CN114322446B - Cooling system fault early warning method and device, cooling system and working machine - Google Patents
Cooling system fault early warning method and device, cooling system and working machine Download PDFInfo
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- 230000002159 abnormal effect Effects 0.000 claims description 27
- 239000010720 hydraulic oil Substances 0.000 claims description 16
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Abstract
The invention provides a cooling system fault early warning method, a cooling system fault early warning device, a cooling system and a working machine, wherein the method comprises the following steps: acquiring working condition information of carrier equipment corresponding to a cooling system; selecting a corresponding fault early warning mode according to the working condition information of carrier equipment corresponding to the cooling system; the fault early warning mode comprises a cold start early warning mode, a transition early warning mode and a dynamic early warning mode; and carrying out fault early warning on the cooling water temperature abnormality in the cooling system based on the selected fault early warning mode. Through setting up multiple trouble early warning mode, can select corresponding trouble early warning mode according to the operating mode information of the carrier equipment that cooling system corresponds to realize cooling water temperature unusual early warning, compare in single trouble early warning mode, this early warning scheme is more pointed, and the fault detection result is more accurate, reliable.
Description
Technical Field
The present invention relates to the field of fault detection technologies, and in particular, to a cooling system fault early warning method and apparatus, a cooling system, and a working machine.
Background
The cooling system is used as a part which is helpful for heat dissipation of the core part of the equipment, the working stability of the cooling system is an important factor which affects the safe operation of the equipment, and if the cooling system is not found abnormal in time, the core part of the equipment can be damaged, and even safety accidents are caused.
However, the existing cooling system fault detection method has the problems of single fault detection and early warning modes and low reliability of fault detection results, and is difficult to meet the actual application demands.
Disclosure of Invention
The invention provides a cooling system fault early warning method and device, a cooling system and an operation machine, which are used for solving the defects that in the prior art, the fault detection method of the cooling system is single in fault detection and early warning mode and the reliability of a fault detection result is low.
In a first aspect, the present invention provides a cooling system fault early warning method, including:
acquiring working condition information of carrier equipment corresponding to a cooling system;
selecting a corresponding fault early warning mode according to the working condition information of the carrier equipment corresponding to the cooling system; the fault early warning mode comprises a cold start early warning mode, a transition early warning mode and a dynamic early warning mode;
and carrying out fault early warning on the cooling water temperature abnormality in the cooling system based on the selected fault early warning mode.
According to the cooling system fault early warning method provided by the invention, the working condition information of the carrier equipment corresponding to the cooling system comprises the access time length of the cooling system and the equipment operation data.
According to the cooling system fault early warning method provided by the invention, a corresponding fault early warning mode is selected according to the working condition information of carrier equipment corresponding to the cooling system, and the method comprises the following steps:
judging whether the access time length of the cooling system is lower than a preset time length lower limit value, and if the access time length of the cooling system is lower than the time length lower limit value, selecting a cold start early warning mode;
if the access time length of the cooling system is between a preset time length lower limit value and a time length upper limit value or the access time length of the cooling system is higher than the time length lower limit value and the equipment operation data lacks part of fields, a transition early warning mode is selected;
and if the access duration of the cooling system is higher than the duration upper limit threshold, selecting a dynamic early warning mode.
According to the cooling system fault early warning method provided by the invention, when the selected fault early warning mode is a cold start early warning mode, the fault early warning is carried out on the cooling water temperature abnormality occurring in the cooling system based on the selected fault early warning mode, and the method comprises the following steps:
acquiring cooling water temperature data of the cooling system within a preset period;
judging whether the cooling water temperature highest value in the cooling water temperature data is higher than a preset unified water temperature threshold value or not, and judging whether the duration of the cooling water temperature highest value higher than the unified water temperature threshold value exceeds a preset overrun duration threshold value or not, if so, judging that the cooling water temperature is abnormal and performing fault early warning.
According to the cooling system fault early warning method provided by the invention, when the selected fault early warning mode is a transition early warning mode, fault early warning is carried out on cooling water temperature abnormality occurring in the cooling system based on the selected fault early warning mode, and the method comprises the following steps:
acquiring cooling water temperature data and historical water temperature data of the cooling system within a preset period;
judging whether the cooling water temperature highest value in the cooling water temperature data is higher than the water temperature highest threshold value in the historical water temperature data and the time length higher than the water temperature highest threshold value exceeds a preset overrun time length threshold value, if so, judging that the cooling water temperature is abnormal and performing fault early warning.
