KR102045823B1 - method for monitoring of diagnosing pulverizer and system - Google Patents

Abstract

According to one embodiment, a differentiator damage diagnosis method comprises: a step of receiving vibration information regarding a differentiator from an acceleration sensor attached to the differentiator in a terminal; a step of filtering vibration information about the differentiator; a step of determining whether or not the differentiator is damaged by comparing the filtered vibration information with a gear mesh frequency (GMF) related to the differentiator for whether there is an error within a predetermined range; and a step of outputting information on whether or not the determined differentiator is damaged at the terminal. Therefore, preventive maintenance or rapid measure of the differentiator is possible by predicting or diagnosing the damage of the differentiator on the basis of the vibration information.

Description

Method for monitoring of diagnosing pulverizer and system

The present invention relates to a differentiator fault diagnosis monitoring method. More specifically, the present invention relates to a method of determining whether or not a failure of a differentiator machine is made through vibration information of an acceleration sensor attached to the differentiator.

In coal-fired power plants, fuels such as coal must be combusted to generate electricity. Pulverizers for pulverizing solid fuel input are one of the main facilities of coal-fired power plants. Crush into pieces.

However, the pulverizer for pulverizing bituminous coal is often damaged by foreign matters such as lumps of stone, bolts, scrap metal, etc. mixed with bituminous coal, which may cause a problem in supplying bituminous coal.

Therefore, by using the vibration signal to monitor the operating state of the differentiator in real time and the operation state before the failure occurs to prevent the failure in advance, even if a failure occurs, the warning signal is immediately transmitted from the vibration sensor to spread the failure There is a need to develop a system to prevent the occurrence of damage and to maintain the equipment.

The facility diagnostic monitoring method using the vibration information has a wider use area than other systems, and can directly measure the vibration of the structure by directly attaching it to the object under test, and can quickly identify the source of the direct failure.

Vibration measurement system using acceleration sensor is optimized for monitoring as it is widely used for condition monitoring of mechanical moving objects in plant industry such as pump and motor, from R & D to automobile and aircraft.

In order to solve the above problems, the differentiator failure diagnosis method according to the present invention, by filtering the vibration information about the differentiator can predict or prepare a failure through the information on whether the differentiator failure.

Differentiator failure diagnosis method according to an embodiment, the terminal receiving vibration information about the differentiator from the acceleration sensor attached to the differentiator; Filtering vibration information about the differentiator; Determining whether the differentiator is broken by comparing the filtered vibration information with a gear mesh frequency (GMF) related to the differentiator and whether there is an error within a predetermined range; And outputting, from the terminal, information on whether the determined differentiator has failed.

The determining of whether or not the differentiator malfunctions may include determining that the filtered vibration information is not a failure when the filtered vibration information and the gear engagement frequency GMF relating to the differentiator are within a first error range, and the filtered vibration information. And when the gear engagement frequency GMF with respect to the differentiator is within a second error range, it is determined to be a failure, and the second error range includes the first error range.

In addition, the acceleration sensor attached to the differentiator may be a high temperature acceleration sensor of 145 degrees Celsius.

The comparing of the gear engagement frequency of the differentiator with an error within a predetermined range may be performed by comparing the error history information of the differentiator and the structure information of the differentiator with an error within a predetermined range.

The method may further include determining whether the differentiator is broken by using a natural frequency of the bearing installed in the differentiator.

According to another embodiment, a failure diagnosis system of a differentiator may include an acceleration sensor attached to the differentiator; A terminal for receiving vibration information about the differentiator from the acceleration sensor; The terminal, the filtering unit for filtering the vibration information on the differentiator; A failure determining unit for comparing the filtered vibration information with a gear mesh frequency (GMF) related to the differentiator to determine whether there is an error within a predetermined range; And an output unit for outputting information on whether or not the differentiator is determined by the failure determination unit.

According to the present invention, there is an advantageous effect that can minimize the failure since the preceding maintenance is carried out irrespective of the actual defect level. It also has the beneficial effect of significantly reducing maintenance costs through state-based predictive maintenance.

