JPH06229791A - Abnormality diagnostic method - Google Patents

Abstract

PURPOSE:To provide an abnormality diagnostic method which can determine an abnormal machine or sensor accurately in a plant or machinery. CONSTITUTION:A machine model 53-1 and an actuator model 54-1 calculate a first prediction data similar to the detection data of a sensor 9 based on detection data from sensors 6, 7, 8. A machine model 53-2 and an actuator model 54-2 calculate a second prediction data similar to the detection data of the sensor 9 based on the detection data of the sensors 6, 8. A diagnostic section 56 then compares the detection data of the sensor 9, the first prediction data, and the second prediction data each other and combine the comparison results to diagnose abnormality in the sensors 6-9.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an abnormality diagnosis method,
In particular, it is useful when applied to abnormality diagnosis of a plant or apparatus having a plurality of devices, actuators and sensors.

[0002]

2. Description of the Related Art FIG. 5 is an explanatory diagram of an abnormality diagnosing method according to a conventional technique.

As shown in FIG. 1, the plant 1 includes equipments 2, 3, an actuator 4, and sensors 5, 6, 7, 8,
Have 9. Of these, the devices 2 and 3 are the control device 10
It operates based on the command signals a and b output from the actuator, and sends a medium such as pressure oil to the actuator 4. Actuator 4
Operates according to the medium sent. The sensors 5, 7, 9 detect the operating state quantities of the devices 2, 3 and the actuator 4 and output detection data. Sensor 9
The detection data of is fed back to the control device 10. The sensors 6 and 8 respectively detect the state quantities of the medium, such as the hydraulic pressure in the pipe section between the device 2 and the device 3 and between the device 3 and the actuator 4, and output detection data.

That is, in the plant 1, the control device 10 performs feedback control so that the actuator 4 performs a predetermined operation.

Next, the abnormality diagnosing device 11 is a device for realizing the abnormality diagnosing method according to the prior art, and includes the device models 12 and 13, the actuator model 14, and the comparator 1.
It has 5, 16, 17, 18, 19 and a diagnostic unit 20.

Of these, the device models 12 and 13 are
It is a mathematical model for inputting command signals a and b from the control device 10 and calculating the operating state quantities of the devices 2 and 3 by a mathematical formula based on the command signals a and b. Calculation data 1 representing the same kind of operating state quantity, etc.
2a, 12b, 13a, 13b are output. The actuator model 14 is a mathematical model that inputs the calculation data 13b and calculates the operating state quantity of the actuator 4 by a mathematical formula based on the calculated data 13b, and represents the same operating state quantity as the detection data of the sensor 9. The data 14a is output. The comparators 15 to 19 are sensors 5 to 5.
9 detection data and calculated data 12a, 12b, 13
a, 13b, and 14a are compared with each other, and the comparison result is output. The diagnosis unit 20 inputs the comparison result, determines whether or not the comparison result largely deviates, and thereby diagnoses whether or not there is an abnormality in the devices 2, 3 and the sensors 5-9.

The abnormality diagnosing method realized by the abnormality diagnosing device 11 as described above is as follows.
Similar to the devices 2 and 3, the command signals a and b output from the control device 10 are respectively input, and the same command signals a and b are input.
Based on the above, the operating state quantities of the devices 2 and 3 and the state quantity of the medium in the pipe section are calculated, the calculated value of the operating state quantity is used as calculation data 12a and 13a, and the calculated value of the state quantity is calculated data 12b. , 13b.

Further, in the actuator model 14,
The calculation data 13b is input, the operating state quantity of the actuator 4 based on the medium sent from the device 3 is calculated, and the calculated value of the operating state quantity is calculated data 14
Output as a.

Next, in the comparators 15 to 19, the sensor 5
9 to 9 and calculated data 12a, 12b, 13
a, 13b, and 14a are compared with each other, and the comparison result is output. For example, the comparator 15 compares the detection data of the sensor 5 with the calculation data 12a of the same type as the detection data, and outputs the comparison result.

