CN115185313A - Trend tracking early warning method and device for bearing bush temperature of hydroelectric generating set - Google Patents
Trend tracking early warning method and device for bearing bush temperature of hydroelectric generating set Download PDFInfo
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- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/20—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature
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Abstract
The disclosure provides a trend tracking and early warning method and device for bearing bush temperature of a hydroelectric generating set, and relates to the technical field of hydroelectric generating sets. The method comprises the following steps: acquiring current equipment parameters of a hydroelectric generating set to be monitored and a corresponding health temperature rise curve; determining the state of the hydroelectric generating set based on the equipment parameters; under the condition that the state of the hydroelectric generating set is a starting operation state, determining a first temperature value at the current moment, a second temperature value at the starting moment and a first time length, wherein the first time length is the time length between the current moment and the starting moment; and determining whether the temperature of the bearing tile of the hydroelectric generating set is normal or not based on the first temperature value at the current moment, the second temperature value and the first duration at the starting-up starting moment and the healthy temperature rise curve. Therefore, the first temperature rise value is obtained based on the healthy temperature rise curve, and whether the temperature of the bearing tile is abnormal or not can be determined according to the comparison result of the first temperature rise value and the temperature rise value in actual operation, so that the accuracy and the reliability of the temperature of the bearing tile of the hydroelectric generating set are improved.
Description
Technical Field
The disclosure relates to the technical field of hydroelectric generating sets, in particular to a trend tracking and early warning method and device for bearing bush temperature of a hydroelectric generating set.
Background
The hydroelectric generating set is used as a core device for energy conversion of the hydropower station, and the components of the hydroelectric generating set are mutually coupled, so that the development trend of complexity and high integration is presented. Meanwhile, the running environment of the hydroelectric generating set is severe and is influenced by coupling factors such as hydraulic force, machinery and electromagnetism, so that safety risks such as abnormal vibration, coupling faults, fatigue deterioration and even structural damage of equipment can be caused, and the safety risks are increasingly prominent.
The bearing tile of the hydroelectric generating set is one of key devices for guaranteeing the normal operation of the hydroelectric generating set, if the temperature of the bearing bush abnormally fluctuates or even rapidly rises due to some reason, the surface of the bearing bush can be burnt, and even the whole hydroelectric generating set can be forced to stop, so that the normal power generation and the operation safety of the hydroelectric generating set are seriously influenced. Therefore, how to monitor the temperature of the bearing tile to ensure the safe operation of the hydroelectric generating set is very important.
Disclosure of Invention
The present disclosure is directed to solving, at least to some extent, one of the technical problems in the related art.
The embodiment of the first aspect of the disclosure provides a trend tracking and early warning method for bearing bush temperature of a hydroelectric generating set, which includes:
acquiring current equipment parameters of a hydroelectric generating set to be monitored and a corresponding health temperature rise curve;
determining a state of the hydroelectric generating set based on the equipment parameter;
under the condition that the state of the hydroelectric generating set is a starting operation state, determining a first temperature value at the current moment, a second temperature value at the starting moment and a first time length, wherein the first time length is the time length between the current moment and the starting moment;
and determining whether the temperature of the bearing tile of the hydroelectric generating set is normal or not based on the first temperature value at the current moment, the second temperature value and the first duration at the starting-up starting moment and the health temperature rise curve.
Optionally, the obtaining of the current device parameter of the hydroelectric generating set to be monitored and the corresponding health temperature rise curve includes:
and determining a healthy temperature rise curve from a preset temperature rise curve library based on the type of the hydroelectric generating set.
Optionally, before determining the healthy temperature rise curve from the preset temperature rise curve library, the method further includes:
inputting each moment in the running period of any type of hydroelectric generating set to a trained neural network model to obtain a reference temperature rise value corresponding to each moment in the running period;
adding a margin value to each reference temperature rise value to obtain a corresponding health temperature rise value;
and fitting each health temperature rise value according to each moment in the operation period to generate a health temperature rise curve.
Optionally, before inputting each time in the operation cycle of any type of hydroelectric generating set into the trained neural network model, the method further includes:
acquiring a historical data set, wherein the historical data set comprises a corresponding labeled temperature rise value at each moment after the hydroelectric generating set of any type is started to operate;
inputting each moment into an initial network model to obtain a predicted temperature rise value corresponding to each moment;
and correcting the initial network model according to the difference between each predicted temperature rise value and the labeled temperature rise value to generate a trained neural network model.
Optionally, the determining whether the temperature of the tile of the bearing of the hydroelectric generating set is normal based on the first temperature value at the current moment, the second temperature value at the starting-up moment, the first duration and the healthy temperature rise curve includes:
determining a first temperature rise value corresponding to the first time length in the healthy temperature rise curve;
fusing the second temperature value with the first temperature rise value to obtain a third temperature value;
determining that the temperature of the tile of the bearing of the hydroelectric generating set is abnormal under the condition that the first temperature value is greater than the third temperature value;
and determining that the temperature of the bearing tile of the hydroelectric generating set is normal under the condition that the first temperature value is less than or equal to the third temperature value.
Optionally, after determining whether the temperature of the bearing tile of the hydroelectric generating set is normal, the method further includes:
and carrying out abnormity early warning under the condition that the temperature of the bearing tile of the hydroelectric generating set is abnormal.
Optionally, after determining the first temperature value at the current time, the second temperature value at the startup time, and the first time length, the method further includes:
acquiring real-time temperature values corresponding to all moments of the hydroelectric generating set within the first time length;
and generating a corresponding actual temperature rise curve based on the difference value between each real-time temperature value and the second temperature value.