In addition, it is possible to solve the difficulties of procuring replacement parts and delaying delivery due to damage of major parts.

1 is a flowchart illustrating a process of diagnosing a differentiator failure according to an embodiment.
2 is a graph showing a general progress measure of a machine failure, according to one embodiment.
3 is a diagram illustrating a configuration of a differentiator failure diagnosis system according to an exemplary embodiment.
4 is a block diagram conceptually illustrating differentiator big data according to an exemplary embodiment.
5 is a flowchart illustrating a process of diagnosing a differentiator failure according to an embodiment.

Terms used herein will be briefly described, and embodiments will be described in detail.

The terminology used herein is to select general terms that are widely used at present, considering the function thereof, but may vary according to the intention or precedent of a person skilled in the art, the emergence of new technologies, and the like. In addition, in certain cases, there is a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the corresponding description. Therefore, the terms used in the present specification should be defined based on the meanings of the terms and the contents throughout the present specification, rather than simple names of the terms.

When any part of the specification is to "include" any component, this means that it may further include other components, except to exclude other components unless otherwise stated. In addition, the terms "... means", "... unit", "module", etc. described in the specification mean a unit for processing at least one function or operation, which may be implemented in hardware or software, or hardware and software. It can be implemented as a combination of.

Hereinafter, various embodiments will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily implement the embodiments. However, embodiments may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In the drawings, parts irrelevant to the description are omitted for clarity of explanation, and like reference numerals designate like parts throughout the specification.

1 is a flowchart illustrating a process of diagnosing a differentiator failure according to an embodiment of the present disclosure.

An accelerometer can be attached to the differentiator. The temperature inside the differentiator rises from 70 degrees Celsius to up to 237 degrees, requiring acceleration sensors that are resistant to thermal shock and thermal fatigue. These acceleration sensors are directly affected by the adhesion surface temperature because they measure vibration while attached to the surface of the differentiator and the peripheral gearhead. In particular, since the acceleration sensor must accurately measure and transmit the state of the differentiator, it is preferable to use an acceleration sensor that can generate a stable output even when temperature changes.

Specifically, the acceleration sensor may be attached to the motor bearing of the differentiator. An acceleration sensor attached to the motor bearing can measure vibration information of the bearing. In addition, the acceleration sensor may be attached to the worm screw bearing of the powder. An acceleration sensor attached to the worm screw bearing can measure vibration information of the bearing. In addition, the acceleration sensor can be attached to the bottom of the bull gear grinding roll of the grinding machine. In addition, the acceleration sensor may be attached to the grinding roll assemblies of the grinding mill. The acceleration sensor attached to each position of the differentiator may be a single or a plurality of acceleration sensors, and are not limited to the same acceleration sensor, and different acceleration sensors may be attached to each position of the differentiator.

Vibration information of the differentiator measured by the acceleration sensor may be received by the device (S110). In this case, the device may be a device capable of receiving vibration information, and for example, a data logger may correspond thereto.

The vibration information of the differentiator received from the data logger device may be transmitted to the file server. In the case of the file server, a server connected to the data logger via wired or wireless connection is sufficient.

The file server may filter vibration information about the differentiator (S120). In this case, the filtering means measures the gear engagement frequency (GMF), RPM, and bearing frequency in advance through analysis of the differentiator machine, and performs bandpass filtering to remove unnecessary frequency parts and cut off noise. do.

The file server may determine the failure of the differentiator by analyzing the filtered vibration information of the differentiator (S130). The failure may be determined based on a difference between a criterion for determining whether the failure is compared with the vibration information previously measured for the differentiator.

The file server may store model information, structural information, and part information about the differentiator in advance. In the case of commercially available mills, since there are many overseas products, information on overseas products can be stored in advance, and information on supply and demand of parts can be stored.

The file server can also machine learn vibration information about the differentiator. Since the magnitude of the vibration of the differentiator may increase as time passes, it is difficult to determine the current status of the differentiator based on the initial vibration information alone.

