CN113569345B - Numerical control system reliability modeling method and device based on multisource information fusion - Google Patents
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Abstract
The application relates to the technical field of numerical control modeling, in particular to a numerical control system reliability modeling method and device based on multi-source information fusion. The method comprises the following steps: collecting fault data of a preset number of numerical control systems; preprocessing the fault data; performing association rule mining on the preprocessed fault data by using a frequent pattern growth method; integrating the results obtained after the association rule mining to obtain a first criterion, a second criterion and a third criterion; obtaining a multisource fusion dispersibility factor affecting the reliability of the numerical control system according to the first criterion, the second criterion and the third criterion; and introducing the multisource fusion dispersibility factors to construct a numerical control system reliability model. The method reduces dependence on manual operation, saves various costs, can comprehensively and accurately model, has good fitting effect by introducing multisource fusion dispersibility factor modeling, and improves the reliability modeling efficiency of the numerical control system.
Description
Technical Field
The application relates to the technical field of numerical control modeling, in particular to a numerical control system reliability modeling method and device based on multi-source information fusion.
Background
The high-grade numerical control system has the characteristics of high reliability and long service life, and the problems of long test time, feedback lag of test results, rapid increase of test cost and the like occur in the reliability research of the high-grade numerical control system, so that the reliability evaluation is difficult to progress, and the built reliability model is low in precision. The reliability research of the traditional numerical control system is poor in application of fault data, the reliability data such as fault reasons of each fault are ignored, and a large enough sample is often needed to support the experiment, so that a large amount of manpower and material resources are consumed, and a good fitting effect cannot be achieved.
The patent with the application number 201710080893.9 as a numerical control machine reliability model modeling method and system based on energy consumption characteristics discloses a numerical control machine reliability model modeling method based on energy consumption characteristics, which is characterized in that acquired power data is segmented and extracted to obtain a feature library, power data is acquired from power segments, a CART decision tree is trained based on the decision tree, the CART decision tree is used for classification, processing cycle duration is obtained through matching of character strings, and finally working time arithmetic averages and fault time arithmetic averages of different working days are obtained to serve as machine reliability model parameters. However, the power data is obtained until the processing period duration, the working time arithmetic average and the fault time arithmetic average are obtained and used as parameters for adjusting the model, so that the model is single, the fault time, the fault position and the fault reason of the numerical control system cannot be comprehensively obtained, namely, the parameters of multi-source fusion cannot be obtained, and the function of the model is imperfect.
In summary, the present application proposes a method and an apparatus for modeling reliability of a numerical control system based on multi-source information fusion to solve the problem.
Disclosure of Invention
In order to achieve the technical purpose, the application provides a numerical control system reliability modeling method based on multi-source information fusion, which comprises the following steps:
collecting fault data of a preset number of numerical control systems;
preprocessing the fault data;
performing association rule mining on the preprocessed fault data by using a frequent pattern growth method;
integrating results obtained after association rule mining to obtain a first criterion, a second criterion and a third criterion, wherein the first criterion comprises judging whether a fault belongs to a statistical object, the second criterion comprises a fault part, a fault reason and fault association strength, and the third criterion comprises a maintenance method and maintenance interval time;
obtaining a multisource fusion dispersibility factor affecting the reliability of the numerical control system according to the first criterion, the second criterion and the third criterion;
and introducing the multisource fusion dispersibility factors to construct a numerical control system reliability model.
Specifically, preprocessing the fault data includes:
processing the fault data by using a fault total time method and a median rank method;
and performing fault tree analysis on the processed data to obtain fault time and fault event weight data.
Preferably, preprocessing the fault data further includes obtaining a cumulative fault distribution of the inter-fault time:
wherein r is i A sequence number indicating the ith fault data, n' indicating the total number of faults, t i Indicating the ith failure time.
Specifically, the association rule mining is carried out on the preprocessed fault data by using a frequent pattern growth method, which comprises the following steps:
forming a fault data set from the preprocessed fault data and storing the fault data set in a database;
and constructing a frequent pattern tree after the fault data set of the database is scanned twice, mining a frequent item set in the frequent pattern tree, and generating an association rule in the frequent item set by using the minimum support degree.