Finally, in the diagnostic section 20, the comparators 15 to
If the comparison results of 19 are input and it can be determined that the comparison results are substantially equal to each other, it is diagnosed that the devices 2, 3, the actuator 4 and the sensors 5 to 9 are all normal, while the comparison results are compared. When it can be determined that any one of the two is largely deviated, the devices 2, 3, the actuator 4 or the sensors 5 to 5 corresponding to the comparison result of the large deviance.
9 is diagnosed as abnormal. For example, when it can be determined that the comparison result of the comparator 15 is significantly deviated, it is diagnosed that the device 2 or the sensor 5 is abnormal.

[0011]

In the abnormality diagnosing method according to the prior art as described above, for example, when only the device 2 is abnormal, the comparison result of the comparator 15 is largely deviated, and as a result, the device 2 or It is diagnosed that the sensor 5 is abnormal. Here, when the device 2 is abnormal, the state quantity of the medium output from the device 2 changes, and accordingly, the detection data of the sensors 6 and 8 that detect the state quantity of the medium also change. . For this reason, the comparison results of the comparators 16 and 18 are also greatly deviated, and as a result, the sensors 6 and 8 are diagnosed as abnormal even though they are actually normal.

As described above, in the abnormality diagnosing method according to the prior art, the actuator 4, the devices 2, 3 and the sensors 5-9 are used.
There is a problem that it may not be possible to accurately diagnose in which of the two cases the abnormality has occurred.

In view of the above-mentioned conventional technique, it is an object of the present invention to provide an abnormality diagnosing method capable of accurately diagnosing which equipment or sensor in a plant or apparatus has an abnormality.

[0014]

In order to achieve the above object, the structure of the present invention detects a plurality of devices and actuators that are remotely or automatically controlled by a control device, and the operating state quantities of these devices and actuators. A plant or apparatus having various sensors, which is an abnormality diagnosis method for diagnosing an abnormality of the device and the sensor, and is attached to the device or the actuator, and the detection data of the sensor for detecting the operating state quantity of these, From the detection data of a plurality of sensors that detect the state quantities of the plurality of sensors, the first prediction data that represents the predicted value of the operating state quantity of the device or the actuator to be compared is calculated, and the plurality of other state quantities are detected. A second value representing the predicted value of the operating state quantity from the detection data of the sensor and the command signal output from the control device. Equipment of the plant or device by calculating measurement data and then comparing the detection data of the sensor that detects the operating state quantity, the first prediction data and the second prediction data, and combining the comparison results. And diagnosing a sensor abnormality.

Further, the detection data of the sensor for detecting the operating state quantity of the device or the actuator to be compared, the first prediction data and the second prediction data are respectively compared, and when the comparison results are combined, they are compared. An equality function for determining the degree of equality of the two data from the difference between the two data is provided, and the value obtained by the equality function is used as the first certainty factor, and from the difference between the other two data to be compared. The other 2
An inequality function for determining the degree of inequality of two data is provided, and the value obtained by the inequality function is used as the second certainty factor. In the comparison combination, "two data are equal and the other two data are unequal. In the case of combining with the word “Katsu” like “”, the smaller value of the first certainty factor and the second certainty factor is selected,
When this is used as an anomaly certainty that indicates the degree of anomaly of a device or sensor, and is combined with the word "or" such as "the two data are equal or the other two data are unequal" in the comparison combination. In the above, the larger value of the first certainty factor and the second certainty factor is selected as the abnormal certainty factor indicating the degree of abnormality of the device or the sensor.

[0016]

According to the present invention having the above-described structure, when, for example, only one of the devices becomes abnormal, the detection data of the sensor for detecting the operation state amount of the device or the actuator to be compared and the first prediction data are provided. When comparing the detected data and the second predicted data, the two are largely deviated and become unequal. Therefore, these comparison results are combined to diagnose that the device is abnormal.

A first certainty factor is obtained from the difference between the detected data and the first prediction data, and an equal sign function, and from the difference between the detected data and the second prediction data and an inequality function. The second certainty factor is obtained, and the smaller one of the first certainty factor and the second certainty factor, whichever is smaller, or the larger one is selected to obtain the abnormal certainty factor of the device.

[0018]

Embodiments of the present invention will now be described in detail with reference to the drawings.