Optionally, the determining whether the temperature of the bearing tile of the hydroelectric generating set is normal includes:
determining that the temperature of the bearing tile of the hydroelectric generating set is normal under the condition that the actual temperature rise curve is located below the healthy temperature rise curve;
and determining that the temperature of the bearing tile of the hydroelectric generating set is abnormal under the condition that the actual temperature rise curve and the healthy temperature rise curve have an intersection or the actual temperature rise curve is positioned above the healthy temperature rise curve.
The embodiment of the second aspect of the present disclosure provides a trend tracking and early warning device for bearing bush temperature of a hydroelectric generating set, including:
the acquisition module is used for acquiring current equipment parameters of the hydroelectric generating set to be monitored and a corresponding health temperature rise curve.
The first determination module is used for determining the state of the hydroelectric generating set based on the equipment parameters;
the second determining module is used for determining a first temperature value at the current moment, a second temperature value at the starting-up starting moment and a first time length under the condition that the state of the hydroelectric generating set is the starting-up running state, wherein the first time length is the time length between the current moment and the starting-up starting moment;
and the fourth determining module is used for determining whether the temperature of the bearing tile of the hydroelectric generating set is normal or not based on the first temperature value at the current moment, the second temperature value and the first duration at the starting-up moment and the health temperature rise curve.
Optionally, the obtaining module is specifically configured to:
and determining a healthy temperature rise curve from a preset temperature rise curve library based on the type of the hydroelectric generating set.
Optionally, the obtaining module is further specifically configured to:
inputting each moment in the operation cycle of any type of hydroelectric generating set to a trained neural network model to obtain a reference temperature rise value corresponding to each moment in the operation cycle;
adding a margin value to each reference temperature rise value to obtain a corresponding health temperature rise value;
and fitting each health temperature rise value according to each moment in the operation period to generate a health temperature rise curve.
Optionally, the obtaining module is further specifically configured to:
acquiring a historical data set, wherein the historical data set comprises a corresponding labeled temperature rise value at each moment after the hydroelectric generating set of any type is started to operate;
inputting each moment into an initial network model to obtain a predicted temperature rise value corresponding to each moment;
and correcting the initial network model according to the difference between each predicted temperature rise value and the labeled temperature rise value to generate a trained neural network model.
Optionally, the third determining module is specifically configured to:
determining a first temperature rise value corresponding to the first time length in the healthy temperature rise curve;
fusing the second temperature value with the first temperature rise value to obtain a third temperature value;
determining that the temperature of the bearing tile of the hydroelectric generating set is abnormal under the condition that the first temperature value is greater than the third temperature value;
and determining that the temperature of the bearing tile of the hydroelectric generating set is normal under the condition that the first temperature value is less than or equal to the third temperature value.
Optionally, the third determining module is further configured to:
and carrying out abnormity early warning under the condition that the temperature of the bearing tile of the hydroelectric generating set is abnormal.
Optionally, the method further includes a generating module, configured to:
acquiring real-time temperature values corresponding to all moments of the hydroelectric generating set within the first time length;
and generating a corresponding actual temperature rise curve based on the difference value between each real-time temperature value and the second temperature value.
Optionally, the third determining module is specifically configured to:
determining that the temperature of the bearing tile of the hydroelectric generating set is normal under the condition that the actual temperature rise curve is located below the healthy temperature rise curve;
and determining that the temperature of the bearing tile of the hydroelectric generating set is abnormal under the condition that the actual temperature rise curve and the healthy temperature rise curve have intersection or the actual temperature rise curve is positioned above the healthy temperature rise curve.
An embodiment of a third aspect of the present disclosure provides a computer device, including: the trend tracking early warning method for the bearing bush temperature of the hydroelectric generating set comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein when the processor executes the program, the trend tracking early warning method for the bearing bush temperature of the hydroelectric generating set is realized.
A fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for tracking and warning a trend of bearing pad temperature of a hydroelectric generating set according to the first aspect of the present disclosure is implemented.
In an embodiment of a fifth aspect of the present disclosure, a computer program product is provided, and when an instruction processor in the computer program product is executed, the trend tracking and early warning method for bearing pad temperature of a hydroelectric generating set according to the embodiment of the first aspect of the present disclosure is executed.
The trend tracking early warning method, the trend tracking early warning device, the computer equipment and the storage medium for the bearing bush temperature of the hydroelectric generating set can firstly acquire current equipment parameters and a corresponding health temperature rise curve of the hydroelectric generating set to be monitored, then can determine the state of the hydroelectric generating set based on the equipment parameters, and determine a first temperature value at the current moment, a second temperature value at the starting-up starting moment and a first duration under the condition that the state of the hydroelectric generating set is in the starting-up running state, wherein the first duration is the duration between the current moment and the starting-up starting moment, and then can determine whether the bearing bush temperature of the hydroelectric generating set is normal or not based on the first temperature value at the current moment, the second temperature value and the first duration at the starting-up starting moment and the health temperature rise curve. Therefore, a corresponding first temperature rise value can be obtained from the health temperature rise curve based on the time length between the current time and the starting time, the temperature rise value corresponding to the current time is determined according to the temperature value of the hydroelectric generating set at the current time and the temperature value of the starting time, and then the temperature rise value is compared with the first temperature rise value, so that whether the temperature of the bearing tile of the hydroelectric generating set at the current time is abnormal or not can be determined, and therefore the accuracy and the reliability of the temperature of the bearing tile of the hydroelectric generating set are improved.
Additional aspects and advantages of the disclosure will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the disclosure.