Further, the multisource fusion dispersibility factor is:
wherein, the degree of correlation is that d is damping, and the value is 0.85; n is a weight coefficient, m is a threshold value, and m and n are obtained through fitting training of historical data; b i Is the mining association strength, k of fault data i Is the weight value of the fault data, c 1 The digging association strength of the maintenance data is shown, and p is the front-to-back ratio of the maintenance time.
Still further, introducing the multisource fusion dispersibility factor to construct a numerical control system reliability model includes:
calculating fault data by adopting a Weibull model;
carrying out parameter estimation on the Weibull model by using a maximum likelihood estimation method;
the Weibull model is adjusted by the multisource fusion dispersibility factor to obtain the optimized fitting effect, and the method comprises the following steps ofWherein beta is the shape parameter of the Weibull distribution, theta is the proportion parameter of the Weibull distribution, t i -t i-1 Is the time interval between two failure times.
Optionally, the method further comprises:
inputting the counted data affecting the reliability of the numerical control system into the Weibull model to obtain new model parameters;
performing model fitting goodness judgment by using root mean square error;
fitting maps were drawn using Matlab tools.
The second aspect of the invention provides a numerical control system reliability modeling device based on multi-source information fusion, which comprises:
the acquisition module is used for acquiring fault data of a preset number of numerical control systems;
the preprocessing module is used for preprocessing the fault data;
the association rule mining module is used for performing association rule mining on the preprocessed fault data by using a frequent pattern growth method;
the criterion establishing module is used for integrating the results obtained after the association rule mining to obtain a first criterion, a second criterion and a third criterion, wherein the first criterion comprises judging whether the fault belongs to a statistical object or not, the second criterion comprises a fault part, a fault reason and fault association strength, and the third criterion comprises a maintenance method and maintenance interval time;
the multisource fusion factor obtaining module is used for obtaining multisource fusion dispersibility factors affecting the reliability of the numerical control system according to the first criterion, the second criterion and the third criterion;
model construction module: the method is used for introducing the multisource fusion dispersibility factors to construct a numerical control system reliability model.
A third aspect of the present invention provides a computer device comprising a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, cause the processor to perform the steps of:
collecting fault data of a preset number of numerical control systems;
preprocessing the fault data;
performing association rule mining on the preprocessed fault data by using a frequent pattern growth method;
integrating results obtained after association rule mining to obtain a first criterion, a second criterion and a third criterion, wherein the first criterion comprises judging whether a fault belongs to a statistical object, the second criterion comprises a fault part, a fault reason and fault association strength, and the third criterion comprises a maintenance method and maintenance interval time;
obtaining a multisource fusion dispersibility factor affecting the reliability of the numerical control system according to the first criterion, the second criterion and the third criterion;
and introducing the multisource fusion dispersibility factors to construct a numerical control system reliability model.
A fourth aspect of the present invention provides a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the steps of:
collecting fault data of a preset number of numerical control systems;
preprocessing the fault data;
performing association rule mining on the preprocessed fault data by using a frequent pattern growth method;
integrating results obtained after association rule mining to obtain a first criterion, a second criterion and a third criterion, wherein the first criterion comprises judging whether a fault belongs to a statistical object, the second criterion comprises a fault part, a fault reason and fault association strength, and the third criterion comprises a maintenance method and maintenance interval time;
obtaining a multisource fusion dispersibility factor affecting the reliability of the numerical control system according to the first criterion, the second criterion and the third criterion;
and introducing the multisource fusion dispersibility factors to construct a numerical control system reliability model.
The beneficial effects of this application are: the method reduces dependence on manual operation, saves various costs, can comprehensively and accurately model, has a good fitting effect by introducing multisource fusion dispersibility factor modeling, and improves the reliability modeling efficiency of the numerical control system.
