CN114997554A - Method and device for evaluating health condition of complex mechanical equipment and storage medium - Google Patents
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Abstract
The application discloses a method and a device for evaluating the health condition of complex mechanical equipment and a storage medium. The technical proposal fully utilizes the prior related equipment operation maintenance data to monitor the health state of the complex mechanical equipment, provides an effective way for the health maintenance and the fault prevention of the complex mechanical equipment, is beneficial to improving the reliable operation capability of an enterprise production workshop, reducing the equipment operation maintenance cost and improving the delivery capacity of a manufacturing enterprise according to the date, thereby improving the core competitiveness of the manufacturing enterprise, when the method and the module are used, index item configuration, knowledge matching rule configuration, knowledge grading setting, knowledge grade coefficient configuration and index weight setting can be carried out according to requirements, the application flexibility is greatly improved, and simultaneously, the method combines the evaluation result and the data of the target knowledge set to give the health risks of the sub-health equipment, and provides a basis for setting an equipment maintenance scheme for equipment maintenance personnel.
Description
Technical Field
The application relates to the technical field of operation and maintenance of complex mechanical equipment, in particular to a method and a device for evaluating the health condition of complex mechanical equipment and a storage medium.
Background
With the development of science and technology and the continuous change of market demand, the structural and functional complexity of many mechanical devices is higher and higher, which results in the great increase of the maintenance difficulty and cost, and meanwhile, the intense market competition requires that enterprises can ensure the manufacturing quality and delivery-on-schedule capability of products, which requires that the mechanical devices manufactured by the enterprises can operate reliably and with high performance. Therefore, the state evaluation of the relevant equipment and the decision guidance are provided before the equipment failure occurs, so that the equipment failure is prevented from occurring, and the method and the device are of great significance for reducing the maintenance cost of the enterprise equipment and improving the core competitiveness of the enterprise. At present, the existing mechanical equipment evaluation method has the following problems:
the method mainly focuses on comprehensive evaluation and early warning of the health state of specific indexes of equipment, the related index ranges of the methods are not comprehensive enough, accurate evaluation results are difficult to obtain, the method is lack of flexibility, and besides, historical data of equipment maintenance are not fully applied to state analysis and potential problem analysis of similar equipment, so that the reliability and integrity of the evaluation results are difficult to guarantee. For more and more complex mechanical equipment, the existing method is more difficult to systematically evaluate the operation state of the equipment and provides a reliable solution.
In summary, although the related art provides an effective way for evaluating the operation state of the device, which can improve the device maintenance level of the manufacturing enterprise to a certain extent, the evaluation and decision requirements of the current complicated and customized device are still met in the aspects of comprehensiveness of evaluation, reliability of guidance, flexibility of application, and the like.
Disclosure of Invention
In view of this, the present application provides a method, an apparatus, and a storage medium for evaluating the health condition of a complex mechanical device, so as to improve the reliability and comprehensiveness of the evaluation.
In a first aspect, the present application provides a method for assessing the health of a complex mechanical device, comprising:
presetting a knowledge base, knowledge grade coefficients and knowledge matching rules, wherein the knowledge base is a historical record about the health state corresponding to the maintenance of mechanical equipment;
acquiring a target knowledge set from the knowledge base according to the knowledge matching rule, the predefined associated index items and the index item weights thereof;
calculating the health state evaluation value of the target equipment according to the target knowledge set;
and acquiring the health state grade of the target equipment according to the health state evaluation value and through the knowledge grade coefficient.
Optionally, the "obtaining the health status level of the target device according to the health status assessment value and through the knowledge level coefficient" further includes:
and determining the object equipment to be subjected to the health risk prediction according to the health state grade and the target knowledge set, so as to perform the health risk prediction on the object equipment to be subjected to the health risk prediction.
Optionally, the "obtaining a target knowledge set from the knowledge base according to the knowledge matching rule, the predefined associated index item and the index item right thereof" specifically includes:
selecting preliminary target knowledge data from the knowledge base according to tag information, wherein the tag information is defined as one or more of equipment type information, equipment model information and equipment specification information;
and selecting the target knowledge set from the preliminary target knowledge data through the knowledge matching rule and in combination with a predefined matching degree rule reflecting the matching degree between the target knowledge and the target equipment information.
