CN116384210A - Equipment life prediction method and device, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure relates to the technical field of equipment life prediction, and provides an equipment life prediction method, an equipment life prediction device, electronic equipment and a medium. The method comprises the following steps: constructing a joint learning frame, and presetting a prediction model to be trained in a joint learning server; processing the relevant data set of the device to obtain an evaluation data set; based on the evaluation data set, carrying out health evaluation on the equipment, and determining a corresponding evaluation result set; acquiring a prediction model to be trained preset in a joint learning server, and training the prediction model to be trained based on equipment data, a preset fault threshold value and an evaluation result set; and determining a life prediction model, and predicting the residual life of the equipment to be predicted based on the life prediction model. According to the method, the service life of the equipment can be accurately predicted, so that risks in production are avoided.
Description
Technical Field
The disclosure relates to the technical field of industrial equipment life prediction, and in particular relates to an equipment life prediction method, an equipment life prediction device, electronic equipment and a storage medium.
Background
The comprehensive energy system is formed by linking various devices and pipelines, and is a system which has various energy inputs, converts and can supply various energy sources to different users. The equipment in the integrated energy system comprises: gas internal combustion engine, waste heat boiler, steam boiler, bromine cooler, photovoltaic equipment, ground source heat pump, wind energy equipment, energy storage equipment and the like.
The health of the equipment may be damaged due to the fact that a large number of equipment works for a long time, frequently starts and stops, environmental changes and the like in the comprehensive energy system. Even when the periodic maintenance time is not reached, the equipment fails, which may cause problems in the whole integrated energy system, so that the evaluation of the health of the equipment is necessary.
Disclosure of Invention
In view of the above, the embodiments of the present disclosure provide a method, an apparatus, an electronic device, and a medium for predicting service life of a device, so as to solve the problem in the prior art that the service life of the device cannot be accurately predicted, which results in risk of operation or running of the device in system production.
In a first aspect of an embodiment of the present disclosure, there is provided a device lifetime prediction method, including:
constructing a joint learning frame, and presetting a prediction model to be trained in a joint learning server;
processing the relevant data set of the device to obtain an evaluation data set; wherein, each relevant data in the relevant data set is used for describing the running state of the equipment at different historical moments; each evaluation data in the evaluation data set is used for evaluating the health condition of the equipment at different historical moments;
based on the evaluation data set, carrying out health evaluation on the equipment, and determining a corresponding evaluation result set;
acquiring a prediction model to be trained preset in a joint learning server, and training the prediction model to be trained based on equipment data, a preset fault threshold value and an evaluation result set;
and determining a life prediction model, and predicting the residual life of the equipment to be predicted based on the life prediction model.
In a second aspect of embodiments of the present disclosure, there is provided an apparatus for predicting lifetime of a device, including:
the construction module is used for constructing a joint learning frame and presetting a prediction model to be trained in a joint learning server;
the data processing module is used for processing the related data set of the equipment to obtain an evaluation data set; wherein, each relevant data in the relevant data set is used for describing the running state of the equipment at different historical moments; each evaluation data in the evaluation data set is used for evaluating the health condition of the equipment at different historical moments;
the evaluation module is used for carrying out health evaluation on the equipment based on the evaluation data set and determining a corresponding evaluation result set;
the training module is used for acquiring a prediction model to be trained preset in the joint learning server, and training the prediction model to be trained based on equipment data, a preset fault threshold value and an evaluation result set;
and the prediction module is used for determining a life prediction model and predicting the residual life of the equipment to be predicted based on the life prediction model.
In a third aspect of the disclosed embodiments, an electronic device is provided, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
In a fourth aspect of the disclosed embodiments, a computer-readable storage medium is provided, which stores a computer program which, when executed by a processor, implements the steps of the above-described method.
Compared with the prior art, the embodiment of the disclosure has the beneficial effects that: the method can effectively solve the problem that the equipment can be found earlier in the application of the comprehensive energy equipment, and the equipment is overhauled in advance on the premise of ensuring the production life, so that the risks of equipment operation and running are greatly reduced, and the production efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are required for the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic diagram of a joint learning architecture provided by embodiments of the present disclosure;
FIG. 2 is a flow chart of a method for predicting device lifetime provided in an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a device life prediction apparatus according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the disclosed embodiments. However, it will be apparent to one skilled in the art that the present disclosure may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.
