JPH0698205B2 - Insulation resistance monitoring device for automatic inspection system of fire extinguishing equipment - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION [Industrial application] The present invention relates to an automatic fire extinguishing equipment inspection system for measuring insulation resistance of a motor for driving a fire extinguishing pump to monitor insulation deterioration during automatic inspection of fire extinguishing equipment. The present invention relates to an insulation resistance monitoring device.

[Prior Art] Conventionally, in an automatic inspection system for fire extinguishing equipment, various inspection items including operation of the fire extinguishing pump equipment are regularly and automatically performed to check whether or not the equipment performance is maintained. There is.

For example, a motor-operated valve connected to the end of the branch pipe is opened by remote control to reduce the pressure in the branch pipe, creating the same condition as if the sprinkler head was activated by the heat of a fire, and a decrease in water pressure is generated by an alarm valve. Detect and start the fire pump.

The insulation resistance of the motor that drives the fire pump is measured during regular automatic inspection, and an alarm is issued when the insulation resistance drops below a predetermined threshold.

[Problems to be Solved by the Invention] However, in a conventional device that issues an alarm when the insulation resistance value measured during such a periodic inspection falls below a predetermined threshold value, the insulation resistance decreases and approaches the threshold value. In such cases, there is a problem that the alarm cannot be properly given.

For example, as shown in FIG. 9, in the first to fifth regular inspections, the measured value of the insulation resistance is above the alarm threshold, so no alarm is issued, but the measured value of the sixth regular inspection does not exceed the alarm threshold. Since it falls below the level, an insulation drop alarm is issued. However, the insulation resistance actually fell below the alarm threshold before the 6th periodic inspection, and since the alarm was not issued at the 5th time, the motor drive with the insulation resistance below the alarm threshold. If the above is performed, there is a high possibility that a dangerous state such as heat generation will occur due to insulation deterioration.

On the other hand, it is possible to set a high alarm threshold to give an early warning of a decrease in insulation resistance.However, for example, when the insulation resistance decreases slowly as shown in Fig. 10, the original alarm threshold Although the following is the 11th time, the alarm will be issued at the 7th time, and there is a problem that an insulation deterioration warning is issued unnecessarily quickly.

The present invention has been made in view of such a conventional problem, and a fire extinguishing equipment automatic inspection for predicting a measurement value of the next periodical inspection based on a measured value of a past insulation resistance and performing a preliminary alarm. It is an object to provide an insulation resistance monitoring device for a system.

[Means for Solving the Problems] First, the present invention is a fire extinguishing facility that periodically measures the insulation resistance of a motor 10 that drives a fire pump 32, and issues an insulation failure alarm when the insulation resistance value falls below a predetermined threshold value. Insulation resistance monitoring equipment for automatic inspection systems is targeted. Incidentally, the reference numerals in the drawings of the examples are also shown.

In the present invention as an insulation resistance monitoring device for such a fire-extinguishing equipment automatic inspection system, a linear prediction expression (single regression or The first predicting means 14 for determining the coefficient of the linear equation) and predicting the measured value of the insulation resistance obtained at the time of the next periodic inspection by the linear prediction equation, and the insulation resistance of the insulation resistance obtained at each periodic inspection. Second, a coefficient of a predetermined non-linear prediction equation (higher-order regression equation) representing the time change of the insulation resistance is determined from the measured value, and the measurement value of the insulation resistance obtained at the next periodic inspection is predicted by the non-linear prediction equation. Of the prediction means 16 and the judgment means 18 for judging which of the prediction means is significant by testing the prediction by the first prediction means 14 and the prediction by the second prediction means 16. The first selected by the judging means 18 Is configured to output a pre-alarm when the predicted value of the next periodic inspection by the second prediction means 14, 16 has determined that more than the predetermined threshold value.

[Operation] According to the insulation resistance monitoring device of the fire extinguishing equipment automatic inspection system according to the present invention having such a configuration, it is used for prediction from the past measured value of the motor insulation resistance at the time of periodic automatic inspection. Determine the coefficient of the linear prediction equation (linear equation) or non-linear prediction equation, select the prediction that has significance, and predict the measurement value of the insulation resistance that will be measured during the next periodic inspection. If it falls below the threshold, a pre-alarm will be used to warn, so it is possible to appropriately predict whether the insulation resistance will fall below the alarm threshold by the next periodic inspection, and drive the motor with the insulation resistance falling below the threshold. It is possible to prevent the occurrence of abnormalities such as heat generation.