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The above and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flow chart of a trend tracking and early warning method for bearing bush temperature of a hydroelectric generating set according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a trend tracking and early warning method for bearing bush temperature of a hydroelectric generating set according to another embodiment of the present disclosure;
fig. 2A is a schematic diagram of a trend tracking early warning process of a bearing bush temperature of a hydroelectric generating set according to an embodiment of the present disclosure;
fig. 3 is a schematic flow chart of a trend tracking and early warning method for bearing bush temperature of a hydroelectric generating set according to another embodiment of the present disclosure;
fig. 3A is a schematic diagram of a trend tracking early warning process of bearing bush temperature of a hydroelectric generating set according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a trend tracking and early warning device for bearing bush temperature of a hydroelectric generating set according to an embodiment of the present disclosure;
FIG. 5 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present disclosure, and should not be construed as limiting the present disclosure.
The trend tracking early warning method, device, computer equipment and storage medium for bearing bush temperature of the hydroelectric generating set according to the embodiments of the present disclosure are described below with reference to the accompanying drawings.
The trend tracking and early warning method for the bearing bush temperature of the hydroelectric generating set is configured in the trend tracking and early warning device for the bearing bush temperature of the hydroelectric generating set for illustration, and the trend tracking and early warning device for the bearing bush temperature of the hydroelectric generating set can be applied to any computer equipment, so that the computer equipment can execute the trend tracking and early warning function for the bearing bush temperature of the hydroelectric generating set.
The Computer device may be a Personal Computer (PC), a cloud device, a mobile device, and the like, and the mobile device may be a hardware device having various operating systems, touch screens, and/or display screens, such as a mobile phone, a tablet Computer, a Personal digital assistant, a wearable device, and an in-vehicle device.
Fig. 1 is a schematic flow diagram of a trend tracking and early warning method for bearing bush temperature of a hydroelectric generating set according to an embodiment of the present disclosure.
As shown in fig. 1, the trend tracking and early warning method for the bearing bush temperature of the hydroelectric generating set may include the following steps:
The hydroelectric generating set may be any type of hydroelectric generating set, such as a mixed-flow hydroelectric generating set, an axial-flow hydroelectric generating set, and the like, which is not limited in this disclosure.
The device parameter may be a parameter value of any component in the hydroelectric generating set to be detected, for example, the device parameter may be a rotation speed of the hydroelectric generating set, and the like, which is not limited in this disclosure.
In addition, temperature rise can be understood as a temperature difference value, a temperature change value, and the like; the health temperature rise curve can be understood as a temperature change curve, which can be a standard curve set in advance, and the like, and the disclosure does not limit this.
And 102, determining the state of the hydroelectric generating set based on the equipment parameters.
The state of the hydroelectric generating set may be various, for example, the hydroelectric generating set may be in a starting operation state, or may also be in a shutdown state, an unoperated state, and the like, which is not limited in this disclosure.
For example, in the case of a rotational speed as the device parameter, if the rotational speed is zero, the state of the hydroelectric generating set may be determined as: stopping the machine and not operating; or, if the rotation speed of the hydroelectric generating set is n1, it may be determined that the current state of the hydroelectric generating set is: a power-on running state, etc. The present disclosure is not limited thereto.
Optionally, the state of the hydroelectric generating set may be monitored, and when the rotation speed of the hydroelectric generating set is a non-zero value, it may be determined that the hydroelectric generating set is started at this time, so that the second temperature value at the time of starting up the hydroelectric generating set may be obtained. For example, the temperature value of the hydroelectric generating set can be obtained through a temperature sensor and the like.
For example, if the current time is t1 and the rotation speed of the hydroelectric generating set is n2, it may be determined that the hydroelectric generating set is in a start-up operation, and the first temperature value at this time is: a1; if the starting-up time of the hydroelectric generating set is the time t2, the second temperature value corresponding to the time t2 is as follows: a2, then it may be determined that the first duration from the power-on start time to the current time is: t2-t1, etc., which the present disclosure does not limit.
And 104, determining whether the temperature of the bearing tile of the hydroelectric generating set is normal or not based on the first temperature value at the current moment, the second temperature value and the first duration at the starting-up moment and the health temperature rise curve.
For example, after the first time length is determined, a first temperature rise value corresponding to the first time length may be searched for in the health temperature rise curve, a second temperature rise value between the first temperature value and the second temperature value may be determined, and the temperature of the bearing tile of the hydroelectric generating set may be determined to be normal when the second temperature rise value is less than or equal to the first temperature rise value; and determining that the temperature of the tile of the bearing of the hydroelectric generating set is abnormal under the condition that the second temperature rise value is greater than the first temperature rise value.
Generally, for different seasons, the bearing tile temperature value of the hydroelectric generating set may change, and if only a single temperature value is used, the bearing tile temperature judgment may be inaccurate. Therefore, in the embodiment of the disclosure, trend tracking and early warning of the bearing bush temperature of the hydroelectric generating set can be performed by using the temperature rise value and the temperature rise curve, so that the occurrence of inaccurate temperature judgment caused by environmental reasons is avoided as much as possible, and the accuracy and reliability of the temperature determination of the bearing tile of the hydroelectric generating set are improved.
According to the embodiment of the disclosure, the current device parameter and the corresponding health temperature rise curve of the hydroelectric generating set to be monitored can be obtained first, then the state of the hydroelectric generating set can be determined based on the device parameter, under the condition that the state of the hydroelectric generating set is in a starting operation state, the first temperature value at the current moment, the second temperature value at the starting moment and the first time length are determined, wherein the first time length is the time length between the current moment and the starting moment, and then whether the temperature of the bearing tile of the hydroelectric generating set is normal or not can be determined based on the first temperature value at the current moment, the second temperature value at the starting moment, the first time length and the health temperature rise curve. Therefore, a corresponding first temperature rise value can be obtained from the health temperature rise curve based on the time length between the current time and the starting time, the temperature rise value corresponding to the current time is determined according to the temperature value of the hydroelectric generating set at the current time and the temperature value of the starting time, and then the temperature rise value is compared with the first temperature rise value, so that whether the temperature of the bearing tile of the hydroelectric generating set at the current time is abnormal or not can be determined, and therefore the accuracy and the reliability of the temperature of the bearing tile of the hydroelectric generating set are improved.