According to the cooling system fault early warning method provided by the invention, when the selected fault early warning mode is a dynamic early warning mode, fault early warning is carried out on cooling water temperature abnormality occurring in the cooling system based on the selected fault early warning mode, and the method comprises the following steps:
acquiring actual operation data of carrier equipment corresponding to the cooling system in a preset period;
based on the actual operation data, obtaining characteristic data of the cooling system and an actual measurement value of the cooling water temperature;
inputting the characteristic data into a cooling water temperature prediction model to obtain a cooling water temperature prediction value output by the cooling water temperature prediction model; the cooling water temperature prediction model is obtained by training and testing a nonlinear regression model based on sample characteristic data and sample label data of the cooling system;
the actually measured cooling water temperature value and the predicted cooling water temperature value are subjected to difference to obtain a water temperature difference value statistical value;
and comparing the water temperature difference statistical value with a preset water temperature difference statistical value threshold, and if the water temperature difference statistical value is larger than the water temperature difference statistical value threshold, judging that the cooling water temperature is abnormal and performing fault early warning.
According to the cooling system fault early warning method provided by the invention, the training process of the cooling water temperature prediction model comprises the following steps:
acquiring original sample data of a cooling system;
extracting sample characteristic data and sample label data from the original sample data; the sample characteristic data comprise one or more of an ambient temperature statistic value, a hydraulic oil temperature statistic value and an engine rotating speed statistic value, and the sample label data comprise a cooling water temperature statistic value;
and training and testing the nonlinear regression model based on the sample characteristic data and the sample label data to obtain a cooling water temperature prediction model.
In a second aspect, the present invention also provides a cooling system fault early warning device, which includes:
the acquisition module is used for acquiring the working condition information of the carrier equipment corresponding to the cooling system;
the first processing module is used for selecting a corresponding fault early warning mode according to the working condition information of the carrier equipment corresponding to the cooling system; the fault early warning mode comprises a cold start early warning mode, a transition early warning mode and a dynamic early warning mode;
and the second processing module is used for carrying out fault early warning on the cooling water temperature abnormality in the cooling system based on the selected fault early warning mode.
In a third aspect, the present invention further provides a cooling system, where the cooling system uses any one of the foregoing cooling system fault early warning methods, and the cooling system uses the foregoing fault early warning method to timely and reliably early warn an abnormal fault of its own cooling water temperature.
In a fourth aspect, the invention also provides a working machine, which comprises the cooling system, and the working machine can timely find abnormal faults of cooling water temperature and timely perform early warning in the working process by using the cooling system, so that the working process is safer.
According to the cooling system fault early warning method, the cooling system and the operation machine, through setting the plurality of fault early warning modes, the corresponding fault early warning mode can be selected according to the working condition information of the carrier equipment corresponding to the cooling system to realize abnormal early warning of the cooling water temperature.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a cooling system fault early warning method provided by the invention;
FIG. 2 is a schematic diagram of a training and testing flow of a cooling water temperature prediction model;
FIG. 3 is a schematic diagram of a data feature extraction process;
FIG. 4 is a schematic structural diagram of a cooling system fault warning device provided by the invention;
fig. 5 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The cooling system fault early warning method, the cooling system and the working machine provided by the invention are described below with reference to fig. 1 to 4.
Fig. 1 shows a cooling system fault early warning method provided by an embodiment of the present invention, where the method includes:
step 110: and acquiring the working condition information of carrier equipment corresponding to the cooling system.
The working condition information of the carrier device corresponding to the cooling system in this embodiment may include access duration data and device operation data of the cooling system. The carrier device herein refers to a device or work machine in which the cooling system is installed, and the device operation data may include one or any one of ambient temperature data, hydraulic oil temperature data, and engine speed data.
Step 120: selecting a corresponding fault early warning mode according to the working condition information of carrier equipment corresponding to the cooling system; the fault early warning modes comprise a cold start early warning mode, a transition early warning mode and a dynamic early warning mode.
In order to realize the setting of a targeted early warning mode, according to the access time length of the cooling system and whether equipment operation data exist, three fault early warning modes are correspondingly set, so that a fault early warning scheme can be started in a targeted manner, and the early warning effect is better.