The joint learning refers to comprehensively utilizing a plurality of AI (Artificial Intelligence ) technologies on the premise of ensuring data safety and user privacy, jointly excavating data value by combining multiparty cooperation, and promoting new intelligent business states and modes based on joint modeling. The joint learning has at least the following characteristics:
(1) The participating nodes control the weak centralized joint training mode of the own data, so that the data privacy safety in the co-creation intelligent process is ensured.
(2) Under different application scenes, a plurality of model aggregation optimization strategies are established by utilizing screening and/or combination of an AI algorithm and privacy protection calculation so as to obtain a high-level and high-quality model.
(3) On the premise of ensuring data safety and user privacy, a method for improving the efficiency of the joint learning engine is obtained based on a plurality of model aggregation optimization strategies, wherein the efficiency method can be used for improving the overall efficiency of the joint learning engine by solving the problems of information interaction, intelligent perception, exception handling mechanisms and the like under a large-scale cross-domain network with parallel computing architecture.
(4) The requirements of multiparty users in all scenes are acquired, the real contribution degree of all joint participants is determined and reasonably evaluated through a mutual trust mechanism, and distribution excitation is carried out.
Based on the mode, AI technical ecology based on joint learning can be established, the industry data value is fully exerted, and the scene of the vertical field is promoted to fall to the ground.
A device lifetime prediction method and apparatus according to embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of a joint learning architecture according to an embodiment of the present disclosure. As shown in fig. 1, the architecture of joint learning may include a server (central node) 101, as well as participants 102, 103, and 104.
In the joint learning process, a basic model may be established by the server 101, and the server 101 transmits the model to the participants 102, 103, and 104 with which a communication connection is established. The basic model may also be uploaded to the server 101 after any party has established, and the server 101 sends the model to the other parties with whom it has established a communication connection. The participants 102, 103 and 104 construct a model according to the downloaded basic structure and model parameters, perform model training using local data, obtain updated model parameters, and encrypt and upload the updated model parameters to the server 101. Server 101 aggregates the model parameters sent by participants 102, 103, and 104 to obtain global model parameters, and transmits the global model parameters back to participants 102, 103, and 104. Participant 102, participant 103 and participant 104 iterate the respective models according to the received global model parameters until the models eventually converge, thereby enabling training of the models. In the joint learning process, the data uploaded by the participants 102, 103 and 104 are model parameters, local data is not uploaded to the server 101, and all the participants can share final model parameters, so that common modeling can be realized on the basis of ensuring data privacy. It should be noted that the number of participants is not limited to the above three, but may be set as needed, and the embodiment of the present disclosure is not limited thereto.
Fig. 2 is a flowchart of a device lifetime prediction method according to an embodiment of the present disclosure. The device lifetime prediction method of fig. 2 may be performed by the participants of fig. 1. As shown in fig. 2, the device lifetime prediction method includes:
s201, constructing a joint learning framework, and presetting a prediction model to be trained in a joint learning server.
Specifically, a joint learning framework is firstly constructed based on the related description of fig. 1, and a prediction model to be trained is preset in a joint learning server playing a role in coordination so as to be downloaded and used by all parties.
S202, processing a relevant data set of the equipment to obtain an evaluation data set;
specifically, based on a wavelet packet decomposition method, processing a related data set of the device to obtain an intermediate data set; the intermediate data set is processed based on an empirical mode decomposition method to obtain an evaluation data set. Wherein, based on wavelet packet decomposition method, the relevant data set of the device is processed to obtain an intermediate data set, which specifically comprises: decomposing each related data in the related data set based on a wavelet packet decomposition method to obtain two groups of orthogonal wavelet packets corresponding to each related data respectively; reconstructing each related data based on two groups of orthogonal wavelet packets corresponding to each related data to obtain each reconstructed data corresponding to each related data; and calculating the energy entropy of each reconstruction data, and determining an intermediate data set based on the energy entropy of each reconstruction data. Based on an empirical mode decomposition method, the intermediate data set is processed to obtain an evaluation data set, specifically comprising: decomposing the energy entropy of each reconstruction data in the intermediate data set based on an empirical mode decomposition method to obtain a plurality of eigenmode function eigenmode functions respectively corresponding to the energy entropy of each reconstruction data; and determining the intrinsic mode functions of the preset number of eigenmode functions as evaluation data, and determining an evaluation data set based on the evaluation data.