FIG. 1 shows an abnormality diagnosis method according to the present invention.
It is explanatory drawing explaining the Example at the time of applying to the plant 1 feedback-controlled by the control apparatus 10 similarly. The abnormality diagnosing device 51 is a device for realizing the abnormality diagnosing method according to the present embodiment, and as shown in the figure, device models 52, 53-1 and 53-2, actuator models 54-1 and 54-. 2, comparator 55 and diagnostic unit 56
Have.

Of these, the device model 52 is the same as the prior art, that is, the same model as the device model 12 of FIG. 5, and the operating state quantity of the device 2 based on the command signal a is calculated by a mathematical expression, and this calculation is performed. The value of the operating state quantity is output as the calculation data 52a. The comparator 55 also compares the calculated data 52a with the detection data of the sensor 5 and outputs the comparison result to the diagnosis unit 56, similarly to the conventional technique, that is, like the comparator 15 of FIG.

The equipment model 53-1 includes sensors 6, 7, and 8.
Is a mathematical model for calculating the state quantity of the medium ejected from the device 3 by a mathematical formula based on the detected data, and outputs the calculated value of the state quantity as calculation data 53-1a. . Device model 53-2
Is a mathematical model for inputting the detection data of the sensors 6 and 8 and the command signal b output from the control device 10 and calculating the state quantity of the medium by a mathematical formula based on these detection data and the command signal b. , And outputs the calculated value of the state quantity as calculation data 53-2a.

The actuator model 54-1 receives the calculation data 53-1a and inputs the calculation data 53-1a.
It is a mathematical model for calculating the operating state quantity of the actuator 4 which is the same kind as the detection signal of the sensor 9 based on a by a mathematical formula, and outputs the value of the calculated operating state quantity as the first prediction data 54-1a. The actuator model 54-2 is a mathematical model that receives the calculated data 53-2a and calculates the operating state quantity of the actuator 4 by a mathematical formula based on the calculated data 53-2a. Value is the second prediction data 5
Output as 4-2a.

The diagnosis unit 56 is a comparative combination diagnosis unit 56.
a and a comparison result diagnosis unit 56b. Among them, the comparison combination diagnosis unit 56a uses the prediction data 54-1.
a, the prediction data 54-2a and the detection data of the sensor 9 are input, and these data are compared and combined to diagnose the abnormality of the device 3 or the sensors 6 to 9. Further, the comparison result diagnosing unit 56b inputs the comparison result from the comparator 55 and diagnoses the abnormality of the device 2 or the sensor 5 based on the comparison result as in the prior art. Note that detailed description of portions similar to those of the conventional art will be omitted.

In the abnormality diagnosing method realized by the abnormality diagnosing device 51 as described above, the detection data of the sensors 6, 7, and 8 are first input in the equipment model 53-1 and the equipment 3 discharges based on these detection data. The state quantity of the medium to be calculated is calculated by a mathematical expression, and the calculation data 53-1a indicating the calculated value of the state quantity is output to the actuator model 54-1. Subsequently, in the actuator model 54-1, the calculation data 53-1a is input, the operation state amount of the actuator 4 is calculated by a mathematical formula based on the calculation data 53-1a, and the calculated value of the operation state amount is shown. First prediction data 54-1
a is output to the diagnosis unit 56.

At the same time, in the device model 53-2, the detection data of the sensors 6 and 8 and the command signal b are input, and the operating state quantity of the device 3 is first calculated by a mathematical formula based on the command signal b, and then calculated. The state quantity of the medium ejected from the device 3 is calculated by a mathematical expression based on the operation state quantity and the detection data, and calculation data 53-2a indicating the calculated value of the state quantity is output to the actuator model 54-2. To do. Subsequently, in the actuator model 54-2, the calculation data 53-2a is input, the operation state amount of the actuator 4 is calculated by a mathematical expression based on the calculation data 53-2a, and the calculated value of the operation state amount is shown. Second prediction data 54-2
a is output to the diagnosis unit 56.