Fig. 2 is a schematic flow diagram of a trend tracking and early warning method for bearing bush temperature of a hydroelectric generating set according to an embodiment of the present disclosure.
As shown in fig. 2, the trend tracking and early warning method for the bearing bush temperature of the hydroelectric generating set may include the following steps:
Optionally, the healthy temperature rise curve may be determined from a preset temperature rise curve library based on the type of the hydroelectric generating set.
Wherein, in the temperature rise curve storehouse, can save many temperature rise curves, each temperature rise curve can correspond with the type of hydroelectric generating set etc. this disclosure does not restrict this.
Therefore, in the embodiment of the present disclosure, the preset temperature rise curve library may be traversed based on the type of the hydro-power generating unit, so as to obtain the temperature rise curve corresponding to the type of the hydro-power generating unit from the temperature rise curve library, and determine the temperature rise curve as the healthy temperature rise curve.
It can be understood that the temperature rise curve can be generated by processing the historical operation data of the hydroelectric generating set and added to the temperature rise curve library, so that in the actual use process, the corresponding healthy temperature rise curve can be obtained from the temperature rise curve library according to the type of the hydroelectric generating set.
Optionally, each time in the operation cycle of any type of hydroelectric generating set may be input to the trained neural network model to obtain a reference temperature rise value corresponding to each time in the operation cycle, then a margin value may be added to each reference temperature rise value to obtain a corresponding health temperature rise value, and then each health temperature rise value is fitted according to each time in the operation cycle to generate a health temperature rise curve.
It can be understood that any time value in any type of hydroelectric generating set operation cycle can be input into the trained neural network model, and the reference temperature rise value corresponding to any time value can be output through the processing of the neural network model.
In addition, the margin value may be set in advance, or may be adjusted according to the actual situation, and the like, which is not limited in this disclosure.
Therefore, in the embodiment of the disclosure, after the reference temperature rise value is obtained, a margin value may be added on the basis of each reference temperature rise value, so as to obtain a healthy temperature rise value corresponding to each reference temperature rise value. For example, if the margin value is 1, the reference temperature rise values are: 7. 10, 12, then the health temperature rise value obtained after adding the margin value may be: 8. 11, 13, etc., to which the present disclosure is not limited.
It will be appreciated that after the healthy temperature rise values are obtained, each healthy temperature rise value can be fitted at various times within the operating cycle to generate a healthy temperature rise curve. The fitting can be performed by a least square method, or the fitting can be performed by software or the like, so as to obtain a healthy temperature rise curve, which is not limited by the disclosure.
It will be appreciated that the initial model may be trained to generate a trained neural network model.
Optionally, a historical data set may be obtained first, where the historical data set includes labeled temperature rise values corresponding to various times after the starting up and the running of any type of hydroelectric generating set, then each time may be input into an initial network model to obtain predicted temperature rise values corresponding to various times, then the initial network model may be corrected according to a difference between each predicted temperature rise value and each labeled temperature rise value to generate a trained neural network model, then each time in a running period of any type of hydroelectric generating set may be input into the trained neural network model to obtain reference temperature rise values corresponding to various times in the running period, and then each reference temperature rise value may be fitted according to various times in the running period to generate a healthy temperature rise curve.
The method comprises the steps of obtaining temperature values corresponding to all moments after each hydroelectric generating set is started and operated, and determining the difference value between the temperature value at each moment and the temperature value at the starting and starting moment after any hydroelectric generating set is started as a marked temperature rise value corresponding to each moment after the hydroelectric generating set is started and operated.
It can be understood that the labeled temperature rise value of different hydroelectric generating sets at different moments after starting can be obtained under the same type according to the type of each hydroelectric generating set.
In addition, the initial network model may be any neural network model, for example, a multi-layer feedforward neural network based on a Back Propagation (BP) algorithm, or any other neural network, and the disclosure does not limit this.
It can be understood that after the same type of hydroelectric generating set is started, each time value is input into the initial network model, so that the predicted temperature rise value corresponding to each time can be output through the processing of the initial network model. And then determining a loss value according to the difference between the predicted temperature rise value and the labeled temperature rise value corresponding to each moment. The initial network model may then be modified based on the loss values to generate a trained neural network model.
It should be noted that specific contents and implementation manners of step 202 and step 203 may refer to descriptions of other embodiments of the present disclosure, and are not described herein again.
It can be understood that the health temperature rise curve may include a temperature rise value corresponding to each time, and a starting point of the health temperature rise curve may be a starting time of the hydro-power generating unit, and then each time value in the health temperature rise curve, that is, a time length corresponding to the starting time of the hydro-power generating unit, and the like.
For example, if the first duration is 5 minutes, a corresponding temperature rise value, that is, a first temperature rise value, at a distance of "5 minutes" from the starting time of the hydroelectric generating set may be searched in the healthy temperature rise curve.
Or, if the current moment of the hydroelectric generating set is: and 6 minutes, the temperature rise value corresponding to the '6 minutes' can be directly searched in the healthy temperature rise curve, namely the first temperature rise value.
It should be noted that the above examples are merely illustrative, and should not be taken as limitations on the first temperature rise value and the like in the embodiments of the present disclosure.
Optionally, for the healthy temperature rise curve, a certain margin value may be set first, and then a first temperature rise value corresponding to the first time length is obtained therefrom, and the like, which is not limited in this disclosure.
And adding the second temperature value and the first temperature rise value to obtain a result, namely a third temperature value, namely the temperature value corresponding to the current time under the health temperature rise curve.