The file server can calculate the overall amplitude of the differentiator. Instead of expressing the sum of amplitudes in a time waveform, the frequency analysis may be performed using a Fast Fourier Transform (FFT), and then may be expressed as a sum of frequency components on a spectrum. When the sum of the frequency components is obtained, a method such as Equation 1 below may be used.

The file server may calculate a time trend with respect to the amplitude of each frequency component. The FFT analysis may set a certain time period such as hourly or daily for the amplitude of each frequency component and calculate the sum of power for each time period. By setting a predetermined time period, such as weekly or monthly, the calculated amplitude power sum can track a time trend (Trend) of the amplitude power value for each time period.

The file server may also store failure history information about the differentiator. If the differentiator has failed in the past, it can store a history of any component failure and vibration information before and after the failure. The file server can store failure history information for the same type of differentiator or related differentiator as well as the failure history information of the differentiator installed in the field. For example, A differentiator is installed in the field, and the failure history information of B differentiator, which shares parts a, b and c, can also be stored.

The file server compares the gear engagement frequency (GMF) with respect to the differentiator and whether there is an error within a predetermined range to determine whether the differentiator has failed. For example, you can compare the preset frequency as a steady state with the calculated gear engagement frequency to determine that there is no failure up to the normal range and up to 10% error, with attention mode, normal range and 50% up to the normal range and 20% error. (Or 30%) errors can be determined in risk mode, and errors in immediate action mode (fault status).

The file server may determine whether there is a failure, and if it is determined to be normal or failure, the file server may output the determined information (S140). The file server can be networked to the web server by wire or wirelessly, and devices such as smartphones and tablet PCs can monitor the failure of the differentiator through the web server.

2 is a graph showing a general progress measure of a machine failure, according to one embodiment.

The noise of the differentiator and the peripheral device is about 140dB, which makes it difficult for the operator to determine whether the failure occurs acoustically even when a failure occurs. Many late failures have already resulted in serious failures of the roller parts or the internal equipment of the mill.

Due to such a breakdown, there are many problems such as an increase in maintenance time for facilities, excessive input of manpower, and disruption of the power generation process as a whole. Rather than judging the failure inside the differentiator, we had to rely on the operator's inspection.

As shown in FIG. 2, it can be seen that the symptoms appearing in the case of a general mechanical failure are generally similar (depending on the machine), and the first change in state is a vibration change.

Over time, the component's life can be extended and vibrations as well as excess friction can occur. This excess friction can be identified by oil analysis. Further breakdowns in the machine generate noise, heat and smoke, and eventually breakdowns, causing great damage.

3 is a diagram illustrating a configuration of a differentiator failure diagnosis system according to an exemplary embodiment.

As shown in FIG. 3, the differentiator failure diagnosis system includes an acceleration sensor 300, a differentiator 301, a data logger 302, a file server 303, a web server 304, and a terminal 305-1 and 305-. 2).

The acceleration sensor 300 detects the vibration signal of the component by installing the component and converting the acceleration change generated during the vibration of the component into an electrical signal and outputting the electrical signal. MEMS accelerometers make a fine structure using semiconductor technology, and when the structure itself is tilted along the direction of acceleration, the resistance value changes so that the value of the current flowing through the structure changes, and the vibration is measured by recognizing the current change as an acceleration. Sensor.

In general, strain gauge type accelerometers utilize a phenomenon in which the electrical resistance of a metal wire or a metal foil changes due to deformation. That is, a strain gauge type accelerometer is a sensor that detects an acceleration change corresponding to component vibration by a change in electrical resistance.

However, strain gage type accelerometers are made of electrical resistance and thus have various problems such as corrosion, electromagnetic effects, and workability. In addition, excessive lead wire operation is expensive and time consuming, and leads to a decrease in reliability of the detection signal. In addition, strain gauge type accelerometers are highly affected by electromagnetic waves, and therefore, when mounted on railroad cars such as subways and high-speed railways, which are used in relatively high voltage environments, noise has a significant effect on detection signals.