Drawings
FIG. 1 shows a schematic flow chart of a method of example 1 of the present application;
FIG. 2 shows a schematic flow chart of the method of embodiment 2 of the present application;
FIG. 3 is a schematic diagram of the acquisition process in embodiment 2 of the present application;
FIG. 4 is a schematic diagram showing the calculation flow of the frequent growth method in embodiment 2 of the present application;
fig. 5 shows a schematic diagram of an association rule mining procedure in embodiment 2 of the present application;
FIG. 6 shows a schematic diagram of a failure analysis fish bone in example 2 of the present application;
FIG. 7 shows a schematic representation of three criteria obtained in example 2 of the present application;
fig. 8 is a schematic view showing the structure of the apparatus of embodiment 3 of the present application;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 10 shows a schematic diagram of a storage medium according to an embodiment of the present application.
Detailed Description
Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. It should be understood that the description is intended to be illustrative only and is not intended to limit the scope of the application. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present application. It will be apparent to one skilled in the art that the present application may be practiced without one or more of these details. In other instances, some features well known in the art have not been described in order to avoid obscuring the present application.
It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present application. As used herein, the singular is intended to include the plural unless the context clearly indicates otherwise. Furthermore, it will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Exemplary embodiments according to the present application will now be described in more detail with reference to the accompanying drawings. These exemplary embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. The figures are not drawn to scale, wherein certain details may be exaggerated and certain details may be omitted for clarity of presentation. The shapes of the various regions, layers and relative sizes, positional relationships between them shown in the drawings are merely exemplary, may in practice deviate due to manufacturing tolerances or technical limitations, and one skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions as actually required.
Example 1:
the embodiment implements a numerical control system reliability modeling method based on multi-source information fusion, as shown in fig. 1, and comprises the following steps:
s1, collecting fault data of a preset number of numerical control systems;
s2, preprocessing the fault data;
s3, performing association rule mining on the preprocessed fault data by using a frequent pattern growth method;
s4, integrating results obtained after association rule mining to obtain a first criterion, a second criterion and a third criterion, wherein the first criterion comprises judging whether a fault belongs to a statistical object, the second criterion comprises a fault part, a fault reason and fault association strength, and the third criterion comprises a maintenance method and maintenance interval time;
s5, obtaining a multisource fusion dispersibility factor affecting the reliability of the numerical control system according to the first criterion, the second criterion and the third criterion;
s6, introducing the multisource fusion dispersibility factors to construct a numerical control system reliability model.
Specifically, preprocessing the fault data includes:
processing the fault data by using a fault total time method and a median rank method;
and performing fault tree analysis on the processed data to obtain fault time and fault event weight data.
Preferably, preprocessing the fault data further comprises obtaining a cumulative fault distribution of the inter-fault time:
wherein r is i A sequence number indicating the ith fault data, n' indicating the total number of faults, t i Indicating the ith failure time.
Specifically, the association rule mining is carried out on the preprocessed fault data by using a frequent pattern growth method, which comprises the following steps:
forming a fault data set from the preprocessed fault data and storing the fault data set in a database;
and constructing a frequent pattern tree after the fault data set of the database is scanned twice, mining a frequent item set in the frequent pattern tree, and generating an association rule in the frequent item set by using the minimum support degree.
Further, the multisource fusion dispersibility factor is:
wherein, the degree of correlation is that d is damping, and the value is 0.85; n is a weight coefficient, m is a threshold value, and m and n are obtained through fitting training of historical data; b i Is the mining association strength, k of fault data i Is the weight value of the fault data, c 1 The digging association strength of the maintenance data is shown, and p is the front-to-back ratio of the maintenance time.
Still further, introducing a multisource fusion dispersibility factor to construct a numerical control system reliability model, comprising:
calculating fault data by adopting a Weibull model;
carrying out parameter estimation on the Weibull model by using a maximum likelihood estimation method;
the Weibull model is adjusted by the multisource fusion dispersibility factor to obtain the optimized fitting effect, and the method comprises the following steps ofWherein beta is the shape parameter of the Weibull distribution, theta is the proportion parameter of the Weibull distribution, t i -t i-1 Is the time interval between two failure times.
Optionally, the method of this embodiment further includes:
inputting the counted data affecting the reliability of the numerical control system into the Weibull model to obtain new model parameters;
performing model fitting goodness judgment by using root mean square error;
fitting maps were drawn using Matlab tools.
