CN115879248B - Full life cycle management method and system suitable for vacuum pump - Google Patents
Full life cycle management method and system suitable for vacuum pump Download PDFInfo
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Abstract
The application relates to a full life cycle management method and a full life cycle management system suitable for a vacuum pump, and belongs to the technical field of full life cycle management of products, wherein the method comprises the following steps: acquiring first data of a vacuum pump in a history database, and preprocessing the first data to obtain target data; constructing a vacuum pump life prediction model based on the target data; calculating to obtain life decay curves of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model; and selecting and processing the processing mode of the target vacuum pump according to the life decay curve to realize the full life cycle management of the target vacuum pump. According to the method and the device, the service life of the target vacuum pump is predicted, so that the performance degradation process of the vacuum pump can be accurately predicted, the full life cycle of the applied vacuum pump can be effectively managed, and the use reliability, safety and management efficiency of the vacuum pump are improved.
Description
Technical Field
The application relates to the technical field of full life cycle management of products, in particular to a full life cycle management method and system suitable for a vacuum pump.
Background
In a system with a vacuum pump, problems such as aging and contamination by sediment may occur in the operation process of the vacuum pump in the full life cycle, so that performance and efficiency of the vacuum pump are reduced and abnormal conditions occur, in the prior art, the abnormal conditions and replacement cycles of the vacuum pump are generally judged through experience of technicians, however, according to different use environments of the vacuum pump, the service life of the vacuum pump is different, the maintenance and replacement cycles of the vacuum pump are difficult to accurately judge by a conventional method, so that the use reliability and safety of the vacuum pump are low, and therefore, a method for managing the full operation life cycle of the vacuum pump by utilizing relevant dynamic information of the vacuum pump is needed to be proposed.
Disclosure of Invention
Based on this, it is necessary to provide a method, a system, a computer device and a storage medium for managing the full life cycle of a vacuum pump, which can accurately judge the performance degradation problem occurring in the operation process of the vacuum pump, thereby facilitating maintenance and management.
In one aspect, a full life cycle management method for a vacuum pump is provided, the method comprising:
step A: acquiring first data of a vacuum pump in a history database, and preprocessing the first data to obtain target data;
And (B) step (B): constructing a vacuum pump life prediction model based on the target data;
step C: calculating to obtain life decay curves of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model;
step D: and selecting and processing the processing mode of the target vacuum pump according to the life decay curve to realize the full life cycle management of the target vacuum pump.
In one embodiment, the method further comprises: the first data includes initial state data, application environment data, running state data and loss data of the vacuum pump, the preprocessing of the first data includes: performing data cleaning on the first data; screening the cleaned first data according to a preset data amount to obtain second data; classifying and sequencing the second data based on a cluster analysis strategy to obtain 4 data sets with different categories, namely a first category, a second category, a third category and a fourth category; and reading and fitting the data set of the target class by using a data adjustment mechanism to obtain the target data.
In one embodiment, the method further comprises: the step of reading and fitting the data set of the target class by using a data adjustment mechanism, and the step of obtaining the target data comprises the following steps: and carrying out normalization processing on the read data set of the target category, wherein the calculation formula is as follows:
wherein ,
normalized coefficient representing mth data attribute, +.>
The custom coefficients representing the nth data,
mth data attribute representing nth data,/->
Weight representing mth data attribute, < ->
Representing the number of data attributes that are to be included,
normalized value of mth data attribute representing nth data, +.>
，/>
Representing the data quantity;
fitting the normalized data by using a fitting function, wherein the fitting function is as follows:
wherein ,
representing fitting function value,/->
Normalized values representing the data sets of the first, second, third and fourth category, respectively, i representing the iteration coefficient, +.>
Fitting coefficients representing a j-th class dataset, < +.>
Characteristic parameters representing a j-th class dataset;
and defining the data value after fitting processing as the target data.
