JP2016003875A - Motor abnormality detecting system, motor abnormality detecting method, and motor abnormality detecting program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a motor abnormality detecting system for detecting any abnormality, such as occurrence of bearing flaw, in motors in a power plant.SOLUTION: A pump abnormality detecting system for detecting any abnormality in pumps in a power plant comprises: model memory means for storing models of normally operating action data during the absence of any pump abnormality, which shows mutual relations among output values of individual vibration sensor means regarding a plurality of vibration sensor means for measuring pump vibration; and motor abnormality detecting means that processes comparison of correlations figured out from models recorded in the model memory means and measured data in a random period obtained from a plurality of vibration sensor means to monitor occurrence of abnormal values with reference to a decay extent of correlation, and extracts a prescribed extent of correlation decay as an event to be notified.

Description

  The present invention relates to a sensing technique for detecting a motor abnormality, and more particularly to a motor abnormality detection system, a motor abnormality detection method, and a motor abnormality detection program for detecting an abnormality of a motor used in a power plant.

  Various motors used in power plants have so far analyzed the output of various sensors (current, voltage, temperature, etc.) attached to the motor in order to identify the cause of the abnormality. In addition, a method of confirming the replacement timing and abnormality of parts etc. by relying on the past experience and expertise of the observer is also performed. Monitoring by an information processing apparatus has also been performed.

  A technique related to motor abnormality monitoring is described in Patent Document 1, for example. This document accepts output signals from various sensors (current, voltage, temperature, etc.) to monitor the current motor status, and predicts the winding temperature used in the motor as the scheduled output is input. An arithmetic device for predicting a deterioration state of a winding is disclosed.

  In these information processing analyses, the problem location is identified and the cause of the abnormality is determined based on various predictions, making use of motor ratings, component specifications, operating conditions, and engineer experience.

JP 05-269719 A

  In a large-scale system such as a power plant system, during operation and periodic inspections, locations that are likely to cause various problems and abnormal locations are identified and switched to a spare system, parts replacement, priority inspections, etc. Is planned as appropriate.

  In such an operation mode, if anomaly detection that leads to detection of minor anomalies or failure prediction can be performed during operation, it will be beneficial in terms of operation.

  On the other hand, the monitoring of the motor alone by the various sensor groups attached to the motor as described above can identify a rough abnormal event, but has an improvement in detecting a minor abnormality and predicting a failure.

  In addition, it is possible to detect breakage and deterioration of parts by artificially setting the analysis target as an important part of the motor and performing frequency analysis etc. on the measured values obtained from various sensors.

  However, when the analysis target is artificially determined to be an important part or a suspicious part as described above, various advance preparations based on design information and installation information and an analysis specialist are required for abnormality detection.

  As a result, the larger the system, the more difficult it is to exhaustively verify and foresee the entire system.

  Further, monitoring by visual inspection or batting inspection is a human subjective evaluation, and quantitative evaluation is difficult. In addition, it is difficult to perform comprehensive and continuous monitoring of all devices.

  Furthermore, the power plant system includes a motor installed at a position where it is difficult for humans to enter.

  SUMMARY OF THE INVENTION An object of the present invention is to provide a motor abnormality detection system that can conveniently detect abnormality that occurs in a motor in a power plant.

  The motor abnormality detection system according to the present invention showed a mutual relationship between the output values of the individual vibration sensor means with respect to a plurality of vibration sensor means for measuring the vibration of the motor provided in the power plant, Model storage means for storing a model of normal operation data in which no abnormality has occurred in the motor, correlation obtained from the model recorded in the model storage means, and an arbitrary obtained from the plurality of vibration sensor means Motor abnormality detection means for comparing the correlation between the measurement data of the period, monitoring the occurrence of an abnormal value based on the amount of correlation collapse, and extracting a predetermined amount of relationship breakdown as a notification event; It is characterized by providing.

  A motor abnormality detection method according to the present invention includes a plurality of vibration sensor means for measuring vibrations of a motor provided in a power plant, collects and accumulates measurement data measured by the vibration sensor means, From the normal operation data accumulated by the sensor information accumulating means in which no abnormality has occurred in the motor, a correlation between each measurement data is extracted, and a model of the normal operation data is constructed. An abnormality that is recorded in the model storage means and is compared with the correlation obtained by the model and the correlation between the measurement data obtained from the plurality of vibration sensor means for an arbitrary period, and is based on the amount of collapse of the correlation. The occurrence of a value is monitored, and a predetermined amount of relationship collapse is extracted as a notification event.

