CN116702597A - Mechanical equipment operation life prediction and health management method, system and medium - Google Patents
Mechanical equipment operation life prediction and health management method, system and medium Download PDFInfo
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Abstract
The embodiment of the application provides a method, a system and a medium for predicting the operation life of mechanical equipment and managing health, wherein the method comprises the following steps: establishing a device running state prediction model through big data, inputting current state information of the mechanical device into the trained prediction model, and generating real-time shaft track data of the mechanical device and predicted shaft track data at the next moment in the predicted state; similarity calculation is carried out on the mechanical equipment shaft track curve and a preset shaft track to obtain a deviation rate; judging whether the deviation rate is larger than a preset deviation rate threshold value or not; if the service life of the mechanical equipment is greater than or equal to the service life of the mechanical equipment, recording the current moment, generating fault information, and inputting a preset service life prediction model according to the fault information to predict the service life of the mechanical equipment; if the current time is smaller than the current time, recording the current time, generating the health management data of the mechanical equipment, and transmitting the health management data of the mechanical equipment to a database; the fault diagnosis is carried out on the equipment by judging the real-time shaft track of the mechanical equipment, the residual service life of the equipment is judged, and the precision is high.
Description
Technical Field
The application relates to the field of mechanical equipment life prediction, in particular to a mechanical equipment operation life prediction and health management method, system and medium.
Background
The mechanical equipment is various, and when the mechanical equipment operates, some parts of the mechanical equipment can even perform mechanical movements in different forms, and in the long-term operation process of the mechanical equipment, the parts can be aged and worn, and finally safety accidents are caused, so that the accurate replacement of the aged parts, namely the accurate prediction of the residual life of the parts, is particularly important.
In the prior art, a method based on a statistical principle is generally adopted for residual life estimation based on a data driving model, information and knowledge implicit in the massive data are captured through collecting the massive data, the current commonly used data driving model is built through an artificial neural network or a support vector machine, a neural network model or a support vector machine model is built, and then the connection weight between layers of the neural network, the number of nodes of the implicit layer and the penalty coefficient of the support vector machine are respectively optimized through corresponding algorithms, such as a genetic algorithm, a particle swarm algorithm or a gradient descent algorithm, so that the data driving model is obtained, but the data collected in the method often has high nonlinear characteristics, the data driving model is easy to fall into a local optimal condition, the obtained prediction result is inaccurate, and most of the existing residual life prediction methods are predicted in an acceleration test or data driving mode, so that a real-time prediction function is difficult to achieve.
Disclosure of Invention
The embodiment of the application aims to provide a method, a system and a medium for predicting the operation life of mechanical equipment and managing health, which can diagnose faults of the equipment by judging the real-time axis track of the mechanical equipment and judge the residual life of the equipment, and has higher precision.
The embodiment of the application also provides a method for predicting the operation life of the mechanical equipment and managing the health, which comprises the following steps:
establishing a device running state prediction model through big data, and training the device running state model to obtain a trained prediction model;
acquiring current state information of the mechanical equipment, inputting the current state information of the mechanical equipment into a trained prediction model, and generating real-time axis track data of the mechanical equipment and predicted axis track data at the next moment in the predicted state;
establishing a mechanical equipment shaft track curve according to the mechanical equipment real-time shaft track data and the predicted shaft track data at the next moment in a predicted state;
similarity calculation is carried out on the mechanical equipment shaft track curve and a preset shaft track to obtain a deviation rate;
judging whether the deviation rate is larger than a preset deviation rate threshold value or not;
if the service life of the mechanical equipment is greater than or equal to the service life of the mechanical equipment, recording the current moment, generating fault information, and inputting a preset service life prediction model according to the fault information to predict the service life of the mechanical equipment;
If the current time is smaller than the current time, recording the current time, generating the health management data of the mechanical equipment, and transmitting the health management data of the mechanical equipment to a database.
Optionally, in the method for predicting the operation life and managing the health of the mechanical device according to the embodiment of the present application, the step of building an equipment operation state prediction model by big data and training the equipment operation state model to obtain a trained prediction model includes:
acquiring historical operation data of the mechanical equipment through the big data;
classifying historical operation data of the mechanical equipment through equipment operation parameters to generate a plurality of data sets;
training the equipment running state model through a plurality of data sets to obtain a training result;
judging whether the training result is converged or not;
if the convergence is achieved, the training is finished;
if the data in the data sets are not converged, extracting the data in the data sets to form a new training set, and training the equipment running state model through the new training set.