In one embodiment, the method further comprises: the construction process of the vacuum pump life prediction model comprises the following steps: pre-constructing a first vacuum pump life prediction model based on a deep neural network; training the first vacuum pump life prediction model based on the target data obtained by the plurality of vacuum pumps; and outputting a second vacuum pump life prediction model obtained after training is completed when the precision of the first vacuum pump life prediction model reaches a preset standard, namely the final vacuum pump life prediction model.
In one embodiment, the method further comprises: the expression of the vacuum pump life prediction model comprises:
wherein ,
lifetime prediction value at time t+1, < ->
Indicating the difference of the target data from time t to time t+1, < >>
Mean life value>
Represents the loss factor at time t+1, < >>
Represents the loss factor at time t, k represents the iteration factor,/->
Representing an index.
In one embodiment, the method further comprises: the calculating the life attenuation curve of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model comprises the following steps: inputting the target data of the target vacuum pump corresponding to each time point obtained through calculation into the vacuum pump life prediction model to obtain a first life prediction value; segmenting a plurality of time points according to a preset interval, and obtaining an average value of the first life predicted values corresponding to each segment to obtain a second life predicted value; and forming a one-to-one mapping relation between the second life predicted value and the middle time point of the segmented interval, and connecting the second life predicted values corresponding to the middle time points to generate a life decay curve.
In one embodiment, the method further comprises: the selecting and processing the processing mode of the target vacuum pump according to the life decay curve comprises the following steps: calculating the slope of the second life prediction value corresponding to two adjacent intermediate time points based on the life decay curve; judging the processing mode of the target vacuum pump according to the slope: when the slope is larger than a first preset value and the service life is smaller than a second preset value, judging that the target vacuum pump is required to be overhauled when the pump fails; and when the slope is larger than a first preset value and the service life is larger than or equal to a second preset value, judging that the target vacuum pump needs to be scrapped.
In another aspect, a full life cycle management system for a vacuum pump is provided, the system comprising:
the preprocessing module is used for acquiring first data of the vacuum pump in the history database, preprocessing the first data and obtaining target data;
the model construction module is used for constructing a vacuum pump life prediction model based on the target data;
the calculation module is used for calculating and obtaining life decay curves of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model;
and the selection module is used for selecting and processing the processing mode of the target vacuum pump according to the life decay curve so as to realize the full life cycle management of the target vacuum pump.
In yet another aspect, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of:
step A: acquiring first data of a vacuum pump in a history database, and preprocessing the first data to obtain target data;
and (B) step (B): constructing a vacuum pump life prediction model based on the target data;
Step C: calculating to obtain life decay curves of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model;
step D: and selecting and processing the processing mode of the target vacuum pump according to the life decay curve to realize the full life cycle management of the target vacuum pump.
In yet another aspect, a computer readable storage medium is provided, having stored thereon a computer program which when executed by a processor performs the steps of:
step A: acquiring first data of a vacuum pump in a history database, and preprocessing the first data to obtain target data;
and (B) step (B): constructing a vacuum pump life prediction model based on the target data;
step C: calculating to obtain life decay curves of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model;
step D: and selecting and processing the processing mode of the target vacuum pump according to the life decay curve to realize the full life cycle management of the target vacuum pump.
The above-mentioned full life cycle management method, system, computer device and storage medium suitable for vacuum pump, the method includes: acquiring first data of a vacuum pump in a history database, and preprocessing the first data to obtain target data; constructing a vacuum pump life prediction model based on the target data; calculating to obtain life decay curves of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model; according to the life decay curve, the processing mode of the target vacuum pump is selected and processed, so that the full life cycle management of the target vacuum pump is realized.
Drawings
FIG. 1 is a diagram of an application environment for a full life cycle management method for a vacuum pump in one embodiment;
FIG. 2 is a flow chart of a full life cycle management method for a vacuum pump according to one embodiment;
FIG. 3 is a block diagram of a full life cycle management system suitable for use with a vacuum pump in one embodiment;
fig. 4 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
It should be understood that throughout this description, unless the context clearly requires otherwise, the words "comprise," "comprising," and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, it is the meaning of "including but not limited to".