Example 2:
the embodiment implements a numerical control system reliability modeling method based on multi-source information fusion, and the general process is shown in fig. 2, and includes the following steps:
step 1: and collecting fault data of a preset number of numerical control systems.
Specifically, the preset number selects 20 numerical control systems of a laboratory, and the fault data of a plurality of sets of numerical control systems can be considered as one fault data because the reliability of the numerical control systems is high. The acquisition method is shown in fig. 3. The acquisition method supports protocols such as FOCAS, OPC, API, modbus and the like of the main stream, and numerical control systems of any model and number can be randomly connected through configuration of corresponding files. The hardware used is as follows: CPU, intel to strong dominant frequency is more than or equal to 2.2Ghz, single core 20 cores, more than 40 threads; memory: 256GB; hard disk: 3 blocks of 2TB SAS 7.2K hard disks support RAID 5; network: gigabit ethernet cards; operating system: windows7; database: mySQL/MongoDB.
The broad numbers are communicated through the MODBUS dynamic link library, and gskm.dll secondary development packages are needed, and the development packages are suitable for the broad number Ethernet series devices. The specific acquisition program is realized by using C# language, a secondary development packet gskm.dll is firstly introduced into a project (because dll is written in c++, an entry of the program needs to be noted), then connection is carried out through a defined handle, a handle larger than 0 is returned after connection is successful, and finally communication is carried out with the numerical control system through the handle. FANUC collection was similar to the broad number, which passed through a FOCAS secondary development kit. The FOCAS is an application program interface function instruction library provided for a user by FANUC, the computer acquires information and data of the numerical control system by calling standard functions in the function instruction library, and FWALIB 32.DLL and FWALIB 64.DLL dynamic link libraries are provided in the function library and are respectively applicable to 32-bit and 64-bit operating systems. When in use, the IP address, the TCP port number, the delay time and the like of the CNC are required to be preset on the CNC system. The acquisition of the SIEMENS numerical control system uses an OPCDAAuto.dll dynamic link library issued by the OPC foundation organization to realize the access to an OPC server. The data collection in China uses a HncNetDllForCSharp.dll dynamic link library, and the specific implementation mode is similar to the broad number. The data collection of Kane Di also uses a dynamic link library, which supports the mode http request of RESTAPI.
Step 2: and preprocessing the fault data.
And carrying out statistics and arrangement on the reliability test data according to the fault judgment standard and the judgment rule, processing the collected fault data by using a total fault time method and a median rank method, and carrying out Fault Tree (FTA) analysis after obtaining usable data to obtain the fault event weight.
According to the principle of the total fault time method, the collected field fault data is assumed to come from m sets of numerical control systems, wherein the fault time interval collected by the ith set of numerical control systems is (0, t) is ]I=1, 2, …, m, co-occurrence of n i Secondary failure, each failure occurrence time is t ij (j＝1,2,…,n i ). The total fault time calculation method corresponding to each fault point comprises the following steps:
T(t ij )＝t ij ×p(t ij )+T s
wherein p (t) ij ) At t ij The number of non-truncated numerical control systems observed at the moment, ts is t ij And the sum of the tail cutting time of all the tail-cutting numerical control systems observed at the moment.
Ordering the obtained data from small to large to obtain time sequence 0<S 1 ≤…≤S k ≤…≤S N Setting the total timing tail-cutting time as T 0 . The time between failures deltat i The adjustment is as follows:
the cumulative fault distribution F (t) of the fault interval time is calculated by adopting a median rank method, and the sequence number r of the ith fault data is calculated i The method comprises the following steps:
where N is the total number of faults (without truncated data); n' =n+1; j is the sequence number of all the collected data (including truncated data); i is the sequence number of the fault data.
The cumulative fault distribution F (t) of the available inter-fault times according to the above derivation is then:
wherein r is i A sequence number indicating the ith fault data, n' indicating the total number of faults, t i Indicating the ith failure time.
Step 3: and carrying out association rule mining on the preprocessed fault data by using a frequent pattern growth method.
