CN116383748A - Engineering machinery cooling liquid abnormality diagnosis method and device and electronic equipment - Google Patents
Engineering machinery cooling liquid abnormality diagnosis method and device and electronic equipment Download PDFInfo
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Abstract
The application provides a method and a device for diagnosing abnormality of engineering machinery cooling liquid and electronic equipment, wherein the method comprises the following steps: acquiring real-time operation data of engineering machinery; inputting the real-time operation data into a cooling liquid abnormality recognition model, and judging the state of the cooling liquid according to the recognition result of the cooling liquid abnormality recognition model; the cooling liquid abnormality recognition model is obtained based on historical operation data training of the engineering machinery; the operation data includes: any one or more of engine speed, handle pilot valve data, instantaneous fuel efficiency and torque, and coolant temperature; the cooling liquid state at least comprises abnormal temperature rise of the cooling liquid and normal temperature of the cooling liquid; and generating a coolant temperature rise abnormality prompt message when the coolant state is judged to be coolant temperature rise abnormality. According to the method and the device, before the engineering machinery cooling liquid exceeds the threshold, the abnormal temperature rise of the cooling liquid is identified, and the abnormal temperature rise prompt information of the cooling liquid is generated, so that related personnel can be indicated to intervene in time to avoid loss caused by the operation of the engineering machinery.
Description
Technical Field
The application relates to the technical field of engineering machinery control, in particular to a method and a device for diagnosing abnormality of engineering machinery cooling liquid and electronic equipment.
Background
The engineering machinery is one of important pillar industries for national economic development, is taken as an important component of equipment industry, is a construction machinery general name for national basic construction engineering, and is mainly applied to urban and rural roads, urban infrastructure construction, national defense, water conservancy, electric power, transportation, energy industry and the like. With the development of engineering machinery equipment technology, internet of things technology and artificial intelligence technology, real-time monitoring of the state of engineering machinery can be provided through a server by sending real-time operation data of the engineering machinery to a remote server. The existing method for diagnosing the abnormality of the engineering machinery cooling liquid is mainly used for judging according to the cooling liquid temperature and the cooling liquid threshold value. In implementing the embodiments of the present application, the discovery technology has at least the following problems: the existing coolant abnormality judgment is delayed, the engineering machinery is abnormally operated for a period of time before the coolant temperature is abnormal, and the coolant abnormality judgment is delayed to easily aggravate the abrasion degree of the engineering machinery.
Disclosure of Invention
The embodiment of the application provides a method and a device for diagnosing engineering machinery cooling liquid abnormality and electronic equipment, so as to solve the problem that the abrasion degree of engineering machinery is easy to be aggravated due to the fact that cooling liquid abnormality judgment is lagged.
In a first aspect, an embodiment of the present application provides a method for diagnosing an abnormality of a cooling liquid of an engineering machine, including:
acquiring real-time operation data of engineering machinery;
inputting real-time operation data into a cooling liquid abnormality recognition model, and judging the state of the cooling liquid according to the recognition result of the cooling liquid abnormality recognition model; the cooling liquid abnormality recognition model is obtained based on historical operation data training of engineering machinery; the operation data includes: any one or more of engine speed, handle pilot valve data, instantaneous fuel efficiency and torque, and coolant temperature; the cooling liquid state at least comprises abnormal temperature rise of the cooling liquid and normal temperature of the cooling liquid;
and generating a coolant temperature rise abnormality prompt message when the coolant state is judged to be coolant temperature rise abnormality.
In one possible implementation, the coolant state further includes a coolant high temperature anomaly;
accordingly, the method further comprises:
when the state of the cooling liquid is judged to be abnormal at the high temperature of the cooling liquid, the state of the thermostat is obtained;
Generating a coolant high-temperature abnormality prompt message when the thermostat is opened; and when the thermostat is closed, generating thermostat abnormality prompt information.
In one possible implementation manner, before the inputting the real-time operation data into the coolant anomaly identification model, the method further includes:
carrying out alignment processing on each parameter data in the real-time operation data according to a time sequence;
carrying out interpolation processing on missing values of the parameter data after the alignment processing;
and carrying out normalization processing on each item of parameter data after interpolation processing.
In one possible implementation, the handle pilot data is a handle pilot pressure or a handle operation indication.
In one possible implementation, the method further includes:
generating an abnormal record when judging that the state of the cooling liquid is abnormal in temperature rise of the cooling liquid;
wherein the anomaly record includes: work machine ID, abnormal date, and operation data in a set period.
In one possible implementation, the method further includes: and updating the cooling liquid abnormality identification model according to the abnormality record.
In one possible implementation manner, the updating the cooling liquid abnormality identification model according to the abnormality record includes:
Dividing the abnormal records into a training set and a verification set;
inputting the training set into the cooling liquid abnormality recognition model for training;
inputting the verification set into a trained cooling liquid abnormality recognition model;
and inputting the verification set into a verified cooling liquid abnormality recognition model, and taking the cooling liquid abnormality recognition model as an updated model when the accuracy rate is larger than a set value.