It should also be appreciated that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
It should be noted that the terms "S1", "S2", and the like are used for the purpose of describing steps only, and are not intended to be limited to the order or sequence of steps or to limit the present application, but are merely used for convenience in describing the method of the present application and are not to be construed as indicating the sequence of steps. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be regarded as not exist and not within the protection scope of the present application.
The full life cycle management method suitable for the vacuum pump can be applied to an application environment shown in fig. 1. The terminal 102 communicates with a data processing platform disposed on the server 104 through a network, where the terminal 102 may be, but is not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the server 104 may be implemented by a stand-alone server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, a full life cycle management method suitable for a vacuum pump is provided, and the method is applied to the terminal in fig. 1 for illustration, and includes the following steps:
s1: and acquiring first data of the vacuum pump in the history database, and preprocessing the first data to obtain target data.
It should be noted that, the first data includes initial state data, application environment data, operation state data and loss data of the vacuum pump, where the initial state data may include one or more of design pre-estimated quality, model, manufacturing quality, acceptance quality, manufacturer production defect, etc., the application environment data may include one or more of environment temperature, humidity, climate characteristics, etc., the operation state data includes one or more of current value, pressure value, usage frequency, etc., the loss data includes one or more of wear value, failure frequency, impact resistance, etc., the weight assignment is performed based on an expert weighting method, where the expert weighting method refers to scoring the target data by using expert in the field, in this embodiment refers to scoring all the target data according to the impact degree of the target data on the life value, and assigning different weights to different indexes according to the scoring result and storing the weights in the historical database;
Further, the preprocessing the first data to obtain target data includes:
the first data is subjected to data cleaning, and in order to process invalid values and missing values, better data are obtained so as to improve the prediction accuracy of a subsequent model;
screening the cleaned first data according to a preset data amount to obtain second data, wherein the preset data amount can be set according to actual needs;
classifying and sorting the second data based on a cluster analysis strategy to obtain 4 data sets of different categories, namely a first category, a second category, a third category and a fourth category, wherein the cluster analysis refers to an analysis process of grouping a set of physical or abstract objects into a plurality of categories consisting of similar objects, the cluster analysis method adopted by the implementation is a conventional method in the field, such as a k-means clustering algorithm, and the like, and the sorting method of the application is not repeated herein, and is sorting according to time, and in addition, the first category corresponds to the initial state data and the like;
and reading and fitting a data set of a target class by using a data adjustment mechanism to obtain the target data, wherein the specific steps are as follows:
And carrying out normalization processing on the read data set of the target category, wherein the calculation formula is as follows:
wherein ,
normalized coefficient representing mth data attribute, +.>
The custom coefficients representing the nth data,
mth data attribute representing nth data,/->
Weight representing mth data attribute, < ->
Representing the number of data attributes that are to be included,
normalized value of mth data attribute representing nth data, +.>
，/>
Representing the data quantity;
fitting the normalized data by using a fitting function, wherein the fitting function is as follows:
wherein ,
representing fitting function value,/->
Normalized values representing the data sets of the first, second, third and fourth category, respectively, i representing the iteration coefficient, +.>
Fitting coefficients representing a j-th class dataset, < +.>
Characteristic parameters representing a j-th class dataset;
and defining the data value after fitting processing as the target data.
S2: and constructing a vacuum pump life prediction model based on the target data.
The construction process of the vacuum pump life prediction model includes:
pre-constructing a first vacuum pump life prediction model based on a deep neural network, wherein the deep neural network comprises: the feature extraction network and the attribute prediction network, the feature extraction network includes: the system comprises a convolution layer, a pooling layer, a nonlinear layer and a full connection layer, wherein the convolution layer extracts new features from input data through linear transformation, the pooling layer can map a plurality of values into one value, the feature space is reduced, the nonlinear layer applies an activation mechanism of biological neurons, and the full connection layer is used for associating and assisting the convolution layer, the pooling layer and the nonlinear layer;
Training the first vacuum pump life prediction model based on the target data obtained by the plurality of vacuum pumps;
when the precision of the first vacuum pump life prediction model reaches a preset standard, the preset standard is preferably that the precision value is 0.97 or more, and a second vacuum pump life prediction model obtained after training is output, namely the final vacuum pump life prediction model.