In this embodiment, the process of selecting the corresponding fault early warning mode according to the working condition information of the carrier device corresponding to the cooling system may specifically include:
judging whether the access time length of the cooling system is lower than a preset time length lower limit value, and if the access time length of the cooling system is lower than the time length lower limit value, selecting a cold start early warning mode;
if the access time length of the cooling system is between a preset time length lower limit value and a time length upper limit value or the access time length of the cooling system is higher than the time length lower limit value and the equipment operation data lacks part of fields, selecting a transition early warning mode;
and if the access time length of the cooling system is higher than the time length upper limit threshold value, selecting a dynamic early warning mode.
In the actual application process, the access time of the cooling system can be judged by the access time of the working machine (namely the carrier equipment) to which the cooling system is attached.
For example, the lower limit value of the time length can be set to be one month, the upper limit value of the time length is one year, when the access time length of the cooling system is lower than one month, the cooling system can be determined to be a newly accessed system, and the cold start early warning mode can be selected because the related working condition data of the cooling system cannot be obtained.
When the access time of the cooling system is longer than one month but less than one year, or the access time of the cooling system is longer than one month or even longer than one year, the related equipment operation data cannot be obtained, and at the moment, because the effective data of the hydraulic system is less than one year, or because the equipment operation data is incomplete, dynamic early warning cannot be carried out in a model training mode based on the existing data of the hydraulic system at present, and only a transition early warning mode can be adopted.
When the access time of the cooling system is longer than one year and relevant equipment operation data exist, a dynamic early warning mode is adopted at the moment.
Step 130: and carrying out fault early warning on the cooling water temperature abnormality in the cooling system based on the selected fault early warning mode.
When the selected fault early-warning mode is a cold start early-warning mode, performing fault early-warning on abnormal cooling water temperature in the cooling system based on the selected fault early-warning mode may include:
acquiring cooling water temperature data of a cooling system in a preset period;
judging whether the cooling water temperature highest value in the cooling water temperature data is higher than a preset unified water temperature threshold value or not, and judging that the cooling water temperature is abnormal and performing fault early warning if the cooling water temperature highest value is higher than the preset unified water temperature threshold value and the duration of the cooling water temperature highest value exceeds a preset overrun duration threshold value.
When the cold start early warning mode is adopted, a preset period can be set as one day, whether the highest value of the daily cooling water temperature of the cooling system is higher than a preset unified water temperature threshold value or not is judged, whether the duration time exceeding the threshold value exceeds the threshold value of the overrun time length or not is judged, if the two judging results are met, the occurrence of water temperature abnormality can be judged, and fault early warning is needed.
In the practical application process, the unified water temperature threshold and the overrun time threshold can be reasonably set according to practical application scenes, and meanwhile, the unified water temperature thresholds of all new access hydraulic systems can be set to be consistent.
When the selected fault early-warning mode is a transition early-warning mode, carrying out fault early-warning on cooling water temperature abnormality in the cooling system based on the selected fault early-warning mode, wherein the fault early-warning comprises the following steps:
acquiring cooling water temperature data and historical water temperature data of a cooling system in a preset period;
judging whether the cooling water temperature highest value in the cooling water temperature data is higher than a water temperature highest threshold value in the historical water temperature data and the time period higher than the water temperature highest threshold value exceeds an overrun time period threshold value, if so, judging that the cooling water temperature is abnormal and performing fault early warning.
When the transition early warning mode is adopted, the preset time period can be set as one day, whether the highest value of the cooling water temperature of the cooling system per day exceeds the highest water temperature threshold value in the historical water temperature data or not is judged, and whether the time length exceeding the highest water temperature threshold value exceeds the threshold value of the overrun time length or not is judged, if the two conditions are met, the abnormal cooling water temperature can be judged, and the fault early warning can be triggered.
It should be noted that, in the transient early warning mode, the highest water temperature threshold is obtained based on the historical data of the cooling system itself, so the thresholds are not uniform and slightly different depending on the cooling system. Of course, in the actual application process, the water temperature threshold value obtained based on the historical data may not be the highest water temperature value in the historical data, and may be set reasonably according to actual needs.
In the practical application process, in order to ensure the reliability of the historical water temperature data, the historical water temperature data in a certain time range close to the preset time period can be selected, for example, when the preset time period is one day, the historical water temperature data in the first half month of the day can be selected, and the data reliability is higher because the time of the selected historical water temperature data is relatively close to the current detection time.
