CN115909678A - Equipment parameter early warning method and device and storage medium - Google Patents
Equipment parameter early warning method and device and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of information, in particular to a method, a device and a storage medium for early warning of equipment parameter of a unit, comprising the following steps: reading historical data of unit equipment parameters; receiving an early warning item added by a user; acquiring an alarm limit value of each target parameter of each early warning item under the corresponding working condition according to historical data; establishing an equipment parameter prediction model, and training the parameter prediction model by using historical data; receiving an early warning period set by a user; after the early warning period is reached, obtaining the predicted values of all parameters of the unit equipment according to the equipment parameter prediction model; and reading each early warning item in sequence, judging whether the predicted value of the target parameter exceeds the alarm limit value, if so, giving an alarm, otherwise, entering the next early warning item. The beneficial technical effects of the invention comprise: the target index alarm high-low limit value is dynamically calculated, the early warning under different working conditions is adapted, and the early warning accuracy is effectively improved.
Description
Technical Field
The invention relates to the technical field of information, in particular to a method and a device for early warning equipment parameters of a set and a storage medium.
Background
The early warning of the parameters of the power plant unit equipment can identify the possible problems of the equipment parameters in advance, and the normal operation of the power plant unit equipment is ensured. Therefore, the method has actual requirements on the parameter early warning of the unit equipment. At present, the parameter early warning of the power plant unit adopts the setting of uniformly setting the index alarm high and low limits. Because the unit is under different operating modes, the equipment parameter warning height limit has the difference. The prior art cannot set corresponding high and low limit values according to different working conditions. The condition of false alarm or missed alarm is caused, and the accuracy and reliability of the equipment parameter early warning are difficult to ensure. At present, a prediction scheme of a real-time value of a unit device parameter of a power plant is lacked, and whether the unit device is likely to have a fault or not can not be judged in advance by combining a predicted value. The processing can be performed only after the unit equipment gives out fault alarm, so that the operation efficiency and the operation reliability of the unit are reduced, and the stable supply of electric power is influenced. In view of the above circumstances, a new power plant unit equipment parameter early warning technology needs to be researched.
Through retrieval, the applicant finds that CN114637791A is the closest prior art to the application, the publication date of the CN114637791A is 2022, 6 and 17, and records a power plant equipment early warning method based on similarity. The method specifically comprises the following steps: the method comprises the following steps: acquiring historical data, acquiring important characteristic data of early warning equipment, and selecting stable working condition data to obtain a stable working condition data set; step two: cleaning abnormal data based on the stable working condition data set in the step I; step three: selecting data in a similar normal range according to key characteristic data of the unit based on the cleaned data obtained in the step two; step four: and judging whether the current running state is a stable working condition, if so, calculating the similarity value of the current characteristic value of the early warning equipment and each piece of data in the step three, selecting the maximum value of the similarity values, and if the value is smaller than a set threshold value, starting early warning. According to the technical scheme, the coupling relation among parameters is considered by adopting a similarity calculation method, and the health degree of equipment is measured by calculating the similarity of distance information and direction information, so that fault early warning is carried out on the power plant equipment. However, the technical scheme cannot set different early warning values according to different unit working conditions, and the situation that false alarm or alarm missing occurs when the unit runs under different working conditions cannot be avoided.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the technical problem that a scheme for dynamically setting an early warning value according to different working conditions of a unit is lacked at present. The method and the device for early warning the equipment parameters of the unit and the storage medium can dynamically set the alarm limit value under different working conditions of the unit, and improve the accuracy and the reliability of the early warning of the equipment parameters.
The technical scheme adopted by the invention is as follows: a device parameter early warning method comprises the following steps:
reading historical data of unit equipment parameters;
receiving an early warning item added by a user, wherein the early warning item comprises a working condition and a plurality of target parameters;
obtaining an alarm limit value of each target parameter of each early warning item under the corresponding working condition according to historical data;
establishing an equipment parameter prediction model, and training the parameter prediction model by using historical data;
receiving an early warning period set by a user;
after the early warning period is reached, obtaining the predicted values of all parameters of the unit equipment according to the equipment parameter prediction model;
and reading each early warning item in sequence, if the working condition of the unit equipment accords with the working condition of the early warning item, further judging whether the predicted value of the target parameter exceeds the alarm limit value, if so, giving an alarm, otherwise, if the working condition of the unit equipment does not accord with the working condition of the early warning item or the predicted value of the target parameter does not exceed the alarm limit value, entering the next early warning item.