The frequent pattern Growth method (FP-Growth) is a classical association rule mining algorithm, and the preprocessed fault data is formed into a fault data set and stored in a database; and constructing a frequent pattern tree (FP-tree) after the fault data set of the database is scanned twice, mining a frequent item set in the frequent pattern tree, and generating association rules in the frequent item set by using the minimum support degree, wherein the specific calculation flow is shown in figures 4 and 5. The method only needs to scan the database twice, and no candidate item set is generated, so that the scanning of the event data set is simplified, and the frequent item set mining efficiency is higher.
Step 4: and integrating the results obtained after the association rule mining to obtain a first criterion, a second criterion and a third criterion, wherein the first criterion comprises judging whether the fault belongs to a statistical object, the second criterion comprises a fault part, a fault reason and fault association strength, and the third criterion comprises a maintenance method and maintenance interval time.
After the data of the reliability data are mined, the mined association rules are required to be integrated, the integration of the reliability data is realized on the basis of fully analyzing the influence of various reliability data on the reliability of the digital system, and the influence of the reliability data can be clearly displayed by using a fishbone method. The reliability data of the common numerical control system comprises fault phenomena, fault judgment, fault parts, fault modes, fault reasons, maintenance times, maintenance modes, interval time and the like. And then the reliability data are classified into three types of fault judgment, fault classification, maintenance condition and the like. The fault phenomenon, the fault classification, the fault judgment and the like are classified into the fault judgment for primarily judging whether the fault is the fault or not. The fault part, the fault reason, the fault mode and the like are classified into fault classification and are used for main content of fault analysis. The maintenance times, maintenance modes, maintenance intervals and the like are classified into maintenance conditions and used for analyzing the influence of historical maintenance on reliability. Through further analysis of the influence of various reliability data, a fish bone map with the "reliability data influence" as a guiding problem is drawn, as shown in fig. 6.
According to the analysis of the reliability data by the fish bone graph analysis method, a first criterion, a second criterion and a third criterion are obtained, and the three criteria can be regarded as a multi-source hierarchical system, as shown in fig. 7, the first layer is a preliminary criterion, and whether the first layer is an internal factor or an artificial factor is primarily judged by using the relativity, so that the fish bone graph analysis method has the functions of yes and no. The second layer is a fault criterion, and a data mining result of a fault part and a fault reason is used and is the core of the multi-source hierarchical system. The third layer is a repair criterion, data mining results using repair methods, and repair intervals. The first layer primary criterion is to judge the internal factor and the artificial effect, i.e. judge whether the fault belongs to the statistical object, so the correlation degree led out by the first layer has the logic function and the data range is 0 or 1. The second layer fault criterion is the function of comprehensive fault type data, namely, the data such as fault modes and the like contained in each fault time data are integrated, so that fault parts, fault reasons and FTA fault tree analysis led out by the second layer have the correlation strength core function, and the data range is 0 to 1. The third layer maintenance criterion is to integrate the maintenance condition into the multi-source hierarchy system, namely to analyze the influence of the history maintenance on the reliability, so the maintenance method and the maintenance interval led out by the third layer have the maintenance condition integration function, the data range is 0 to 1, and the data is the front-back maintenance time interval.
Step 5: and obtaining a multisource fusion dispersibility factor influencing the reliability of the numerical control system according to the first criterion, the second criterion and the third criterion.
Further, the multisource fusion dispersibility factor is:
wherein, the degree of correlation is that d is damping, and the value is 0.85; n is a weight coefficient, m is a threshold value, and m and n are obtained through fitting training of historical data; b i Is the mining association strength, k of fault data i Is the weight value of the fault data, c 1 The digging association strength of the maintenance data is shown, and p is the front-to-back ratio of the maintenance time.
Step 6: and introducing the multisource fusion dispersibility factors to construct a numerical control system reliability model.
The invention selects reliability as the fitting condition of the reliability model and fault data, and because the model established by the invention is the reliability model of the numerical control system based on the fault time, the fusion factor has the function of the fault time, and the larger the fusion factor is, the larger the influence of the corresponding fault time on the reliability model is.
The Weibull model is adopted to analyze and calculate the data, the maximum likelihood estimation method is used for estimating model parameters, and the dispersibility of the model can be adjusted by introducing the multisource fusion dispersibility factor, so that the model has a better fitting effect.