Optionally, in the method for predicting the operation life of a mechanical device and managing health of the embodiment of the present application, if the operation life of the mechanical device is greater than or equal to the operation life, recording the current time, generating fault information, and inputting the fault information into a preset life prediction model to predict the life of the mechanical device, including:
Acquiring operation parameters of mechanical equipment and acquiring equipment operation time-varying information;
analyzing the equipment operation time-varying information to obtain equipment working condition information;
comparing the equipment working condition information with preset first information to obtain first similarity;
judging whether the first similarity is larger than or equal to a first similarity threshold value;
if the current equipment working condition is greater than or equal to the first similarity threshold value, judging that the current equipment working condition is a stable working condition;
if the device working condition information is smaller than the first similarity threshold value, comparing the device working condition information with preset second information to obtain a second similarity threshold value;
determining whether the second similarity threshold is greater than a second similarity threshold,
if the current equipment working condition is larger than the second similarity threshold value, judging that the current equipment working condition is a gradual change working condition;
if the current equipment working condition is smaller than the second similarity threshold value, judging that the current equipment working condition is a sudden change working condition;
the first similarity threshold is greater than the second similarity threshold.
Optionally, in the method for predicting the operation life of the mechanical device and managing the health of the mechanical device according to the embodiment of the present application, if the operation life of the mechanical device is greater than or equal to the operation life of the mechanical device, recording the current time, generating fault information, and inputting a preset life prediction model according to the fault information to predict the life of the mechanical device; comprising the following steps:
Acquiring fault information and generating a fault set;
inputting the fault set into a life prediction model to obtain equipment life loss information;
calculating a creep component and a fatigue component of the equipment life loss according to the life loss information;
predicting the residual life of the equipment according to the creep component and the fatigue component;
judging the difference value between the residual life of the equipment and a preset life threshold;
if the difference value is smaller than the preset difference value, stopping the machine to replace important parts of the mechanical equipment;
if the difference is larger than the preset difference, generating equipment life loss rate, and monitoring the equipment life in real time.
Optionally, in the method for predicting the operation life and managing the health of a mechanical device according to the embodiment of the present application, the obtaining the current state information of the mechanical device, inputting the current state information of the mechanical device into a trained prediction model, generating real-time axis track data of the mechanical device and predicted axis track data at the next time in the predicted state includes:
acquiring state data of mechanical equipment and generating a data sequence;
decomposing the data sequence to obtain trend components and detail components;
constructing an initial boundary of the data feature according to the trend component and the detail component;
performing interval construction according to the detail components, and calculating interval evaluation indexes;
Judging whether the interval evaluation index is larger than a preset index value or not;
if the data is larger than the optimal data estimation interval, an optimal data estimation interval is established;
if the value is smaller than the preset value, the interval range is adjusted.
Optionally, in the method for predicting the operation life of a mechanical device and managing health of a mechanical device according to the embodiment of the present application, if the number is smaller than, recording the current moment, generating the health management data of the mechanical equipment, and transmitting the health management data of the mechanical equipment to a database; comprising the following steps:
acquiring equipment health management data, and generating an equipment health operation expert knowledge base according to the equipment health management data;
generating a healthy running curve graph of the equipment according to the expert knowledge base;
comparing whether the equipment health running curve graph is compared with a preset curve to obtain similarity;
judging whether the similarity is larger than or equal to a preset similarity threshold value;
if the data is greater than or equal to the preset value, saving the equipment health management data;
and if the device health management data is smaller than the set value, extracting the device health management data of the corresponding time node, and correcting the device health management data of the corresponding time node.
In a second aspect, embodiments of the present application provide a system for predicting operational life and managing health of a mechanical device, the system comprising: the system comprises a memory and a processor, wherein the memory comprises a program of a mechanical equipment operation life prediction and health management method, and the program of the mechanical equipment operation life prediction and health management method realizes the following steps when being executed by the processor:
Establishing a device running state prediction model through big data, and training the device running state model to obtain a trained prediction model;
acquiring current state information of the mechanical equipment, inputting the current state information of the mechanical equipment into a trained prediction model, and generating real-time axis track data of the mechanical equipment and predicted axis track data at the next moment in the predicted state;
establishing a mechanical equipment shaft track curve according to the mechanical equipment real-time shaft track data and the predicted shaft track data at the next moment in a predicted state;
similarity calculation is carried out on the mechanical equipment shaft track curve and a preset shaft track to obtain a deviation rate;
judging whether the deviation rate is larger than a preset deviation rate threshold value or not;
if the service life of the mechanical equipment is greater than or equal to the service life of the mechanical equipment, recording the current moment, generating fault information, and inputting a preset service life prediction model according to the fault information to predict the service life of the mechanical equipment;
if the current time is smaller than the current time, recording the current time, generating the health management data of the mechanical equipment, and transmitting the health management data of the mechanical equipment to a database.
Optionally, in the system for predicting the operation life of a mechanical device and managing health of the embodiment of the present application, the step of building a device operation state prediction model by big data and training the device operation state model to obtain a trained prediction model includes:
Acquiring historical operation data of the mechanical equipment through the big data;
classifying historical operation data of the mechanical equipment through equipment operation parameters to generate a plurality of data sets;
training the equipment running state model through a plurality of data sets to obtain a training result;
judging whether the training result is converged or not;
if the convergence is achieved, the training is finished;
if the data in the data sets are not converged, extracting the data in the data sets to form a new training set, and training the equipment running state model through the new training set.