Next, in the comparison / combination diagnosis unit 56a, the first prediction data 54-1a and the second prediction data 5 are obtained.
The detection data of the sensor 4-2a and the sensor 9 is input, these data are compared with each other, and the data are combined to make the device 3
Alternatively, the abnormality of the sensors 6 to 9 is diagnosed. For example, device 3
If only one becomes abnormal, the detection data of the sensor 9 and the first
Comparing with the prediction data 54-1a of the above, both are almost equal, but the detection data of the sensor 9 and the second prediction data 5
Comparing with 4-2a, these two are greatly shifted and unequal. Therefore, these comparison results are combined to diagnose that the device 3 is abnormal.

An abnormality of the device 2 or the sensor 5 is diagnosed by the comparison result diagnosis section 56b based on the comparison result of the comparator 55.

Next, a turning device will be taken as an example of a specific abnormality diagnosis target, and the abnormality diagnosis method according to the embodiment of the present invention applied to the turning device will be described.

FIG. 2 is an explanatory view showing the relationship among the turning device 1, the abnormality diagnosis device 61 and the control device 40. As shown in the figure, the swivel device 1 includes a pump 22, a valve 23, a motor 24, pipes 32a, 32b, 32c, a swash plate sensor 25,
It has a valve displacement sensor 27, a rotation speed sensor 29 and pressure sensors 26, 28a, 28b and 30.

Of these, the pump 22 discharges pressure oil for rotating the motor 24. The discharge amount of the pressure oil is controlled by a swash plate 22 provided on the pump 22. The valve 23 controls the flow rate of the pressure oil and switches the flowing direction. The motor 24 is rotated by the pressure oil.

That is, the pressure oil discharged from the pump 22 flows in the order of the pipe 32a, the valve 23, and the pipe 32b (or the pipe 32c), and after the motor 24 is normally rotated (or reversely rotated), the pipe 32c (or the pipe 32c). 32b) It is discharged to the tank 31 through the valve 23.

The swash plate sensor 25 detects the displacement of the swash plate 21 and outputs detection data Φ _a . Valve displacement sensor 27
Detects the displacement of the valve 23 and outputs the detection data X _a . The rotation speed sensor 29 detects the rotation speed of the motor,
The detection data N _a is output. Pressure sensors 26, 28a,
28b and 30 detect the pressure of the pressure oil in the pipes 32a, 32b and 32c and the pressure of the pressure oil discharged to the tank 31, respectively, and output detection data P _pa , P _1a , P _2a and P _T.

The control device 40 outputs the command signals Φ _D and X _D to the swash plate 21 and the valve 23, respectively, while feeding back the detection data N _a of the rotation speed sensor 29 so as to detect the displacement of the swash plate 21 and the valve 23. The flow rate of the pressure oil flowing into the motor 24, that is, the rotation speed of the motor 24 is controlled by the control.

The abnormality diagnosing device 61 is a device for realizing the above-described abnormality diagnosing method, and is the detection data Φ _a , X _a , N _a , P _pa , P _1a , P from the turning device 1 and the control device 40.
_2a , P _T and command signals Φ _D , X _D are input to turn device 1
The abnormality diagnosis of each device and sensor is performed.

FIG. 3 is a block diagram showing an example of the internal configuration of the abnormality diagnosis device 61. In the figure, a mathematical model for calculating the displacement of the swash plate 21, which is the same as the conventional technique,
The description of the comparator for comparing the calculated data of the mathematical model and the detected data Φ _a and the comparison result diagnosis unit for diagnosing the abnormality of the swash plate 21 or the swash plate sensor 25 by inputting the comparison result from the comparator is omitted. .

As shown in FIG. 3, the abnormality diagnosing device 61 includes valve models 63-1, 63-2, 63-3, 63-4,
Motor models 64-1, 64-2, 64-3, 64-4
And a comparative combination diagnosis unit 66a.

Of these, the valve model 63-1 is
Detection data X _a, P _1a, type a P _pa, a mathematical model for calculating the hydraulic fluid flow rate Q _1S passing through the supply side of the valve 23 on the basis of the equation. Equation (1) is an example of the above equation. The valve model 63-2 is a mathematical model for inputting the detection data P _1a , P _pa and the command signal X _D , and calculating the pressure oil flow rate Q _1c passing through the supply side of the valve 23 based on a mathematical formula. Equation (2) is an example of the above equation.