  In the motor abnormality detection program according to the present invention, the control unit of the information processing apparatus is between the output values of the individual vibration sensor means with respect to the plurality of vibration sensor means for measuring the vibration of the motor provided in the power plant. Correlation obtained by the model stored in the model storage means storing the model of normal operation data in which no abnormality has occurred in the motor, showing the mutual relationship, and obtained from the plurality of vibration sensor means Motor abnormality detection means that compares the correlation between measured data for any period of time, monitors the occurrence of abnormal values based on the correlation breakdown amount, and extracts a predetermined amount of relationship breakdown as a notification event It is made to operate as.

  ADVANTAGE OF THE INVENTION According to this invention, the motor abnormality detection system which made it possible to detect the abnormality which arises in the motor in a power station convenient.

1 is a block diagram showing a motor abnormality detection system according to a first embodiment of the present invention. It is a flowchart which illustrates the processing operation of the motor abnormality detection system concerning a 1st embodiment. It is a block diagram which shows the motor abnormality detection system concerning the 2nd Embodiment of this invention. It is explanatory drawing which shows the motor abnormality detection system concerning an Example. It is the flowchart which illustrated the processing operation of the sensor information storage part. It is the flowchart which illustrated the processing operation of the model construction part. It is the flowchart which illustrated the processing operation of the motor abnormality detection part.

  Hereinafter, a motor abnormality detection system according to an embodiment of the present invention will be described with reference to the drawings.

[First Embodiment]
FIG. 1 is a block diagram illustrating a motor abnormality detection system 10 according to the first embodiment.

  The motor abnormality detection system 10 includes a motor abnormality detection unit 20 and a model storage unit 30 that receive a plurality of vibration sensor units that measure vibrations of a motor provided in a power plant as inputs and detect an abnormality. Contains and is composed.

  The model storage unit 30 records a normal-time model indicating the relationship between the measurement data between the output values of an arbitrary period for each vibration sensor (1-1, 1-2,... 1-n). Has been. The normal model may be generated from the operation data of the vibration sensor group that does not indicate an abnormal operation of the motor as one method.

  The motor abnormality detection unit 20 obtains a correlation between measurement data of measurement data of an arbitrary period of a plurality of vibration sensor means to be monitored, such as measurement values having real-time characteristics or accumulated measurement values. The comparison processing is performed with the correlation obtained from the normal model recorded in the model storage unit 30. The motor abnormality detection unit 20 detects a predetermined amount of relationship breakdown based on the correlation collapse amount (amount of mismatched portion) in the comparison result, and extracts it as a notification event when an abnormal value occurs. Moreover, you may extract abnormality generation | occurrence | production on the basis of how to collapse with the amount of collapse. Moreover, you may consider the increase / decrease (change amount) per hour of collapse amount. The extracted notification event may be recorded in a predetermined storage unit, automatically notified to a person in charge, or notified to another system. Depending on the degree of abnormality, a signal for automatically stopping the motor or increasing or decreasing the output may be output. You may make it notify as an alerting | reporting event which has an urgency level, when the collapse amount and change amount per time exceed predetermined value.

Next, an operation example of the motor abnormality detection system 10 according to the first embodiment will be described.
FIG. 2 is a flowchart illustrating the processing operation of the motor abnormality detection system 10 according to the first embodiment.

  The motor abnormality detection unit 20 receives measurement data of a vibration sensor group to be monitored (S101). The measurement data input at this time may be real-time measurement data or measurement data collected in the past in an arbitrary section.

  In parallel, the motor abnormality detection unit 20 acquires a normal-time model recorded in the model storage unit 30 (S102).

  The motor abnormality detection unit 20 identifies the received sensor value group and obtains a correlation (S103).

  Next, the motor abnormality detection unit 20 compares the obtained correlation with the correlation obtained from the normal model, calculates a value indicating the collapse of the relationship, and whether the relationship is broken or not. Discrimination processing such as / where is / when is / is performed (S104).