When the selected fault early-warning mode is a dynamic early-warning mode, performing fault early-warning on cooling water temperature abnormality occurring in the cooling system based on the selected fault early-warning mode, including:
acquiring actual operation data of carrier equipment corresponding to a cooling system in a preset period;
based on the actual operation data, obtaining characteristic data of the cooling system and an actual measurement value of the cooling water temperature;
inputting the characteristic data into a cooling water temperature prediction model to obtain a cooling water temperature prediction value output by the cooling water temperature prediction model; the cooling water temperature prediction model is obtained by training and testing a nonlinear regression model based on sample characteristic data and sample label data of a cooling system;
the actual measurement value of the cooling water temperature is differenced with the predicted value of the cooling water temperature, and a water temperature difference value statistic value is obtained;
and comparing the water temperature difference statistical value with a preset water temperature difference statistical value threshold, and if the water temperature difference statistical value is larger than the water temperature difference statistical value threshold, judging that the cooling water temperature is abnormal and performing fault early warning.
When the dynamic early warning mode is adopted, a cooling water temperature prediction model of each carrier device can be established by taking the carrier device (i.e. the working machine attached by the cooling system) as a unit, and the process of training and testing the model can comprise the following steps:
firstly, acquiring original sample data of a cooling system; the original sample data can comprise one or more of environmental temperature data, hydraulic oil temperature data and engine rotating speed data, and cooling water temperature data;
then, extracting sample characteristic data and sample label data from the original sample data; the sample characteristic data comprise one or more of an ambient temperature statistic value, a hydraulic oil temperature statistic value and an engine rotating speed statistic value, and the sample label data comprise a cooling water temperature statistic value;
and finally, training and testing the nonlinear regression model based on the sample characteristic data and the sample label data to obtain a cooling water temperature prediction model.
In this embodiment, the flow of model training and model testing of the cooling water temperature prediction model is shown in fig. 2, and specifically includes:
step 201: the method comprises the steps of acquiring original sample data, acquiring sensor data of different columns (namely different types) to obtain related data, namely the original sample data, of a hydraulic system in normal operation, wherein the data acquisition frequency can be reasonably set according to actual needs, and partial data in the original sample data can be taken as training data.
Step 202: and preprocessing training data, namely setting screening rules for the data of each column sensor, removing noise points in the data, and ensuring the accuracy of a training data set. In this embodiment, taking the case that the original sample data includes the temperature of hydraulic oil and the temperature of cooling water as an example, the set screening rules are as follows:
50 ℃ < cooling water temperature <113 ℃;
30 ℃ < hydraulic oil temperature <90 ℃;
the standard deviation of the cooling water temperature is more than 0;
hydraulic oil temperature standard deviation >0;
the number of data sampling points >100.
Step 203: and training data feature extraction, wherein for each cooling system, data features are extracted in a sliding window mode, and a specific feature extraction principle is shown in fig. 3, wherein the original data are two-dimensional structured data, the data of different types of sensors at the same moment in each behavior are listed as the data of each sensor at different moments. The dashed box is a time sliding window, which includes the amount of raw data used for extracting each feature data, in order to ensure a sufficient amount of data in the feature data of the training set, the size of the time sliding window may be 2 hours, and the size of the window may be 1 day during online operation. The offset between each window is a sliding window time span, the offset of the training data is typically set to half the window size, and the on-line run offset may be set to 1 day.
The feature data and the label corresponding to each data example are shown in fig. 3, and in the feature extraction section, fnn represents feature data extracted from a time sliding window, and mainly includes one or any of an ambient temperature statistic value, a hydraulic oil temperature statistic value, and an engine speed statistic value, and specifically may include an ambient temperature fraction (may be 99% fraction), an ambient temperature median, an ambient temperature standard deviation, a hydraulic oil temperature fraction value (may be 99% fraction), a hydraulic oil temperature median, a hydraulic oil temperature standard deviation, an engine speed fraction (may be 99% fraction), an engine speed median, an engine speed standard deviation, and a current month. labeln represents label data corresponding to a time sliding window, and the label is a cooling water temperature statistic value, specifically may be a cooling water temperature quantile value (may be 99% quantile).
Step 204: and establishing a regression model between the features and the water temperature, training the nonlinear regression model by using the extracted feature data and the label data, and establishing the nonlinear regression model of the feature data and the label data.
Step 205: and calculating a residual error between the model predicted value and the true value, inputting characteristic data in the training data into the model based on the obtained nonlinear regression model, obtaining a corresponding cooling water temperature predicted value, and differentiating the cooling water temperature predicted value and an actual measured value (namely the true value) in the training data to obtain residual error data.