Therefore, it may be advantageous to use a MEMS acceleration sensor rather than a strain gauge type acceleration sensor in terms of processing the measured information and measuring vibration information of the differentiator 301 accurately.

The temperature inside the differentiator rises from 70 degrees Celsius to up to 237 degrees, requiring acceleration sensors that are resistant to thermal shock and thermal fatigue. These acceleration sensors are directly affected by the adhesion surface temperature because they measure vibration while attached to the surface of the differentiator and the peripheral gearhead. In particular, since the accelerometer must accurately measure and transmit the state of the differentiator, it is preferable to use an accelerometer for a high temperature of 145 degrees Celsius that can generate a stable output even when temperature changes.

In addition, the acceleration sensor 300 not only continuously measures the vibration information of the differentiator, but also needs to communicate with the data logger 302, and uses a low noise cable that can be equipped with a battery or block external noise for a long time. Accelerometers that can be powered by being directly connected to a power source are desirable.

The differentiator 301 is a machine for crushing fuel such as coal in a thermal power plant, and the acceleration sensor 300 may be attached to the surface of the differentiator and the peripheral reducer. One or more acceleration sensors may be attached to the inside or outside of the differentiator 301, and specifically, may be attached at positions of the motor bearing, the worm screw bearing, the bull gear grinding roll bottom, the grinding roll assembly, and the like of the differentiator. Accelerometers can also be attached to locations where failures frequently occur in the differentiator.

The data logger 302 receives vibration information of the differentiator from an acceleration sensor attached to the differentiator. The acceleration sensor may be attached to the data logger in a state of being connected to the data logger in a wired form, and may transmit vibration information through a near field communication with the data logger in a wireless form.

The data logger 302 may remove noise from the signal received from the acceleration sensor, convert the analog signal into digital, and transmit the digital signal to the file server 303. The data logger 302 is equipped with a robust frame structure and a process capable of handling 400k sampling per second to facilitate field installation, and can be a highly compatible device by providing various interfaces.

The file server 303 may be connected to the data logger 302 in one form. For example, the file server 303 may be connected to a serial network such as USB, a wired LAN, or wireless communication. File server 303 converts the information received from data logger 302 into meaningful information.

File server 303 may be configured to perform algorithmic software for failure analysis of the differentiator. The gearing frequency (GMF) between the gears in the differentiator can be used to determine whether there is a failure. More specifically, the ideal value (frequency) of the gear and the gear engagement frequency can be calculated using the rotational speed (RPM) of the differentiator drive motor and the number of gear teeth of the differentiator.

The file server 303 sets the calculated ideal value as a reference value, and compares the vibration value measured in the differentiator with the reference value. The file server 303 may determine whether the differentiator 301, in which the vibration value is measured, has a failure according to the error value. For example, it can be determined to be normal within the 10% error range and the reference value, and can be determined as the state requiring attention (attention mode) within the reference value and the 20% error range. Dangerous mode), if more errors occur, it can be determined to be a failure and to be taken immediately (failure state). In the present disclosure, for convenience of description, an arbitrary numerical value has been described as an example, but it is not necessarily limited thereto, and it is preferable to understand that the definition criteria for the state may be set differently for each different branch.

The file server 303 may determine whether there is a failure based on a bearing vibration value protecting the differentiator 301 gear. By analyzing the natural frequency of the bearing installed in the differentiator 301, it is possible to measure the frequency value of the normal state. The natural frequencies of the bearings at this time include the rolling bearing ball pass frequency inner race (BPFI), the rolling bearing ball pass frequency outer race (BPFO), the rolling bearing cage frequency (FTF) and It may include ball spin frequency (BSF) information.