Wherein the attribute prediction network includes the vacuum pump life prediction model, and can be described by the following expression:
wherein ,
lifetime prediction value at time t+1, < ->
Indicating the difference of the target data from time t to time t+1, < >>
Mean life value>
Represents the loss factor at time t+1, < >>
Represents the loss factor at time t, k represents the iteration factor,/->
Representing an index.
S3: and calculating to obtain life decay curves of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model.
It should be noted that this step specifically includes:
inputting the target data of the target vacuum pump corresponding to each time point obtained through calculation into the vacuum pump life prediction model to obtain a first life prediction value;
segmenting a plurality of time points according to a preset interval, and calculating the average value of the first life predicted value corresponding to each segment to obtain a second life predicted value, wherein the preset interval is 8-10 minutes, and the time points are defined as 8-1 minutes, 8-2 minutes and … … -8-10 minutes;
And forming a one-to-one mapping relation between the second life predicted value and the middle time point of the segmented interval, wherein the middle time point of the segmented interval is 8 hours and 5 minutes, connecting the second life predicted values corresponding to a plurality of middle time points to generate a life attenuation curve, and placing the life attenuation curve in a rectangular coordinate system so as to facilitate subsequent calling.
S4: and selecting and processing the processing mode of the target vacuum pump according to the life decay curve to realize the full life cycle management of the target vacuum pump.
It should be noted that this step specifically includes:
calculating a slope of the second lifetime prediction value corresponding to two adjacent intermediate time points based on the lifetime decay curve, and as an example, the two adjacent intermediate time points may be 8 hours 5 minutes and 8 hours 15 minutes;
judging the processing mode of the target vacuum pump according to the slope:
when the slope is larger than a first preset value and the service life is smaller than a second preset value, judging that the target vacuum pump is required to be overhauled when the pump fails;
when the slope is larger than a first preset value and the service life is larger than or equal to a second preset value, judging that the target vacuum pump needs to be scrapped;
And when the slope is smaller than the first preset value and the service life is larger than or equal to or smaller than the second preset value, no processing is performed.
In the above-mentioned full life cycle management method suitable for a vacuum pump, the method includes: acquiring first data of a vacuum pump in a history database, and preprocessing the first data to obtain target data; constructing a vacuum pump life prediction model based on the target data; calculating to obtain life decay curves of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model; according to the life decay curve, the processing mode of the target vacuum pump is selected and processed, so that the full life cycle management of the target vacuum pump is realized.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
In one embodiment, as shown in FIG. 3, a full life cycle management system for a vacuum pump is provided, comprising: the system comprises a preprocessing module, a model construction module, a calculation module and a selection module, wherein:
the preprocessing module is used for acquiring first data of the vacuum pump in the history database, preprocessing the first data and obtaining target data;
the model construction module is used for constructing a vacuum pump life prediction model based on the target data;
the calculation module is used for calculating and obtaining life decay curves of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model;
and the selection module is used for selecting and processing the processing mode of the target vacuum pump according to the life decay curve so as to realize the full life cycle management of the target vacuum pump.
As a preferred implementation manner, in the embodiment of the present invention, the preprocessing module is specifically configured to:
performing data cleaning on the first data;
screening the cleaned first data according to a preset data amount to obtain second data;
classifying and sequencing the second data based on a cluster analysis strategy to obtain 4 data sets with different categories, namely a first category, a second category, a third category and a fourth category;
Reading and fitting a data set of a target class by using a data adjustment mechanism to obtain the target data;
the first data includes initial state data, application environment data, operating state data, and wear data of the vacuum pump.