Optionally, the knowledge matching rules include an option value comparison rule, a discrete value comparison rule, and a continuous value comparison rule;
the calculation mode of the option value comparison rule is as follows:
in the above formula, OpIa represents the value of a certain boolean or option type indicator of the target device, and OpIb represents the value of a corresponding indicator of a certain knowledge in the knowledge database;
the calculation mode of the discrete value comparison rule is as follows:
in the above formula, DIa represents the value of a certain discrete index of the target device, DIb represents the value of a corresponding index of a certain knowledge in the knowledge database, DImax and DImin represent the maximum value and the minimum value of the DIb and the DIa respectively;
wherein, the calculation mode of the continuous value comparison rule is as follows:
in the above formula, CIa represents the value of a certain continuous index of the target device, CIb represents the value of a corresponding index of a certain knowledge in the knowledge database, and RV represents the reference value of the index.
Optionally, the matching degree rule is calculated as follows:
in the above formula, TE and REi respectively represent each item of associated index data of the target device and each item of index data of the ith knowledge obtained by preliminary screening, TE (Ik) and REi (Ik) respectively represent values of the index Ik in TE and REi, wk represents a weight of the index Ik, n represents the number of associated index items configured in the step 4.1), and when MD (TE, REi) is greater than a threshold, REi is added to the target knowledge set to participate in the health state assessment of the target device.
Optionally, the method for calculating the health status assessment value is as follows:
in the above formula, m 1 And m 2 Respectively representing the number of target knowledge of the fault state level identification and the number of target knowledge of the operational state level identification in the target knowledge set, x and y respectively representing the index of the target knowledge of the fault state level identification and the index of the target knowledge of the operational state level identification in the target knowledge set, f _ RE x And f _ c x Target knowledge and fault state coefficients, h _ RE, representing the x-th fault state level identification, respectively y And h _ c y Target knowledge and operational state coefficients, h _ c, representing the y-th operational state level indicator, respectively max Representing the maximum possible value of the coefficient of runnability.
Optionally, the health risk prediction method specifically includes:
searching target knowledge of the fault state level identification in a target knowledge set, and acquiring fault information in the target knowledge set;
retrieving the continuous associated knowledge of all target knowledge in a target knowledge set, and acquiring fault information in the target knowledge, wherein the target knowledge is defined as the associated index information of a corresponding device at a certain time node, and the associated index information record of the device after the time node is the continuous associated knowledge of the corresponding target knowledge;
and outputting all the acquired fault information and the classification and feature description thereof to a user.
In a second aspect, the present application provides an apparatus for assessing the health of a complex mechanical device, the apparatus comprising:
the system comprises a presetting module, a data processing module and a data processing module, wherein the presetting module is used for presetting a knowledge base, knowledge grade coefficients and knowledge matching rules, and the knowledge base is a historical record about a health state corresponding to maintenance of mechanical equipment;
the acquisition module is used for acquiring a target knowledge set from the knowledge base according to the knowledge matching rule, the predefined associated index items and the index item weights thereof;
the calculation module is used for calculating the health state evaluation value of the target equipment according to the target knowledge set;
and the obtaining module is used for obtaining the health state grade of the target equipment according to the health state evaluation value and through the knowledge grade coefficient.
In a third aspect, the present application provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method described above.
The technical proposal fully utilizes the prior related equipment operation maintenance data to monitor the health state of the complex mechanical equipment, provides an effective way for the health maintenance and the fault prevention of the complex mechanical equipment, is beneficial to improving the reliable operation capability of an enterprise production workshop, reducing the equipment operation maintenance cost and improving the delivery capacity of a manufacturing enterprise according to the date, thereby improving the core competitiveness of the manufacturing enterprise, when the method and the module are used, index item configuration, knowledge matching rule configuration, knowledge grading setting, knowledge grade coefficient configuration and index weight setting can be carried out according to requirements, thereby greatly increasing the application flexibility of the method and the module, and simultaneously, the method combines the evaluation result and the data of the target knowledge set to give the health risks of the sub-health equipment, and provides a basis for setting an equipment maintenance scheme for equipment maintenance personnel.