The motor abnormality detection unit 20 notifies a predetermined device as a notification event when the relationship collapse / progress of collapse / remarkable collapse at a predetermined position / etc. Is a predetermined amount or more (S105).
Thus, according to the present embodiment, a detection system that can easily and accurately detect an abnormality of the motor from the measurement values of the plurality of vibration sensors installed in the motor can be obtained. At this time, even finer events than the previous system can be captured. Further, as in the conventional system, it is possible to determine an abnormality without requiring an analysis focusing on a specific portion or a determination by an analysis specialist. In addition, a real-time monitoring system can be constructed with low resources.

  In addition, this system should just analyze for every motor, and can also monitor several motors collectively and simultaneously.

[Second Embodiment]
Next, a second embodiment will be described. In addition, description is simplified or abbreviate | omitted about the same location as 1st Embodiment.
The second embodiment includes means for constructing a normal model in which the abnormality of the motor used in the first embodiment does not occur.

  FIG. 3 is a block diagram showing the control rod monitoring system 10 of the second embodiment.

  The motor abnormality detection system 10 includes a motor abnormality detection unit 20, a model storage unit 30, a sensor information storage unit 40, and a model construction unit 50.

  The sensor information accumulation unit 40 collects and accumulates measurement data groups measured by a plurality of vibration sensors associated with individual motors.

The model construction unit 50 obtains between each measurement data for each data group from each operation data group at normal time when no abnormality has occurred in one or all motors accumulated by the sensor information accumulation unit 40. The correlation is extracted, and a normal model of each motor of the normal operation data is constructed and recorded in the model storage unit 30.
The model construction unit 50 may generate and process an approximate expression and a fit value indicating the correlation between the measurement data measured by the vibration sensor group for each motor, with respect to the normal model of each motor. The generated approximate expression and fit value are recorded in the model storage unit 30 as a model of each motor. Further, the model construction unit 50 preferably has an algorithm that is used as a normal model when the generated fit value is compared with a predetermined threshold value and the fit value is equal to or larger than the predetermined threshold value.

  By generating a model using an approximate expression in this way, processing resources can be reduced and real-time processing performance can be improved.

In this way, when using the normal model using the approximate expression, the motor abnormality detection unit 20 may be operated for each motor as follows.
The motor abnormality detection unit 20 uses, for example, one of measurement data measured by an arbitrary vibration sensor (for example, a sensor in the vicinity of an element of interest such as the periphery of a bearing) for each motor as an approximate expression of a normal model of the motor. substitute. By this substitution, each predicted value that should be measured by the vibration sensor can be calculated.
Next, the motor abnormality detection unit 20 determines the amount of collapse of the correlation by comparing the quantity of the difference between the predicted value and the actual measurement value with a predetermined threshold value. Abnormality is determined when the amount of correlation collapse exceeds a predetermined threshold.

  Thus, according to the present embodiment, a detection system that can easily and accurately detect an abnormality of the motor from the measurement values of the plurality of vibration sensors installed in the motor can be obtained. At this time, even finer events than the previous system can be captured. Further, as in the conventional system, it is possible to determine an abnormality without requiring an analysis focusing on a specific portion or a determination by an analysis specialist. In addition, a real-time monitoring system can be constructed with low resources.

  Next, the present invention will be described with reference to one embodiment. This example is a system capable of monitoring in real time the slight scratches and grease consumption in the bearing that may occur in one motor in the second embodiment. A plurality of vibration sensors are arranged in the vicinity of a bearing (bearing) with the pump.

  In other words, the system of this embodiment operates as a bearing flaw detection device.

  As shown in FIG. 4, a motor 2 that operates the pump 3 and a control device 4 that adjusts the output of the motor 2 are provided as part of the power generation equipment. The pump 3 obtains power from the motor 2 and sends out fluid. The adjuster in the figure adjusts the fluid velocity in the pipe.

  The motor abnormality detection system (bearing flaw detection device) monitors the motor 2. Vibration sensors 1-1 and 1-2 are attached to the motor 2. At least one of the vibration sensors is provided so as to measure vibration in the vicinity of the bearing. The present embodiment will be described using a minimum of two vibration sensors. Three or more vibration sensors may be installed in the motor 2. Other types of sensors (ammeters, voltmeters, temperature sensors, etc.) may also be provided. Moreover, it is good also as a structure which takes in measured values, such as an electric current, a voltage, and rotation speed, from the control apparatus 4. FIG. In the present embodiment, the control device 4 that adjusts the output of the motor 2 controls the motor output based on the rotational speed of the motor shaft. The regulator will be described as a control valve that controls the water pressure in the pipe.