[0038]

[Equation 1]

The valve model 63-3 inputs the detection data P _2a , P _T , X _a and inputs the detection data P _2a , P _T , X _a to the valve 23 based on the mathematical formula.
₂ is a mathematical model for calculating a flow rate Q _2S of pressure oil passing through the return side of. Expression (3) is an example of the above expression. The valve model 63-4 uses the detection data P _2a , P _T and the command signal X.
It is a mathematical model for inputting _D and calculating the pressure oil flow rate Q _2C passing through the return side of the valve 23 based on the mathematical formula. (4)
The formula is an example of the above formula.

[0040]

[Equation 2]

Motor models 64-1, 64-2, 64--
3, 64-4 are the pressure oil flow rates Q _1S , Q _1C ,
This is a mathematical model for inputting Q _2S and Q _2C and calculating prediction data N _1S , N _1C , N _2S and N _2C of the rotation speed of the motor 24 based on the mathematical formula. Equation (5) is an example of the above equation.

[0042]

[Equation 3]

The comparator combination diagnosis unit 66a, the type the predictive data _{N 1S, N 1C, N 2S} , N 2C and detection data N _a, the abnormality diagnosis by combining comparing them.

By using the abnormality diagnosis device 61 as described above,
The abnormality diagnosis method that appears is first described in the valve model 63-1.
The detection data X_a, P_1a, P_paEnter (1)
Based on the formula, ie detection data X_aOf the valve 22
Function f of valve opening area on the supply side with respect to displacement₁Assigned to
Then, the valve opening area A_1SAnd then
Opening area A_1S, Detection data P_1a, P_paAnd each constant,
That is, the flow coefficient of the valve 22, gravity acceleration g, specific gravity of oil
From r to pressure oil flow rate Q_1STo calculate. Also valve model 63
-2, the detection data P_1a, P_paAnd command signal X_a
Input, based on the equation (2), that is, the command signal X_DTo
Predicted valve displacement value X by substituting it into the first-order lag function_CAnd calculate
After this, the predicted valve displacement value X_CIs the function f₁Assigned to
And valve opening area A_1CAnd then open the valve.
Mouth area A_1C, Detection data P_1a, P _paAnd from the above constants
Pressure oil flow rate Q_1CTo calculate.

At the same time, in the valve model 63-3,
Outgoing data X_a, P_2a, P_TEnter, and based on formula (3)
I.e. detection data X_aTo the displacement of the valve 22
Function f of valve opening area on the return side₂Substitute in
Valve opening area A_2SAnd then the valve opening surface
Product A_2S, Detection data P_2a, P_TAnd pressure oil from the above constants
Flow rate Q_2STo calculate. Also in the valve model 63-4
Detection data P_2a, P _TAnd command signal X_DEnter
Based on the equation (4), that is, the command signal X_DThe primary delay
Substitute in the number to predict the valve displacement X_CAfter calculating
Predicted valve displacement value X_CIs the function f₂Substitute in the valve
Opening area A_2CAnd then the valve opening area
A_2C, Detection data P_2a, P_TAnd the flow rate of pressure oil from the constant
Q_2CTo calculate.

Next, the motor models 64-1, 64-2,
64-3 and 64-4, respectively, the pressure oil flow rate Q _1S ,
Input Q _1C , Q _2S , Q _2C , and based on the equation (5), that is, the capacity D _{M of the} motor 24 and the pressure oil flow rate Q _1S , Q _1C , Q
_2S , Q _2C and predicted data N _1S of the rotation speed of the motor 24,
Calculate N _1C , N _2S and N _2C .

Finally, in the comparison / combination diagnosing unit 66a, the detection data N _a and the prediction data N _1S , N _1C , N _2S ,
By inputting N _{2 C} and comparing and combining these, abnormality diagnosis of the valve 23, the valve displacement sensor 27, the rotation speed sensor 29, and the pressure sensors 26, 28a, 28b is performed.