Drawings
The technical solution and other advantages of the present application will become apparent from the detailed description of the embodiments of the present application with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of an evaluation method according to an embodiment of the present application.
Fig. 2 is a block diagram of a frame of an evaluation apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present application, it is to be understood that the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or as implying that the number of indicated technical features is indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; may be mechanically connected, may be electrically connected or may be in communication with each other; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
The following disclosure provides many different embodiments or examples for implementing different features of the application. In order to simplify the disclosure of the present application, specific example components and arrangements are described below. Of course, they are merely examples and are not intended to limit the present application. Moreover, the present application may repeat reference numerals and/or letters in the various examples, such repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. In addition, examples of various specific processes and materials are provided herein, but one of ordinary skill in the art may recognize applications of other processes and/or use of other materials.
Referring to fig. 1, which is a schematic flow chart of the method for evaluating the health condition of a complex mechanical device according to the embodiment of the present application, an execution subject according to the embodiment of the present application may be a user device, or may be a server, and the like. The method comprises the following steps:
and 102, presetting a knowledge base, knowledge grade coefficients and knowledge matching rules, wherein the knowledge base is a history record about the health state corresponding to the maintenance of the mechanical equipment.
It should be noted that the repository may be implemented as a database, for example, the database is MSSQL, and the development method is asp.
Here, the history on the health state is defined as tag information, device state information, and device index information of the mechanical device, a health state level, a health failure, and the like corresponding to the tag information. A piece of historical state data corresponds to a piece of knowledge.
The tag information corresponds to ID information of the machine. The label information includes, but is not limited to, one or more of device type information, device model information, and device specification information.
Here, the index information may include 6 items, that is, KIs _1, ki _2, ki _3, ki _4, ki _5, ki _6, the values of each index and the data in the knowledge database are randomly generated by writing a computer program, the weight values of each index are respectively set to Ws {0.1,0.3,0.2,0.1,0.1,0.2}, the equipment fault levels are classified into a minor fault, a general fault, and a major fault, the corresponding state level coefficients are respectively set to f _ mc ═ 4, f _ gc ═ 6, f _ sc ═ 8, the equipment operational state levels are classified into sub-healthy, general healthy, and very healthy, the corresponding state level coefficients are respectively set to h _ sh ═ 1, h _ gh ═ 3, and h _ vh ═ 5, and the setting of the above state coefficients is determined using the effect of the present application according to some test data.
As shown in table 1, table 2, and table 3, which are an apparatus tag information table, an apparatus status information table, and an apparatus index information table, respectively, data redundancy can be effectively reduced and flexibility in recording data can be improved by this data structure arrangement.
Table 1 device tag information table M _ LabelInfo
Table 2 device status information table M _ StateInfo
Table 3 equipment index information table M _ lndinfo
As described above, the knowledge level coefficient is a quantitative description of different state levels, and the evaluation value interval is set by associating the operable state level with the device health state evaluation value, so as to realize conversion of the evaluation value into the operable state level, which can eliminate error of the evaluation value to some extent and make the evaluation result more intuitive, as shown in fig. 4, a knowledge level coefficient table, in this embodiment, the operable state levels h1, h2, and h3 correspond to the device health state evaluation value intervals [0, 0.4], (0.4,0.6], (0.6, 1), respectively.
Table 4 knowledge grade coefficient table Kg _ Level
Rules are matched for knowledge. The knowledge matching rules mainly comprise an option value comparison rule, a discrete value comparison rule and a continuous value comparison rule shown in figure 1, different calculation methods are adopted for different rules, namely different calculation programs are called, the Class names of the different calculation programs are CalMatVal _ Class, and the method names of the programs corresponding to the three are OptionMatCal (), DiscreMatCal () and ContinuousMatCal (), respectively.