  The motor abnormality detection system 10 receives the output values of the vibration sensors 1-1 and 1-2 as inputs and detects an abnormality. Further, the output of the control device 4 is used together with the output values of the vibration sensors 1-1 and 1-2 for the construction of the normal model.

  In the sensor information storage unit 40, the output values of the vibration sensors 1-1 and 1-2 and the time data of the measurement time are sequentially stored in association with each other.

  The model construction unit 50 obtains measurement data for a certain period of time when no bearing damage has occurred from the sensor information storage unit 40, extracts the correlation of the measurement data together with the output value of the control device 4, and is normal. Build a time model.

  The motor abnormality detection unit 20 is operated as a bearing flaw detection means. The motor abnormality detection unit 20 receives the extracted correlation from the model storage unit 30 and also receives measurement data for a predetermined time from the sensor information storage unit 40, and detects the occurrence of bearing flaws in the motor 2 based on these. .

  The motor abnormality detection unit 20 includes a notification unit that notifies a device such as an alarm device or a display as necessary, and notifies the occurrence of the detected bearing flaw with an alarm sound or a message. Further, for example, it is possible to log only a scratch having a predetermined value or less.

Next, the operations of the vibration sensors 1-1 and 1-2 and the sensor information storage unit 40 will be described with reference to FIG.
First, the vibration sensors 1-1 and 1-2 always measure the vibration of the motor 2 (step S501).
Next, the sensor information storage unit 40 receives each measurement data measured by the vibration sensors 1-1 and 1-2 together with time data of the measurement time (step S502).
Next, the sensor information storage unit 40 stores the data (measurement data and time data) received from the vibration sensors 1-1 and 1-2 (step S503).
The operations in steps S501 to S503 described above are always repeated.
The accumulated information is composed of output values from the vibration sensors 1-1 and 1-2 and measurement times, and is generally referred to as time series data.
As a mode of information storage by the sensor information storage unit 40, a mechanism such as a relational database may be used, or a simple text file may be used.

Next, the operation of the model construction unit 50 will be described with reference to FIG.
First, during a normal operation in which no bearing damage is generated in the motor 2, the control device 4 is adjusted to change the rotation speed of the motor 2 little by little (step S601). At this time, based on the control state of the adjuster, the flow velocity in the pipe, the pressure, and the like, the operation is performed so that the water pressure in the pipe increases and decreases.

Next, the vibration sensors 1-1 and 1-2 read the vibrations changed by the operation of the control device 4, and the sensor data storage unit 40 stores the measurement data and time data as needed (step S602, step of FIG. 5). S501 to S503).
Next, the model construction unit 50 receives measurement data from the sensor information storage unit 40 during a period in which the rotational speed is changed by operating the control device 4 (a period in which variation data during normal operation is recorded) (Step S1). S603).
Next, the model construction unit 50 identifies whether there is a correlation between the vibration values measured for the motor 2 from the received measurement data (step S604).

  Here, the model construction unit 50 uses a mathematical formula-based approximation such as B = f (A) as a correlation between two points from time series data of a fixed time of two points obtained from the sensor information storage unit 40. Generate an expression. As a method for generating the approximate expression, for example, a method called linear regression may be used. Various other methods have been proposed, and any method may be adopted.

  Further, the model construction unit 50 generates a fit value, which is an index of how much the approximate expression can approximate the actual data, from the generated approximate expression and the time series data used at the time of generation. When approximation is performed using the least square method as linear regression, the fit value can be a determination coefficient in the least square method.

  Next, the fit value is compared with a predetermined threshold value, and if it is equal to or greater than the threshold value (N in step S605), the relationship between the two points (approximation formula and fit value) is stored as a model, and the process is terminated ( Step S606). If the fit value is less than or equal to the threshold value (Y in step S605), the process ends. When three or more vibration sensors are used, a model may be generated using a relationship between multiple points. Hereinafter, the stored relationship between two normal points is referred to as a model.

Next, the operation of the motor abnormality detection unit 20 that performs bearing flaw detection will be described with reference to FIG.
As an operating condition of the motor abnormality detection unit 20, a normal model is constructed, and measurement data from the vibration sensors 1-1 and 1-2 are constantly accumulated in the sensor information accumulation unit 50. .