After the participant obtains the relevant data set of the corresponding device, the relevant data set of the device is firstly processed based on a wavelet packet decomposition method to obtain an intermediate data set corresponding to the relevant data set. Wherein, each relevant data in the relevant data set is used for describing the running state of the equipment at different historical moments; each evaluation data in the evaluation data set is used for evaluating the health condition of the equipment at different historical moments
S203, carrying out health evaluation on the equipment based on the evaluation data set, and determining a corresponding evaluation result set;
specifically, a weight vector and a proportion parameter vector corresponding to the evaluation data are determined; determining a minimum quantization error of the evaluation data based on the weight vector; wherein the minimum quantization error is used to describe the degradation of the device; an evaluation result set of the device is determined based on the minimum quantization error and the scale parameter vector.
After the evaluation data set is obtained, an evaluation result corresponding to each evaluation data in the evaluation data set is determined to construct an evaluation result set of the apparatus. It can be understood that the evaluation data in the evaluation data set and the evaluation result in the evaluation result set are in one-to-one correspondence.
S204, acquiring a prediction model to be trained preset in the joint learning server, and training the prediction model to be trained based on equipment data, a preset fault threshold value and an evaluation result set;
specifically, the model gradient of the trained prediction model to be trained is uploaded to a joint learning server, so that the joint learning server aggregates the model gradients of all sides, and model parameters of the prediction model to be trained are updated to obtain a life prediction model.
S205, determining a life prediction model, and predicting the residual life of the equipment to be predicted based on the life prediction model;
specifically, the model gradient of the trained prediction model to be trained is uploaded to a joint learning server, so that the joint learning server aggregates model gradients of all parties, model parameters of the prediction model to be trained are updated, and after a life prediction model participant obtains a residual life prediction model from the joint learning server, the residual life prediction model is applied. And when the real-time parameters of the equipment to be predicted are input, predicting the service life of the equipment to be predicted through a service life prediction model.
According to the technical scheme provided by the embodiment of the disclosure, the problems possibly occurring in the equipment can be found earlier in the application of the comprehensive energy equipment by the method, and the equipment is overhauled in advance on the premise of ensuring production and life, so that the risks of equipment operation and running are greatly reduced, and the production efficiency is improved.
In some embodiments, prior to processing the relevant data set of the device, the method further comprises: determining equipment attributes of equipment, and acquiring related equipment data based on the equipment attributes; analyzing the device data to determine outliers in the device data; and adjusting the abnormal value to obtain a related data set corresponding to the equipment data.
Specifically, device data of the device is selectively acquired according to the characteristics of the device attribute. Wherein device data of the device may be collected from a SCADA (data acquisition and monitoring control system); the device data includes, but is not limited to, any one or more of the following: equipment operation time length data, historical maintenance data, current operation time, report maintenance data, equipment operation speed, equipment operation temperature, energy consumption data, environmental data and the like.
Further, after acquiring the device data of the device, analyzing the device data to determine whether an outlier exists in the device data; wherein the outliers include, but are not limited to, data misses or data anomalies. In addition, the abnormal value is judged by the threshold value set in advance, whether the equipment data exceeds the threshold value is judged, so that whether the equipment has faults or not is directly judged, if the equipment has faults, the equipment data is directly reported, and if the equipment has no faults, the equipment data is determined to be a related data set. If the equipment data has data missing, the regression type method can be used for numerical interpolation. After the device data is adjusted, a relevant data set is obtained.
In some embodiments, the relevant data sets of the device are processed to obtain an evaluation data set, specifically including: processing the relevant data set of the device based on a wavelet packet decomposition method to obtain an intermediate data set; the intermediate data set is processed based on an empirical mode decomposition method to obtain an evaluation data set.
Further, based on the wavelet packet decomposition method, the relevant data set of the device is processed to obtain an intermediate data set, which specifically includes: decomposing each related data in the related data set based on a wavelet packet decomposition method to obtain two groups of orthogonal wavelet packets corresponding to each related data respectively; reconstructing each related data based on two groups of orthogonal wavelet packets corresponding to each related data to obtain each reconstructed data corresponding to each related data; and calculating the energy entropy of each reconstruction data, and determining an intermediate data set based on the energy entropy of each reconstruction data.
Specifically, each of the related data in the related data set is decomposed based on a wavelet packet decomposition method. For related data
The decomposition of (2) is shown in the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
low-pass orthogonal wavelet packet corresponding to t-time related data, and +.>
A high-pass orthogonal wavelet packet corresponding to the related data at the time t, h k (k-2 t) represents a low-pass filter window sum, g k (k-2 t) represents a high-pass filter window.