Shown in table 5 is a knowledge matching rules table, as follows:
TABLE 5 knowledge matching rules Table Kg _ MatRules
The calculation mode of the option value comparison rule is as follows:
in the above formula, OpIa represents the value of a certain boolean or option type indicator of the target device, and OpIb represents the value of a corresponding indicator of a certain knowledge in the knowledge database;
the calculation mode of the discrete value comparison rule is as follows:
in the above formula, DIa represents a value of a certain discrete index of the target device, DIb represents a value of a corresponding index of a certain knowledge in the knowledge database, and DImax and DImin represent a maximum value and a minimum value which can be obtained by the DIb and the DIa, respectively;
wherein, the calculation mode of the continuous value comparison rule is as follows:
in the above formula, CIa represents the value of a certain continuous index of the target device, CIb represents the value of a corresponding index of a certain knowledge in the knowledge database, and RV represents the reference value of the index.
And 104, acquiring a target knowledge set from the knowledge base according to the knowledge matching rule, the predefined associated index items and the index item weights thereof.
Here, the predefined specific manner of the predefined associated indicator item and the indicator item weight thereof may include three items of content, namely, indicator item selection, reference value setting, and indicator weight setting, and table 6 shows a configuration table of the associated indicator item, where the configuration manner and the key point of the three items of content are as follows:
the method comprises the following steps that firstly, an index item is selected, whether strong correlation exists between each index and the current running state of equipment is evaluated, generally, the index item corresponding to the equipment of the same type is fixed and unchanged, and a [ index name ] field shown in a table 6 corresponds to a [ index name ] field in a table 3, namely, the name of the same index is identical in the two tables. For example, the six index names in the present embodiment are ki _1, ki _2, ki _3, ki _4, ki _5, and ki _6, respectively, and they are also ki _1, ki _2, ki _3, ki _4, ki _5, and ki _6, respectively, in the field [ index name ] in table 3 and table 6.
The reference value setting corresponds to the reference value in the continuous value comparison rule, and the value setting makes the continuous value comparison rule reflect the degree of deviation of the corresponding index value of the target equipment and the corresponding index value of the target knowledge from the standard value, which is more suitable for practical situations, for example, the surface hardness requirement of a certain part is 40HRC, the target part is 42HRC, the index value corresponding to a certain item of target knowledge in the knowledge base is 50HRC, the matching degree between the index value and the item of target knowledge is inaccurate by using 42/50 ═ 0.84, and the value calculated by using the continuous value comparison rule is (42-40)/(50-40) ═ 0.2.
And thirdly, setting the weight of the indexes is mainly determined by combining the experience of an experienced expert with the historical data, the weight needs to highlight the importance of each index on the healthy operation of the equipment, in order to reduce the influence of subjective factors, a plurality of experts are needed to participate in decision making, and the final weight of each index is determined after the opinions of a plurality of experts are integrated.
Table 6 associated index item configuration table Config _ reid
Here, "acquiring a target knowledge set from the knowledge base according to the knowledge matching rule, the predefined associated index items and their index item weights" specifically includes:
selecting preliminary target knowledge data from the knowledge base according to the label information;
and selecting the target knowledge set from the preliminary target knowledge data through the knowledge matching rule and in combination with a predefined matching degree rule reflecting the matching degree between the target knowledge and the target equipment information.
Specifically, to evaluate the target device by using the knowledge data, the relevant knowledge needs to be screened out, and the screening process is divided into two stages: the first stage is to search the knowledge bases in tables 1, 2 and 3 by fuzzy matching using the label information such as the equipment type, equipment model, equipment specification, etc., because the data volume of the knowledge base is often huge, the search efficiency is greatly reduced if the matching degree calculation is directly performed by using the methods shown in formulas (1) to (4), therefore, before knowledge screening is performed by using the matching degree, the quick search needs to be performed by using the label information related to the equipment to obtain the preliminary target knowledge data.
In the second stage, the data and configuration information in tables 3, 5 and 6 are accurately filtered by the calculation method of the knowledge matching rules shown in formulas (1) to (4). As for the matching degree rule reflecting the matching degree between the target knowledge and the target device information, the calculation method is as follows:
in the above formula, TE and REi respectively represent each item of associated index data of the target device and each item of index data of the ith knowledge obtained by preliminary screening, TE (Ik) and REi (Ik) respectively represent values of the index Ik in TE and REi, wk represents a weight of the index Ik, n represents the number of associated index items configured in the step 4.1), and when MD (TE, REi) is greater than a threshold, REi is added to the target knowledge set to participate in the health state assessment of the target device.