In a second aspect, an embodiment of the present application provides an engineering machinery coolant abnormality diagnosis device, including:
the acquisition module is used for acquiring real-time operation data of the engineering machinery;
the abnormal recognition module is used for inputting the real-time operation data into the cooling liquid abnormal recognition model and judging the state of the cooling liquid according to the recognition result of the cooling liquid abnormal recognition model; the cooling liquid abnormality recognition model is obtained based on historical operation data training of engineering machinery; the operating data includes engine speed, handle pilot valve data, instantaneous fuel efficiency, torque, and coolant temperature; the cooling liquid state at least comprises abnormal temperature rise of the cooling liquid and normal temperature of the cooling liquid;
and the control module is used for generating abnormal prompt information when judging that the state of the cooling liquid is abnormal in temperature rise of the cooling liquid.
In one possible implementation, the coolant state further includes a coolant high temperature anomaly;
correspondingly, the control module is also used for acquiring the state of the thermostat when judging that the state of the cooling liquid is abnormal at high temperature of the cooling liquid; generating a coolant high-temperature abnormality prompt message when the thermostat is opened; and when the thermostat is closed, generating thermostat abnormality prompt information.
In one possible implementation, the method further includes: the preprocessing module is used for carrying out alignment processing on each parameter data in the real-time operation data according to a time sequence before the real-time operation data are input into the cooling liquid abnormality recognition model;
carrying out interpolation processing on missing values of the parameter data after the alignment processing;
and carrying out normalization processing on each item of parameter data after interpolation processing.
In one possible implementation, the handle pilot data is a handle pilot pressure or a handle operation indication.
In one possible implementation manner, the control module is further configured to generate an abnormality record when the cooling liquid state is determined to be abnormal in temperature rise of the cooling liquid;
wherein the anomaly record includes: work machine ID, abnormal date, and operation data in a set period.
In a possible implementation manner, the device further comprises an updating module, configured to update the cooling liquid abnormality identification model according to the abnormality record.
In one possible implementation manner, the updating module is specifically configured to:
dividing the abnormal records into a training set and a verification set;
inputting the training set into the cooling liquid abnormality recognition model for training;
inputting the verification set into a trained cooling liquid abnormality recognition model;
and inputting the verification set into a verified cooling liquid abnormality recognition model, and taking the cooling liquid abnormality recognition model as an updated model when the accuracy rate is larger than a set value.
In a third aspect, embodiments of the present application provide an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to the first aspect or any one of the possible implementations of the first aspect, when the computer program is executed by the processor.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above in the first aspect or any one of the possible implementations of the first aspect.
According to the method, the device and the electronic equipment for diagnosing the cooling liquid abnormality of the engineering machinery, model training is carried out on the basis of cooling liquid temperature and one or more of engine rotating speed, handle pilot valve data, instantaneous fuel efficiency and torque before the cooling liquid abnormality diagnosis of the engineering machinery, acquired real-time operation data of the engineering machinery are input into a cooling liquid abnormality identification model, cooling liquid heating abnormality can be identified before the cooling liquid of the engineering machinery exceeds a threshold value, cooling liquid heating abnormality prompt information is generated, related personnel are instructed to continue to carry out abnormal maintenance of the engineering machinery or adjust an operation scheme of the engineering machinery, and the fact that the cooling liquid temperature rises to exceed the threshold value is avoided, so that loss of each device generated during abnormal operation of the engineering machinery is reduced, and the service life of the engineering machinery is prolonged as a whole.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required for the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an implementation of a method for diagnosing an abnormality of a cooling fluid of an engineering machine according to an embodiment of the present disclosure;
FIGS. 2a and 2b are schematic diagrams of data of normal temperature rise and abnormal temperature rise of the cooling liquid according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a construction of an abnormality diagnosis device for a coolant of an engineering machine according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
The terms first, second and the like in the description and in the claims of the embodiments and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe embodiments of the present application described herein. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion.
The term "plurality" means two or more, unless otherwise indicated. The character "/" indicates that the front and rear objects are an "or" relationship. For example, A/B represents: a or B. The term "and/or" is an associative relationship that describes an object, meaning that there may be three relationships. For example, a and/or B, represent: a or B, or, A and B.
The words used in this application are merely for describing embodiments and are not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a," "an," and "the" (the) are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, when used in this application, the terms "comprises," "comprising," and/or "includes," and variations thereof, mean that the stated features, integers, steps, operations, elements, and/or components are present, but that the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof is not precluded. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of other like elements in a process, method or apparatus comprising such elements.
In this application, each embodiment focuses on the differences from other embodiments, and the same similar parts between the embodiments may be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method sections disclosed in the embodiments, the description of the method sections may be referred to for relevance.
In this application, the types of work machines include, but are not limited to, excavators, heavy transportation vehicles, large cranes, bulldozers, road rollers, loaders, and the like. Along with the improvement of engineering machinery equipment technology and Internet of things technology, real-time operation data of engineering machinery can be sent to a cloud server, and the real-time operation data of the engineering machinery collected by the server monitor the temperature and the temperature change state of engineering machinery cooling liquid.