[Embodiment] FIG. 1 is a configuration diagram of an embodiment showing one embodiment of the present invention.

In FIG. 1, 12 is an insulation resistance value storage unit,
The measured insulation resistance of the pump drive motor obtained during the periodic inspection of the automatic inspection system for fire extinguishing equipment as shown in the figure is stored.

Reference numeral 14 denotes a first predicting means, which is a linear prediction formula (single regression formula or 1) that represents a change over time of the insulation resistance from the measured value of the motor insulation resistance stored in the insulation resistance value storage unit 12 obtained at each periodic inspection. The following equation) y = α + βx The coefficient α and β are determined, and the insulation resistance measurement value y = y ₀ obtained at the time of the next periodic inspection x = x ₀ by the linear equation substituting the determined coefficient values. Predict.

Reference numeral 16 denotes a second predicting means, which is a predetermined non-linear predictive equation (higher-order regression equation) that expresses the time change of the insulation resistance from the measured value of the motor insulation resistance obtained at each periodic inspection in the insulation resistance storage unit 12. , For example, Y = A × B ^x coefficients A and B are determined, and the insulation resistance obtained at the next periodical inspection x = x ₀ by the nonlinear prediction formula in which the determined values of the coefficients A and B are substituted. Predict the value y = y ₀ .

Reference numeral 18 denotes a determination means, which tests the prediction by the first prediction means 14 and the prediction by the second prediction means 16 to determine which prediction means has significance.

Furthermore, 24 is an alarm means, and the first predicting means 14 or the second predicting means 16 selected by the judging means 18 as having significance.
The predicted value of x = x ₀ at the time of the next regular inspection by either one of the above is compared with a predetermined threshold value, and when the predicted value falls below the threshold value, the decrease of the motor insulation resistance is warned by the pre-alarm output. I am trying.

FIG. 2 is an equipment configuration diagram showing an example of a fire extinguishing equipment targeted by the automatic inspection system of the present invention.

In FIG. 2, reference numeral 10 denotes a motor, which drives the fire extinguishing pump 32 to supply the fire extinguishing water from the water storage tank 30 to the water supply main 36 under pressure. The motor 10 is controlled by the pump control panel 400.
An automatic inspection relay board 200 is installed side by side with the pump control board 400.

The water supply main 36 is connected to an elevated water tank 38 on the roof, and a branch pipe 40 is drawn out for each floor. The branch pipe 40 shows only the first floor.

The branch pipe 40 is drawn into the floor via an alarm valve 42 and connects a plurality of sprinkler heads 44. An end testing device 46 is provided at the end of the branch pipe 40. The terminal test device 46 has a sluice valve 48, a motor-operated valve 50, and an orifice 52, a pressure gauge 54, a pressure sensor 56, and a local repeater that determines the amount of water corresponding to the operation of one sprinkler head 44.
300 will be provided. The terminal testing device 46 opens the motorized valve 50 and receives the sprinkler head from the branch pipe 40 when the automatic inspection relay board 200 receives an automatic inspection command from the center-side automatic inspection management board 100 associated with pump operation. A test is conducted in which the same amount of water as when one of the 44 is operated is supplied, and at this time the motor 10 is driven by the detection signal output from the alarm valve 42 to operate the fire pump.

Immediately after the discharge of the fire pump 32, a test pipe 58 is provided, the test pipe 58 is provided with an electric valve 60 and a flow meter 62, and the electric valve 60 is provided with the sluice valve 64 provided in the water main 36 closed. By opening and operating the fire pump 32, the fire pump actually
The pressurized water from 32 is passed through the test pipe 58 to perform a pump performance test.

Further, a pressure tank 66 is installed in a branch pipe of the water supply main 36 from the secondary side of the sluice valve 64, and the pressure tank 66 receives the water pressure of the water supply main 36 to compress the internal air, and the pressure of the compressed air is When the sprinkler head operates, the detection output of the pressure sensor 68 falls below a predetermined value, the fire pump 32 is started by the motor 10, and as a result, pressurized water is supplied to the main water supply pipe 36.
Fire can be extinguished by supplying to a predetermined sprinkler head through the inside.

An electric valve 70 for partitioning is also provided in the branch pipe of the pressure tank 66, and by opening the electric valve 70, the pressure in the pressure tank 66 is released so that the fire pump starting test by the motor 10 can be performed. A water tank 72 is provided for the fire pump 32.

FIG. 3 is a system configuration diagram of the automatic inspection system of the present invention for the fire extinguishing equipment of FIG.