  First, the motor abnormality detection unit 20 acquires measurement data for a past certain period of time from a certain time t when it is desired to detect a bearing flaw from the sensor information storage unit 50 (step S701). Here, a certain time t is a time slightly past the current time. If the measurement data at the current time is always stored in the sensor information storage unit 50, the time t may be the current time.

  Next, a normal model stored in the model construction unit 50 is acquired (step S702).

  Next, the relationship between the vibration sensors 1-1 and 1-2 at two points A and B (approximate expression B = f (A) and fit value) is acquired from the model (step S703).

  Next, the motor abnormality detection unit 20 substitutes the value of one vibration sensor A included in the measurement data obtained from the sensor information storage unit 50 into the approximate expression B = f (A), and the other vibration sensor as a result. A predicted value of B is obtained (step S704).

  Next, the motor abnormality detection unit 20 calculates a difference R between the calculated predicted value of the vibration sensor B and the actual measurement value of the vibration sensor B obtained from the sensor information storage unit 40 (step S705).

  Next, when the difference R exceeds a predetermined threshold (Y in step S706), the motor abnormality detection unit 20 determines that the relationship of the approximate expression B = f (A) is not established, and the motor 2 determines that there is a possibility that a bearing flaw has occurred, and executes a predetermined notification action (step S707).

  Next, the motor abnormality detection unit 20 advances the time t by a predetermined time Δt after notifying in step S707 and when the difference R does not exceed a predetermined threshold (step S706N). Then, the processing from step S701 is repeated.

  Here, Δt is set from an interval at which bearing flaws are desired to be detected, but needs to be larger than the interval at which the vibration sensors 10 and 20 notify the sensor information storage unit 50 of the measurement results.

  Further, the motor abnormality detection system 10 obtains models in advance such as the approximate expression B = f (A), the approximate expression A = f (B), and the respective fit values, and the vibration sensor A and the vibration sensor. The vibration value used as the reference of B may be switched and monitored sequentially.

  In the bearing flaw detection device of the present embodiment obtained in this way, the bearing flaw is detected by the motor 2 using the measurement data of the existing vibration sensors 1-1 and 1-2 installed to measure vibration. It is possible to detect that has occurred. In addition, grease consumption can be recognized. In addition, when using three or more measured values, there is also an event in which the propagation source of the collapse can be identified by bilateral analysis.

  The correlation with various sensors related to the motor may be similarly analyzed. For example, a detection value of a motor control system, a pressure sensor that measures a pressure at an input port of a pump or a pressure at an output port, an audible sound sensor, or the like can be used.

  Further, in the bearing flaw detection device of the present embodiment, it can be detected at the initial stage of occurrence of the bearing flaw. It is also possible to detect that the wear of the grease has exceeded a predetermined value.

  Also, it is possible to detect a bearing abnormality at an arbitrary location without consuming a large amount of computing resources. This detection includes foreseeing the occurrence of problems.

  It is very difficult for human ability to recognize an initial stage wound without using such an analysis technique. Moreover, it is necessary to make it into a test object as an abnormal part, and it is very difficult to do all to an installation as resource allocation. On the other hand, if the value of each sensor is monitored as in this method, there is an advantage that various matters such as identification of an abnormal part and the degree of abnormality can be intensively monitored over a wide range.

  In addition, what is necessary is just to implement | achieve each part of a motor abnormality detection system using the combination of hardware and software. In the form of a combination of hardware and software, a motor abnormality detection program is developed in the RAM, and each unit is realized as various means by operating hardware such as a control unit (CPU) based on the program. The program may be recorded in a fixed manner on a storage medium and distributed. The program recorded on the recording medium is read into a memory via a wired, wireless, or recording medium itself, and operates a control unit or the like. Examples of the recording medium include an optical disk, a magnetic disk, a semiconductor memory device, and a hard disk.

  In other words, the information processing apparatus operating as the motor abnormality detection system is based on the motor abnormality detection program developed in the RAM, and motor abnormality detection means, model storage means, sensor information storage means. It can be realized by operating the control unit as a model construction means.

  As described above, the information processing apparatus to which the present invention is applied can provide a motor abnormality detection system that can conveniently detect abnormality that occurs in a motor in a power plant.