In the specific implementation process, a controller of the engineering machinery is connected with each component through a CAN bus, and operation data of each component are collected in real time and sent to a server.
During the operation of the engineering machine, before the temperature of the cooling liquid is abnormal at a high temperature, part of components in the engineering machine are in an abnormal operation state for a period of time, and during the abnormal operation state, the components of the engineering machine are lost. Early warning can influence the life-span of engineering machinery when coolant temperature exceeds the threshold value based on traditional scheme, and need shut down the inspection when coolant temperature influences the engineering progress.
Therefore, the scheme aims to provide a scheme for timely finding out the running state of the cooling liquid with high temperature abnormality and carrying out abnormality prompt in the running process of the engineering machinery so as to instruct related personnel to timely check or adjust the engineering machinery control scheme to reduce the abrasion of devices and avoid influencing the engineering progress in the working process.
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the following description will be made with reference to the accompanying drawings by way of specific embodiments.
Fig. 1 is a flowchart for implementing a method for diagnosing abnormality of a cooling fluid of an engineering machine according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps:
s101, acquiring real-time operation data of the engineering machinery.
The execution main body of the engineering machinery cooling liquid abnormality diagnosis method provided by the embodiment of the application is a remote server, the server is in communication connection with the engineering machinery, and the engineering machinery uploads real-time operation data according to set time. The server executes the process of cooling liquid abnormality diagnosis, thereby reducing the operation pressure of the local data of the engineering machinery.
Optionally, the time period of uploading the real-time operation data by the engineering machine, namely the set time is 3 s-5 s. Optionally, the setting time is 3s, 4s or 5s, and the setting time is not too long, so that the data can accurately reflect the state of the engineering machinery cooling liquid.
The server can manage one or more engineering machines, and is responsible for collecting and storing historical operation data of the engineering machines, training and updating the cooling liquid abnormality identification model, and training the model on the premise of not affecting operation of the engineering machines.
S102, inputting real-time operation data into a cooling liquid abnormality recognition model, and judging the state of the cooling liquid according to the recognition result of the cooling liquid abnormality recognition model; the cooling liquid abnormality recognition model is obtained based on historical operation data training of the engineering machinery; the operation data includes: any one or more of engine speed, handle pilot valve data, instantaneous fuel efficiency and torque, and coolant temperature; the cooling liquid state at least comprises abnormal temperature rise of the cooling liquid and normal temperature of the cooling liquid.
In the embodiment of the application, based on the specific composition of different engineering machines and the different cooling liquid circulation loops, corresponding cooling liquid abnormality identification models are arranged corresponding to different engineering machine types. Thus, in one possible implementation, before inputting the real-time operation data into the coolant anomaly identification model, further comprising: and selecting a corresponding cooling liquid abnormality identification model according to the type of the engineering machinery.
In common vehicles and engineering machinery, the common cooling liquid is used for protecting the engine from normal and good operation, and circulates in the engine water tank, thereby having the effects of freezing prevention, boiling prevention, rust prevention, corrosion prevention and the like. Therefore, the coolant anomaly identification model is typically obtained based on engine speed in historical operating data of the work machine, or, engine speed and torque training.
In other possible implementations, the construction machine is distinguished from a conventional vehicle and has components associated with the construction operation, which generate high heat or wear during construction implementation, so that the coolant is used for protecting the engine from normal good operation and for normal operation of other components of the construction machine. Similarly, coolant temperature is also affected by other component operating data, such as: handle pilot valve data, instantaneous fuel efficiency, etc.
In the embodiment of the application, the experiment verification is performed on the verification efficiency of the cooling liquid abnormality recognition model by taking the excavator as an example, wherein the operation data comprise: engine speed, handle pilot valve data, instantaneous fuel efficiency, torque, and coolant temperature. By constructing the coolant abnormality recognition model based on various operation data, accuracy in judging the coolant abnormality integrity can be improved.
In the specific implementation process, the temperature rising speeds of the cooling liquid are different based on different states of the engineering machinery cooling liquid during temperature rising. The state of the engineering machinery coolant when the temperature is raised can be broadly divided into a cold vehicle idle speed no operation, a cold vehicle acceleration no operation and a cold vehicle acceleration no operation.
Under normal operation, the engineering machinery completes the starting of the engineering machinery in a cold idle non-operation state (for example, the pilot hydraulic system is preheated by controlling the oil temperature in the pilot system to stably rise), wherein the cold idle non-operation state is that the engine speed is lower than the set speed, the handle pilot valve is not operated or the pressure is lower than the set pressure, the instantaneous fuel efficiency is lower than the set efficiency and the torque is lower than the set torque. The engine is in idle running state when the rotation speed of the engine is lower than the set rotation speed and the instantaneous fuel efficiency is lower than the set efficiency, and the fuel injection quantity of the fuel injection valve is relatively small, so that the stable rising of the oil temperature is ensured; the handle pilot valve does not act or the pressure is smaller than the set pressure, namely the operator does not control the bucket of the excavator to act; the torque is smaller than the set torque, i.e. the bucket does not excavate or does not carry (i.e. the load is zero), and if the bucket does excavate or carries, the larger the load, the larger the torque.