The file server 303 derives the measured multiple of the bearing frequency in the steady state, calculates the frequency multiple derived within 0 to 2 KHz, adds the peak value of the frequency corresponding to the multiple, and changes the peak value. Can be analyzed. For example, the file server 303 may determine a normal state when the change in the peak value is within the 10% range with the reference value, and warns when the change is within the 20% range with the reference value and a warning state when the peak value is within the 20% range. If it is more than the state, it can be determined that the failure.

The file server 303 may transmit determination information regarding the presence or absence of a failure to the monitoring terminals 305-1 and 305-2 through the web server 304. The user of the terminals 305-1 and 305-2 does not have a visual or auditory sense of the powder 301, and there is a failure based on vibration information of the powder 301 obtained through the terminals 305-1 and 305-2. Can be visually confirmed. In addition, the operator does not judge the failure of the differentiator 301 through diagnosis every time, but compares and analyzes the vibration value of the steady state based on the continuously measured vibration information to predict and maintain the failure in advance. Has an advantageous effect.

In the present disclosure, the file server 303 and the web server 304 for network communication are illustrated separately from each other. However, the file server 303 and the web server 304 are described separately to explain the role of each server. ) And the web server 304 may be operated simultaneously.

4 is a block diagram conceptually illustrating differentiator big data according to an exemplary embodiment.

The file server 303 may not only store the vibration information measured in the DB while measuring vibration information of the differentiator, but also store information 400 about various differentiators. As the information about the differentiator 400, the differentiation model information 410, differentiation part information 420, differentiation structure information 430, and differentiator failure history information 440 may be stored.

The differentiator model information 410 may be model information of the differentiator from which vibration information is measured. The model information may correspond to the installation year information and the workshop (heat power plant) information.

In addition to the corresponding differentiator, the differentiator model information 410 may further include differentiation information or related differentiation information of the same model. For example, when a differentiator of the same model is installed in another work site (thermal power plant), it is possible to store various information about the differentiator device, such as the information of the differentiator or other differentiator information installed by the same installer.

The differentiator part information 420 may correspond to information on each part and accessories in the differentiator. This may include arrangement position information of a specific part, replacement history information, presence of other compatible parts, supply and demand information of the part, and the like.

The differentiator structure information 430 may include design structure information of the differentiator. This could be the design structure of the differentiator, or the place of installation of the differentiator in the workplace.

The differentiator failure history information 440 may include past failure history information of the differentiator, symptom information before and after the failure, and action information on the failure. In addition to failure history information such as when the past breakdown of the differentiator that measured the vibration information occurred, which part was the problem, and the replacement of parts for the failure, The failure history information of the differentiator can also be saved in the file server by network connection or user configuration.

The differentiator failure history information 440 may include not only information on when a failure has occurred but also information on how the countermeasure is taken in a state where a failure is foreseen. The main object of the present invention is to anticipate the failure of the differentiator in advance and to take measures in advance so that the failure does not occur, but not to predict the time when the failure occurs and to cope with the failure.

5 is a flowchart illustrating a process of diagnosing a differentiator failure according to an embodiment.

Differentiation fault diagnosis can be divided into two processes. The first step is to monitor the current status of the differentiator, and the second step is to take action if a fault is detected or foreseen.

First, as described above with reference to FIGS. 1 to 4, the process of monitoring the state of the differentiator may be performed by acquiring vibration information, that is, data from a acceleration sensor attached to the differentiator, and processing and characterizing the signal. Signal processing includes regenerating the vibration data into meaningful information through identification, compression, signal expansion, and filtering, and analyzing the data based on various criteria such as time base.

Secondly, the process of action includes the process of predicting a failure and maintaining it by replacing or refurbishing the troubled part if it has not yet occurred, and repairing and resuming normal operation after the failure occurs. .

As such a series of processes are continuously accumulated and stored in the DB over time, the accuracy of predicting failure increases, and even in the event of an actual failure, there is an advantageous effect of enabling a quick repair based on existing failure history information. Can be increased.

Operations described herein are described as being sequentially operated for convenience of description, but are not necessarily limited thereto. According to an embodiment, a plurality of operations may be simultaneously performed and some operations may be different from those shown or described. The order may be implemented to be changed.