For example, if the second temperature value corresponding to the starting time of the hydroelectric generating set is: at 20 ℃ (DEG C), if the first time length between the current time and the starting time of the hydroelectric generating set is as follows: and 10 minutes, and determining that a first temperature rise value corresponding to the '10 minutes' is as follows based on the healthy temperature rise curve: and 5 ℃, according to the healthy temperature rise curve, determining that the third temperature value corresponding to the current moment is: 25 ℃, etc., as the disclosure does not limit.
And step 206, determining that the temperature of the bearing tile of the hydroelectric generating set is normal under the condition that the first temperature value is less than or equal to the third temperature value.
For example, if the first temperature value at the current time is 15 ℃, the determined third temperature value is 20 ℃ based on the healthy temperature rise curve and the second temperature value at the startup time, and since the temperature of 15 ℃ is less than 20 ℃, it may be determined that the temperature of the bearing tile of the hydroelectric generating set is normal at the current time, and so on, which is not limited by the disclosure.
And step 207, determining that the temperature of the bearing tile of the hydroelectric generating set is abnormal under the condition that the first temperature value is greater than the third temperature value.
For example, if the first temperature value at the current time is 30 ℃, the determined third temperature value is 20 ℃ based on the healthy temperature rise curve and the second temperature value at the startup time, and since the temperature of 30 ℃ is greater than 20 ℃, it may be determined that the temperature of the bearing tile of the hydroelectric generating set is abnormal at the current time, and the like, which is not limited by the disclosure.
And step 208, performing abnormity early warning under the condition that the temperature of the bearing tile of the hydroelectric generating set is abnormal.
It can be understood that, because the bearing tiles of the hydroelectric generating set are key devices for guaranteeing the normal operation of the hydroelectric generating set, if the temperature of the bearing bush abnormally fluctuates or even rapidly rises for some reason, the surface of the bearing bush may be burnt, and even the whole hydroelectric generating set may be forced to stop, thereby seriously affecting the normal power generation and the operation safety of the hydroelectric generating set. Therefore, in the embodiment of the disclosure, when the temperature of the bearing tile of the hydroelectric generating set is abnormal, abnormal early warning can be performed.
The abnormal early warning has various forms, such as voice reminding, or flashing through an indicator light to realize the abnormal early warning, for example, flashing red light of the indicator light can be used for representing the occurrence of abnormality; or, the display can be performed on a display interface; or a notification, such as a short message form, a mail form and the like, can be sent to terminal equipment associated with the staff; or may also be pushed by an associated terminal device, etc., which is not limited by this disclosure.
Optionally, after the healthy temperature rise curve is obtained, a certain margin value can be set, on the basis of the healthy temperature rise curve, a second temperature value at the starting-up starting time is added to obtain a standard temperature curve, and then the actual temperature rise curve can be matched with the standard temperature curve to determine the temperature condition of the bearing tile of the hydroelectric generating set.
For example, if the actual temperature rise curve is below the standard temperature curve, as shown in fig. 2A, it may be determined that the bearing tile temperature of the hydroelectric generating set is normal. If the actual temperature rise curve is located above the standard temperature curve, or an intersection exists, it can be determined that the temperature of the bearing tile of the hydroelectric generating set is abnormal, and the like, which is not limited by the disclosure.
According to the embodiment of the disclosure, a current device parameter and a corresponding health temperature rise curve of a hydroelectric generating set to be monitored can be obtained first, then, a state of the hydroelectric generating set can be determined based on the device parameter, a first temperature value at the current moment, a second temperature value at the starting-up starting moment and a first duration are determined under the condition that the state of the hydroelectric generating set is in the starting-up running state, the first duration is the duration between the current moment and the starting-up starting moment, then, a first temperature rise value corresponding to the first duration in the health temperature rise curve can be determined, the second temperature value and the first temperature rise value are fused to obtain a third temperature value, and under the condition that the first temperature value is smaller than or equal to the third temperature value, the temperature of a bearing tile of the hydroelectric generating set is determined to be normal, or under the condition that the first temperature value is larger than the third temperature value, the temperature of the bearing tile of the hydroelectric generating set is determined to be abnormal, and abnormal early warning is performed. Therefore, a corresponding first temperature rise value can be obtained from the health temperature rise curve based on the time length between the current time and the starting time, a third temperature rise value obtained by fusing the temperature value of the hydroelectric generating set at the current time with the first temperature rise value is matched with the third temperature value and the first temperature value at the current time, and the temperature condition of the bearing tile of the hydroelectric generating set at the current time can be determined according to the matching result, so that the accuracy and the reliability of the temperature of the bearing tile of the hydroelectric generating set are improved.
Fig. 3 is a schematic flow diagram of a trend tracking and early warning method for bearing bush temperature of a hydroelectric generating set according to an embodiment of the present disclosure.
As shown in fig. 3, the trend tracking and early warning method for the bearing bush temperature of the hydroelectric generating set may include the following steps:
and 301, acquiring current equipment parameters of the hydroelectric generating set to be monitored and a corresponding health temperature rise curve.
It should be noted that specific contents and implementation manners of steps 301 to 303 may refer to descriptions of other embodiments of the present disclosure, and are not described herein again.
And 304, acquiring real-time temperature values of the hydroelectric generating set at all times in the first duration.
For example, if the current time is "10 minutes", that is, 10 minutes from the startup of the hydroelectric generating set, the real-time temperature values corresponding to each time within "10 minutes" of the startup of the hydroelectric generating set may be obtained. For example, the value of the temperature sensor at each time can be obtained, and the disclosure is not limited thereto.