Step 206: and determining a residual error threshold, wherein the difference between the actually measured cooling water temperature value and the predicted cooling water temperature value can be calculated through nonlinear regression model prediction to obtain residual error data, the statistical value of the selected residual error can be used as the residual error threshold, and the residual error threshold can be used as the statistical value threshold of the water temperature difference value for judging whether the water temperature is abnormal or not.
In the step of determining the residual error threshold value, the method can also be realized by using a verification data set, namely, part of data is taken from original sample data as the verification data set, after the same preprocessing and feature extraction as those of training data, feature data and tag data of the verification data set are obtained, the feature data is input into a nonlinear regression model obtained through training, a cooling water temperature verification value can be obtained, and then the cooling water temperature verification value and an actual measurement value in the tag data are subjected to difference to obtain the residual error data, so that the residual error threshold value can be determined.
Step 207: and constructing a test data set, and taking part of data in the original sample data as test data.
Step 208: the test data preprocessing, namely removing the screening condition of the upper limit threshold value of the cooling water temperature in the test data preprocessing process, and removing noise points in the data through screening, wherein the specific screening rule can be as follows:
the cooling water temperature is more than 50 ℃;
30 ℃ < hydraulic oil temperature <90 ℃;
the standard deviation of the cooling water temperature is more than 0;
hydraulic oil temperature standard deviation >0;
the number of data sampling points >100.
Step 209: and (3) extracting the characteristics of the test data, wherein the characteristic extraction mode is approximately consistent with the characteristic extraction mode of the training data given in the step 203, and finally, a test data set for model test, which is composed of the characteristic data and the label data, is obtained.
Step 210: model prediction, wherein the feature data is predicted through a nonlinear regression model obtained through training, so as to obtain a model test value.
Step 211: and calculating the difference between the model test value and the true value (namely the label data) in the test data set to obtain residual data.
Step 212: comparing the obtained residual error threshold value with the residual error data obtained in the step 211 to determine whether each residual error in the residual error data exceeds the residual error threshold value, if so, performing fault early warning of water temperature abnormality, and thus completing model test to obtain a cooling water temperature prediction model.
The implementation flow of the cooling system fault early warning method is described in detail below by a specific example.
In this embodiment, taking the cooling system failure prediction of a stake machine as an example, the equipment user submitted a failure order at day 4 and 8 of 2021, and confirmed that the equipment failed on that day.
And firstly, acquiring sampling data of each sensor related to a cooling system on the pile driver to obtain original data. The sensor mainly comprises an environment temperature sensor, a cooling water temperature sensor, an engine rotating speed sensor and a hydraulic oil temperature sensor which correspond to the cooling system.
And secondly, dividing the data set, taking the data collected by 2019.1.1-2020.3.1 as a training data set, taking the data collected by 2020.3.1-2021.3.1 as a verification data set and taking the data collected by 2021.3.1-2021.5.1 as a test data set according to the time-slicing data set.
Thirdly, data screening and feature extraction are carried out on the data set, wherein the time sliding window of the training set is 2 hours in size, 6607 data examples are obtained, and the lightgbm regression model is obtained by training the 6607 data examples.
And fourthly, carrying out data screening and feature extraction on the verification data set, wherein the size of a time sliding window of the verification set is 2 hours, 5120 data instances are obtained, the verification features are predicted by adopting a training-obtained lightgbm regression model, the difference between the actual cooling water temperature and the predicted cooling water temperature in the verification data set is obtained, a residual curve is drawn, and in order to avoid the influence of noise points in the data, 99% quantile of residual data is taken as a residual threshold value.
And fifthly, carrying out data screening and feature extraction on the test data set, wherein the cooling water temperature parameter is not screened, the size of a time sliding window of the test set is 1 day, predicting test features by adopting a model obtained through training to obtain the difference between the actual cooling water temperature and the predicted cooling water temperature in the test data set, drawing a residual curve, comparing the residual threshold obtained in the fourth step with each residual point in the residual curve, and judging whether each residual point exceeds the residual threshold, if the residual threshold is exceeded, the water temperature abnormality is indicated, and fault early warning is needed.
Sixth, in the last measured result, the residual error of 2021, 4 and 1 exceeds the residual error threshold, and the fault early warning time is about seven days earlier than the actual fault time. Therefore, the early warning method can timely and accurately early warn the abnormal water temperature faults of the cooling system in advance.
Therefore, according to the cooling system fault early warning method provided by the embodiment of the invention, by setting a plurality of fault early warning modes, cold start of fault prediction can be performed on the newly-accessed cooling system, meanwhile, as the access time length becomes longer, a more reliable fault early warning function can be realized by utilizing historical data of the system, and the precision of fault early warning is effectively improved.