In the actual operation process, in order to shorten the starting time of the engineering machine, an operator of the engineering machine can turn the throttle to pull the engine speed up at idle speed, and meanwhile, the instantaneous fuel efficiency is improved. At the moment, the state corresponding to the temperature rise of the engineering machinery cooling liquid is the idle speed non-operation state of the cold vehicle.
In addition, the action of controlling the action of the bucket in the starting process of the engineering machinery is not lacked, and the temperature rising speed of the cooling liquid is further influenced. At the moment, the state corresponding to the temperature rise of the engineering machinery cooling liquid is the idle speed operating state of the cold car.
Therefore, under the conventional operation, the temperature rising speed of the cooling liquid is relatively stable, and when the water tank is to be cleaned or the water pump is to be replaced, the temperature rising of the cooling liquid is abnormal, and at the moment, abnormal prompt of the temperature rising of the cooling liquid is required to be timely carried out so as to prompt related personnel to perform repair and cleaning work on the water pump or the water tank. When an engineering operator is in idle speed in order to shorten the starting time of the engineering machinery, the throttle is used for pulling the engine rotating speed up or controlling the action of the bucket, the temperature rising speed of the cooling liquid is greatly fluctuated, and the temperature rising speed is higher, so that the temperature rising speed is not caused by the reasons that the water tank is to be cleaned or the water pump is to be replaced, but caused by personal behaviors of the operator.
In order to facilitate the judgment of the abnormal temperature of the cooling liquid, the abnormal temperature of the cooling liquid is known in time, and at least the temperature of the cooling liquid is required to be input into the cooling liquid abnormal recognition model when the cooling liquid abnormal recognition model is constructed and the abnormal judgment is carried out based on the model. In addition, any one or more of engine speed, handle pilot valve data, instantaneous fuel efficiency and torque are input into the coolant abnormality recognition model to realize abnormal temperature rise judgment in the coolant temperature rise process, and when the temperature rises rapidly, the abnormal operation is judged.
In different embodiments, the handle pilot valve data is represented differently.
In one possible implementation, the handle pilot data is a handle pilot pressure. When the operator does not have the operation handle, the pressure of the handle pilot valve is zero or a certain smaller fixed value, and when the operator has the operation handle, the pressure of the handle pilot valve is increased.
In one possible implementation, the handle pilot valve data is a handle operation indication. Wherein, when the operator has an operation handle behavior, the handle operation instruction is 1, and when the operator has no operation handle behavior, the handle operation instruction is 0.
In addition, in view of the fact that the temperature rise of the engineering machinery cooling liquid is a continuous process, model training is required to be performed based on time series data when the cooling liquid abnormality recognition model training is performed, and in a specific process, model training is performed based on a long-short-term memory (Long Short Term Memory, LSTM) recurrent neural network. Alternatively, model training is performed by an autoregressive moving average (Auto Regressive Moving Average, ARMA) model, a differentially integrated moving average autoregressive (Autoregressive Integrated Moving Average, ARIMA) model, a seasonal differential autoregressive moving average (Seasonal Autoregressive Integrated Moving Average, SARIMA) model, or the like.
And S103, when the cooling liquid state is judged to be abnormal in cooling liquid temperature rise, generating cooling liquid temperature rise abnormal prompt information.
The server can send the abnormal temperature rise prompt information of the cooling liquid to the engineering machine side or the mobile terminal of an engineering machine operator, so that the operator can know the abnormal temperature rise of the cooling liquid in time in the operation process of the engineering machine, and can adjust a control scheme or stop maintenance in time based on operation experience, thereby avoiding the stop of the engineering machine or reducing the abnormal operation time of the engineering machine caused by the fact that the temperature of the cooling liquid exceeds a threshold value, reducing the abrasion degree of devices, and prolonging the service life of the engineering machine as a whole.
Optionally, the coolant temperature rise abnormality prompt information includes a coolant temperature rise abnormality identification result and operation data corresponding to a set time period, so that an operator can conveniently determine a further operation scheme based on the operation data. The set time period is a period corresponding to abnormal temperature rise of the cooling liquid, and is optionally 1-3 min. Optionally, the set time period is 1min, 2min or 3min.
In the embodiment, model training is performed before the abnormality diagnosis of the engineering machinery cooling liquid based on the cooling liquid temperature and one or more of the engine speed, the handle pilot valve data, the instantaneous fuel efficiency and the torque, and the acquired real-time operation data of the engineering machinery are input into the cooling liquid abnormality recognition model, so that the abnormal temperature rise of the cooling liquid can be recognized before the engineering machinery cooling liquid exceeds a threshold value, and the abnormal temperature rise prompt information of the cooling liquid is generated, so that related personnel can be instructed to continue to carry out abnormal maintenance of the engineering machinery or adjust the operation scheme of the engineering machinery, the shutdown of the engineering machinery caused by the fact that the temperature rise of the cooling liquid exceeds the threshold value is avoided, the loss of each device generated in abnormal operation of the engineering machinery is reduced, and the service life of the engineering machinery is prolonged as a whole.