  The specific configuration of the present invention is not limited to the above-described embodiments and examples, and may be changed such as separation / merging of block configurations and replacement of procedures without departing from the gist of the present invention. The above description is not intended to limit the present invention. For example, each component (such as a motor abnormality detection unit and a sensor information storage unit) may be configured by an information processing device.

DESCRIPTION OF SYMBOLS 1 ... Vibration sensor 2 ... Motor 3 ... Pump 4 ... Control apparatus (adjustment means)
DESCRIPTION OF SYMBOLS 10 ... Motor abnormality detection system 20 ... Motor abnormality detection part (motor abnormality detection means)
30 ... Model storage unit (model storage means)
40: Sensor information storage unit (sensor information storage means)
50 ... Model building section (model building means)

Claims (10)

Regarding the plurality of vibration sensor means for measuring the vibration of the motor provided in the power plant, the normality in which no abnormality has occurred in the motor, showing the mutual relationship between the output values of the individual vibration sensor means Model storage means for storing a model of hourly motion data;
The correlation obtained by the model recorded in the model storage means and the correlation between the measurement data of the arbitrary period obtained from the plurality of vibration sensor means are compared, and the amount of correlation collapse is used as a reference. Motor abnormality detecting means for monitoring occurrence of abnormal values and extracting a predetermined amount of relationship breakdown as a notification event;
A motor abnormality detection system comprising: Sensor information storage means for collecting and storing measurement data measured by the plurality of vibration sensor means;
A correlation between each measurement data is extracted from normal operation data accumulated by the sensor information accumulating means and no abnormality occurs in the motor, and a model of the normal operation data is constructed. Model building means for recording in the model storage means;
The motor abnormality detection system according to claim 1, further comprising:   2. The model construction unit generates an approximate expression and a fit value indicating a correlation between measurement data measured by the vibration sensor unit, and stores the approximate expression and the fit value as a model. Or the motor abnormality detection system of Claim 2.   4. The model construction unit compares the generated fit value with a predetermined threshold value, and stores the model when the fit value is equal to or greater than a predetermined threshold value. The motor abnormality detection system described in 1.   The motor abnormality detection system according to any one of claims 1 to 4, further comprising an adjusting unit that changes a rotational speed of the motor as required.   The motor abnormality detection system according to any one of claims 1 to 3, further comprising notification means for notifying a predetermined target when abnormality is detected by the motor abnormality detection means.   The motor abnormality detection system according to any one of claims 1 to 3, wherein the motor operates a pump that delivers liquid.   The motor abnormality detection means substitutes one of the measurement data measured by the vibration sensor means into the approximate expression generated by the model construction means, so that other than the vibration sensor means used for the substitution Calculating a predicted value of one or a plurality of measurement data, comparing the predicted value with an actual measurement value of each of the plurality of vibration sensors, and calculating the correlation by an amount whose difference exceeds a predetermined threshold value; The motor abnormality detection system according to claim 6 or 7, wherein the amount of collapse of the motor is determined. A plurality of vibration sensor means for measuring the vibration of a motor provided in the power plant is provided, and measurement data measured by the vibration sensor means are collected and accumulated,
A correlation between each measurement data is extracted from normal operation data accumulated by the sensor information accumulating means and no abnormality occurs in the motor, and a model of the normal operation data is constructed. Recorded in the model storage means,
The correlation obtained by the model and the correlation between the measurement data of arbitrary periods obtained from the plurality of vibration sensor means are compared to monitor the occurrence of an abnormal value based on the correlation collapse amount, A motor abnormality detection method, wherein a predetermined amount of relationship breakdown is extracted as a notification event. The control unit of the information processing device
Regarding the plurality of vibration sensor means for measuring the vibration of the motor provided in the power plant, the normality in which no abnormality has occurred in the motor, showing the mutual relationship between the output values of the individual vibration sensor means The correlation obtained by the model recorded in the model storage means storing the model of the hourly motion data is compared with the correlation between the measurement data of any period obtained from the plurality of vibration sensor means, and the correlation is performed. A motor abnormality detection program that monitors the occurrence of an abnormal value based on the amount of disruption of relationship and operates as a motor abnormality detection means that extracts a predetermined amount of relationship disruption as a notification event.

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