In FIG. 3, an automatic inspection management board 100 is provided on the center side such as a central monitoring room, and the automatic inspection management board 100 automatically inspects an automatic inspection relay board 200 installed on site by a polling method at predetermined time intervals. A command is given. As shown in FIG. 2, the automatic inspection relay board 200 includes a local repeater 300 provided in the terminal testing device 46, a fire pump control board 400,
A display operation unit 500, various terminal devices 600 such as a motor-operated valve and a pressure sensor for automatic inspection, and an analog type terminal device 700 for detecting various states such as voltage, current and pressure are connected. Further, an insulation resistance measurement control unit 20 and an insulation resistance meter 22 are connected to monitor the insulation resistance of the motor 10 according to the present invention.

The automatic inspection relay board 200 is provided with a CPU 80 for overall control, and further has an input / output interface 82 with the automatic inspection management board 100, ROM 84, RAM 86, serial interface 88 with the automatic inspection management board 100, and local relay. Interface 90 for device 300, watchdog circuit 92 for monitoring runaway of CPU 80, timer 9 for generating reference clock
4, an input / output interface 96 with the display operation unit 500, an input / output interface 98a for the terminal device 600 and the fire pump control panel 400, an input / output interface 98n for the insulation resistance measurement control unit 20 targeted by the present invention, and further an insulation resistance Appropriate analog end equipment including measurement control unit 20
Converts the detected analog signal from 700 to a digital signal A
The / D converter 102 is provided.

The insulation resistance monitoring device of the present invention shown in FIG.
For example, it is realized by the program control of the CPU 80 provided in the automatic inspection relay board 200.

Next, the monitoring process based on the prediction calculation of the motor insulation resistance according to the embodiment of the present invention shown in FIG. 1 will be described in detail with reference to the flowchart of FIG.

[Step S1] First, in step S1 of FIG. 4, the measurement data to be used for the prediction calculation of the motor insulation resistance is collected and the calculation is arranged. For example, if automatic inspection is performed once a week and the measured value of motor insulation resistance is obtained, 1
The automatic inspection is performed four times in a month, and the insulation resistance measurement value for each month is created and stored as an average value of the measurement values of the four motor insulation resistances obtained thereby. Furthermore, for the monthly measurement data, one month data is created as a six-month moving average.

For example, as shown in Fig. 5, when the month data y1 to y12 for one year from January to December is obtained, the months for three months before and after the month for which the six-month moving average is calculated are obtained. A 6-month moving average is calculated assuming that data exists. Specifically, since there are three month data before and after the April data y4, the six-month moving average Y4 is calculated as shown in the right column of FIG. In this six-month moving average calculation, the two data y2, y3 and y5, y6 on both sides of the April data y4 for which the moving average is calculated are added as they are. For the data y1 and y7 for January and July at both ends, half of the data is added and divided by 6 to obtain the moving average. This calculation is performed when the number of months for moving average is even, and when it is odd, the month data is added as it is to obtain the average value.

As shown in Fig. 5, if the insulation resistance data for each month is calculated and stored as the 6-month moving average, the average value is calculated at the stage when the 12-month month data is obtained by the 6-month moving average. Is the yearly data.

When the monthly data for 12 months can be collected by the data collection in step S1, the prediction calculation of the monthly insulation resistance is started, while on the other hand, when the annual data for several years can be collected, Insulation resistance prediction calculation starts.

Of course, the data collection period that enables the prediction calculation can be appropriately determined as necessary.

[Step S2] Next, if, for example, 12 months of monthly data can be collected by the data collection of step S1, the process proceeds to step S2, and the first
The simple regression equation (linear prediction equation, 1
Next, the coefficients α and β of y = α + βx are calculated.

The determination of the coefficients α and β of the simple regression equation y = α + βx is performed as follows.

First, the average value of x (month or year) and y (insulation resistance value) is calculated by the following formula.

Here, when x is a month, for example, the value of x = 1 to 12 is taken for the first to December of the first year, and the value of x = 13 to 24 is taken to the first to December of the second year. Is decided.

Next, the sum of squares S _xx and S _yy are obtained by the following formula.

Further, the deviation product sum S _{xy is obtained} by the following formula.

Finally, the coefficients α and β of the linear regression equation are Can be asked as

[Step S3] Next, in step S3, the correlation coefficient r and the coefficient of determination R ² are calculated according to the following equations.