The solution provided by the above embodiment mainly introduces a detection for detecting abnormal temperature rise of the cooling liquid, wherein the reason that the water pump is damaged or the water tank is to be cleaned to cause abnormal temperature rise is mentioned. In the specific implementation process, the abnormal condition that the cooling liquid cannot circulate due to the damage of the thermostat exists.
In one possible implementation, the coolant state further includes a coolant high temperature anomaly;
correspondingly, the method further comprises the steps of:
when the state of the cooling liquid is judged to be abnormal at the high temperature of the cooling liquid, the state of the thermostat is obtained;
generating a coolant high-temperature abnormality prompt message when the thermostat is opened; when the thermostat is closed, abnormal prompt information of the thermostat is generated.
In one possible implementation, when the coolant high temperature abnormality is identified according to the coolant abnormality identification model, the coolant high temperature abnormality is determined based on a comparison of the set temperature threshold and the coolant temperature in the real-time operation data. The set temperature threshold is larger than the thermostat action threshold and smaller than the rated cooling liquid threshold.
The thermostat is a valve for controlling the flow path of cooling liquid, and is a temperature-regulating device, and generally comprises a temperature-sensing component for opening and closing the flow of air, gas or liquid by means of thermal expansion or contraction. When the thermostat is normal, the temperature of the static cooling liquid rises to a certain temperature to reach the action threshold of the thermostat, and the thermostat is opened and controls the flow of the cooling liquid so as to improve the cooling efficiency; when the thermostat is abnormal, the static cooling liquid temperature rises to a certain temperature, the thermostat does not act, the cooling liquid cannot flow, the engineering machinery continuously runs, the cooling liquid temperature can continuously rise and is judged to be in a cooling liquid high-temperature abnormal state when the temperature exceeds a set temperature threshold value, and in the case, if the water pump or the water tank is in a good state, the temperature rising process of the cooling liquid is normal before the cooling liquid reaches the temperature set value of the temperature sensing component of the thermostat. When the cooling liquid reaches the temperature set value of the temperature sensing assembly of the thermostat, if the thermostat does not normally operate, abnormal temperature rise of the cooling liquid and even abnormal high temperature occur.
In the embodiment, the situation that the temperature of the cooling liquid is abnormal due to the abnormality of the thermostat in consideration of the normal operation of the cooling liquid in the temperature rising stage before the abnormality of the temperature of the cooling liquid is caused is perfected, the abnormality prompting scheme of engineering machinery is improved, the accuracy of the abnormality prompt of the cooling liquid is improved, and the error prompt of the cooling liquid due to the abnormality of the thermostat is avoided.
Similarly, in another possible implementation manner, the method further includes:
when the state of the cooling liquid is judged to be abnormal at the high temperature of the cooling liquid, the state of the water pump is obtained;
generating a coolant high-temperature abnormality prompt message when the water pump is in normal operation; when the water pump cannot be started, generating water pump abnormality prompt information.
In the embodiment, the situation that the cooling liquid is abnormal due to the abnormality of the water pump in the normal operation of the cooling liquid heating stage before the abnormality of the cooling liquid is considered, so that the engineering machinery abnormality prompting scheme is perfected, the accuracy of the cooling liquid high temperature abnormality prompt is improved, and the cooling liquid high temperature false prompt caused by the abnormality of the water pump is avoided.
In one possible implementation, before inputting the real-time operation data into the coolant anomaly identification model, the method further includes:
carrying out alignment processing on each parameter data in the real-time operation data according to a time sequence;
Carrying out interpolation processing on missing values of the parameter data after the alignment processing;
and carrying out normalization processing on each item of parameter data after interpolation processing.
In view of the fact that a continuous temperature rising process exists before the cooling liquid is abnormal in high temperature, real-time operation data are needed to be input into a cooling liquid abnormality identification model according to time sequence, and therefore monitoring of the temperature rising process of the cooling liquid is achieved. In addition, there are various devices or factors that affect the temperature rise of the cooling liquid, and each component uploads data to the controller through the CAN bus respectively, so that each item of parameter data in the real-time operation data is mutually independent, and each item of parameter data needs to be aligned according to time sequence.
Secondly, in view of collecting multiple parameter data, incomplete, inconsistent, abnormal and deviated data exist on the surface of the original data, and the problem data can influence the data mining execution efficiency and even influence the recognition result of a cooling liquid abnormal recognition model. Therefore, the interpolation processing is continued on the missing value in the data preprocessing process, and the cooling liquid abnormality recognition efficiency and accuracy can be improved.