Preferably, the method for calculating the alarm limit value of the target parameter comprises:
reading all data which meet the working condition of the early warning item from the historical data to obtain all historical data of corresponding target parameters;
calculating the mean value of the historical data of each target parameter
And standard deviation s i ；
The alarm upper limit value of the target parameter is
The lower alarm limit value of the target parameter is->
Preferably, the value of k belongs to the interval [2,5].
Preferably, k has a value of 4.
Preferably, after reading the historical data of the unit equipment parameters, preprocessing the historical data, removing the special values, and supplementing the missing values by interpolation of numerical values before and after the missing values.
Preferably, the method for removing the special value includes:
sorting the data values of the historical data according to the generation time;
setting the window length L1, respectively reading L1/2 data values before and after the data value, and calculating an average value;
and if the data value exceeds the interval [ k 1-average value, k 2-average value ], judging that the data value is a special value, and removing the special value.
Preferably, the method of supplementing the missing value includes:
sorting the data values of the historical data according to the generation time;
setting a window length L2, and respectively reading L2/2 data values before and after the missing value to obtain L2 data values;
interpolation at missing values of quadratic interpolation is used.
Preferably, the method for establishing the device parameter prediction model comprises the following steps:
reading historical data of unit equipment parameters, and establishing a training matrix K, K = [ X ] 1 M (1) ,X 2 M (2) ,X 3 M (3) ,…,X (m) M (m) ]M represents the number of device parameters contained in the training matrix;
a health matrix D is established and,
n represents a data line;
obtaining M according to the training matrix K and the health matrix D 1 ,M 2 ,M 3 ,…,M m A value of (d);
setting a normalization factor according to the parameters of the equipment to be predicted;
predicting the real-time value Y, Y = [ X ] of the normalization factor according to an algorithm 1(n+1) M (1) ,X 2(n+1) M (2) ,X 3(n+1) M (3) ,…,X (m)(n+1) M (m) ]；
Real-time values Y and M based on predicted normalization factors 1 To M m To obtain X 1(n+1) ,X 2(n+1) ,X 3(n+1) ,…,X (m)(n+1) The value of (b) is the value to be predicted of the equipment parameter.
The device parameter early warning device comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein when the computer program is executed by the processor, the device parameter early warning method is realized.
A storage medium storing a computer program which, when executed by a processor, implements a set of apparatus parameter warning methods as set forth above.
The beneficial technical effects of the invention comprise: according to different set early warning items, the online data of equipment parameters are collected in combination with the current working condition of the unit, the target index alarm high-low limit value is dynamically calculated, and the method is suitable for early warning under different working conditions. Meanwhile, an equipment parameter prediction model is provided, and early warning is carried out according to the predicted value of the equipment parameter, so that the equipment parameter early warning has more lead, and the working efficiency of the equipment and the early warning accuracy of the equipment under different working conditions are effectively improved.
Other features and advantages of the present invention will be disclosed in more detail in the following detailed description of the invention and the accompanying drawings.
Drawings
The invention is further described below with reference to the accompanying drawings:
fig. 1 is a schematic flow chart of a unit equipment parameter early warning method according to an embodiment of the present invention.
FIG. 2 is a flowchart illustrating a method for calculating an alarm limit value of a target parameter according to an embodiment of the present invention.
Fig. 3 is a flowchart illustrating a method for removing a singular value according to an embodiment of the present invention.
FIG. 4 is a flowchart illustrating a method for supplementing missing values according to an embodiment of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention are explained and illustrated below with reference to the drawings of the embodiments of the present invention, but the following embodiments are only preferred embodiments of the present invention, and not all embodiments. Other embodiments obtained by persons skilled in the art without making creative efforts based on the embodiments in the implementation belong to the protection scope of the invention.
In the following description, the appearances of the indicating orientation or positional relationship such as the terms "inner", "outer", "upper", "lower", "left", "right", etc. are only for convenience in describing the embodiments and for simplicity in description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and are not to be construed as limiting the present invention.
An early warning method for equipment parameter of a set, please refer to fig. 1, comprising the following steps:
step A01) reading historical data of unit equipment parameters;
step A02) receiving an early warning item added by a user, wherein the early warning item comprises a working condition and a plurality of target parameters;
step A03) obtaining an alarm limit value of each target parameter of each early warning item under the corresponding working condition according to historical data;
step A04) establishing an equipment parameter prediction model, and training the parameter prediction model by using historical data;
step A05), receiving an early warning period set by a user;
step A06) obtaining the predicted values of all parameters of the unit equipment according to the equipment parameter prediction model after the early warning period is reached;
step A07) reading each early warning item in sequence, if the working condition of the unit equipment accords with the working condition of the early warning item, further judging whether the predicted value of the target parameter exceeds the alarm limit value, if so, giving an alarm, otherwise, if the working condition of the unit equipment does not accord with the working condition of the early warning item or the predicted value of the target parameter does not exceed the alarm limit value, entering the next early warning item.