It should be understood that, after the preliminary target knowledge data is calculated according to the knowledge matching rule and the matching degree rule, filtering the calculation result by using a preset threshold, for example, setting the threshold to 0.8, so as to ensure the result accuracy of the final target knowledge set.
Therefore, the matching degree of the target knowledge and each index of the target equipment is realized through the knowledge matching rule, and the matching performance of the target knowledge and each state grade of each target knowledge of the target equipment, each target knowledge and faults occurring in the continuous associated knowledge of each target knowledge is ensured through the matching degree rule.
As shown in table 7, where each associated fault type identifier represents a class of device faults, the threshold value in this embodiment is set to 0.8, and the target knowledge indexed 1, 4, 5, 6, 7, 10, 11, and 12 in table 7 collectively constitutes a target knowledge set for target device status evaluation.
TABLE 7 target knowledge-related data retrieved in the example
And 106, calculating the health state evaluation value of the target equipment according to the target knowledge set.
The method of calculating the health state evaluation value is as follows:
in the above formula, m 1 And m 2 Respectively representing the number of target knowledge of the fault state level identification and the number of target knowledge of the operational state level identification in the target knowledge set, x and y respectively representing the index of the target knowledge of the fault state level identification and the index of the target knowledge of the operational state level identification in the target knowledge set, f _ RE x And f _ c x Target knowledge and fault state coefficient, h _ RE, respectively representing the x-th fault state level identifier y And h _ c y Target knowledge and operational state coefficient respectively representing the y-th operational state grade mark, h _ c max Representing the maximum possible value of the coefficient of runnability.
And 108, obtaining the health state grade of the target equipment according to the health state evaluation value and through the knowledge grade coefficient.
The target device's health status evaluation value was 0.23 based on the data shown in table 7 and equation 5, and the target device's operable status level was h1, i.e., sub-health status, based on the correspondence between the operable status level and the device health status evaluation value.
In an exemplary embodiment, the obtaining the health status rating of the target device according to the health status assessment value and through the knowledge rating coefficient further includes:
and determining the object equipment to be subjected to health risk prediction according to the health state grade and the target knowledge set, so as to predict the health risk of the object equipment to be subjected to health risk prediction.
Specifically, the health risk prediction method here may be:
searching target knowledge of the fault state level identification in a target knowledge set, and acquiring fault information in the target knowledge set;
retrieving the continuous associated knowledge of all target knowledge in a target knowledge set, and acquiring fault information in the target knowledge, wherein the target knowledge is defined as the associated index information of a corresponding device at a certain time node, and the associated index information record of the device after the time node is the continuous associated knowledge of the corresponding target knowledge;
and outputting all the acquired fault information and the classification and feature description thereof to a user.
Since the evaluation result of the target device in this embodiment is sub-healthy, it needs to be subjected to health risk prediction, and the health risks that the target device may be prompted to have according to the associated fault identifiers indexed as 1, 4, 5, 6, 7, 10, 11, and 12 in table 7 include a _01(3 times), a _02(1 times), a _04(1 times), a _05(1 times), B _01(1 times), B _03(2 times), B _08(1 times), C _02(1 times), C _03(1 times), C _05(1 times), C _06(2 times), D _01(1 times), D _02(1 times), and D _05(1 times), measures need to be taken to avoid similar faults during subsequent operations of the device, especially faults of a _01, B _03, and C _06 that occur more frequently than 1.
The invention provides a knowledge set-based method and a knowledge set-based module for evaluating the health state of complex mechanical equipment, which aim at the problems of high difficulty and low accuracy in monitoring the health of the complex mechanical equipment, realize the evaluation of the comprehensive state of the complex mechanical equipment in the operation process on the basis of knowledge formed by historical data of equipment operation and by combining a corresponding parameter configuration method, a knowledge filtering method and an equipment state evaluation value calculation method, and design a health risk prediction method, thereby providing an effective way for reasonable decision making of enterprises and having good flexibility. According to actual requirements, an enterprise maintains equipment operation historical data, configures knowledge grade coefficients, knowledge matching rules and associated index items according to the method and the module described in the invention, realizes comprehensive assessment and health risk analysis of different types of equipment operation states, can effectively solve the health monitoring problem of complex mechanical equipment, is beneficial to improving the operation and maintenance efficiency of the enterprise on the equipment, reducing corresponding operation and maintenance cost, and is also beneficial to ensuring the continuous operation capability of the equipment, thereby improving the delivery capability of the enterprise according to the date.