Here, if the correlation coefficient r is a positive value, it is a straight line rising to the right, and if it is a negative value, it is a straight line falling to the right. That is, if the correlation coefficient r is positive, it can be determined that it is an upward tendency, and if it is negative, it can be determined that it is a downward tendency. Further, it can be evaluated that the closer the correlation coefficient r or the determination coefficient R ² is to ± 1, the higher the correlation between the actual measurement data and the straight line obtained by the simple regression equation obtained according to the equations (1) to (5).

[Step S4] Next, proceeding to step S4, an analysis of variance table is created. This analysis of variance table is used to examine whether or not the simple regression equation obtained in step S2 is an appropriate equation, and to apply another equation if it is not appropriate.

The analysis of variance table created in step S4 is as shown in FIG.

The stored data used to create this analysis of variance table corresponds to the case where a plurality of data exist at the same time (for example, a month) and are repeated. For example, there is a case where there are two insulation resistance values y1 and y2 as shown in the January data when the level i is, for example, i = 1 as shown in FIG. Therefore, such data is regarded as one-way arrangement, and the analysis of variance table shown in FIG. 6 is created according to the following calculation formula.

First, S _T is calculated according to the following equation.

Next, S _A is calculated.

For example, when S _A is calculated according to the values in the table of FIG. 7, it becomes as follows.

Further, the table shown in FIG. 8 is prepared. Here, when there is only one measurement data of the insulation resistance y at the same level, the calculation of the equations (8) to (10) is not performed and the sixth equation is not calculated.
Only the linear regression part of the ANOVA table in the figure and the ANOVA table in FIG. 8 are calculated, and the linear regression test of step S6, which will be explained later, is executed. If the linear regression is significant, step The process will proceed to S7.

[Step S5] If the analysis of variance table shown in FIGS. 6 and 8 can be created in step S4, the process proceeds to step S5 to test the significance of higher-order regression.
In this test, the variance ratio F ₀ is used to determine whether the poor fit of higher-order regression shown in the analysis of variance table of FIG. 6 is significant or insignificant.
Test with the value of. That is, If so, the higher-order regression becomes significant, and it is determined that the single regression model does not fit, and the process proceeds to step S9. on the other hand, If it is, the higher-order regression is not significant and step S6
Proceed to and perform a linear regression test.

[Steps S6, S7, S8] If it is determined in step S5 that the higher-order regression is not significant, the process proceeds to step S6 to perform a linear regression test. That is, If so, the linear regression becomes significant, and the procedure proceeds to step S7, where the simple regression equation (linear equation) y = α + βx obtained in step S2 is used to determine the insulation resistance at the next periodic inspection x = x ₀ . Calculate the predicted value y = y ₀ .

If the predicted value y ₀ by the simple regression equation is obtained in step S7, the process proceeds to step S8, and section prediction is performed.

This interval prediction is a prediction interval with a reliability rate of 100 (1-Υ) for the predicted value y = y ₀ of the insulation resistance that will be realized in the future when x = x ₀ is specified. Ask as. Here, if Υ = 0.05, the prediction interval of 95% reliability is determined.

For example, if regression analysis is performed using data up to x = 12, the 95% prediction interval of the predicted value y realized at the next time x = 13 is Becomes

In this way, for example, the minimum value of the 95% prediction interval is compared with the final warning threshold value, which is obtained by equation (15), and if the predicted value y ₀ is below the warning threshold value, a pre-alarm will be issued. .

On the other hand, in the single regression test of step S6, F ₀ <F (1,
N-2; 0.05) If (16), it is determined that the change in insulation resistance due to the passage of time is not in the period until the next periodic inspection, and the series of processing is terminated.

[Step S9] In the determination of the significance of the higher-order regression in step S5, if the above equation (13) is satisfied, the process proceeds to step S9, and the higher-order regression equation (non-linear prediction equation) Y = A × B ^x Ask for. Specifically, the values of the coefficients A and B are calculated.

That is, if the natural logarithm of both sides of Y = A × B ^x is taken, logY = lo
g (A) + x · log (B) (17) Here, y = logY, a = log (A), b = log (B)
Then, y = a + bx (18) can be expressed in a linear form. Therefore, when the linear form of the equation (18) is corrected, the values of the coefficients a and b are obtained according to the above (1) to (5) in the same manner as in step S2 described above, and the equation of logY = a + bx (19) is obtained. .

[Steps S10, S11] If a high-order regression equation is determined in step S9,
In step S10, the correlation coefficient and the coefficient of determination are obtained in accordance with the equations (6) and (7) in the same manner as in step S3, and then, in step S11, the analysis of variance table shown in FIGS. Is created in accordance with the equations (8) to (11), as in step S4.