Optionally, in the system for predicting the operation life of a mechanical device and managing health of the embodiment of the present application, if the operation life of the mechanical device is greater than or equal to the operation life, recording the current time, generating fault information, and inputting the fault information into a preset life prediction model to predict the life of the mechanical device, including:
acquiring operation parameters of mechanical equipment and acquiring equipment operation time-varying information;
analyzing the equipment operation time-varying information to obtain equipment working condition information;
comparing the equipment working condition information with preset first information to obtain first similarity;
judging whether the first similarity is larger than or equal to a first similarity threshold value;
If the current equipment working condition is greater than or equal to the first similarity threshold value, judging that the current equipment working condition is a stable working condition;
if the device working condition information is smaller than the first similarity threshold value, comparing the device working condition information with preset second information to obtain a second similarity threshold value;
determining whether the second similarity threshold is greater than a second similarity threshold,
if the current equipment working condition is larger than the second similarity threshold value, judging that the current equipment working condition is a gradual change working condition;
if the current equipment working condition is smaller than the second similarity threshold value, judging that the current equipment working condition is a sudden change working condition;
the first similarity threshold is greater than the second similarity threshold.
In a third aspect, an embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium includes a mechanical device operation lifetime prediction and health management method program, where the mechanical device operation lifetime prediction and health management method program, when executed by a processor, implements the steps of the mechanical device operation lifetime prediction and health management method according to any one of the above.
As can be seen from the above, according to the method, system and medium for predicting the operation life and managing the health of the mechanical equipment provided by the embodiments of the present application, the equipment operation state prediction model is established through big data, the current state information of the mechanical equipment is input into the trained prediction model, and the real-time axis track data of the mechanical equipment and the predicted axis track data at the next time in the predicted state are generated; similarity calculation is carried out on the mechanical equipment shaft track curve and a preset shaft track to obtain a deviation rate; judging whether the deviation rate is larger than a preset deviation rate threshold value or not; if the service life of the mechanical equipment is greater than or equal to the service life of the mechanical equipment, recording the current moment, generating fault information, and inputting a preset service life prediction model according to the fault information to predict the service life of the mechanical equipment; if the current time is smaller than the current time, recording the current time, generating the health management data of the mechanical equipment, and transmitting the health management data of the mechanical equipment to a database; the fault diagnosis is carried out on the equipment by judging the real-time shaft track of the mechanical equipment, the residual service life of the equipment is judged, and the precision is high.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for predicting the operation life of a mechanical device and managing health according to an embodiment of the present application;
FIG. 2 is a training flow chart of an equipment operation state model of a mechanical equipment operation life prediction and health management method according to an embodiment of the present application;
FIG. 3 is a flow chart of equipment condition judgment of a method for predicting the operation life and managing the health of a mechanical equipment according to an embodiment of the present application;
FIG. 4 is a flowchart of predicting the remaining life of a mechanical device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a mechanical device operation lifetime prediction and health management system according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for predicting operation life and managing health of a mechanical device according to some embodiments of the application. The mechanical equipment operation life prediction and health management method is used in terminal equipment and comprises the following steps:
s101, building a device running state prediction model through big data, and training the device running state model to obtain a trained prediction model;
s102, acquiring current state information of the mechanical equipment, inputting the current state information of the mechanical equipment into a trained prediction model, and generating real-time axis track data of the mechanical equipment and predicted axis track data of the next moment in a predicted state;
s103, establishing a mechanical equipment shaft track curve according to the mechanical equipment real-time shaft track data and the predicted shaft track data at the next moment in a predicted state;
s104, similarity calculation is carried out on the mechanical equipment axis track curve and a preset axis track, so that a deviation rate is obtained;
s105, judging whether the deviation rate is larger than a preset deviation rate threshold value;
s106, if the service life of the mechanical equipment is greater than or equal to the service life of the mechanical equipment, recording the current moment, generating fault information, and inputting a preset service life prediction model according to the fault information to predict the service life of the mechanical equipment; if the current time is smaller than the current time, recording the current time, generating the health management data of the mechanical equipment, and transmitting the health management data of the mechanical equipment to a database.
It should be noted that, the similarity of the motion trail in the motion process of the mechanical equipment is judged through big data, and the threshold value judgment is carried out on the motion trail, so that the mechanical equipment can be accurately distinguished in fault grade, the targeted processing strategies of different fault grades are realized, meanwhile, the operation fault analysis of the shaft ends of the mechanical equipment is judged through the similarity of the motion trail, the life prediction is carried out on the mechanical equipment according to a fault model, the dynamic compensation of the rotating shaft is carried out according to the fault analysis result, the healthy operation and management of the mechanical equipment are realized, an expert database is generated, and the data reference comparison is convenient when the next fault occurs.
Referring to fig. 2, fig. 2 is a device operation state model training flowchart of a method for predicting operation life and managing health of a mechanical device according to some embodiments of the present application. According to the embodiment of the application, a device running state prediction model is established through big data, and the device running state model is trained to obtain a trained prediction model, which comprises the following steps:
s201, acquiring historical operation data of mechanical equipment through big data;
s202, classifying historical operation data of mechanical equipment through equipment operation parameters to generate a plurality of data sets;
S203, training the equipment running state model through a plurality of data sets to obtain a training result;
s204, judging whether the training result is converged; if the convergence is achieved, the training is finished; if the data in the data sets are not converged, extracting the data in the data sets to form a new training set, and training the equipment running state model through the new training set.