In particular embodiments, the interpolation process operates differently for different sampling periods. Optionally, when the time axes of the data of different operation parameters are aligned to the same second, if the sampling interval of the data is 1 second, interpolation is performed on missing data generated after alignment; if the sampling interval of the data is greater than 1 second, the missing data generated after alignment is deleted.
Finally, each parameter has different dimensions and dimension units, the influence degree on the cooling liquid rise is different, and normalization processing is carried out before the data subjected to difference processing is input into the cooling liquid anomaly identification model, so that each parameter can be ensured to be in the same order of magnitude, the comprehensive comparison and evaluation are suitable, and the cooling liquid anomaly identification efficiency and accuracy are improved.
In this embodiment, before the real-time operation data is input to the coolant anomaly identification model, a data preprocessing process including alignment, interpolation and normalization is performed, so that the coolant anomaly identification efficiency and accuracy are improved.
As shown in fig. 2a and 2b, fig. 2b illustrates an exemplary situation in which one operating parameter causes a change in the temperature rising rate of the coolant in a non-cold idle non-operating state with operator intervention control. The triangle corresponding curve is a cooling liquid temperature curve, the diamond corresponding curve is any one of engine speed, torque, handle pilot valve data or instantaneous fuel efficiency, the temperature of the cooling liquid is increased in the temperature rising process, after the final state is stable, the temperature of the cooling liquid is relatively stable, the result of the diamond corresponding parameter item can be determined according to the recognition result of the cooling liquid abnormal recognition model, and when the influence of the parameter item on the temperature rising speed is determined to be in a normal range, no cooling liquid temperature rising abnormal prompt information is generated. In other embodiments, there are also cases where the temperature rise of the coolant is abnormal due to two or more operating parameter changes, which can be identified by the coolant abnormality identification model.
When other items are normal, the cooling liquid still has abnormal temperature rise, which may be caused by blockage of the cooling liquid circulating component, and specifically, the abnormal temperature rise condition of the cooling liquid can be cleared by cleaning the water tank, the cooling liquid circulating pipeline and the like.
In the embodiment, when the abnormal temperature rise of the cooling liquid is determined based on the cooling liquid abnormal recognition model, an abnormal processing scheme can be further determined according to various parameter data in the reference data, so that the maintenance efficiency of engineering machinery is improved, the service life of parts is prolonged, and meanwhile, the construction period delay caused by the maintenance of multiple parts is avoided.
In one possible implementation, when the coolant state is determined to be abnormal in temperature rise of the coolant, an abnormality record is generated;
wherein the exception record includes: work machine ID, abnormal date, and operation data in a set period.
In the use process of the engineering machinery, the abrasion of parts is inevitably generated, compared with the heat generated in the same time when the engineering machinery leaves a factory newly or the temperature rising speed of the cooling liquid is changed, therefore, in the use process of the engineering machinery, the cooling liquid abnormality identification model needs to be updated based on recorded abnormality data so as to improve the identification efficiency of the cooling liquid abnormality identification model on the cooling liquid temperature rising abnormality.
The abnormal records comprise engineering machine IDs, so that the server can realize classified management of the data of the engineering machines when managing the engineering machines. When the state of the cooling liquid is determined to be abnormal temperature rise of the cooling liquid, the data in a continuous period of time is correspondingly determined, so that when the abnormal temperature rise of the cooling liquid is detected, the operation data in a corresponding set period of time is acquired, and reference basis is provided for updating training of a subsequent model. Since the coolant state includes a coolant high-temperature abnormality in addition to the coolant temperature increase abnormality in the different embodiments, the vehicle coolant temperature abnormality recognition result is recorded simultaneously when the abnormality record is generated.
In the specific implementation process, the referential property is reduced when the abnormal date is far from the updated date, so that the range of the referential data is determined when the abnormal date is recorded in the abnormal record so as to be convenient for updating. In addition, when relevant personnel review engineering machinery abnormality, specific abnormality occurrence information is known, for example: engineering machinery manufacturers can provide after-sales services as soon as possible based on the anomaly records, and in addition, production lines or in-production component purchasing can be improved based on the newly added anomaly records; performance manager can adjust or check performance records of related operators based on the abnormal records; operators or on-site maintenance personnel can quickly lock the maintenance part, so that the use safety of the instrument is improved.
In this embodiment, when the cooling liquid state is judged to be abnormal temperature rise of the cooling liquid, an abnormal record is generated, so that relevant personnel can check the history of use of the engineering machinery to better provide maintenance service or improve use safety. In addition, the cooling liquid abnormality recognition model can be updated based on the abnormality record, so that the recognition efficiency of the cooling liquid temperature rise abnormality can be improved.
In other possible implementations, when the state of the cooling liquid is judged to be the cooling liquid high-temperature abnormality, a high-temperature abnormality record is also generated so as to update the cooling liquid abnormality recognition model based on the high-temperature abnormality record, and accuracy of recognition of the cooling liquid high-temperature abnormality is improved.
