CN116228183A - Equipment operation and maintenance system, equipment and storage medium based on fault knowledge base - Google Patents
Equipment operation and maintenance system, equipment and storage medium based on fault knowledge base Download PDFInfo
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Abstract
The invention relates to a device operation and maintenance system based on a fault knowledge base, which comprises: the fault knowledge base comprises equipment types, fault types related to each equipment type and recommended maintenance means related to each fault type; the equipment maintenance database comprises equipment identification codes and equipment types and fault records which are associated with the equipment identification codes; the fault prediction module is used for predicting the target equipment to obtain a prediction result; the early warning module is used for acquiring the associated recommended maintenance means from the fault knowledge base and outputting the recommended maintenance means and the predicted result; the fault rate monitoring module is used for counting and judging whether the fault rate is in the confidence interval or not, and if not, updating the recommended maintenance means of the fault type in the fault knowledge base. The system can predict the predicted fault time point according to the historical fault time point, so that operation and maintenance personnel can make a maintenance plan with dynamically changed maintenance period according to the predicted fault time point.
Description
Technical Field
The present invention relates to the field of device management technologies, and in particular, to a device operation and maintenance system, a device, and a storage medium based on a fault knowledge base.
Background
The equipment operation and maintenance is an important component in the production and life of a factory, and the existing equipment operation and maintenance mainly comprises two parts of preventive maintenance and sudden maintenance. Wherein, the traditional preventive maintenance refers to the periodical maintenance measures formulated for eliminating the reasons of equipment failure and production unplanned interruption so as to perform early discovery and early maintenance on the abnormal shape of the equipment; the sudden maintenance is a maintenance measure for repairing after the equipment fails, and the product quality and delivery period are easily affected, so that a certain degree of loss is brought to enterprises. In actual production and life of a factory, in order to stabilize the production rhythm and ensure normal operation of equipment, the equipment of the factory needs to be frequently subjected to preventive maintenance work so as to reduce the frequency of sudden maintenance. The factory usually makes a maintenance plan manually according to experience, and the content of the maintenance plan generally comprises lubrication, replacement of spare parts, maintenance and the like, and the maintenance plan is executed according to a fixed period after the planning.
However, in actual production, as the running time of the equipment increases, the failure rate of the equipment increases along with the increase of the service life of the equipment, but the current preventive maintenance period is mostly a fixed period determined based on human experience, and too short period can cause frequent maintenance to occupy maintenance resources, and meanwhile, the equipment itself is also damaged in a loss or unpredictable way, so that the service life of the equipment is influenced; the overlong period can raise the risk of equipment failure and shutdown, affect the production efficiency and the order delivery period, and even cause safety accidents. In addition, the existing preventive maintenance plan is formulated by depending on the historical maintenance experience of operation and maintenance personnel corresponding to the equipment, and once personnel replacement occurs, the failure rate of the equipment is easily increased in the transitional period of personnel replacement due to insufficient historical maintenance experience.
Disclosure of Invention
First, the technical problem to be solved
In view of the above-mentioned drawbacks and shortcomings of the prior art, the present invention provides an equipment operation and maintenance system based on a fault knowledge base, which solves the technical problem that the maintenance period of equipment cannot be well determined in the preventive maintenance of the prior art.
(II) technical scheme
In order to achieve the above purpose, the main technical scheme adopted by the invention comprises the following steps:
in a first aspect, an embodiment of the present invention provides an equipment operation and maintenance system based on a fault knowledge base, including:
the fault knowledge base comprises a plurality of equipment types, fault types related to each equipment type and recommended maintenance means related to each fault type;
the equipment maintenance database comprises an equipment identification code of each equipment in the industrial field, and equipment types and fault records associated with the equipment identification codes, wherein the fault records comprise historical fault time points, historical fault types and historical maintenance means of the equipment;
the fault prediction module is used for predicting any target equipment in the industrial field based on a fault prediction algorithm to obtain a prediction result, wherein the prediction result comprises a prediction fault type and a prediction fault time point of the target equipment;
the early warning module is used for acquiring related recommended maintenance means from a fault knowledge base based on the predicted fault type of the target equipment and the equipment type of the target equipment, and outputting the recommended maintenance means, the predicted fault time point and the predicted fault type as early warning information of the target equipment;
the fault rate monitoring module is used for counting the fault rate of each historical fault type based on the fault record in the equipment maintenance database, judging whether the fault rate is in the confidence interval or not, and if not, updating the recommended maintenance means of the fault type in the fault knowledge base.