[Steps S12, S13, S14, S15] When the creation of the analysis of variance table for the higher-order regression equation is completed in step S11, the process proceeds to step S12, as in step S5.
The higher-order regression test is performed according to the equation (13), and if significant is determined, the process proceeds to step S13, and the higher-order regression formula obtained in step S9 is substituted by x = x ₀ at the time of the next automatic inspection. The predicted resistance value y = y ₀ is obtained, and the section prediction is performed by the equation (15) as in step S8, and when 95% reliability is obtained, it is compared with the alarm threshold value.

On the other hand, if it is determined in step S12 that there is no pseudo regression of higher-order regression, the process proceeds to step S14, and steps S3 and S
The correlation coefficients of the simple regression equation and the higher-order regression equation obtained in 10 are compared. Here, if the correlation coefficient of the simple regression equation (first-order equation) is large, the process proceeds to S15, and the first-order section prediction is performed as shown in steps S7 and S8. On the other hand, when the correlation coefficient of the higher-order regression equation is large, the process proceeds to step S13 to perform the higher-order section prediction. That is, even if both the simple regression equation and the higher-order regression equation have no significance, the one with the larger correlation value is used to make the minimum necessary prediction.

Although the single regression test is performed in step S6 and the higher-order regression test is performed in step S12, a data abnormality alarm may of course be output when the result of the test is determined to be insignificant.

[Effects of the Invention] As described above, according to the present invention, a linear prediction equation (linear equation, single regression equation) or By determining the coefficient of the nonlinear prediction formula (higher-order formula) and selecting the prediction that has significance, you can predict the measurement value of the insulation resistance that will be measured during the next periodic inspection. If it falls below the range, a pre-alarm will give an alarm.Before the next periodical inspection, drive the motor with the insulation resistance falling below the threshold value and cause abnormalities such as heat generation. It can be prevented.

[Brief description of drawings]

FIG. 1 is a block diagram of an embodiment showing an embodiment of the present invention; FIG. 2 is a block diagram of a fire extinguishing facility targeted by the present invention; FIG. 3 is a system block diagram of an automatic inspection system of the present invention; FIG. 4 is a flow chart showing the processing operation of the present invention; FIG. 5 is an explanatory diagram of 6-month moving average data; FIG. 6 is an explanatory diagram of an ANOVA table obtained by the present invention; FIG. 8 is an explanatory view of the second analysis of variance table; FIGS. 9 and 10 are explanatory views showing problems of conventional insulation resistance measurement. 10: Motor 12: Insulation resistance value storage section 14: First prediction means 16: Second prediction means 18: Judgment means 20: Insulation resistance measurement unit 22: Insulation resistance meter 24: Alarm means 30: Water tank 32: Fire extinguishing Pump 36: Water main 38: Elevated water tank 40: Branch pipe 42: Alarm valve 44: Sprinkler head 46: Terminal test device 48, 64: Gate valve 50, 60, 70: Motorized valve 52: Orifice 56, 68: Pressure sensor 58: Test piping 62: Flow meter 66: Pressure tank 80: CPU 100: Automatic inspection management board 200: Automatic inspection relay board 300: Local relay 400: Fire pump control board

Claims (1)

[Claims] 1. An insulation resistance monitoring device for an automatic inspection system for a fire extinguishing system, which periodically measures the insulation resistance of a motor for driving a fire extinguishing pump and issues an alarm as insulation failure when the insulation resistance value falls below a predetermined threshold value. , The coefficient of the linear prediction formula representing the time change of the insulation resistance is determined from the measured value of the insulation resistance obtained at each periodic inspection, and the measured value of the insulation resistance obtained at the next periodic inspection is calculated by the linear prediction formula. First predicting means for predicting; determining a coefficient of a predetermined non-linear prediction equation representing a time change of the insulation resistance from the measured value of the insulation resistance obtained at each periodic inspection, and using the non-linear prediction equation Second predicting means for predicting a measured value of insulation resistance obtained at the time of regular inspection; Which predicting means is significant by testing the prediction by the first predicting means and the prediction by the second predicting means Determining means for determining; and outputting a pre-alarm when the predicted value at the time of the next periodical inspection by the first or second predicting means selected by the determining means is predicted to be less than or equal to the predetermined threshold value. Insulation resistance monitoring device for automatic inspection system of fire extinguishing equipment.