The cooling system fault early-warning device provided by the invention is described below, and the cooling system fault early-warning device described below and the cooling system fault early-warning method described above can be referred to correspondingly.
Fig. 4 shows a cooling system fault early warning device provided by an embodiment of the present invention, where the device includes:
an obtaining module 410, configured to obtain operating condition information of carrier equipment corresponding to the cooling system;
the first processing module 420 is configured to select a corresponding failure pre-warning mode according to the working condition information of the carrier device corresponding to the cooling system; the fault early warning mode comprises a cold start early warning mode, a transition early warning mode and a dynamic early warning mode;
the second processing module 430 is configured to perform fault early warning on an abnormal cooling water temperature occurring in the cooling system based on the selected fault early warning mode.
Specifically, the working condition information of the carrier device corresponding to the cooling system may include access duration data and device operation data of the cooling system.
In an exemplary embodiment, the first processing module 420 is specifically configured to:
judging whether the access time length of the cooling system is lower than a preset time length lower limit value, and if the access time length of the cooling system is lower than the time length lower limit value, selecting a cold start early warning mode;
if the access time length of the cooling system is between a preset time length lower limit value and a time length upper limit value or the access time length of the cooling system is higher than the time length lower limit value and the running data of the cooling system equipment lacks part of fields, selecting a transition early warning mode;
and if the access time length of the cooling system is higher than the time length upper limit threshold value, selecting a dynamic early warning mode.
When the selected fault early warning mode is a cold start early warning mode, the second processing module 430 is specifically configured to:
acquiring cooling water temperature data of a cooling system in a preset period;
judging whether the cooling water temperature highest value in the cooling water temperature data is higher than a preset unified water temperature threshold value or not, and judging that the cooling water temperature is abnormal and performing fault early warning if the cooling water temperature highest value is higher than the preset unified water temperature threshold value and the duration of the cooling water temperature highest value exceeds a preset overrun duration threshold value.
When the selected fault early warning mode is the transition early warning mode, the second processing module 430 is specifically configured to:
acquiring cooling water temperature data and historical water temperature data of a cooling system in a preset period;
judging whether the cooling water temperature highest value in the cooling water temperature data is higher than a water temperature highest threshold value in the historical water temperature data and the time period of the cooling water temperature highest value is higher than a preset overrun time period threshold value or not, if so, judging that the cooling water temperature is abnormal and performing fault early warning.
When the selected fault early warning mode is a dynamic early warning mode, the second processing module 430 is specifically configured to:
acquiring actual operation data of carrier equipment corresponding to a cooling system in a preset period;
based on the actual operation data, obtaining characteristic data of the cooling system and an actual measurement value of the cooling water temperature;
inputting the characteristic data into a cooling water temperature prediction model to obtain a cooling water temperature prediction value output by the cooling water temperature prediction model; the cooling water temperature prediction model is obtained by training and testing a nonlinear regression model based on sample characteristic data and sample label data of a cooling system;
the actual measurement value of the cooling water temperature is differenced with the predicted value of the cooling water temperature, and a water temperature difference value statistic value is obtained;
and comparing the water temperature difference statistical value with a preset water temperature difference statistical value threshold, and if the water temperature difference statistical value is larger than the water temperature difference statistical value threshold, judging that the cooling water temperature is abnormal and performing fault early warning.
In an exemplary embodiment, the cooling system fault early warning device further includes a model training module, specifically configured to:
acquiring original sample data of a cooling system;
extracting sample characteristic data and sample label data from the original sample data; the sample characteristic data comprise one or more of an ambient temperature statistic value, a hydraulic oil temperature statistic value and an engine rotating speed statistic value, and the sample label data comprise a cooling water temperature statistic value;
and training and testing the nonlinear regression model based on the sample characteristic data and the sample label data to obtain a cooling water temperature prediction model.
Therefore, the cooling system fault early warning device provided by the embodiment of the invention can select a corresponding fault early warning mode according to the access time of the cooling system, can realize the fault early warning function according to the pertinence of the actual situation, has more perfect functions, and has higher accuracy and reliability of the fault early warning process.
In addition, the embodiment of the invention also provides a cooling system, which can timely and reliably early warn the abnormal faults of the self cooling water temperature by using the cooling system fault early warning method.
The embodiment of the invention also provides a working machine, which comprises the cooling system, wherein the cooling system is arranged on the working machine, the cooling system can be understood as a heat dissipation system on the working machine, abnormal faults of the cooling water temperature can be timely found and early-warned in the working process through the system, and the working process is safer.