The probability density function of the weibull distribution is:
the reliability function of the weibull distribution is:
introducing multisource fusion dispersibility factors to construct a numerical control system reliability model, wherein the method comprises the following steps: calculating fault data by adopting a Weibull model; carrying out parameter estimation on the Weibull model by using a maximum likelihood estimation method; the Weibull model is adjusted by the multisource fusion dispersibility factor to obtain the optimized fitting effect, and the method comprises the following steps ofWherein beta is the shape parameter of the Weibull distribution, theta is the proportion parameter of the Weibull distribution, t i -t i-1 Is the time interval between two failure times.
Optionally, the method of this embodiment further includes: inputting the counted data affecting the reliability of the numerical control system into the Weibull model to obtain new model parameters; model fitting goodness judgment is carried out by using Root Mean Square Error (RMSE); fitting maps were drawn using Matlab tools.
Example 3:
a numerical control system reliability modeling apparatus based on multi-source information fusion, as shown in fig. 8, the apparatus comprises:
the acquisition module 801 is configured to acquire fault data of a preset number of numerical control systems;
a preprocessing module 802, configured to preprocess the fault data;
the association rule mining module 803 is configured to perform association rule mining on the preprocessed fault data by using a frequent pattern growth method;
the criterion establishing module 804 is configured to integrate the results obtained after the association rule mining to obtain a first criterion, a second criterion and a third criterion, where the first criterion includes determining whether the fault belongs to a statistical object, the second criterion includes a fault location, a fault cause and a fault association strength, and the third criterion includes a maintenance method and a maintenance interval time;
a multi-source fusion factor obtaining module 805, configured to obtain a multi-source fusion dispersibility factor affecting reliability of the numerical control system according to the first criterion, the second criterion, and the third criterion;
model building module 806: the method is used for introducing the multisource fusion dispersibility factors to construct a numerical control system reliability model.
Reference is next made to fig. 9, which is a schematic diagram illustrating an electronic device provided in some embodiments of the present application. As shown in fig. 9, the electronic device 2 includes: a processor 200, a memory 201, a bus 202 and a communication interface 203, the processor 200, the communication interface 203 and the memory 201 being connected by the bus 202; the memory 201 stores a computer program that can be run on the processor 200, and when the processor 200 runs the computer program, the numerical control system reliability modeling method based on the multi-source information fusion provided in any of the foregoing embodiments of the present application is executed.
The memory 201 may include a high-speed random access memory (RAM: random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the system network element and at least one other network element is implemented via at least one communication interface 203 (which may be wired or wireless), the internet, a wide area network, a local network, a metropolitan area network, etc. may be used.
Bus 202 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. The memory 201 is configured to store a program, and the processor 200 executes the program after receiving an execution instruction, and the method for modeling reliability of a numerical control system based on multi-source information fusion disclosed in any embodiment of the present application may be applied to the processor 200 or implemented by the processor 200.
The processor 200 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 200 or by instructions in the form of software. The processor 200 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201, and in combination with its hardware, performs the steps of the above method.
The electronic equipment provided by the embodiment of the application and the numerical control system reliability modeling method based on multi-source information fusion provided by the embodiment of the application have the same beneficial effects as the method adopted, operated or realized by the same invention conception.
The present application further provides a computer readable storage medium corresponding to the method for modeling reliability of a numerical control system based on multi-source information fusion provided in the foregoing embodiment, referring to fig. 10, the computer readable storage medium is shown as an optical disc 30, on which a computer program (i.e. a program product) is stored, where the computer program, when executed by a processor, performs the method for modeling reliability of a numerical control system based on multi-source information fusion provided in any of the foregoing embodiments.
Examples of the computer readable storage medium may also include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory, or other optical and magnetic storage medium, which are not described in detail herein.
It should be noted that: the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may also be used with the teachings herein. The required structure for the construction of such devices is apparent from the description above. In addition, the present application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and the above description of specific languages is provided for disclosure of preferred embodiments of the present application. In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description. Similarly, it should be appreciated that in the above description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed application requires more features than are expressly recited in each claim.