Further, after two sets of orthogonal wavelet packets corresponding to each relevant data are obtained, reconstructing each relevant data based on the two sets of orthogonal wavelet packets corresponding to each relevant data, so as to obtain each reconstructed data corresponding to each relevant data. Wherein the calculation of the reconstructed data is determined by the following formula:
where h (k-2 t) is a low-pass filter correlation scale function and g (k-2 t) is a high-pass filter correlation wavelet function.
Further, an energy entropy of each reconstructed data is calculated. Wherein the calculation of the energy entropy of the reconstructed data is determined by the following formula:
first, the energy of each reconstructed data is calculated:
wherein Ei (t) is the j-th layer at the moment t, the energy of the i-th reconstruction data, and N is the length of the reconstruction function.
Wherein S is EN (t) the energy entropy of the j-th layer, i-th reconstructed data,
is the probability distribution of the ith wavelet energy of the jth layer at the moment t.
Further, the energy entropy of all the reconstructed data is calculated to determine the intermediate data set corresponding to each reconstructed data.
Further, based on the empirical mode decomposition method, the intermediate data set is processed to obtain an evaluation data set, specifically including: decomposing the energy entropy of each reconstruction data in the intermediate data set based on an empirical mode decomposition method to obtain a plurality of inherent mode functions respectively corresponding to the energy entropy of each reconstruction data; and determining a preset number of natural mode functions as evaluation data, and determining an evaluation data set based on the evaluation data.
Specifically, the intermediate data set is decomposed into an intrinsic mode function and a remaining portion based on an empirical mode decomposition method. The following formula is shown:
wherein ci (t) is the ith natural mode function (IMF) at time t,
and rn (t) is the residual equation of the n mode corresponding to the t moment, which is a plurality of inherent mode functions corresponding to the t moment related data.
Further, a preset number of natural mode functions after the t moment are selected as evaluation data. For example, if the preset number is m, the evaluation data is:
T(t)＝[c n-m+1 (t)，c n-m+2 (t)，…，c n-1 (t)，c n (t)]
wherein T (T) is the evaluation data corresponding to the relevant data at the moment T.
Further, evaluation data corresponding to the energy entropy of all the reconstruction data in the intermediate data set is determined to determine an evaluation data set corresponding to the relevant data set.
In some embodiments, based on the evaluation data set, health evaluation is performed on the device, and a corresponding evaluation result set is determined to construct a health index set of the device, specifically including: determining weight vectors and proportional parameter vectors corresponding to the weights of the eigen mode functions in the evaluation data; determining a minimum quantization error of the evaluation data based on the weight vector; wherein the minimum quantization error is used to describe the degradation of the device; determining a health index of the equipment based on the minimum quantization error and the proportion parameters of each eigen-mode function in the evaluation data preset by the proportion parameter vector; based on the health index, a health index set evaluation result set is constructed.
Specifically, after determining the evaluation data corresponding to each relevant data, the weight vector of the best matching unit corresponding to the data is evaluated first. The weight vector includes weights corresponding to the respective natural mode functions in the evaluation data. Based on the weight corresponding to the intrinsic mode function, determining the minimum quantization error of the evaluation data, wherein the calculation of the minimum quantization error is determined by the following formula:
MQE(t)＝||T(t)-m BMU ||
wherein MQE (t) is the minimum quantization error corresponding to the evaluation data at time t, m BMU And the weight vector of the best matching unit corresponding to the evaluation data at the time t.
Further, after determining the minimum quantization error of the evaluation data, the evaluation result of the apparatus is determined based on the minimum quantization error and the scale parameter vector. Specifically, the corresponding scale parameters in the minimum quantization error and scale parameter vector are brought into the following formula:
wherein CV (t) is an evaluation result corresponding to the data related to the time t, and c is a proportion parameter corresponding to the evaluation data. It should be noted that the proportional parameter is obtained from MQE under the normal condition of the device and may be obtained by field device experiment or experience.
Further, health indexes corresponding to all evaluation data in the evaluation data set are determined so as to determine an evaluation result set corresponding to the evaluation data set.
In some embodiments, the determined evaluation result set is used to train the prediction model to be trained in cooperation with the corresponding device data, and therefore, each participant needs to acquire a preset prediction model to be trained from the joint learning server before training. It should be noted that, the initial prediction model may have various options, for example, BP (inverse neural network), FNN (fuzzy neural network), and the like, which may be the initial prediction model.
In some embodiments, after the prediction model to be trained is acquired in the joint learning server, the prediction model to be trained is trained based on the device data, the preset failure threshold and the evaluation result set.