In the practical application process, the working machine can be a pile machine, such as a rotary drilling rig, and when the access time length of the cooling system is determined, the access time length data of the whole working machine can be utilized, so that the abnormal cooling water temperature of the pile machine is detected and pre-warned in a parallel mode of a plurality of fault pre-warning modes.
Fig. 5 illustrates a physical schematic diagram of an electronic device, as shown in fig. 5, which may include: processor 510, communication interface (Communications Interface) 520, memory 530, and communication bus 540, wherein processor 510, communication interface 520, memory 530 complete communication with each other through communication bus 540. Processor 510 may invoke logic instructions in memory 530 to perform a cooling system fault warning method comprising: acquiring working condition information of carrier equipment corresponding to a cooling system; selecting a corresponding fault early warning mode according to the working condition information of carrier equipment corresponding to the cooling system; the fault early warning mode comprises a cold start early warning mode, a transition early warning mode and a dynamic early warning mode; and carrying out fault early warning on the cooling water temperature abnormality in the cooling system based on the selected fault early warning mode.
Further, the logic instructions in the memory 530 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the cooling system fault warning method provided by the above methods, the method comprising: acquiring working condition information of carrier equipment corresponding to a cooling system; selecting a corresponding fault early warning mode according to the working condition information of carrier equipment corresponding to the cooling system; the fault early warning mode comprises a cold start early warning mode, a transition early warning mode and a dynamic early warning mode; and carrying out fault early warning on the cooling water temperature abnormality in the cooling system based on the selected fault early warning mode.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the cooling system fault warning methods provided above, the method comprising: acquiring working condition information of carrier equipment corresponding to a cooling system; selecting a corresponding fault early warning mode according to the working condition information of carrier equipment corresponding to the cooling system; the fault early warning mode comprises a cold start early warning mode, a transition early warning mode and a dynamic early warning mode; and carrying out fault early warning on the cooling water temperature abnormality in the cooling system based on the selected fault early warning mode.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. A cooling system failure warning method, comprising:
acquiring working condition information of carrier equipment corresponding to a cooling system;
selecting a corresponding fault early warning mode according to the working condition information of the carrier equipment corresponding to the cooling system; the fault early warning mode comprises a cold start early warning mode, a transition early warning mode and a dynamic early warning mode;
based on the selected fault early warning mode, carrying out fault early warning on cooling water temperature abnormality in the cooling system;
the working condition information of the carrier equipment corresponding to the cooling system comprises the access time length of the cooling system and the equipment operation data;
according to the working condition information of the carrier equipment corresponding to the cooling system, selecting a corresponding fault early warning mode, wherein the fault early warning mode comprises the following steps:
judging whether the access time length of the cooling system is lower than a preset time length lower limit value, and if the access time length of the cooling system is lower than the time length lower limit value, selecting a cold start early warning mode;
if the access time length of the cooling system is between a preset time length lower limit value and a time length upper limit value or the access time length of the cooling system is higher than the time length lower limit value and the equipment operation data lacks part of fields, a transition early warning mode is selected;
if the access time length of the cooling system is higher than the time length upper limit value, a dynamic early warning mode is selected;
when the selected fault early-warning mode is a cold start early-warning mode, performing fault early-warning on cooling water temperature abnormality occurring in the cooling system based on the selected fault early-warning mode, including:
acquiring cooling water temperature data of the cooling system within a preset period;
judging whether the cooling water temperature highest value in the cooling water temperature data is higher than a preset unified water temperature threshold value or not and the time length higher than the unified water temperature threshold value exceeds a preset overrun time length threshold value, if so, judging that the cooling water temperature is abnormal and performing fault early warning;
when the selected fault early-warning mode is a transition early-warning mode, carrying out fault early-warning on cooling water temperature abnormality in the cooling system based on the selected fault early-warning mode, wherein the fault early-warning comprises the following steps:
acquiring cooling water temperature data and historical water temperature data of the cooling system within a preset period;
judging whether the cooling water temperature highest value in the cooling water temperature data is higher than a water temperature highest threshold value in the historical water temperature data and the time period of the water temperature highest threshold value exceeds a preset overrun time period threshold value or not, if so, judging that the cooling water temperature is abnormal and carrying out fault early warning;
when the selected fault early-warning mode is a dynamic early-warning mode, carrying out fault early-warning on cooling water temperature abnormality in the cooling system based on the selected fault early-warning mode, wherein the fault early-warning comprises the following steps:
acquiring actual operation data of carrier equipment corresponding to the cooling system in a preset period;
based on the actual operation data, obtaining characteristic data of the cooling system and an actual measurement value of the cooling water temperature;
inputting the characteristic data into a cooling water temperature prediction model to obtain a cooling water temperature prediction value output by the cooling water temperature prediction model; the cooling water temperature prediction model is obtained by training and testing a nonlinear regression model based on sample characteristic data and sample label data of the cooling system;
the actually measured cooling water temperature value and the predicted cooling water temperature value are subjected to difference to obtain a water temperature difference value statistical value;
and comparing the water temperature difference statistical value with a preset water temperature difference statistical value threshold, and if the water temperature difference statistical value is larger than the water temperature difference statistical value threshold, judging that the cooling water temperature is abnormal and performing fault early warning.