The second temperature value is a temperature value at the starting time of the hydroelectric generating set, each real-time temperature value is a temperature value corresponding to each time after the hydroelectric generating set is started, each real-time temperature value and the second temperature value can be respectively differenced to obtain each difference value, and the difference values are connected or fitted according to each time to generate a corresponding actual temperature rise curve.
The actual temperature rise curve can be used for representing the temperature rise value of the hydroelectric generating set corresponding to each moment from the starting-up moment to the current moment in the actual operation process.
And step 306, determining that the temperature of the bearing tile of the hydroelectric generating set is normal under the condition that the actual temperature rise curve is located below the healthy temperature rise curve.
It can be understood that if the actual temperature rise curve is always located below the healthy temperature rise curve, the temperature change trend of the bearing tiles of the hydroelectric generating set in actual operation can be considered to be smaller than the temperature change trend corresponding to the healthy temperature rise curve, and the temperature of the bearing tiles of the hydroelectric generating set can be considered to be normal.
For example, if the actual temperature rise curve and the healthy temperature rise curve are as shown in fig. 3A, and the actual temperature rise curve is located below the healthy temperature rise curve, it may be determined that the temperature of the tile of the bearing of the hydro-power generating unit is normal, and the like, which is not limited by the present disclosure.
And 307, determining that the temperature of the tile of the bearing of the hydroelectric generating set is abnormal under the condition that an intersection exists between the actual temperature rise curve and the healthy temperature rise curve or the actual temperature rise curve is located above the healthy temperature rise curve.
It can be understood that if the intersection of the actual temperature rise curve and the healthy temperature rise curve occurs, it can be considered that the temperature change of the bearing tiles in the hydroelectric generating set at a certain moment or a certain period is too large in the actual operation process and exceeds the temperature change of the bearing tiles in the healthy temperature rise curve, and it can be considered that the temperature of the bearing tiles of the hydroelectric generating set is abnormal.
Or, if the actual temperature rise curve is located above the healthy temperature rise curve, the temperature change trend of the bearing tile of the hydroelectric generating set in actual operation can be considered, the temperature change trend is different from the temperature change trend corresponding to the healthy temperature rise curve, and the temperature change trend is possibly abnormal, so that the temperature of the bearing tile of the hydroelectric generating set can be considered to be abnormal.
Optionally, for the healthy temperature rise curve, a certain margin value may be added, and then the comparison is performed with the actual temperature rise curve, which is not limited by the present disclosure.
Optionally, after the real-time temperature values corresponding to the hydroelectric generating set at each time within the first time period are obtained, the difference values between the real-time temperature values corresponding to each time and the second temperature values can be determined, then the health temperature rise values at each time in the health temperature rise curve can be obtained, and then the difference values corresponding to each time can be compared with the health temperature rise values at the corresponding time. If the difference value corresponding to the moment is smaller than or equal to the healthy temperature rise value at the same moment, the temperature of the bearing tile of the hydroelectric generating set can be considered to be normal; if the corresponding difference value at a certain moment is larger than the corresponding healthy temperature rise value, the temperature of the bearing tile of the hydroelectric generating set can be considered to be abnormal, and the like.
For example, after the hydroelectric generating set is started and operated for 60 minutes, if the difference value between the real-time temperature value and the second temperature value is 30 ℃ in the 60 th minute, the healthy temperature rise value corresponding to the 60 th minute is determined to be 24 ℃ from the healthy temperature rise curve, and the temperature of the tile of the bearing of the hydroelectric generating set is determined to be abnormal if the temperature of the tile is higher than 24 ℃ in the 30 th minute. Or if the hydroelectric generating set is started to operate for 100 minutes, if the difference value between the real-time temperature value and the second temperature value is 25 ℃ in the 100 th minute, the healthy temperature rise value corresponding to the 100 th minute is determined to be 40 ℃ from the healthy temperature rise curve, and the temperature of the tile of the bearing of the hydroelectric generating set is determined to be normal when the temperature of the tile is lower than 40 ℃ in the 25 ℃ range.
It should be noted that the above examples are only illustrative, and should not be taken as limiting the manner of determining the temperature of the bearing tile of the hydroelectric generating set in the embodiments of the present disclosure.
And 308, performing abnormity early warning under the condition that the temperature of the bearing tile of the hydroelectric generating set is abnormal.
The method comprises the steps of obtaining current equipment parameters and corresponding health temperature rise curves of a hydroelectric generating set to be monitored, then determining the state of the hydroelectric generating set based on the equipment parameters, determining a first temperature value at the current moment, a second temperature value at the starting-up starting moment and a first time length under the condition that the state of the hydroelectric generating set is in a starting-up running state, wherein the first time length is the time length between the current moment and the starting-up starting moment, then obtaining real-time temperature values corresponding to all moments of the hydroelectric generating set in the first time length, generating corresponding actual temperature rise curves based on the difference value between each real-time temperature value and each second temperature value, determining that the temperature of tiles of the bearing of the hydroelectric generating set is normal under the condition that the actual temperature rise curves are located below the health temperature rise curves, and determining that the temperature of the bearing tiles of the hydroelectric generating set is abnormal under the condition that the actual temperature rise curves are intersected with the health temperature rise curves or the actual temperature rise curves are located above the health temperature rise curves. Therefore, an actual temperature rise curve can be generated based on the difference value between the real-time temperature value and the second temperature value of each moment in the current moment and the starting-up starting moment, and then the temperature condition of the bearing tile of the hydroelectric generating set at the current moment can be determined according to the matching condition of the actual temperature rise curve and the healthy temperature rise curve, so that the accuracy and the reliability of the temperature of the bearing tile of the hydroelectric generating set are improved.
In order to realize the embodiment, the disclosure further provides a trend tracking and early warning device for the bearing bush temperature of the hydroelectric generating set.
