calculating the warning of a level indicating attention or monitoring of the rotating machine, and the secondary warning value is a level at which the rotating machine reaches a dangerous value and stops the equipment. Corresponding to the value for calculating the warning of, the method of detecting a defect in a rotating machine. The method of claim 1,
The warning includes at least one warning corresponding to the state of the rotating machine,
Step (c)
When the predictive maintenance value is included in a preset section, a first warning indicating caution or monitoring of the rotating machine is output, and
If the predictive maintenance value is more than a preset value, the rotating machine reaches a dangerous value and outputs a second warning for stopping the equipment. In the rotating machine defect inspection apparatus for detecting a defect in a rotating machine,
A vibration sensor coupled to the rotating machine and measuring a vibration value in a preset time unit; And
Receives a plurality of time-series vibration values measured for each preset time unit from a vibration sensor coupled with the rotating machine, and predicts based on a preset predictive maintenance value calculation formula through frequency analysis of the plurality of time-series vibration values. When the maintenance value is calculated and the predictive maintenance value is greater than or equal to a preset value, a warning notifying that the possibility of a defect of the rotating machine is high is output, and the apparatus for detecting a defect of the rotating machine responds to the output warning, the Receiving input from the manager whether or not an actual defect has occurred in the rotating machine, the time-series vibration value includes an analysis unit for a continuous vibration value measured for a specific time,
The process of calculating the predictive maintenance value
After converting each of the time series vibration values into a frequency domain, a peak value is extracted from the converted data, and a peak value is extracted for each time series vibration value, and an average value of a plurality of peak values and a standard of a plurality of peak values After calculating the reference value by applying the deviation, it is applied to the formula for calculating the predictive maintenance value,
The first predictive maintenance value calculation formula for calculating the first predictive maintenance value corresponding to the operating state of the rotating machine at a specific point in time is the reference value and the peak value corresponding to the vibration value calculated from the vibration sensor at a specific point in time. Is calculated by applying to Equation 2 below,
In the process of receiving input from the manager whether or not a defect has occurred in the rotating machine
Extracting the number of times the warning was output from the rotating machine defect inspection device but the actual defect did not occur, and when the number of times that the actual defect did not occur compared to the number of times the warning was output exceeds a preset number, the predictive maintenance A rotating machine defect inspection device for downwardly correcting the preset value for calculating a value.
[Equation 2]

The method of claim 8,
The rotating machine defect inspection apparatus further comprises a notification unit including at least one of a speaker module or a display module. The method of claim 9,
When the notification unit includes the display module,
The display module displays at least one or more of the warning information, a vibration value measured in a time unit, and a predictive maintenance value. delete delete The method of claim 1,
The actual defect includes at least one of a defect directly generated in the rotating machine or a defect generated in a product produced by the rotating machine.

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