The equipment operation and maintenance system provided by the embodiment of the invention predicts the predicted fault type and the predicted fault time point of the target equipment according to the historical fault time point and the historical fault type in the fault record based on the fault prediction module, and outputs the recommended maintenance means, the predicted fault time point and the predicted fault type as the early warning information of the target equipment based on the early warning module, so that a reference is provided for an operation and maintenance person to formulate a maintenance plan of the equipment. The equipment operation and maintenance system can predict and obtain the predicted fault time point according to the historical fault time point, so that operation and maintenance personnel can make a maintenance plan with dynamic change of a maintenance period according to the predicted fault time point, and the risk of equipment fault shutdown is reduced by more reasonably utilizing maintenance resources.
Optionally, in the fault prediction module, the fault prediction algorithm is a gray prediction algorithm, and the fault prediction module predicts a predicted fault time and a predicted fault type of the target device according to a historical fault record of the target device in a device maintenance database.
Optionally, the fault prediction module includes:
the data extraction unit is used for obtaining the fault record of the target equipment from the equipment maintenance database; extracting original sequence data of the target equipment based on the fault record; the original sequence data comprises all historical fault types of the target equipment and a time sequence which corresponds to each historical fault type and consists of a plurality of historical fault time points;
the prediction unit is used for predicting the next fault time point of each historical fault type of the target equipment according to the original sequence data based on a gray prediction algorithm, outputting the historical fault type as a predicted fault type, and outputting the next fault time point corresponding to the fault type as a predicted fault time point;
the sorting unit is used for sorting the priorities according to the predicted fault time points corresponding to each predicted fault type and the sequence of the predicted fault time points, wherein the earlier the predicted fault time point is, the higher the predicted fault type priority is, and all the predicted fault types and the corresponding predicted fault time points are arranged according to the sequence of the priorities and are output as a predicted result.
Optionally, in the data extraction unit, the time sequence is in the format of:
wherein ,x(0) A time series representing a certain historical fault type of the target device,
a historical fault time point representing the nth time of the fault type;
in the prediction unit, the predicting, based on the gray prediction algorithm, a next fault time point of each historical fault type of the target device according to the original sequence data includes:
traversing all the historical fault types of the target equipment, accumulating according to a formula (1) based on the time sequence of the historical fault types to obtain a 1-AGO sequence,
the formula (1) is:
wherein ,
a value representing the kth element in the 1-AGO sequence;
the 1-AGO sequence is expressed as:
constructing a data vector Y based on a time series of the historical fault types n Constructing a data matrix B based on the 1-AGO sequence of the historical fault type; wherein,
the data matrix B and the data vector Y are processed n Substituting the parameter matrix into the formula (2) to calculate to obtain the parameter matrix
From the parameter matrix->
Extracting to obtain parameters a and b;
the formula (2) is:
obtaining the next fault time point of the historical fault type based on a formula (3)
The formula (3) is:
in the formula (3),
and />
The result is obtained by the formula (4),
the formula (4) is:
optionally, in the fault knowledge base, a plurality of recommended maintenance means are associated with each fault type; the plurality of recommended maintenance means are arranged according to a preset priority sequence;
in the early warning module, the recommended maintenance means for acquiring the association from the fault knowledge base are as follows: acquiring the associated recommended maintenance means with the highest priority from a fault knowledge base;
in the fault rate monitoring module, the recommended maintenance means for updating the fault type in the fault knowledge base comprises: adjusting the priority sequence of the recommended maintenance means associated with the fault type, and adjusting the recommended maintenance means with the highest priority sequence to the lowest priority sequence; and/or when receiving the adjustment operation of the priority order of the recommended maintenance means by the user, adjusting the priority order of the recommended maintenance means.
Optionally, the device operation and maintenance system further includes:
the fault knowledge base updating module is used for extracting all the historical fault types associated with each equipment type and all the historical maintenance means associated with each historical fault type based on fault records in the equipment maintenance database; comparing the historical fault type corresponding to the equipment type with the fault types in a fault knowledge base, judging whether the historical fault type is a newly added fault type, and if so, adding the newly added fault type to the equipment type in the fault knowledge base; comparing the historical maintenance means corresponding to the historical fault type with the recommended maintenance means in the fault knowledge base, judging whether the historical maintenance means is a newly added maintenance means, and if so, adding the newly added maintenance means to the fault knowledge base under the fault type.
Optionally, the device operation and maintenance system further includes:
and the retrieval module is used for extracting the fault type with the highest matching degree from the fault knowledge base based on a cosine similarity algorithm according to the keywords about the fault information input by the user, and outputting the fault type and the recommended maintenance means related to the fault type.
In a second aspect, an embodiment of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, which when executed by the processor, implements the fault knowledge base based device operation and maintenance system of the first aspect.
In a third aspect, an embodiment of the present invention provides a computer readable storage medium, where a computer program is stored on the computer readable storage medium, where the computer program when executed by a processor implements the device operation and maintenance system based on a fault knowledge base according to the first aspect.