Specifically, based on the evaluation result set, regression is performed to determine a health change curve, and through a preset fault threshold value, when the health change curve touches the fault threshold value in the future is predicted, so that the possible residual service life of the equipment can be predicted.
In some embodiments, determining a life prediction model specifically includes: and uploading the model gradient of the trained prediction model to be trained to a joint learning server so that the joint learning server aggregates the model gradients of all sides and updates the model parameters of the prediction model to be trained to obtain a life prediction model.
Specifically, the joint learning referred to in the application can be used for supporting multiple users to perform multiparty cooperation, and the data value is mined by combining the multiparty cooperation through an AI technology, so that intelligent joint modeling is established. After each participant performs distributed initial prediction model training according to the equipment data of the acquisition equipment, the model gradient of the trained prediction model to be trained is uploaded to the joint learning server, so that the joint learning server aggregates the model gradients of each participant, and model parameters of the initial prediction model are updated to obtain a life prediction model.
Further, after the joint learning server trains to obtain the residual service life prediction model, the model is returned to each participant so that each participant updates the model.
In some embodiments, each participant, when real-time parameters of the device to be predicted are input, can predict the remaining life of the device to be predicted through the remaining life prediction model.
Any combination of the above optional solutions may be adopted to form an optional embodiment of the present application, which is not described herein in detail.
The following are device embodiments of the present disclosure that may be used to perform method embodiments of the present disclosure. For details not disclosed in the embodiments of the apparatus of the present disclosure, please refer to the embodiments of the method of the present disclosure.
Fig. 3 is a schematic diagram of an apparatus for predicting lifetime of a device according to an embodiment of the disclosure. As shown in fig. 3, the device lifetime prediction apparatus includes:
the construction module 301 is configured to construct a joint learning framework, and preset a prediction model to be trained in a joint learning server;
a data processing module 302, configured to process the relevant data set of the device to obtain an evaluation data set; wherein, each relevant data in the relevant data set is used for describing the running state of the equipment at different historical moments; each evaluation data in the evaluation data set is used for evaluating the health condition of the equipment at different historical moments;
an evaluation module 303, configured to perform health evaluation on the device based on the evaluation data set, and determine a corresponding evaluation result set;
the training module 304 is configured to obtain a prediction model to be trained preset in the joint learning server, and train the prediction model to be trained based on the device data, a preset failure threshold value and an evaluation result set;
the prediction module 305 is configured to determine a life prediction model, and predict a remaining life of the device to be predicted based on the life prediction model.
According to the technical scheme provided by the embodiment of the disclosure, the problems possibly occurring in the equipment can be found earlier in the application of the comprehensive energy equipment through the device, and the equipment is overhauled in advance on the premise of ensuring the production life, so that the risks of equipment operation and running are greatly reduced, and the production efficiency is improved.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not constitute any limitation on the implementation process of the embodiments of the disclosure.
Fig. 4 is a schematic diagram of an electronic device 4 provided by an embodiment of the present disclosure. As shown in fig. 4, the electronic apparatus 4 of this embodiment includes: a processor 401, a memory 402 and a computer program 403 stored in the memory 402 and executable on the processor 401. The steps of the various method embodiments described above are implemented by processor 401 when executing computer program 403. Alternatively, the processor 401, when executing the computer program 403, performs the functions of the modules/units in the above-described apparatus embodiments.
Illustratively, the computer program 403 may be partitioned into one or more modules/units, which are stored in the memory 402 and executed by the processor 401 to complete the present disclosure. One or more of the modules/units may be a series of computer program instruction segments capable of performing a specific function for describing the execution of the computer program 403 in the electronic device 4.
The electronic device 4 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The electronic device 4 may include, but is not limited to, a processor 401 and a memory 402. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the electronic device 4 and is not meant to be limiting of the electronic device 4, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may also include an input-output device, a network access device, a bus, etc.
The processor 401 may be a central processing unit (Central Processing Unit, CPU) or other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 402 may be an internal storage unit of the electronic device 4, for example, a hard disk or a memory of the electronic device 4. The memory 402 may also be an external storage device of the electronic device 4, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the electronic device 4. Further, the memory 402 may also include both internal storage units and external storage devices of the electronic device 4. The memory 402 is used to store computer programs and other programs and data required by the electronic device. The memory 402 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other manners. For example, the apparatus/electronic device embodiments described above are merely illustrative, e.g., the division of modules or elements is merely a logical functional division, and there may be additional divisions of actual implementations, multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over 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 disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present disclosure may implement all or part of the flow of the method of the above-described embodiments, or may be implemented by a computer program to instruct related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of the method embodiments described above. The computer program may comprise computer program code, which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
The above embodiments are merely for illustrating the technical solution of the present disclosure, and are not limiting thereof; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the disclosure, and are intended to be included in the scope of the present disclosure.