The device running state model is trained through the data set, so that the prediction result of the device running state model is more accurate.
Referring to fig. 3, fig. 3 is a flow chart illustrating a device operation condition determination method for predicting an operation lifetime of a mechanical device according to some embodiments of the application. According to the embodiment of the application, if the service life of the mechanical equipment is greater than or equal to the service life of the mechanical equipment, the current moment is recorded, fault information is generated, and the service life of the mechanical equipment is predicted according to a preset service life prediction model input by the fault information, wherein the method comprises the following steps:
s301, acquiring operation parameters of mechanical equipment, acquiring equipment operation time-varying information, and analyzing the equipment operation time-varying information to obtain equipment working condition information;
s302, comparing the equipment working condition information with preset first information to obtain a first similarity;
s303, judging whether the first similarity is larger than or equal to a first similarity threshold value;
S304, if the current equipment working condition is greater than or equal to the first similarity threshold value, judging that the current equipment working condition is a stable working condition; if the device working condition information is smaller than the first similarity threshold value, comparing the device working condition information with preset second information to obtain a second similarity threshold value;
s305, judging whether the second similarity threshold is larger than the second similarity threshold,
s306, if the current equipment working condition is larger than the second similarity threshold value, judging that the current equipment working condition is a gradual change working condition; if the current equipment working condition is smaller than the second similarity threshold value, judging that the current equipment working condition is a sudden change working condition;
the first similarity threshold is greater than the second similarity threshold.
The stable working condition can be understood as a normal running state of the equipment, and the slow-change working condition can be understood as a tiny change of the working condition of the equipment, namely, the temperature change quantity in a normal interval of the equipment and the vibration change quantity in a normal interval of the equipment; the abrupt change working condition can be understood as fault information after the equipment has an emergency.
Referring to fig. 4, fig. 4 is a flow chart of a device remaining life prediction method for predicting the operation life of a mechanical device according to some embodiments of the application. According to the embodiment of the application, if the service life of the mechanical equipment is greater than or equal to the service life of the mechanical equipment, the current moment is recorded, fault information is generated, and the service life of the mechanical equipment is predicted by inputting a preset service life prediction model according to the fault information; comprising the following steps:
S401, obtaining fault information, generating a fault set,
s402, inputting a fault set into a life prediction model to obtain equipment life loss information;
s403, calculating a creep component and a fatigue component of the service life loss of the equipment according to the service life loss information;
s404, predicting the residual life of the equipment according to the creep component and the fatigue component;
s405, judging the difference value between the residual life of the equipment and a preset life threshold;
s406, if the difference value is smaller than a preset difference value, stopping the machine to replace important parts of the mechanical equipment; if the difference is larger than the preset difference, generating equipment life loss rate, and monitoring the equipment life in real time.
The method adopts mechanical equipment strength performance and operation parameters to calculate stress distribution under each working condition, and calculates creep component and fatigue component of service life loss of the component according to analysis results of component operation history data and start-stop times of corresponding equipment, so as to predict service life of the component.
The life loss rate of the component is T/T when the component is subjected to stress sigma and temperature T r The life loss rate is independent when the stress and the temperature are continuously changed, and the total life loss rate D c Is the integral of each part.
Wherein T represents stress sigma and run time at temperature T, T r The stress sigma and the break time at temperature T are indicated.
The fatigue component is calculated by a linear superposition principle, and the calculation formula is as follows:
wherein k is the number of working condition changes, n i Indicating the number of cycles in which fission occurs, N i Indicating the number of operating conditions in actual operation.
And calculating the allowable value of the loss accumulation of the total service life according to the total service life loss rate and the fatigue component, wherein the calculation formula is as follows:
D＝D c +D f 。
according to the embodiment of the invention, the current state information of the mechanical equipment is obtained, the current state information of the mechanical equipment is input into the trained prediction model, and the real-time axis track data of the mechanical equipment and the predicted axis track data at the next moment in the predicted state are generated, which comprises the following steps:
acquiring state data of mechanical equipment and generating a data sequence;
decomposing the data sequence to obtain trend components and detail components;
constructing an initial boundary of the data feature according to the trend component and the detail component;
performing interval construction according to the detail components, and calculating interval evaluation indexes;
judging whether the interval evaluation index is larger than a preset index value;
if the data is larger than the optimal data estimation interval, an optimal data estimation interval is established;
if the value is smaller than the preset value, the interval range is adjusted.
According to the embodiment of the invention, if the current time is smaller than the preset time, the current time is recorded, the health management data of the mechanical equipment is generated, and the health management data of the mechanical equipment is transmitted to a database; comprising the following steps:
Acquiring equipment health management data, and generating an equipment health operation expert knowledge base according to the equipment health management data;
generating a healthy running curve graph of the equipment according to the expert knowledge base;
comparing whether the equipment health running curve graph is compared with a preset curve to obtain similarity;
judging whether the similarity is larger than or equal to a preset similarity threshold value;
if the data is greater than or equal to the preset value, saving the equipment health management data;
and if the device health management data is smaller than the set value, extracting the device health management data of the corresponding time node, and correcting the device health management data of the corresponding time node.