Referring to fig. 2, the method for calculating the alarm limit of the target parameter includes:
step B01) reading all data which meet the working condition of the early warning item from the historical data to obtain all corresponding historical data of the target parameter;
step B02) of calculating the mean value of the historical data of each target parameter
And standard deviation s i ；
Step B03) the alarm upper limit value of the target parameter is
The lower alarm limit value of the target parameter is
Preferably, the value of k belongs to the interval [2,5]. Preferably, the value of k is chosen to be 4. I.e. the upper alarm limit value of the target parameter is
Alarm lower limit value of target parameter is>
Step A01) reading the historical data of the unit equipment parameters, preprocessing the historical data, removing special values, and supplementing missing values by interpolation of numerical values before and after the missing values.
Referring to fig. 3, the method for removing the singular value includes:
step C01) sorting the data values of the historical data according to the generation time;
step C02) setting a window length L1, respectively reading L1/2 data values before and after the data value, and calculating an average value;
and C03) judging the data value to be a special value if the data value exceeds the interval [ k1 mean value, k2 mean value ], and removing the special value.
Referring to fig. 4, the method for supplementing missing values includes:
step D01) sorting the data values of the historical data according to the generation time;
step D02) setting a window length L2, and respectively reading L2/2 data values before and after the missing value to obtain L2 data values;
step D03) uses interpolation at missing values of quadratic interpolation.
The working condition tag0 meets the following conditions: tag0 is more than or equal to K1 and less than or equal to K2, and K1 and K2 are set conditions. And screening all data of the target parameters meeting the working condition. The target parameters are tag1 to tagm, tag1= [ x = [) 11 ,x 12 ,x 13 ,..,x 1n ],tag1＝[x 21 ,x 22 ,x 23 ,..,x 2n ],…,tagm＝[x m1 ,x m2 ,x m3 ,..,x mn ]Calculating the mean value and the standard deviation mean value of the data of the qualified target parameters:
wherein->
The mean value is indicated.
Calculating the standard deviation:
the dynamic alarm upper limit value is as follows:
the dynamic alarm lower limit value is as follows:
for example:
the historical data of the screened target parameters under the corresponding working conditions is assumed to be [100,90,110]And obtaining an average value:
calculating a standard deviation: s i =8.16. The dynamic alarm upper limit value is as follows: 100+8.16 + 4=132, and the lower limit value of the state alarm is as follows: 100-8.16 × 4=67.34.
The alarm limit value of the target parameter is dynamically determined according to the historical data of the normal operation of the target parameter under the corresponding working condition, and the alarm limit value of each target parameter under different working conditions is obtained, so that the method has higher accuracy and adaptability. The method is beneficial to reducing missed alarm and false alarm, and improves the accuracy and reliability of parameter early warning of the unit equipment.
The method for establishing the equipment parameter prediction model comprises the following steps:
reading historical data of unit equipment parameters, and establishing a training matrix K, K = [ X ] 1 M (1) ,X 2 M (2) ,X 3 M (3) ,…,X (m) M (m) ]M represents the number of device parameters contained in the training matrix;
a health matrix D is established and,
n represents a data line;
obtaining M according to the training matrix K and the health matrix D 1 ,M 2 ,M 3 ,…,M m A value of (d);
setting a normalization factor according to the parameters of the equipment to be predicted;
predicting real-time values of normalization factors according to an algorithmY，Y＝[X 1(n+1) M (1) ,X 2(n+1) M (2) ,X 3(n+1) M (3) ,…,X (m)(n+1) M (m) ]；
Real-time values Y and M based on predicted normalization factor 1 To M m To obtain X 1(n+1) ,X 2(n+1) ,X 3(n+1) ,…,X (m)(n+1) The value of (b) is the value to be predicted of the equipment parameter.
The machine set equipment parameter early warning device comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the computer program is executed by the processor to realize the machine set equipment parameter early warning method.
A storage medium stores a computer program, and the computer program is executed by a processor to implement the device parameter pre-warning method.