Fig. 4 is a schematic structural diagram of a trend tracking and early warning device for bearing bush temperature of a hydroelectric generating set according to an embodiment of the present disclosure.
As shown in fig. 4, the trend tracking and early warning device 100 for bearing pad temperature of a hydroelectric generating set may include: an acquisition module 110, a first determination module 120, a second determination module 130, and a fourth determination module 140.
The obtaining module 110 is configured to obtain a current device parameter of the hydroelectric generating set to be monitored and a corresponding health temperature rise curve.
A first determining module 120, configured to determine a state of the hydroelectric generating set based on the device parameter;
a second determining module 130, configured to determine a first temperature value at the current time, a second temperature value at a start-up time, and a first duration when the state of the hydroelectric generating set is a start-up operating state, where the first duration is a duration between the current time and the start-up time;
the fourth determining module 140 is configured to determine whether the temperature of the bearing tile of the hydroelectric generating set is normal based on the first temperature value at the current time, the second temperature value and the first duration at the startup time, and the health temperature rise curve.
Optionally, the obtaining module 110 is specifically configured to:
and determining a healthy temperature rise curve from a preset temperature rise curve library based on the type of the hydroelectric generating set.
Optionally, the obtaining module 110 is further specifically configured to:
inputting each moment in the operation cycle of any type of hydroelectric generating set to a trained neural network model to obtain a reference temperature rise value corresponding to each moment in the operation cycle;
adding a margin value to each reference temperature rise value to obtain a corresponding health temperature rise value;
and fitting each healthy temperature rise value according to each moment in the operation period to generate a healthy temperature rise curve.
Optionally, the obtaining module 110 is further specifically configured to:
acquiring a historical data set, wherein the historical data set comprises marked temperature rise values corresponding to various moments after the hydroelectric generating set of any type is started to operate;
inputting each moment into an initial network model to obtain a predicted temperature rise value corresponding to each moment;
and correcting the initial network model according to the difference between each predicted temperature rise value and the labeled temperature rise value to generate a trained neural network model.
Optionally, the third determining module 140 is specifically configured to:
determining a first temperature rise value corresponding to the first time length in the healthy temperature rise curve;
fusing the second temperature value and the first temperature rise value to obtain a third temperature value;
determining that the temperature of the bearing tile of the hydroelectric generating set is abnormal under the condition that the first temperature value is greater than the third temperature value;
and determining that the temperature of the bearing tile of the hydroelectric generating set is normal under the condition that the first temperature value is less than or equal to the third temperature value.
Optionally, the third determining module 140 is further configured to:
and carrying out abnormity early warning under the condition that the temperature of the bearing tile of the hydroelectric generating set is abnormal.
Optionally, the method further includes a generating module, configured to:
acquiring real-time temperature values corresponding to all moments of the hydroelectric generating set in the first duration;
and generating a corresponding actual temperature rise curve based on the difference value between each real-time temperature value and the second temperature value.
Optionally, the third determining module 140 is specifically configured to:
determining that the temperature of the bearing tile of the hydroelectric generating set is normal under the condition that the actual temperature rise curve is located below the healthy temperature rise curve;
and determining that the temperature of the bearing tile of the hydroelectric generating set is abnormal under the condition that the actual temperature rise curve and the healthy temperature rise curve have intersection or the actual temperature rise curve is positioned above the healthy temperature rise curve.
The functions and specific implementation principles of the modules in the embodiments of the present disclosure may refer to the embodiments of the methods, and are not described herein again.
The trend tracking early warning device for the bearing bush temperature of the hydroelectric generating set can firstly acquire current equipment parameters and corresponding health temperature rise curves of the hydroelectric generating set to be monitored, then determine the state of the hydroelectric generating set based on the equipment parameters, and determine a first temperature value at the current moment, a second temperature value at the starting time and a first duration under the condition that the state of the hydroelectric generating set is in the starting running state, wherein the first duration is the duration between the current moment and the starting time, and then determine whether the bearing bush temperature of the hydroelectric generating set is normal or not based on the first temperature value at the current moment, the second temperature value at the starting time, the first duration and the health temperature rise curves. Therefore, a corresponding first temperature rise value can be obtained from the health temperature rise curve based on the time length between the current time and the starting time, the temperature rise value corresponding to the current time is determined according to the temperature value of the hydroelectric generating set at the current time and the temperature value of the starting time, and then the temperature rise value is compared with the first temperature rise value, so that whether the temperature of the bearing tile of the hydroelectric generating set at the current time is abnormal or not can be determined, and therefore the accuracy and the reliability of the temperature of the bearing tile of the hydroelectric generating set are improved.
In order to implement the foregoing embodiments, the present disclosure also provides a computer device, including: the trend tracking and early warning method for the bearing bush temperature of the hydroelectric generating set is realized when the processor executes the program.
In order to implement the foregoing embodiments, the present disclosure further provides a non-transitory computer-readable storage medium storing a computer program, where the computer program, when executed by a processor, implements the method for tracking and warning the trend of the bearing pad temperature of the hydroelectric generating set according to the foregoing embodiments of the present disclosure.
In order to implement the foregoing embodiments, the present disclosure further provides a computer program product, which when executed by an instruction processor in the computer program product, executes the trend tracking and early warning method for bearing pad temperature of a hydroelectric generating set according to the foregoing embodiments of the present disclosure.
FIG. 5 illustrates a block diagram of an exemplary computer device suitable for use to implement embodiments of the present disclosure. The computer device 12 shown in fig. 5 is only one example and should not bring any limitations to the functionality or scope of use of the embodiments of the present disclosure.
As shown in FIG. 5, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination of which may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described in this disclosure.
The processing unit 16 executes various functional applications and data processing, for example, implementing the methods mentioned in the foregoing embodiments, by executing programs stored in the system memory 28.