It should be noted that, the expert knowledge base is generated according to the device health management data, and the expert knowledge base is used for storing the running state of the device health data and the diagnosis result of each fault diagnosis of the device, and the diagnosis process can provide reference information for the next fault diagnosis and life prediction.
According to the embodiment of the invention, the collected bearing data come from different test tables, the amplitude of the bearing vibration signals in the steady operation section has great difference, and in order to enable the distances between the distributions calculated by the data on the different test tables to be comparable, the following normalization processing is needed to be carried out on the vibration signals before the vibration signal distribution difference is calculated, and the normalization processing formula is as follows:
Wherein X is i As the original vibration signal SX i To normalize the processed vibration signal, max (X normal ) Is the maximum of the first third vibration signal.
The Wasperstein Distance (WD) can effectively reflect the difference between the distributions, and therefore the WD between the vibration signal distributions is used as a characteristic index to reflect different degradation states of the bearing. For two vibration signals x= { X 1 ,x 2 ,…x n Sum y= { Y 1 ,y 2 ,…y m WD between its vibration signal distributions may be obtained by:
wherein f ij The following conditional constraints need to be satisfied:
wherein i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n,and->The probability values for X and Y at the respective magnitudes, respectively.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a system for predicting operation life of a mechanical device and managing health of the mechanical device according to some embodiments of the application. In a second aspect, embodiments of the present application provide a mechanical device operational life prediction and health management system 5 comprising: the memory 51 and the processor 52, the memory 51 includes a program of a mechanical equipment operation life prediction and health management method, and the program of the mechanical equipment operation life prediction and health management method realizes the following steps when executed by the processor:
establishing a device running state prediction model through big data, and training the device running state model to obtain a trained prediction model;
Acquiring current state information of the mechanical equipment, inputting the current state information of the mechanical equipment into a trained prediction model, and generating real-time axis track data of the mechanical equipment and predicted axis track data at the next moment in the predicted state;
establishing a mechanical equipment shaft track curve according to the mechanical equipment real-time shaft track data and the predicted shaft track data at the next moment in a predicted state;
similarity calculation is carried out on the mechanical equipment shaft track curve and a preset shaft track to obtain a deviation rate;
judging whether the deviation rate is larger than a preset deviation rate threshold value or not;
if the service life of the mechanical equipment is greater than or equal to the service life of the mechanical equipment, recording the current moment, generating fault information, and inputting a preset service life prediction model according to the fault information to predict the service life of the mechanical equipment;
if the current time is smaller than the current time, recording the current time, generating the health management data of the mechanical equipment, and transmitting the health management data of the mechanical equipment to a database.
It should be noted that, the similarity of the motion trail in the motion process of the mechanical equipment is judged through big data, and the threshold value judgment is carried out on the motion trail, so that the mechanical equipment can be accurately distinguished in fault grade, the targeted processing strategies of different fault grades are realized, meanwhile, the operation fault analysis of the shaft ends of the mechanical equipment is judged through the similarity of the motion trail, the life prediction is carried out on the mechanical equipment according to a fault model, the dynamic compensation of the rotating shaft is carried out according to the fault analysis result, the healthy operation and management of the mechanical equipment are realized, an expert database is generated, and the data reference comparison is convenient when the next fault occurs.
According to the embodiment of the invention, a device running state prediction model is established through big data, and the device running state model is trained to obtain a trained prediction model, which comprises the following steps:
acquiring historical operation data of the mechanical equipment through the big data;
classifying historical operation data of the mechanical equipment through equipment operation parameters to generate a plurality of data sets;
training the equipment running state model through a plurality of data sets to obtain a training result;
judging whether the training result is converged or not;
if the convergence is achieved, the training is finished;
if the data in the data sets are not converged, extracting the data in the data sets to form a new training set, and training the equipment running state model through the new training set.
The device running state model is trained through the data set, so that the prediction result of the device running state model is more accurate.
According to the embodiment of the invention, if the service life of the mechanical equipment is greater than or equal to the service life of the mechanical equipment, the current moment is recorded, fault information is generated, and the service life of the mechanical equipment is predicted according to a preset service life prediction model input by the fault information, wherein the method comprises the following steps:
acquiring operation parameters of mechanical equipment and acquiring equipment operation time-varying information;
Analyzing the equipment operation time-varying information to obtain equipment working condition information;
comparing the equipment working condition information with preset first information to obtain first similarity;
judging whether the first similarity is larger than or equal to a first similarity threshold value;
if the current equipment working condition is greater than or equal to the first similarity threshold value, judging that the current equipment working condition is a stable working condition;
if the device working condition information is smaller than the first similarity threshold value, comparing the device working condition information with preset second information to obtain a second similarity threshold value;
determining whether the second similarity threshold is greater than the second similarity threshold,
if the current equipment working condition is larger than the second similarity threshold value, judging that the current equipment working condition is a gradual change working condition;
if the current equipment working condition is smaller than the second similarity threshold value, judging that the current equipment working condition is a sudden change working condition;
the first similarity threshold is greater than the second similarity threshold.