Method according to an embodiment is implemented in the form of program instructions that can be executed by various computer means may be recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on the media may be those specially designed and constructed for the purposes of the present invention, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic disks, such as floppy disks. Magneto-optical media, and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.

Although various embodiments have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements of those skilled in the art using the basic concepts of the present invention defined in the following claims are also provided. Belongs to the range.

300: acceleration sensor
301: powder
302: data logger
303: file server
304: web server
305-1, 305-2: terminal

Claims (10)

In the differential diagnosis method,
Receiving vibration information regarding the differentiator from an acceleration sensor attached to the differentiator at a terminal;
Filtering vibration information about the differentiator;
Calculating an ideal gear mesh frequency (GMF) for the differentiator using the rotational speed of the drive motor of the differentiator and the number of gear teeth of the differentiator;
Comparing the filtered vibration information with the calculated gear engagement frequency and an error within a preset range to determine whether the differentiator is broken;
Outputting, by the terminal, information on whether the differentiator has failed; And
Machine learning the time-varying change in the amount of vibration of the differentiator represented by the sum of the frequency components in the vibration information included in the vibration information, which component failed when a failure occurred, and the history of vibration information before and after the failure;
Differential failure diagnosis method comprising a. The method of claim 1,
Determining whether the differentiator is broken,
If the filtered vibration information and the gear engagement frequency (GMF) with respect to the differentiator and within the first error range, determine that it is not a failure,
If the filtered vibration information and the gear engagement frequency (GMF) with respect to the differentiator and the second error range, it is determined that the failure,
And the second error range includes the first error range. The method of claim 1,
The acceleration sensor attached to the differentiator,
Differential failure diagnosis method, characterized in that the acceleration sensor for high temperature 145 degrees Celsius. The method of claim 1,
Comparing the gear engagement frequency with respect to the differentiator and whether there is an error within a predetermined range,
Differentiator failure diagnostic method, characterized in that for comparing the error history within the predetermined range by using the failure history information and the structure information of the differentiator. The method of claim 1,
By using the natural frequency of the bearing installed in the differentiator,
The method for diagnosing a differentiator failure, characterized in that it further comprises the step of determining whether the failure of the differentiator. In the fault diagnosis system of the differentiator,
An acceleration sensor attached to the differentiator;
A terminal for receiving vibration information about the differentiator from the acceleration sensor;
The terminal,
A filtering unit for filtering vibration information regarding the differentiator;
The filtered vibration information has an error in an ideal gear mesh frequency (GMF) and a predetermined range for the differentiator calculated using the rotational speed of the drive motor of the differentiator and the gear teeth of the differentiator. A failure determining unit for determining whether or not the differentiator has a failure; And
And an output unit for outputting information on whether or not the differentiator is determined by the failure determination unit.
The change of the amount of vibration of the differentiator represented by the sum of the frequency components in the vibration information included in the vibration information over time, which component failed when a failure occurred, and the history of the vibration information before and after the failure was determined by the differentiator failure diagnosis system. Differential fault diagnosis system, machine-learned by file server within. The method of claim 6,
The failure determination unit,
If the filtered vibration information and the gear engagement frequency (GMF) with respect to the differentiator and within the first error range, determine that it is not a failure,
If the filtered vibration information and the gear engagement frequency (GMF) with respect to the differentiator and the second error range, it is determined that the failure,
And the second error range includes the first error range. The method of claim 6,
The acceleration sensor,
Differential fault diagnosis system, characterized in that the acceleration sensor for 145 degrees Celsius high temperature. The method of claim 6,
The failure determination unit,
The failure diagnosis system of the differentiator, characterized in that comparing the error history information and the structure information of the differentiator with respect to the differentiator whether there is an error within a predetermined range. The method of claim 6,
The failure determination unit,
By using the natural frequency of the bearing installed in the differentiator,
The failure diagnosis system of the differentiator, characterized in that it further comprises the step of determining whether the failure of the differentiator.

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