According to the technical scheme, the current equipment parameters and the corresponding health temperature rise curve of the hydroelectric generating set to be monitored can be obtained firstly, then the state of the hydroelectric generating set can be determined based on the equipment parameters, under the condition that the state of the hydroelectric generating set is in a starting operation state, the first temperature value at the current moment, the second temperature value at the starting moment and the first time length are determined, wherein the first time length is the time length between the current moment and the starting moment, and then whether the temperature of the bearing tile of the hydroelectric generating set is normal or not can be determined based on the first temperature value at the current moment, the second temperature value at the starting moment, the first time length and the health temperature rise curve. Therefore, a corresponding first temperature rise value can be obtained from the health temperature rise curve based on the time length between the current time and the starting time, the temperature rise value corresponding to the current time is determined according to the temperature value of the hydroelectric generating set at the current time and the temperature value of the starting time, and then the temperature rise value is compared with the first temperature rise value, so that whether the temperature of the bearing tile of the hydroelectric generating set at the current time is abnormal or not can be determined, and therefore the accuracy and the reliability of the temperature of the bearing tile of the hydroelectric generating set are improved.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, "plurality" means at least two, e.g., two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer-readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure, and that changes, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present disclosure.
Claims (10)
1. A trend tracking early warning method for bearing bush temperature of a hydroelectric generating set is characterized by comprising the following steps:
acquiring current equipment parameters of a hydroelectric generating set to be monitored and a corresponding health temperature rise curve;
determining a state of the hydroelectric generating set based on the equipment parameter;
under the condition that the state of the hydroelectric generating set is a starting operation state, determining a first temperature value at the current moment, a second temperature value at the starting moment and a first time length, wherein the first time length is the time length between the current moment and the starting moment;
and determining whether the temperature of the bearing tile of the hydroelectric generating set is normal or not based on the first temperature value at the current moment, the second temperature value and the first duration at the starting-up starting moment and the health temperature rise curve.
2. The method according to claim 1, wherein the obtaining of the current device parameters and the corresponding health temperature rise curve of the hydroelectric generating set to be monitored comprises:
and determining a healthy temperature rise curve from a preset temperature rise curve library based on the type of the hydroelectric generating set.
3. The method of claim 2, wherein prior to said determining a healthy temperature rise profile from a library of preset temperature rise profiles, further comprising:
inputting each moment in the operation cycle of any type of hydroelectric generating set to a trained neural network model to obtain a reference temperature rise value corresponding to each moment in the operation cycle;
adding a margin value to each reference temperature rise value to obtain a corresponding health temperature rise value;
and fitting each health temperature rise value according to each moment in the operation period to generate a health temperature rise curve.
4. The method of claim 3, wherein prior to inputting each time within an operational cycle of any type of hydroelectric generating set into the trained neural network model, further comprising:
acquiring a historical data set, wherein the historical data set comprises marked temperature rise values corresponding to various moments after the hydroelectric generating set of any type is started to operate;
inputting each moment into an initial network model to obtain a predicted temperature rise value corresponding to each moment;
and correcting the initial network model according to the difference between each predicted temperature rise value and the labeled temperature rise value to generate a trained neural network model.
5. The method of claim 1, wherein determining whether the bearing tile temperature of the hydro-power unit is normal based on the first temperature value at the current time, the second temperature value and the first duration at the start-up time, and the healthy temperature rise profile comprises:
determining a first temperature rise value corresponding to the first time length in the healthy temperature rise curve;
fusing the second temperature value and the first temperature rise value to obtain a third temperature value;
determining that the temperature of the bearing tile of the hydroelectric generating set is abnormal under the condition that the first temperature value is greater than the third temperature value;
and determining that the temperature of the bearing tile of the hydroelectric generating set is normal under the condition that the first temperature value is less than or equal to the third temperature value.
6. The method of claim 1, further comprising, after the determining whether the hydro-power unit bearing tile temperature is normal:
and under the condition that the temperature of the bearing tile of the hydroelectric generating set is abnormal, performing abnormity early warning.
7. The method of claim 1, wherein after the determining the first temperature value at the current time, the second temperature value at the power-on start time, and the first duration, further comprising:
acquiring real-time temperature values corresponding to all moments of the hydroelectric generating set within the first time length;
and generating a corresponding actual temperature rise curve based on the difference value between each real-time temperature value and the second temperature value.
8. The method of claim 7, wherein the determining whether the hydro-power unit bearing tile temperature is normal comprises:
determining that the temperature of the bearing tile of the hydroelectric generating set is normal under the condition that the actual temperature rise curve is located below the healthy temperature rise curve;
and determining that the temperature of the bearing tile of the hydroelectric generating set is abnormal under the condition that the actual temperature rise curve and the healthy temperature rise curve have intersection or the actual temperature rise curve is positioned above the healthy temperature rise curve.
9. The utility model provides a trend tracking early warning device of hydroelectric generating set bearing bush temperature which characterized in that includes:
the acquisition module is used for acquiring current equipment parameters of the hydroelectric generating set to be monitored and a corresponding health temperature rise curve;
the first determination module is used for determining the state of the hydroelectric generating set based on the equipment parameters;
the second determining module is used for determining a first temperature value at the current moment, a second temperature value at the starting-up starting moment and a first time length under the condition that the state of the hydroelectric generating set is the starting-up running state, wherein the first time length is the time length between the current moment and the starting-up starting moment;
and the third determining module is used for determining whether the temperature of the bearing tile of the hydroelectric generating set is normal or not based on the first temperature value at the current moment, the second temperature value and the first duration at the starting-up moment and the health temperature rise curve.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the program, implements the method for trend-based early warning of bearing pad temperature in a hydroelectric generating set according to any of claims 1 to 8.
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