As a preferred implementation manner, in the embodiment of the present invention, the preprocessing module is specifically further configured to:
and carrying out normalization processing on the read data set of the target category, wherein the calculation formula is as follows:
wherein ,
normalized coefficient representing mth data attribute, +.>
The custom coefficients representing the nth data,
mth data attribute representing nth data,/->
Weight representing mth data attribute, < ->
Representing the number of data attributes that are to be included,
normalized value of mth data attribute representing nth data, +.>
，/>
Representing the data quantity;
fitting the normalized data by using a fitting function, wherein the fitting function is as follows:
wherein ,
representing fitting function value,/->
Normalized values representing the data sets of the first, second, third and fourth category, respectively, i representing the iteration coefficient, +.>
Fitting coefficients representing a j-th class dataset, < +.>
Characteristic parameters representing a j-th class dataset;
And defining the data value after fitting processing as the target data.
As a preferred implementation manner, in the embodiment of the present invention, the model building module is specifically further configured to:
pre-constructing a first vacuum pump life prediction model based on a deep neural network;
training the first vacuum pump life prediction model based on the target data obtained by the plurality of vacuum pumps;
outputting a second vacuum pump life prediction model obtained after training is completed when the precision of the first vacuum pump life prediction model reaches a preset standard, namely the final vacuum pump life prediction model;
wherein, the expression of the vacuum pump life prediction model comprises:
wherein ,
lifetime prediction value at time t+1, < ->
Indicating the difference of the target data from time t to time t+1, < >>
Mean life value>
Represents the loss factor at time t+1, < >>
Represents the loss factor at time t, k represents the iteration factor,/->
Representing an index.
As a preferred implementation manner, in the embodiment of the present invention, the computing module is specifically configured to:
inputting the target data of the target vacuum pump corresponding to each time point obtained through calculation into the vacuum pump life prediction model to obtain a first life prediction value;
Segmenting a plurality of time points according to a preset interval, and obtaining an average value of the first life predicted values corresponding to each segment to obtain a second life predicted value;
and forming a one-to-one mapping relation between the second life predicted value and the middle time point of the segmented interval, and connecting the second life predicted values corresponding to the middle time points to generate a life decay curve.
As a preferred implementation manner, in the embodiment of the present invention, the selection module is specifically configured to:
calculating the slope of the second life prediction value corresponding to two adjacent intermediate time points based on the life decay curve;
judging the processing mode of the target vacuum pump according to the slope:
when the slope is larger than a first preset value and the service life is smaller than a second preset value, judging that the target vacuum pump is required to be overhauled when the pump fails;
and when the slope is larger than a first preset value and the service life is larger than or equal to a second preset value, judging that the target vacuum pump needs to be scrapped.
For specific limitations on the full life cycle management system applicable to the vacuum pump, reference may be made to the above limitation on the full life cycle management method applicable to the vacuum pump, and the detailed description thereof will be omitted. The various modules described above as being suitable for use in a full life cycle management system for a vacuum pump may be implemented in whole or in part in software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, a display screen, and an input system connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a full life cycle management method for a vacuum pump. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input system of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the structures shown in FIG. 4 are block diagrams only and do not constitute a limitation of the computer device on which the present aspects apply, and that a particular computer device may include more or less components than those shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of when executing the computer program:
s1: acquiring first data of a vacuum pump in a history database, and preprocessing the first data to obtain target data;
s2: constructing a vacuum pump life prediction model based on the target data;
s3: calculating to obtain life decay curves of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model;
s4: and selecting and processing the processing mode of the target vacuum pump according to the life decay curve to realize the full life cycle management of the target vacuum pump.
In one embodiment, the processor when executing the computer program further performs the steps of:
Performing data cleaning on the first data;
screening the cleaned first data according to a preset data amount to obtain second data;
classifying and sequencing the second data based on a cluster analysis strategy to obtain 4 data sets with different categories, namely a first category, a second category, a third category and a fourth category;
reading and fitting a data set of a target class by using a data adjustment mechanism to obtain the target data;
the first data includes initial state data, application environment data, operating state data, and wear data of the vacuum pump.