Accordingly, in another possible implementation manner, when the coolant state is determined to be abnormal in temperature rise of the coolant or abnormal in temperature rise of the coolant, an abnormality record is generated;
wherein the exception record includes: the method comprises the steps of engineering machinery ID, abnormal date, operation data in a set time period and abnormal identification results of the temperature of the cooling liquid of the vehicle; the vehicle coolant temperature abnormality recognition result includes coolant temperature rise abnormality and coolant high temperature abnormality.
In one possible implementation, the method further includes: and updating the cooling liquid abnormality identification model according to the abnormality record.
Wherein the exception record includes: work machine ID, abnormal date, and operation data in a set period. Specifically, when the cooling liquid abnormality recognition model is updated according to the abnormality record, the cooling liquid abnormality recognition model is required to be updated by selecting the operation data of the same engineering machine or the same kind of engineering machines based on the engineering machine ID; selecting data in the near term based on the abnormal date to update the cooling liquid abnormal recognition model; and meanwhile, determining data corresponding to the same abnormal recognition result based on the abnormal recognition result of the temperature of the cooling liquid of the vehicle, and updating the cooling liquid abnormal recognition model.
In one possible implementation, updating the coolant anomaly identification model based on the anomaly record includes:
dividing the abnormal records into a training set and a verification set;
inputting the training set into a cooling liquid anomaly identification model for training;
inputting the verification set into a trained cooling liquid abnormality recognition model;
inputting the verification set into the verified cooling liquid abnormality recognition model, and taking the cooling liquid abnormality recognition model as an updated model when the accuracy rate is larger than a set value.
In the model training process, the accuracy of the cooling liquid abnormality recognition model should reach more than 95% so as to realize accurate recognition of the cooling liquid abnormality.
In the specific implementation process, the abnormal record data is a reference basis for identifying the abnormal condition of the cooling liquid, in addition, the normal operation data is combined for representing the abnormal condition of the cooling liquid, in order to realize diversified deployment, when the abnormal condition identification model of the cooling liquid is updated or the initial model is trained, the historical data is acquired based on different sampling periods, and optionally, the sampling periods comprise 1s, 2s and 3s, and 3 versions of abnormal condition identification models of the cooling liquid are correspondingly generated.
The following are device embodiments of the present application, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 3 is a schematic structural diagram of a cooling fluid abnormality diagnosis device for an engineering machine according to an embodiment of the present application, as shown in fig. 3, and for convenience of explanation, only a portion related to the embodiment of the present application is shown, as shown in fig. 3, the device includes:
an acquisition module 301, configured to acquire real-time operation data of the engineering machine;
the anomaly identification module 302 is configured to input real-time operation data to the coolant anomaly identification model, and determine a coolant state according to an identification result of the coolant anomaly identification model; the cooling liquid abnormality recognition model is obtained based on historical operation data training of the engineering machinery; the operating data includes engine speed, handle pilot valve data, instantaneous fuel efficiency, torque, and coolant temperature; the cooling liquid state at least comprises abnormal temperature rise of the cooling liquid and normal temperature of the cooling liquid;
The control module 303 is configured to generate abnormality notification information when it is determined that the coolant state is abnormal in temperature rise of the coolant.
In one possible implementation, the coolant state further includes a coolant high temperature anomaly;
correspondingly, the control module 303 is further configured to obtain a thermostat state when the coolant state is determined to be abnormal in coolant temperature; generating a coolant high-temperature abnormality prompt message when the thermostat is opened; when the thermostat is closed, abnormal prompt information of the thermostat is generated.
In one possible implementation, the method further includes: the preprocessing module is used for carrying out alignment processing on each parameter data in the real-time operation data according to a time sequence before the real-time operation data are input into the cooling liquid abnormality recognition model;
carrying out interpolation processing on missing values of the parameter data after the alignment processing;
and carrying out normalization processing on each item of parameter data after interpolation processing.
In one possible implementation, wherein the handle pilot data is a handle pilot pressure or a handle operation indication.
In a possible implementation manner, the control module 303 is further configured to generate an abnormality record when the coolant state is determined to be abnormal in temperature rise of the coolant;
Wherein the exception record includes: work machine ID, abnormal date, and operation data in a set period.
In one possible implementation, the apparatus further includes an updating module configured to update the coolant anomaly identification model based on the anomaly record.
In one possible implementation, the updating module is specifically configured to:
dividing the abnormal records into a training set and a verification set;
inputting the training set into a cooling liquid anomaly identification model for training;
inputting the verification set into a trained cooling liquid abnormality recognition model;
inputting the verification set into the verified cooling liquid abnormality recognition model, and taking the cooling liquid abnormality recognition model as an updated model when the accuracy rate is larger than a set value.