Referring to fig. 2, an evaluation apparatus according to an embodiment of the present application includes:
the presetting module 202 is used for presetting a knowledge base, knowledge grade coefficients and knowledge matching rules, wherein the knowledge base is a historical record about the health state corresponding to the maintenance of mechanical equipment;
an obtaining module 204, configured to obtain a target knowledge set from the knowledge base according to the knowledge matching rule, and predefined associated index items and index item weights thereof;
a calculating module 206, configured to calculate a health status evaluation value of the target device according to the target knowledge set;
an obtaining module 208, configured to obtain a health status level of the target device according to the health status evaluation value and through the knowledge level coefficient.
In view of the one-to-one correspondence between the evaluation device and the method, that is, the functions of the modules involved in the computing device can be corresponded by the steps included in the method, which is not described in detail herein.
An embodiment of the present application provides an electronic device including a processor and a memory connected by a system bus. Wherein, the processor is used for providing calculation and control capability and supporting the operation of the whole electronic equipment. The memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program can be executed by a processor for implementing an image generation method provided in the following embodiments. The internal memory provides a cached execution environment for the operating system computer programs in the non-volatile storage medium. The electronic device may be a cell phone, a tablet computer or a personal digital assistant or a wearable device or an embedded computer or the like.
The implementation of each module in the image generation apparatus provided in the embodiment of the present application may be in the form of a computer program. The computer program may be run on a terminal or a server. The program modules constituted by the computer program may be stored on the memory of the terminal or the server. Which when executed by a processor, performs the steps of the method described in the embodiments of the present application.
The embodiment of the application also provides a computer readable storage medium. One or more non-transitory computer-readable storage media containing computer-executable instructions that, when executed by one or more processors, cause the processors to perform the steps of the image generation method.
Any reference to memory, storage, database, or other medium used by embodiments of the present application may include non-volatile and/or volatile memory. Suitable non-volatile 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), which acts as 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 (DDR SDRAM), Enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (9)
1. A method for assessing the health of a complex mechanical device, the method comprising:
presetting a knowledge base, knowledge grade coefficients and knowledge matching rules, wherein the knowledge base is a historical record about the health state corresponding to the maintenance of mechanical equipment;
acquiring a target knowledge set from the knowledge base according to the knowledge matching rule, the predefined associated index items and the index item weights thereof;
calculating the health state evaluation value of the target equipment according to the target knowledge set;
and acquiring the health state grade of the target equipment according to the health state evaluation value and through the knowledge grade coefficient.
2. The method according to claim 1, wherein the obtaining the health status rating of the target device according to the health status assessment value and through the knowledge rating coefficient further comprises:
and determining the object equipment to be subjected to the health risk prediction according to the health state grade and the target knowledge set, so as to perform the health risk prediction on the object equipment to be subjected to the health risk prediction.
3. The method according to claim 1, wherein the "obtaining a target knowledge set from the knowledge base according to the knowledge matching rule, and the predefined associated index items and their index item weights" specifically includes:
selecting preliminary target knowledge data from the knowledge base according to tag information, wherein the tag information is defined as one or more of equipment type information, equipment model information and equipment specification information;
and selecting the target knowledge set from the preliminary target knowledge data through the knowledge matching rule and in combination with a predefined matching degree rule reflecting the matching degree between the target knowledge and the target equipment information.
4. The method of claim 1, wherein the knowledge matching rules comprise option value comparison rules, discrete value comparison rules, continuous value comparison rules;
the calculation mode of the option value comparison rule is as follows:
in the above formula, OpIa represents the value of a certain boolean or option type indicator of the target device, and OpIb represents the value of a corresponding indicator of a certain knowledge in the knowledge database;
the calculation mode of the discrete value comparison rule is as follows:
in the above formula, DIa represents a value of a certain discrete index of the target device, DIb represents a value of a corresponding index of a certain knowledge in the knowledge database, and DImax and DImin represent a maximum value and a minimum value which can be obtained by the DIb and the DIa, respectively;
wherein, the calculation mode of the continuous value comparison rule is as follows:
in the above formula, CIa represents the value of a certain continuous index of the target device, CIb represents the value of a corresponding index of a certain knowledge in the knowledge database, and RV represents the reference value of the index.