The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A numerical control system reliability modeling method based on multi-source information fusion is characterized by comprising the following steps:
collecting fault data of a preset number of numerical control systems;
preprocessing the fault data;
performing association rule mining on the preprocessed fault data by using a frequent pattern growth method;
integrating results obtained after association rule mining to obtain a first criterion, a second criterion and a third criterion, wherein the first criterion comprises judging whether a fault belongs to a statistical object, the second criterion comprises a fault part, a fault reason and fault association strength, and the third criterion comprises a maintenance method and maintenance interval time;
obtaining a multisource fusion dispersibility factor affecting the reliability of the numerical control system according to the first criterion, the second criterion and the third criterion;
and introducing the multisource fusion dispersibility factors to construct a numerical control system reliability model.
2. The method for modeling reliability of a numerical control system based on multi-source information fusion according to claim 1, wherein preprocessing the fault data comprises:
processing the fault data by using a fault total time method and a median rank method;
and performing fault tree analysis on the processed data to obtain fault time and fault event weight data.
3. The method for modeling reliability of a numerical control system based on multi-source information fusion according to claim 2, wherein preprocessing the fault data further comprises obtaining a cumulative fault distribution of a fault interval time:
wherein r is i A sequence number indicating the ith fault data, n' indicating the total number of faults, t i Indicating the ith failure time.
4. The method for modeling reliability of a numerical control system based on multi-source information fusion according to claim 1, wherein the performing association rule mining on the preprocessed fault data by using a frequent pattern growth method comprises:
forming a fault data set from the preprocessed fault data and storing the fault data set in a database;
and constructing a frequent pattern tree after the fault data set of the database is scanned twice, mining a frequent item set in the frequent pattern tree, and generating an association rule in the frequent item set by using the minimum support degree.
5. The method for modeling reliability of a numerical control system based on multi-source information fusion according to claim 1, wherein the multi-source fusion dispersibility factor is:
wherein a is 1 The degree of correlation is that d is damping, and the value is 0.85; n is a weight coefficient, m is a threshold value, and m and n are obtained through fitting training of historical data; b i Is the mining association strength, k of fault data i Is the weight value of the fault data, c 1 The digging association strength of the maintenance data is shown, and p is the front-to-back ratio of the maintenance time.
6. The method for modeling reliability of a numerical control system based on multi-source information fusion according to claim 5, wherein introducing the multi-source fusion dispersion factor constructs a numerical control system reliability model, comprising:
calculating fault data by adopting a Weibull model;
carrying out parameter estimation on the Weibull model by using a maximum likelihood estimation method;
the Weibull model is adjusted by the multisource fusion dispersibility factor to obtain the optimized fitting effect, and the method comprises the following steps ofWherein beta is the shape parameter of the Weibull distribution, theta is the proportion parameter of the Weibull distribution, t i -t i-1 Is the time interval between two failure times.
7. The method for modeling reliability of a numerical control system based on multi-source information fusion according to claim 6, further comprising:
inputting the counted data affecting the reliability of the numerical control system into the Weibull model to obtain new model parameters;
performing model fitting goodness judgment by using root mean square error;
fitting maps were drawn using Matlab tools.
8. A numerical control system reliability modeling device based on multi-source information fusion, the device comprising:
the acquisition module is used for acquiring fault data of a preset number of numerical control systems;
the preprocessing module is used for preprocessing the fault data;
the association rule mining module is used for performing association rule mining on the preprocessed fault data by using a frequent pattern growth method;
the criterion establishing module is used for integrating the results obtained after the association rule mining to obtain a first criterion, a second criterion and a third criterion, wherein the first criterion comprises judging whether the fault belongs to a statistical object or not, the second criterion comprises a fault part, a fault reason and fault association strength, and the third criterion comprises a maintenance method and maintenance interval time;
the multisource fusion factor obtaining module is used for obtaining multisource fusion dispersibility factors affecting the reliability of the numerical control system according to the first criterion, the second criterion and the third criterion;
model construction module: the method is used for introducing the multisource fusion dispersibility factors to construct a numerical control system reliability model.
9. A computer device comprising a memory and a processor, wherein the memory has stored therein computer readable instructions which, when executed by the processor, cause the processor to perform the method of any of claims 1-7.
10. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method of any of claims 1-7.
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