In one embodiment, the processor when executing the computer program further performs the steps of:
and carrying out normalization processing on the read data set of the target category, wherein the calculation formula is as follows:
wherein ,
normalized coefficient representing mth data attribute, +.>
The custom coefficients representing the nth data,
mth data attribute representing nth data,/->
Weight representing mth data attribute, < ->
Representing the number of data attributes that are to be included,
normalized value of mth data attribute representing nth data, +.>
，/>
Representing the data quantity;
fitting the normalized data by using a fitting function, wherein the fitting function is as follows:
wherein ,
representing fitting function value,/->
Normalized values representing the data sets of the first, second, third and fourth category, respectively, i representing the iteration coefficient, +.>
Fitting coefficients representing a j-th class dataset, < +.>
Characteristic parameters representing a j-th class dataset;
and defining the data value after fitting processing as the target data.
In one embodiment, the processor when executing the computer program further performs the steps of:
pre-constructing a first vacuum pump life prediction model based on a deep neural network;
training the first vacuum pump life prediction model based on the target data obtained by the plurality of vacuum pumps;
outputting a second vacuum pump life prediction model obtained after training is completed when the precision of the first vacuum pump life prediction model reaches a preset standard, namely the final vacuum pump life prediction model;
wherein, the expression of the vacuum pump life prediction model comprises:
wherein ,
lifetime prediction value at time t+1, < ->
Indicating the difference of the target data from time t to time t+1, < >>
Mean life value>
Represents the loss factor at time t+1, < >>
Represents the loss factor at time t, k represents the iteration factor,/- >
Representing an index.
In one embodiment, the processor when executing the computer program further performs the steps of:
inputting the target data of the target vacuum pump corresponding to each time point obtained through calculation into the vacuum pump life prediction model to obtain a first life prediction value;
segmenting a plurality of time points according to a preset interval, and obtaining an average value of the first life predicted values corresponding to each segment to obtain a second life predicted value;
and forming a one-to-one mapping relation between the second life predicted value and the middle time point of the segmented interval, and connecting the second life predicted values corresponding to the middle time points to generate a life decay curve.
In one embodiment, the processor when executing the computer program further performs the steps of:
calculating the slope of the second life prediction value corresponding to two adjacent intermediate time points based on the life decay curve;
judging the processing mode of the target vacuum pump according to the slope:
when the slope is larger than a first preset value and the service life is smaller than a second preset value, judging that the target vacuum pump is required to be overhauled when the pump fails;
and when the slope is larger than a first preset value and the service life is larger than or equal to a second preset value, judging that the target vacuum pump needs to be scrapped.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
s1: acquiring first data of a vacuum pump in a history database, and preprocessing the first data to obtain target data;
s2: constructing a vacuum pump life prediction model based on the target data;
s3: calculating to obtain life decay curves of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model;
s4: and selecting and processing the processing mode of the target vacuum pump according to the life decay curve to realize the full life cycle management of the target vacuum pump.