The stable working condition can be understood as a normal running state of the equipment, and the slow-change working condition can be understood as a tiny change of the working condition of the equipment, namely, the temperature change quantity in a normal interval of the equipment and the vibration change quantity in a normal interval of the equipment; the abrupt change working condition can be understood as fault information after the equipment has an emergency.
According to the embodiment of the invention, if the service life of the mechanical equipment is greater than or equal to the service life of the mechanical equipment, the current moment is recorded, fault information is generated, and the service life of the mechanical equipment is predicted by inputting a preset service life prediction model according to the fault information; comprising the following steps:
Acquiring fault information, generating a fault set,
inputting the fault set into a life prediction model to obtain equipment life loss information;
calculating a creep component and a fatigue component of the equipment life loss according to the life loss information;
predicting the residual life of the equipment according to the creep component and the fatigue component;
judging the difference value between the residual life of the equipment and a preset life threshold value;
if the difference value is smaller than the preset difference value, stopping the machine to replace important parts of the mechanical equipment;
if the difference is larger than the preset difference, generating equipment life loss rate, and monitoring the equipment life in real time.
The method adopts the geometric dimension of the strength performance value of the component steel and the operation parameters to calculate the stress distribution under each working condition, and calculates the creep component and the fatigue component of the service life loss of the component according to the analysis result of the operation history data of the component and the start-stop times of corresponding equipment, thereby predicting the service life of the component.
The life loss rate of the component is T/T when the component is subjected to stress sigma and temperature T r The life loss rate is independent when the stress and the temperature are continuously changed, and the total life loss rate D c Is the integral of each part.
Wherein T represents stress sigma and run time at temperature T, T r The stress sigma and the break time at temperature T are indicated.
The fatigue component is calculated by a linear superposition principle, and the calculation formula is as follows:
wherein k is the number of working condition changes, n i Indicating the number of cycles in which fission occurs, N i Indicating the number of operating conditions in actual operation.
And calculating the allowable value of the loss accumulation of the total service life according to the total service life loss rate and the fatigue component, wherein the calculation formula is as follows:
D＝D c +D f 。
according to the embodiment of the invention, the current state information of the mechanical equipment is obtained, the current state information of the mechanical equipment is input into the trained prediction model, and the real-time axis track data of the mechanical equipment and the predicted axis track data at the next moment in the predicted state are generated, which comprises the following steps:
acquiring state data of mechanical equipment and generating a data sequence;
decomposing the data sequence to obtain trend components and detail components;
constructing an initial boundary of the data feature according to the trend component and the detail component;
performing interval construction according to the detail components, and calculating interval evaluation indexes;
judging whether the interval evaluation index is larger than a preset index value;
if the data is larger than the optimal data estimation interval, an optimal data estimation interval is established;
if the value is smaller than the preset value, the interval range is adjusted.
According to the embodiment of the invention, if the current time is smaller than the preset time, the current time is recorded, the health management data of the mechanical equipment is generated, and the health management data of the mechanical equipment is transmitted to a database; comprising the following steps:
Acquiring equipment health management data, and generating an equipment health operation expert knowledge base according to the equipment health management data;
generating a healthy running curve graph of the equipment according to the expert knowledge base;
comparing whether the equipment health running curve graph is compared with a preset curve to obtain similarity;
judging whether the similarity is larger than or equal to a preset similarity threshold value;
if the data is greater than or equal to the preset value, saving the equipment health management data;
and if the device health management data is smaller than the set value, extracting the device health management data of the corresponding time node, and correcting the device health management data of the corresponding time node.
It should be noted that, the expert knowledge base is generated according to the device health management data, and the expert knowledge base is used for storing the running state of the device health data and the diagnosis result of each fault diagnosis of the device, and the diagnosis process can provide reference information for the next fault diagnosis and life prediction.
According to the embodiment of the invention, the collected bearing data come from different test tables, the amplitude of the bearing vibration signals in the steady operation section has great difference, and in order to enable the distances between the distributions calculated by the data on the different test tables to be comparable, the following normalization processing is needed to be carried out on the vibration signals before the vibration signal distribution difference is calculated, and the normalization processing formula is as follows:
Wherein X is i As the original vibration signal SX i To normalize the processed vibration signal, max (X normal ) Is the maximum of the first third vibration signal.
The Wasperstein Distance (WD) can effectively reflect the difference between the distributions, and therefore the WD between the vibration signal distributions is used as a characteristic index to reflect different degradation states of the bearing. For two vibration signals x= { X 1 ,x 2 ,…x n Sum y= { Y 1 ,y 2 ,…y m WD between its vibration signal distributions may be obtained by:
wherein f ij The following conditional constraints need to be satisfied:
wherein i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n,and->The probability values for X and Y at the respective magnitudes, respectively.