5. The method of claim 1, wherein the matching degree rule is calculated as follows:
in the above formula, TE and REi respectively represent each item of associated index data of the target device and each item of index data of the ith knowledge obtained by preliminary screening, TE (Ik) and REi (Ik) respectively represent values of the index Ik in TE and REi, wk represents a weight of the index Ik, n represents the number of associated index items configured in the step 4.1), and when MD (TE, REi) is greater than a threshold, REi is added to the target knowledge set to participate in the health state assessment of the target device.
6. The method according to claim 1, wherein the health status assessment value is calculated as follows:
in the above formula, m 1 And m 2 Respectively representing the number of target knowledge of the fault state level identification and the number of target knowledge of the operational state level identification in the target knowledge set, x and y respectively representing the index of the target knowledge of the fault state level identification and the index of the target knowledge of the operational state level identification in the target knowledge set, f _ RE x And f _ c x Target knowledge and fault state coefficients, h _ RE, representing the x-th fault state level identification, respectively y And h _ c y Target knowledge and operational state coefficient respectively representing the y-th operational state grade mark, h _ c max Representing the maximum possible value of the coefficient of runnability.
7. The method according to claim 1, wherein the health risk prediction method is specifically:
searching target knowledge of the fault state level identification in a target knowledge set, and acquiring fault information in the target knowledge set;
retrieving continuous associated knowledge of all target knowledge in a target knowledge set, and acquiring fault information in the target knowledge set, wherein the target knowledge is defined as associated index information of corresponding equipment at a certain time node, and the associated index information record of the equipment after the time node is the continuous associated knowledge of the corresponding target knowledge;
and outputting all the acquired fault information and the classification and feature description thereof to a user.
8. An apparatus for assessing the health of a complex machine, the apparatus comprising:
the system comprises a presetting module, a data processing module and a data processing module, wherein the presetting module is used for presetting a knowledge base, knowledge grade coefficients and knowledge matching rules, and the knowledge base is a historical record about the health state corresponding to the maintenance of mechanical equipment;
the acquisition module is used for acquiring a target knowledge set from the knowledge base according to the knowledge matching rule, the predefined associated index items and the index item weights thereof;
the calculation module is used for calculating the health state evaluation value of the target equipment according to the target knowledge set;
and the obtaining module is used for obtaining the health state grade of the target equipment according to the health state evaluation value and through the knowledge grade coefficient.
9. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, realizing the steps of the method of any one of claims 1 to 7.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105809255A (en) * | 2016-03-07 | 2016-07-27 | 大唐淮南洛河发电厂 | IoT-based heat-engine plantrotary machine health management method and system |
| CN110766277A (en) * | 2018-10-24 | 2020-02-07 | 中国核电工程有限公司 | Health assessment and diagnosis system and mobile terminal for nuclear industry field |
| CN112101764A (en) * | 2020-09-07 | 2020-12-18 | 中国人民解放军海军工程大学 | Ship technical condition comprehensive evaluation system based on state monitoring |
| CN112884325A (en) * | 2021-02-22 | 2021-06-01 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Method and system for application analysis and health condition evaluation of customer station equipment |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105809255A (en) * | 2016-03-07 | 2016-07-27 | 大唐淮南洛河发电厂 | IoT-based heat-engine plantrotary machine health management method and system |
| CN110766277A (en) * | 2018-10-24 | 2020-02-07 | 中国核电工程有限公司 | Health assessment and diagnosis system and mobile terminal for nuclear industry field |
| CN112101764A (en) * | 2020-09-07 | 2020-12-18 | 中国人民解放军海军工程大学 | Ship technical condition comprehensive evaluation system based on state monitoring |
| CN112884325A (en) * | 2021-02-22 | 2021-06-01 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Method and system for application analysis and health condition evaluation of customer station equipment |
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