In one embodiment, the computer program when executed by the processor further performs the steps of:
performing data cleaning on the first data;
screening the cleaned first data according to a preset data amount to obtain second data;
classifying and sequencing the second data based on a cluster analysis strategy to obtain 4 data sets with different categories, namely a first category, a second category, a third category and a fourth category;
reading and fitting a data set of a target class by using a data adjustment mechanism to obtain the target data;
The first data includes initial state data, application environment data, operating state data, and wear data of the vacuum pump.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and carrying out normalization processing on the read data set of the target category, wherein the calculation formula is as follows:
wherein ,
normalized coefficient representing mth data attribute, +.>
The custom coefficients representing the nth data,
mth data attribute representing nth data,/->
Weight representing mth data attribute, < ->
Representing the number of data attributes that are to be included,
normalized value of mth data attribute representing nth data, +.>
，/>
Representing the data quantity;
fitting the normalized data by using a fitting function, wherein the fitting function is as follows:
wherein ,
representing fitting function value,/->
Normalized values representing the data sets of the first, second, third and fourth category, respectively, i representing the iteration coefficient, +.>
Fitting coefficients representing a j-th class dataset, < +.>
Characteristic parameters representing a j-th class dataset;
and defining the data value after fitting processing as the target data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
Pre-constructing a first vacuum pump life prediction model based on a deep neural network;
training the first vacuum pump life prediction model based on the target data obtained by the plurality of vacuum pumps;
outputting a second vacuum pump life prediction model obtained after training is completed when the precision of the first vacuum pump life prediction model reaches a preset standard, namely the final vacuum pump life prediction model;
wherein, the expression of the vacuum pump life prediction model comprises:
wherein ,
lifetime prediction value at time t+1, < ->
Indicating the difference of the target data from time t to time t+1, < >>
Mean life value>
Represents the loss factor at time t+1, < >>
Represents the loss factor at time t, k represents the iteration factor,/->
Representing an index.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the target data of the target vacuum pump corresponding to each time point obtained through calculation into the vacuum pump life prediction model to obtain a first life prediction value;
segmenting a plurality of time points according to a preset interval, and obtaining an average value of the first life predicted values corresponding to each segment to obtain a second life predicted value;
And forming a one-to-one mapping relation between the second life predicted value and the middle time point of the segmented interval, and connecting the second life predicted values corresponding to the middle time points to generate a life decay curve.
In one embodiment, the computer program when executed by the processor further performs the steps of:
calculating the slope of the second life prediction value corresponding to two adjacent intermediate time points based on the life decay curve;
judging the processing mode of the target vacuum pump according to the slope:
when the slope is larger than a first preset value and the service life is smaller than a second preset value, judging that the target vacuum pump is required to be overhauled when the pump fails;
and when the slope is larger than a first preset value and the service life is larger than or equal to a second preset value, judging that the target vacuum pump needs to be scrapped.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application.
Claims (4)
1. A full life cycle management method for a vacuum pump, the method comprising:
acquiring first data of a vacuum pump in a history database, and preprocessing the first data to obtain target data;
constructing a vacuum pump life prediction model based on the target data;
calculating to obtain life decay curves of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model;
selecting and processing the processing mode of the target vacuum pump according to the life decay curve to realize the full life cycle management of the target vacuum pump;
The construction process of the vacuum pump life prediction model comprises the following steps:
pre-constructing a first vacuum pump life prediction model based on a deep neural network;
training the first vacuum pump life prediction model based on the target data obtained by the plurality of vacuum pumps;
outputting a second vacuum pump life prediction model obtained after training is completed when the precision of the first vacuum pump life prediction model reaches a preset standard, namely the final vacuum pump life prediction model;
the expression of the vacuum pump life prediction model comprises:
wherein ,
lifetime prediction value at time t+1, < ->
Indicating the difference of the target data from time t to time t+1, < >>
Mean life value>
Represents the loss factor at time t+1, < >>
Represents the loss factor at time t, k represents the iteration factor,/->
Representing an index;
the calculating the life attenuation curve of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model comprises the following steps:
inputting the target data of the target vacuum pump corresponding to each time point obtained through calculation into the vacuum pump life prediction model to obtain a first life prediction value;
segmenting a plurality of time points according to a preset interval, and obtaining an average value of the first life predicted values corresponding to each segment to obtain a second life predicted value;
Forming a one-to-one mapping relation between the second life predicted value and the middle time point of the segmented interval, and connecting the second life predicted values corresponding to a plurality of middle time points to generate a life decay curve;
the selecting and processing the processing mode of the target vacuum pump according to the life decay curve comprises the following steps:
calculating the slope of the second life prediction value corresponding to two adjacent intermediate time points based on the life decay curve;
judging the processing mode of the target vacuum pump according to the slope:
when the slope is larger than a first preset value and the service life is smaller than a second preset value, judging that the target vacuum pump is required to be overhauled when the pump fails;
and when the slope is larger than a first preset value and the service life is larger than or equal to a second preset value, judging that the target vacuum pump needs to be scrapped.