A third aspect of the present invention provides a computer readable storage medium, the readable storage medium including a mechanical device operation lifetime prediction and health management method program, which when executed by a processor, implements the steps of a mechanical device operation lifetime prediction and health management method as in any one of the above.
The invention discloses a method, a system and a medium for predicting the operation life and managing the health of mechanical equipment, wherein an equipment operation state prediction model is established through big data, current state information of the mechanical equipment is input into the trained prediction model, and real-time axis track data of the mechanical equipment and predicted axis track data at the next moment in the predicted state are generated; similarity calculation is carried out on the mechanical equipment shaft track curve and a preset shaft track to obtain a deviation rate; judging whether the deviation rate is larger than a preset deviation rate threshold value or not; if the service life of the mechanical equipment is greater than or equal to the service life of the mechanical equipment, recording the current moment, generating fault information, and inputting a preset service life prediction model according to the fault information to predict the service life of the mechanical equipment; if the current time is smaller than the current time, recording the current time, generating the health management data of the mechanical equipment, and transmitting the health management data of the mechanical equipment to a database; the fault diagnosis is carried out on the equipment by judging the real-time shaft track of the mechanical equipment, the residual service life of the equipment is judged, and the precision is high.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of units is only one logical function division, and there may be other divisions in actual implementation, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to 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 be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
Claims (10)
1. A method for predicting the operational life and managing the health of a mechanical device, comprising:
establishing a device running state prediction model through big data, and training the device running state model to obtain a trained prediction model;
acquiring current state information of the mechanical equipment, inputting the current state information of the mechanical equipment into a trained prediction model, and generating real-time axis track data of the mechanical equipment and predicted axis track data at the next moment in the predicted state;
establishing a mechanical equipment shaft track curve according to the mechanical equipment real-time shaft track data and the predicted shaft track data at the next moment in a predicted state;
similarity calculation is carried out on the mechanical equipment shaft track curve and a preset shaft track to obtain a deviation rate;
judging whether the deviation rate is larger than a preset deviation rate threshold value or not;
if the service life of the mechanical equipment is greater than or equal to the service life of the mechanical equipment, recording the current moment, generating fault information, and inputting a preset service life prediction model according to the fault information to predict the service life of the mechanical equipment;
if the current time is smaller than the current time, recording the current time, generating the health management data of the mechanical equipment, and transmitting the health management data of the mechanical equipment to a database.
2. The method for predicting the operation life and managing the health of a mechanical device according to claim 1, wherein the steps of building a device operation state prediction model by big data and training the device operation state model to obtain a trained prediction model include:
Acquiring historical operation data of the mechanical equipment through the big data;
classifying historical operation data of the mechanical equipment through equipment operation parameters to generate a plurality of data sets;
training the equipment running state model through a plurality of data sets to obtain a training result;
judging whether the training result is converged or not;
if the convergence is achieved, the training is finished;
if the data in the data sets are not converged, extracting the data in the data sets to form a new training set, and training the equipment running state model through the new training set.
3. The method for predicting the service life and managing the health of the mechanical equipment according to claim 2, wherein if the service life of the mechanical equipment is greater than or equal to the service life, recording the current moment, generating fault information, and inputting a preset service life prediction model according to the fault information to predict the service life of the mechanical equipment, wherein the method comprises the following steps:
acquiring operation parameters of mechanical equipment and acquiring equipment operation time-varying information;
analyzing the equipment operation time-varying information to obtain equipment working condition information;
comparing the equipment working condition information with preset first information to obtain first similarity;
judging whether the first similarity is larger than or equal to a first similarity threshold value;
If the current equipment working condition is greater than or equal to the first similarity threshold value, judging that the current equipment working condition is a stable working condition;
if the device working condition information is smaller than the first similarity threshold value, comparing the device working condition information with preset second information to obtain a second similarity threshold value;
determining whether the second similarity threshold is greater than a second similarity threshold,
if the current equipment working condition is larger than the second similarity threshold value, judging that the current equipment working condition is a gradual change working condition;
if the current equipment working condition is smaller than the second similarity threshold value, judging that the current equipment working condition is a sudden change working condition;
the first similarity threshold is greater than the second similarity threshold.
4. The method for predicting the service life and managing the health of the mechanical equipment according to claim 3, wherein if the service life of the mechanical equipment is greater than or equal to the service life, recording the current moment, generating fault information, and inputting a preset service life prediction model according to the fault information to predict the service life of the mechanical equipment; comprising the following steps:
acquiring fault information, generating a fault set,
inputting the fault set into a life prediction model to obtain equipment life loss information;
calculating a creep component and a fatigue component of the equipment life loss according to the life loss information;
predicting the residual life of the equipment according to the creep component and the fatigue component;
Judging the difference value between the residual life of the equipment and a preset life threshold;
if the difference value is smaller than the preset difference value, stopping the machine to replace important parts of the mechanical equipment;
if the difference is larger than the preset difference, generating equipment life loss rate, and monitoring the equipment life in real time.