2. The cooling system failure warning method according to claim 1, wherein the training process of the cooling water temperature prediction model includes:
acquiring original sample data of a cooling system;
extracting sample characteristic data and sample label data from the original sample data; the sample characteristic data comprise one or more of an ambient temperature statistic value, a hydraulic oil temperature statistic value and an engine rotating speed statistic value, and the sample label data comprise a cooling water temperature statistic value;
and training and testing the nonlinear regression model based on the sample characteristic data and the sample label data to obtain a cooling water temperature prediction model.
3. A cooling system malfunction early warning device, characterized by comprising:
the acquisition module is used for acquiring the working condition information of the carrier equipment corresponding to the cooling system;
the first processing module is used for selecting a corresponding fault early warning mode according to the working condition information of the carrier equipment corresponding to the cooling system;
the fault early warning mode comprises a cold start early warning mode, a transition early warning mode and a dynamic early warning mode;
the second processing module is used for carrying out fault early warning on the cooling water temperature abnormality in the cooling system based on the selected fault early warning mode;
the carrier equipment corresponding to the cooling system comprises access duration data and equipment operation data of the cooling system;
the first processing module is used for:
judging whether the access time length of the cooling system is lower than a preset time length lower limit value, and if the access time length of the cooling system is lower than the time length lower limit value, selecting a cold start early warning mode;
if the access time length of the cooling system is between a preset time length lower limit value and a time length upper limit value or the access time length of the cooling system is higher than the time length lower limit value and the running data of the cooling system equipment lacks part of fields, selecting a transition early warning mode;
if the access time length of the cooling system is higher than the time length upper limit threshold value, a dynamic early warning mode is selected;
when the selected fault early warning mode is a cold start early warning mode, the second processing module is configured to:
acquiring cooling water temperature data of a cooling system in a preset period;
judging whether the cooling water temperature highest value in the cooling water temperature data is higher than a preset unified water temperature threshold value or not and the time length higher than the unified water temperature threshold value exceeds a preset overrun time length threshold value, if so, judging that the cooling water temperature is abnormal and performing fault early warning;
when the selected fault early warning mode is a transition early warning mode, the second processing module is configured to:
acquiring cooling water temperature data and historical water temperature data of a cooling system in a preset period;
judging whether the highest value of cooling water temperature in cooling water temperature data is higher than a highest water temperature threshold value in historical water temperature data and the time period of the highest water temperature threshold value exceeds a preset overrun time period threshold value or not, if so, judging that the cooling water temperature is abnormal and performing fault early warning;
when the selected fault early warning mode is a dynamic early warning mode, the second processing module is configured to:
acquiring actual operation data of carrier equipment corresponding to a cooling system in a preset period;
based on the actual operation data, obtaining characteristic data of the cooling system and an actual measurement value of the cooling water temperature;
inputting the characteristic data into a cooling water temperature prediction model to obtain a cooling water temperature prediction value output by the cooling water temperature prediction model; the cooling water temperature prediction model is obtained by training and testing a nonlinear regression model based on sample characteristic data and sample label data of a cooling system;
the actual measurement value of the cooling water temperature is differenced with the predicted value of the cooling water temperature, and a water temperature difference value statistic value is obtained;
and comparing the water temperature difference statistical value with a preset water temperature difference statistical value threshold, and if the water temperature difference statistical value is larger than the water temperature difference statistical value threshold, judging that the cooling water temperature is abnormal and performing fault early warning.
4. A cooling system, characterized in that the cooling system uses a cooling system failure warning method according to any one of claims 1 to 2.
5. A work machine comprising a cooling system according to claim 4.
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