2. The full life cycle management method of claim 1, wherein the first data includes initial state data, application environment data, running state data, and loss data of the vacuum pump, and the preprocessing the first data to obtain target data includes:
Performing data cleaning on the first data;
screening the cleaned first data according to a preset data amount to obtain second data;
classifying and sequencing the second data based on a cluster analysis strategy to obtain 4 data sets with different categories, namely a first category, a second category, a third category and a fourth category;
and reading and fitting the data set of the target class by using a data adjustment mechanism to obtain the target data.
3. The full life cycle management method for a vacuum pump according to claim 2, wherein the reading and fitting the data set of the target class using the data adjustment mechanism to obtain the target data comprises:
and carrying out normalization processing on the read data set of the target category, wherein the calculation formula is as follows:
wherein ,
normalized coefficient representing mth data attribute, +.>
Custom coefficients representing the nth data, +.>
Mth data attribute representing nth data,/->
Weight representing mth data attribute, < ->
Representing the number of data attributes>
Normalized value of mth data attribute representing nth data, +.>
，/>
Representing the data quantity;
fitting the normalized data by using a fitting function, wherein the fitting function is as follows:
wherein ,
representing fitting function value,/->
Normalized values representing the data sets of the first, second, third and fourth category, respectively, i representing the iteration coefficient, +.>
Fitting coefficients representing a j-th class dataset, < +.>
Characteristic parameters representing a j-th class dataset;
and defining the data value after fitting processing as the target data.
4. A full life cycle management system for a vacuum pump, the system comprising:
the preprocessing module is used for acquiring first data of the vacuum pump in the history database, preprocessing the first data and obtaining target data;
the model construction module is used for constructing a vacuum pump life prediction model based on the target data;
the calculation module is used for calculating and obtaining life decay curves of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model;
the selection module is used for selecting and processing the processing mode of the target vacuum pump according to the life decay curve so as to realize the full life cycle management of the target vacuum pump;
the construction process of the vacuum pump life prediction model comprises the following steps:
pre-constructing a first vacuum pump life prediction model based on a deep neural network;
Training the first vacuum pump life prediction model based on the target data obtained by the plurality of vacuum pumps;
outputting a second vacuum pump life prediction model obtained after training is completed when the precision of the first vacuum pump life prediction model reaches a preset standard, namely the final vacuum pump life prediction model;
the expression of the vacuum pump life prediction model comprises:
wherein ,
lifetime prediction value at time t+1, < ->
Indicating the difference of the target data from time t to time t+1, < >>
Mean life value>
Represents the loss factor at time t+1, < >>
Represents the loss factor at time t, k represents the iteration factor,/->
Representing an index;
the calculating the life attenuation curve of the target vacuum pump in a plurality of time periods by using the vacuum pump life prediction model comprises the following steps:
inputting the target data of the target vacuum pump corresponding to each time point obtained through calculation into the vacuum pump life prediction model to obtain a first life prediction value;
segmenting a plurality of time points according to a preset interval, and obtaining an average value of the first life predicted values corresponding to each segment to obtain a second life predicted value;
forming a one-to-one mapping relation between the second life predicted value and the middle time point of the segmented interval, and connecting the second life predicted values corresponding to a plurality of middle time points to generate a life decay curve;
The selecting and processing the processing mode of the target vacuum pump according to the life decay curve comprises the following steps:
calculating the slope of the second life prediction value corresponding to two adjacent intermediate time points based on the life decay curve;
judging the processing mode of the target vacuum pump according to the slope:
when the slope is larger than a first preset value and the service life is smaller than a second preset value, judging that the target vacuum pump is required to be overhauled when the pump fails;
and when the slope is larger than a first preset value and the service life is larger than or equal to a second preset value, judging that the target vacuum pump needs to be scrapped.
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