5. The method for predicting the operation life and managing the health of a machine according to claim 4, wherein the obtaining the current state information of the machine, inputting the current state information of the machine into the trained prediction model, and generating real-time axis trajectory data of the machine and predicted axis trajectory data at the next time in the predicted state, comprises:
acquiring state data of mechanical equipment and generating a data sequence;
decomposing the data sequence to obtain trend components and detail components;
constructing an initial boundary of the data feature according to the trend component and the detail component;
performing interval construction according to the detail components, and calculating interval evaluation indexes;
judging whether the interval evaluation index is larger than a preset index value or not;
if the data is larger than the optimal data estimation interval, an optimal data estimation interval is established;
if the value is smaller than the preset value, the interval range is adjusted.
6. The method for predicting the operation life and managing the health of a mechanical device according to claim 5, wherein if the operation life of the mechanical device is smaller than the predetermined value, recording the current time, generating mechanical device health management data, and transmitting the mechanical device health management data to a database; comprising the following steps:
Acquiring equipment health management data, and generating an equipment health operation expert knowledge base according to the equipment health management data;
generating a healthy running curve graph of the equipment according to the expert knowledge base;
comparing whether the equipment health running curve graph is compared with a preset curve to obtain similarity;
judging whether the similarity is larger than or equal to a preset similarity threshold value;
if the data is greater than or equal to the preset value, saving the equipment health management data;
and if the device health management data is smaller than the set value, extracting the device health management data of the corresponding time node, and correcting the device health management data of the corresponding time node.
7. A system for predicting operational life and health of a mechanical device, the system comprising: the system comprises a memory and a processor, wherein the memory comprises a program of a mechanical equipment operation life prediction and health management method, and the program of the mechanical equipment operation life prediction and health management method realizes the following steps when being executed by the processor:
establishing a device running state prediction model through big data, and training the device running state model to obtain a trained prediction model;
acquiring current state information of the mechanical equipment, inputting the current state information of the mechanical equipment into a trained prediction model, and generating real-time axis track data of the mechanical equipment and predicted axis track data at the next moment in the predicted state;
Establishing a mechanical equipment shaft track curve according to the mechanical equipment real-time shaft track data and the predicted shaft track data at the next moment in a predicted state;
similarity calculation is carried out on the mechanical equipment shaft track curve and a preset shaft track to obtain a deviation rate;
judging whether the deviation rate is larger than a preset deviation rate threshold value or not;
if the service life of the mechanical equipment is greater than or equal to the service life of the mechanical equipment, recording the current moment, generating fault information, and inputting a preset service life prediction model according to the fault information to predict the service life of the mechanical equipment;
if the current time is smaller than the current time, recording the current time, generating the health management data of the mechanical equipment, and transmitting the health management data of the mechanical equipment to a database.
8. The system for predicting the operational life and health of a machine according to claim 7, wherein said creating a model for predicting the operational state of the machine from big data and training the model for predicting the operational state of the machine to obtain a trained model for predicting the operational life of the machine comprises:
acquiring historical operation data of the mechanical equipment through the big data;
classifying historical operation data of the mechanical equipment through equipment operation parameters to generate a plurality of data sets;
training the equipment running state model through a plurality of data sets to obtain a training result;
Judging whether the training result is converged or not;
if the convergence is achieved, the training is finished;
if the data in the data sets are not converged, extracting the data in the data sets to form a new training set, and training the equipment running state model through the new training set.
9. The system for predicting the operation life of a mechanical device and managing health of the mechanical device according to claim 8, wherein if the operation life of the mechanical device is greater than or equal to the operation life of the mechanical device, recording the current time, generating fault information, and inputting a preset life prediction model according to the fault information to predict the life of the mechanical device, comprising:
acquiring operation parameters of mechanical equipment and acquiring equipment operation time-varying information;
analyzing the equipment operation time-varying information to obtain equipment working condition information;
comparing the equipment working condition information with preset first information to obtain first similarity;
judging whether the first similarity is larger than or equal to a first similarity threshold value;
if the current equipment working condition is greater than or equal to the first similarity threshold value, judging that the current equipment working condition is a stable working condition;
if the device working condition information is smaller than the first similarity threshold value, comparing the device working condition information with preset second information to obtain a second similarity threshold value;
determining whether the second similarity threshold is greater than a second similarity threshold,
If the current equipment working condition is larger than the second similarity threshold value, judging that the current equipment working condition is a gradual change working condition;
if the current equipment working condition is smaller than the second similarity threshold value, judging that the current equipment working condition is a sudden change working condition;
the first similarity threshold is greater than the second similarity threshold.
10. A computer readable storage medium, wherein a mechanical device operation life prediction and health management method program is included in the computer readable storage medium, and when the mechanical device operation life prediction and health management method program is executed by a processor, the steps of the mechanical device operation life prediction and health management method according to any one of claims 1 to 6 are implemented.
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| CN117290803B (en) * | 2023-11-27 | 2024-03-26 | 深圳鹏城新能科技有限公司 | Energy storage inverter remote fault diagnosis method, system and medium |
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