The beneficial technical effects of the embodiment include: according to different set early warning items, the online data of equipment parameters are collected in combination with the current working condition of the unit, the target index alarm high-low limit value is dynamically calculated, and the method is suitable for early warning under different working conditions. Meanwhile, an equipment parameter prediction model is provided, and early warning is carried out according to the predicted value of the equipment parameter, so that the equipment parameter early warning has more lead, and the working efficiency of the equipment and the early warning accuracy of the equipment under different working conditions are effectively improved.
While the invention has been described with reference to specific embodiments thereof, it will be understood by those skilled in the art that the invention is not limited thereto, and may be embodied in many different forms without departing from the spirit and scope of the invention as set forth in the following claims. Any modifications which do not depart from the functional and structural principles of the present invention are intended to be included within the scope of the claims.
Claims (10)
1. A device parameter early warning method is characterized in that,
the method comprises the following steps:
reading historical data of unit equipment parameters;
receiving an early warning item added by a user, wherein the early warning item comprises a working condition and a plurality of target parameters;
acquiring an alarm limit value of each target parameter of each early warning item under the corresponding working condition according to historical data;
establishing an equipment parameter prediction model, and training the parameter prediction model by using historical data;
receiving an early warning period set by a user;
after the early warning period is reached, obtaining the predicted values of all parameters of the unit equipment according to the equipment parameter prediction model;
and reading each early warning item in sequence, if the working condition of the unit equipment accords with the working condition of the early warning item, further judging whether the predicted value of the target parameter exceeds the alarm limit value, if so, giving an alarm, otherwise, if the working condition of the unit equipment does not accord with the working condition of the early warning item or the predicted value of the target parameter does not exceed the alarm limit value, entering the next early warning item.
2. The device parameter early warning method as claimed in claim 1,
the method for calculating the alarm limit value of the target parameter comprises the following steps:
reading all data which meet the working condition of the early warning item from the historical data to obtain all historical data of corresponding target parameters;
calculating the mean value of the historical data of each target parameter
And standard deviation s i ；
The alarm upper limit value of the target parameter is
The lower alarm limit value of the target parameter is->
3. The method of claim 2, wherein the early warning of the device parameters is performed by a computer,
the value of k belongs to the interval [2,5].
4. The device parameter early warning method as claimed in claim 2,
the value of k is 4.
5. The device parameter warning method according to any one of claims 1 to 4,
and after reading the historical data of the unit equipment parameters, preprocessing the historical data, removing special values, and supplementing missing values by interpolation of numerical values before and after the missing values.
6. The method of claim 5, wherein the early warning of the device parameters is performed by a computer,
the method for removing the special value comprises the following steps:
sorting the data values of the historical data according to the generation time;
setting the window length L1, respectively reading L1/2 data values before and after the data value, and calculating an average value;
and if the data value exceeds the interval [ k 1-average value, k 2-average value ], judging that the data value is a special value, and removing the special value.
7. The device parameter early warning method as claimed in claim 5,
the method for supplementing missing values comprises the following steps:
sorting the data values of the historical data according to the generation time;
setting a window length L2, and respectively reading L2/2 data values before and after the missing value to obtain L2 data values;
interpolation at missing values of quadratic interpolation is used.
8. The device parameter warning method according to any one of claims 1 to 4,
the method for establishing the equipment parameter prediction model comprises the following steps:
reading historical data of unit equipment parameters, and establishing a training matrix K, K = [ X ] 1 M (1) ,X 2 M (2) ,X 3 M (3) ,…,X (m) M (m) ]M represents the number of device parameters contained in the training matrix;
a health matrix D is established and,
n represents a data line;
obtaining M according to the training matrix K and the health matrix D 1 ,M 2 ,M 3 ,…,M m A value of (d);
setting a normalization factor according to the parameters of the equipment to be predicted;
predicting a real-time value of the normalization factor Y, Y = [ X ] according to an algorithm 1(n+1) M (1) ,X 2(n+1) M (2) ,X 3(n+1) M (3) ,…,X (m)(n+1) M (m) ]；
Real-time values Y and M based on predicted normalization factors 1 To M m To obtain X 1(n+1) ,X 2(n+1) ,X 3(n+1) ,…,X (m)(n+1) The value of (b) is the value to be predicted of the equipment parameter.
9. A group device parameter warning apparatus, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements a group device parameter warning method as claimed in any one of claims 1 to 8.
10. A storage medium, characterized in that the storage medium stores a computer program, which when executed by a processor implements a set device parameter warning method according to any one of claims 1 to 7.
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