In the embodiment, model training is performed before the abnormality diagnosis of the engineering machinery cooling liquid based on the cooling liquid temperature and one or more of the engine speed, the handle pilot valve data, the instantaneous fuel efficiency and the torque, and the acquired real-time operation data of the engineering machinery are input into the cooling liquid abnormality recognition model, so that the abnormal temperature rise of the cooling liquid can be recognized before the engineering machinery cooling liquid exceeds a threshold value, and the abnormal temperature rise prompt information of the cooling liquid is generated, so that related personnel can be instructed to continue to carry out abnormal maintenance of the engineering machinery or adjust the operation scheme of the engineering machinery, the shutdown of the engineering machinery caused by the fact that the temperature rise of the cooling liquid exceeds the threshold value is avoided, the loss of each device generated in abnormal operation of the engineering machinery is reduced, and the service life of the engineering machinery is prolonged as a whole. Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 4, the electronic apparatus 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in the memory 41 and executable on the processor 40. The processor 40 executes the computer program 42 to implement the steps in the embodiments of the method for diagnosing a coolant abnormality of a construction machine, for example, steps S101 to S103 shown in fig. 1. Alternatively, the processor 40 may perform the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules 301 to 303 shown in fig. 3, when executing the computer program 42.
Illustratively, the computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used to describe the execution of the computer program 42 in the electronic device 4. For example, the computer program 42 may be partitioned into modules 301 to 303 shown in fig. 3.
The electronic device 4 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The electronic device 4 may include, but is not limited to, a processor 40, a memory 41. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the electronic device 4 and is not meant to be limiting of the electronic device 4, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may further include an input-output device, a network access device, a bus, etc.
The processor 40 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the electronic device 4, such as a hard disk or a memory of the electronic device 4. The memory 41 may be an external storage device of the electronic device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 41 is used for storing the computer program and other programs and data required by the electronic device. The memory 41 may also be used for temporarily storing data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other manners. For example, the apparatus/electronic device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the foregoing embodiment, or may be implemented by instructing related hardware by a computer program, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of the method embodiment of diagnosing coolant abnormality of each engineering machine when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should 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 application, and are intended to be included in the scope of the present application.
Claims (10)
1. The engineering machinery coolant abnormality diagnosis method is characterized by comprising the following steps:
acquiring real-time operation data of engineering machinery;
inputting real-time operation data into a cooling liquid abnormality recognition model, and judging the state of the cooling liquid according to the recognition result of the cooling liquid abnormality recognition model; the cooling liquid abnormality recognition model is obtained based on historical operation data training of engineering machinery; the operation data includes: any one or more of engine speed, handle pilot valve data, instantaneous fuel efficiency and torque, and coolant temperature; the cooling liquid state at least comprises abnormal temperature rise of the cooling liquid and normal temperature of the cooling liquid;
And generating a coolant temperature rise abnormality prompt message when the coolant state is judged to be coolant temperature rise abnormality.
2. The method of claim 1, wherein the coolant condition further comprises a coolant high temperature anomaly;
accordingly, the method further comprises:
when the state of the cooling liquid is judged to be abnormal at the high temperature of the cooling liquid, the state of the thermostat is obtained;
generating a coolant high-temperature abnormality prompt message when the thermostat is opened; and when the thermostat is closed, generating thermostat abnormality prompt information.
3. The method of claim 1, further comprising, prior to said inputting the real-time operational data into the coolant anomaly identification model:
carrying out alignment processing on each parameter data in the real-time operation data according to a time sequence;
carrying out interpolation processing on missing values of the parameter data after the alignment processing;
and carrying out normalization processing on each item of parameter data after interpolation processing.
4. The method of claim 1, wherein the handle pilot data is a handle pilot pressure or a handle operation indication.
5. The method according to any one of claims 1 to 4, further comprising:
Generating an abnormal record when judging that the state of the cooling liquid is abnormal in temperature rise of the cooling liquid;
wherein the anomaly record includes: work machine ID, abnormal date, and operation data in a set period.
6. The method of claim 5, wherein the method further comprises: and updating the cooling liquid abnormality identification model according to the abnormality record.
7. The method of claim 6, wherein updating the coolant anomaly identification model based on the anomaly record comprises:
dividing the abnormal records into a training set and a verification set;
inputting the training set into the cooling liquid abnormality recognition model for training;
inputting the verification set into a trained cooling liquid abnormality recognition model;
and inputting the verification set into a verified cooling liquid abnormality recognition model, and taking the cooling liquid abnormality recognition model as an updated model when the accuracy rate is larger than a set value.
8. An abnormality diagnosis device for a coolant of an engineering machine, comprising:
the acquisition module is used for acquiring real-time operation data of the engineering machinery;
the abnormal recognition module is used for inputting the real-time operation data into the cooling liquid abnormal recognition model and judging the state of the cooling liquid according to the recognition result of the cooling liquid abnormal recognition model; the cooling liquid abnormality recognition model is obtained based on historical operation data training of engineering machinery; the operating data includes engine speed, handle pilot valve data, instantaneous fuel efficiency, torque, and coolant temperature; the cooling liquid state at least comprises abnormal temperature rise of the cooling liquid and normal temperature of the cooling liquid;
And the control module is used for generating abnormal prompt information when judging that the state of the cooling liquid is abnormal in temperature rise of the cooling liquid.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of the preceding claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any of the preceding claims 1 to 7.
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