Claims (10)
1. A method for predicting device life, comprising:
constructing a joint learning frame, and presetting a prediction model to be trained in a joint learning server;
processing the relevant data set of the device to obtain an evaluation data set; wherein each relevant data in the relevant data set is used for describing the running state of the equipment at different historical moments; each evaluation data in the evaluation data set is used for evaluating the health condition of the equipment at different historical moments;
based on the evaluation data set, carrying out health evaluation on the equipment, and determining a corresponding evaluation result set;
acquiring a prediction model to be trained preset in the joint learning server, and training the prediction model to be trained based on the equipment data, a preset fault threshold and the evaluation result set;
and determining a life prediction model, and predicting the residual life of the equipment to be predicted based on the life prediction model.
2. Method according to claim 1, characterized in that the relevant data set of the device is processed to obtain an evaluation data set, in particular comprising:
processing the relevant data set of the device based on a wavelet packet decomposition method to obtain an intermediate data set;
the intermediate data set is processed based on an empirical mode decomposition method to obtain an evaluation data set.
3. Method according to claim 2, characterized in that the relevant data sets of the device are processed based on a wavelet packet decomposition method to obtain intermediate data sets, in particular comprising:
decomposing each related data in the related data set based on a wavelet packet decomposition method to obtain two groups of orthogonal wavelet packets corresponding to each related data respectively;
reconstructing each related data based on two groups of orthogonal wavelet packets corresponding to each related data to obtain each reconstructed data corresponding to each related data;
and calculating the energy entropy of each reconstruction data, and determining the intermediate data set based on the energy entropy of each reconstruction data.
4. The method according to claim 2, characterized in that the intermediate data set is processed based on an empirical mode decomposition method to obtain an evaluation data set, in particular comprising:
decomposing the energy entropy of each reconstruction data in the intermediate data set based on an empirical mode decomposition method to obtain a plurality of intrinsic mode functions respectively corresponding to the energy entropy of each reconstruction data;
and determining a preset number of natural mode functions as evaluation data, and determining an evaluation data set based on the evaluation data.
5. The method according to claim 1, wherein the device is subjected to health assessment based on the assessment dataset, and wherein determining the corresponding assessment result set comprises:
determining a weight vector and a proportion parameter vector corresponding to the evaluation data;
determining a minimum quantization error of the evaluation data based on the weight vector; wherein the minimum quantization error is used to describe a degradation condition of the device;
and determining an evaluation result set of the equipment based on the minimum quantization error and the proportional parameter vector.
6. The method according to claim 1, wherein determining a life prediction model, in particular comprises:
and uploading the trained model gradient of the prediction model to be trained to a joint learning server so that the joint learning server aggregates the model gradients of all sides and updates the model parameters of the prediction model to be trained to obtain a life prediction model.
7. The method of claim 1, wherein prior to processing the relevant dataset for the device, the method further comprises:
determining equipment attributes of the equipment, and acquiring related equipment data based on the equipment attributes;
analyzing the device data to determine outliers in the device data;
and adjusting the abnormal value to obtain a related data set corresponding to the equipment data.
8. A device life prediction apparatus, comprising:
the construction module is used for constructing a joint learning frame and presetting a prediction model to be trained in a joint learning server;
the data processing module is used for processing the related data set of the equipment to obtain an evaluation data set; wherein each relevant data in the relevant data set is used for describing the running state of the equipment at different historical moments; each evaluation data in the evaluation data set is used for evaluating the health condition of the equipment at different historical moments;
the evaluation module is used for carrying out health evaluation on the equipment based on the evaluation data set and determining a corresponding evaluation result set;
the training module is used for acquiring a prediction model to be trained preset in the joint learning server, and training the prediction model to be trained based on the equipment data, the preset fault threshold and the evaluation result set;
and the prediction module is used for determining a life prediction model and predicting the residual life of the equipment to be predicted based on the life prediction model.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 7.
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| CN202111572529.7A CN116384210A (en) | 2021-12-21 | 2021-12-21 | Equipment life prediction method and device, electronic equipment and storage medium |
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