Table 1 shows the result of the abnormality diagnosis. As shown in the table, in CASE 1, for example, the detected data N _a
And the predicted data N _1C are compared with each other, the detected data N _a and the predicted data N _1S are further compared, and the comparison results are combined to _satisfy “N _a = N _1C and N _a ≠ N _1S ”,
N _a ≠ N _1S from the pressure sensor 28a, 26, one of the valve displacement sensor 27 and speed sensor 29 can see that there is an abnormality, among these, the pressure sensor 28a from N _a = N _1C, 26 and the rotational speed Since it is known that the sensor 29 is normal, the remaining valve displacement sensor 27 is diagnosed as abnormal.

[0049]

[Table 1]

Similarly, in CASE 2 "N_a≠ N_1CAnd
N_a= N_1SWhen the comparison combination of
Diagnoses that the valve 23 body is abnormal, and
Of "N _a= N_1CAnd N_a≠ N_2CThe comparison combination
If, the pressure sensor 28b is abnormal.
Diagnose.

In CASE 5, "N _a ≠ N _1C and N _a =
When the comparison combination of "N _2C " is established, it is not possible to diagnose that either the pressure sensor 28a or the pressure sensor 26 is abnormal, and it is not possible to specify one of them, but in this case, it is specified as follows. be able to.

First, the direction in which the pressure oil flows by the valve 23
Is switched to reversely rotate the motor 24. Then another valve
Using the model, the detection data P at this time_pa, P_2a，
P_1a, P _TAnd command signal X_DAnd enter in the formula
Therefore, the flow rate of the pressure oil passing through the supply side when the valve 23 flows backward
Q '_2C, The pressure oil flow passing through the return side of the valve 23 at the time of reverse flow
Quantity Q '_1CTo calculate. Equation (6) is an example of the above equation.
It That is, the command signal X_DFrom valve displacement predicted value X_CTo
After calculating, this valve displacement predicted value X_CEach function
f₁, F₂To the pressure oil flow rate Q ′_1C, Q '_2CCalculate
Put out. Also, by using another motor model, the pressure oil flow rate
Q '_1C, Q '_2CWhen you enter the
Data N ′ of the rotation speed of the motor 24 of_1C, N '_2CCalculate
Put out. Expression (7) is an example of the above expression. Ie
Capacity of pump 24 D_MAnd pressure oil flow rate Q '_1C, Q '_2CFrom each
Prediction data N '_1C, N '_2CTo calculate.

[0053]

[Equation 4]

Finally, in the comparison / combination diagnosing unit 66a, the predicted data _N'1C and _N'2C are input, and by comparing and combining them, the pressure sensors 28a and 26a.
Which is abnormal is diagnosed. That is, as shown in Table 1, when the comparative combination of CASE 6 "N _a ≠ N ' _1C and N _a = N' _2C " is established, the pressure sensor 28b
Is diagnosed as abnormal.

In Table 1, all the comparison results are combined with the word "and", but it may be necessary to combine them with the word "or" depending on the configuration of the diagnosis target.

Next, a specific example of a method of calculating an abnormality certainty factor indicating the degree of abnormality of a device and a sensor when performing abnormality diagnosis by comparison and combination in the above abnormality diagnosis method will be specifically described.

FIG. 4 is an explanatory diagram showing an example of the equal sign function and the inequality sign function. As shown in FIG.
This is a function in which the more the two data to be compared are equal to each other, that is, the smaller the difference between the two data is, the larger the value is (maximum value = 1). The inequality function 72 is such that the two data to be compared are not equal to each other. That is, the larger the difference between the two data, the larger the value (maximum value = 1).

Taking CASE1 in Table 1 as an example, first, the difference ΔN ₁ between the detected data N _a and the predicted data N _1C is calculated, and the first conviction is calculated from this ΔN ₁ and the equal sign function 71. Find the degree τ ₁ . Next, the difference ΔN ₂ between the detected data N _a and the predicted data N _1S is calculated, and this ΔN ₂ and the inequality function 72 are calculated.
Then, the second certainty factor τ ₂ is obtained from. Finally, since CASE1 is combined with the word "Katsu",
Of the certainty factor τ ₁ and the second certainty factor τ ₂ whichever is smaller. This means that an area in which the respective comparison results are commonly established is obtained, and the value obtained by the selection represents the degree of abnormality of the valve displacement sensor 27, that is, the degree of abnormality confidence. When CASE1 is not established, the abnormality certainty factor of the valve displacement sensor 27 is close to zero. Conversely, when CASE1 is established, the abnormality certainty factor is close to one.

As described above, the comparison result may be combined by the word "or" depending on the configuration of the comparison object. In that case, the first certainty factor obtained from the equality function 71 and The larger one of the second certainty factors obtained from the inequality function 72 is selected, and this is set as the abnormal certainty factor.

[0060]

As described above in detail with the embodiments, according to the present invention, when only a certain device becomes abnormal, only the abnormality of the device is diagnosed and other devices or sensors are also abnormal. There is no such misdiagnosis. Therefore, it is possible to perform accurate abnormality diagnosis of the device and sensor to be diagnosed. Further, the degree of abnormality of the device and the sensor can be grasped by obtaining the abnormality certainty factor using the equal sign function and the inequality sign function.

[Brief description of drawings]

FIG. 1 is an explanatory diagram of an abnormality diagnosis method according to an embodiment of the present invention.

FIG. 2 is a turning device 1, an abnormality diagnosis device 61, and a control device 4.
It is explanatory drawing which shows the relationship of 0.

FIG. 3 is a block diagram showing an example of an internal configuration of an abnormality diagnosis device 61.

FIG. 4 is an explanatory diagram showing an example of an equal sign function and an inequality sign function.

FIG. 5 is an explanatory diagram of an abnormality diagnosis method according to a conventional technique.

[Explanation of symbols]

 1 Plant 2, 3 Equipment 5-9 Sensors 10, 40 Control Equipment 51, 61 Abnormality Diagnosis Equipment 52, 53-1, 53-2 Equipment Model 54-1, 54-2 Actuator Model 55 Comparator 56 Diagnostic Section 56a, 66a Comparison combination diagnosis part 56b Comparison result diagnosis part 22 Pump 63-1-4 Valve model 64-1-4 Motor model 71 Equal function 72 Inequality function

Claims (2)

[Claims] 1. A plant or apparatus having a plurality of devices and actuators that are remotely or automatically controlled by a control device, and various sensors that detect the operating state amount of these devices and actuators, and the devices and sensors. An abnormality diagnosis method for diagnosing an abnormality of, the detection data of the sensor attached to the device or the actuator, for detecting these operation state amount, and the detection data of a plurality of sensors for detecting other state amount, The first prediction data representing the predicted value of the operation state quantity of the device or the actuator to be compared is calculated, and the detection data of the plurality of sensors for detecting the other state quantity and the command signal output from the control device are calculated. Second predicted data representing a predicted value of the operating state quantity is calculated from the above, and then the operating state quantity is detected. Abnormality diagnosis characterized by diagnosing abnormality of the equipment and sensor of the plant or device by comparing the detection data of the sensor, the first prediction data and the second prediction data, and combining the comparison results. Method. 2. The detection data of a sensor for detecting the operating state quantity of a device or an actuator to be compared, the first prediction data and the second prediction data are respectively compared, and when the comparison results are combined, they are compared with each other. An equality function for determining the degree of equality of the two data from the difference between the two data is provided, and the value obtained by the equality function is used as the first certainty factor, and from the difference between the other two data to be compared. An inequality function that obtains the degree of inequality of the other two data is provided, and the value obtained by the inequality function is used as the second certainty factor. In the comparison combination, "the two data are equal and the other two data are In the case of combining with the word “and” such as “unequal”, the smaller value of the first certainty factor and the second certainty factor is selected, and this is selected as a device or a sensor. In the case of combining with the word “or”, such as “the two data are equal or the other two data are unequal” in the comparison combination, The abnormality diagnosis according to claim 1, wherein a larger value of the certainty factor of 1 and the second certainty factor is selected as an abnormality certainty factor indicating a degree of abnormality of the device or the sensor. Method.