(III) beneficial effects
In the embodiment of the invention, the equipment operation and maintenance system predicts the predicted fault type and the predicted fault time point of the target equipment according to the historical fault time point and the historical fault type in the fault record based on the fault prediction module, and outputs the recommended maintenance means, the predicted fault time point and the predicted fault type as the early warning information of the target equipment based on the early warning module, so as to provide reference for operation and maintenance personnel to formulate a maintenance plan of the equipment. The equipment operation and maintenance system can predict and obtain the predicted fault time point according to the historical fault time point, so that operation and maintenance personnel can make a maintenance plan with dynamic change of a maintenance period according to the predicted fault time point, the risk of equipment fault shutdown is reduced by more reasonably utilizing maintenance resources, and the defect in manually making the maintenance plan is overcome.
In the embodiment provided by the invention, the effectiveness of each fault type in the fault knowledge base on the associated recommended maintenance means is monitored based on the fault rate monitoring module, if the fault rate of a certain fault type is within the confidence range, the recommended maintenance means associated with the current fault type is indicated to be effective, otherwise, the current recommended maintenance means is indicated to be possibly not matched with the fault type, so that the recommended maintenance means associated with the fault type is updated to reduce the fault rate of the fault type.
Drawings
FIG. 1 is a schematic diagram of an architecture of a device operation and maintenance system based on a fault knowledge base according to an embodiment;
fig. 2 is a schematic architecture diagram of another device operation and maintenance system based on a fault knowledge base according to an embodiment.
Detailed Description
The invention will be better explained by the following detailed description of the embodiments with reference to the drawings.
In order that the above-described aspects may be better understood, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Example 1
As shown in fig. 1, the present embodiment provides an equipment operation and maintenance system based on a fault knowledge base, where the system of the present embodiment may be implemented on any electronic equipment, and the electronic equipment may be specifically a computer equipment, and the system of the present embodiment includes a fault knowledge base, an equipment maintenance database, a fault prediction module, an early warning module, and a fault rate monitoring module, specifically:
the fault knowledge base comprises a plurality of equipment types, fault types associated with each equipment type and recommended maintenance means associated with each fault type. The fault knowledge base is used for providing fault types associated with each equipment type and recommended maintenance means associated with each fault type, wherein the fault types associated with each equipment type can be one or more, and the recommended maintenance means associated with each fault type can be one or more. The data in the fault knowledge base can be directly transplanted from the existing expert knowledge base or standard base in the corresponding field, and can also be automatically established for operation and maintenance personnel based on the fault record of the equipment. In particular, in the petrochemical field, the device types of the fault knowledge base may include: metering pump, heating furnace, speed reducer and stirrer; the fault types corresponding to the metering pump comprise that the motor is not used for turning the pump, the adjustment is not flexible, the pipeline joint leaks liquid and lubricating oil, the temperature is high, the pressure gauge does not have pressure indication, and the fault types corresponding to the heating furnace comprise liner falling, nozzle damage, furnace tube blockage and furnace tube leakage; the fault types corresponding to the speed reducer comprise large vibration or abnormal sound; the fault types corresponding to the stirrer comprise abnormal stirring, large vibration, vibration and abnormal sound.
And the equipment maintenance database comprises equipment identification codes of each equipment in the industrial field, and equipment types and fault records which are associated with the equipment identification codes, wherein the fault records comprise historical fault time points, historical fault types and historical maintenance means of the equipment. The equipment maintenance database is used for recording fault records of each equipment in the industry, and is also a data source of a fault prediction module, a fault rate monitoring module and other modules.
The fault prediction module is used for predicting any target equipment in the industrial field based on a fault prediction algorithm to obtain a prediction result, wherein the prediction result comprises a prediction fault type and a prediction fault time point of the target equipment. The fault prediction algorithm is used for predicting the fault time point of each fault type of the target equipment so as to provide reference for operation and maintenance personnel to make a maintenance plan. The fault prediction algorithm may be a model-based fault prediction algorithm or a probability statistics-based fault prediction algorithm. Specifically, the model in the model-based fault prediction algorithm may be a gray model, an LSTM (Long Short-Term Memory network) model, or other neural network model; the fault prediction algorithm based on probability statistics can be a time sequence prediction algorithm, a regression prediction algorithm and the like.
And the early warning module is used for acquiring the associated recommended maintenance means from a fault knowledge base based on the predicted fault type of the target equipment and the equipment type of the target equipment, and outputting the recommended maintenance means, the predicted fault time point and the predicted fault type as early warning information of the target equipment. The early warning module is used for calling corresponding recommended maintenance means from the fault knowledge base based on the prediction result and outputting the recommended maintenance means together with the prediction result, so that maintenance personnel can reasonably formulate the maintenance time and the maintenance means of the target equipment in time according to early warning information when formulating a maintenance plan.
The fault rate monitoring module is used for counting the fault rate of each historical fault type based on the fault record in the equipment maintenance database, judging whether the fault rate is in the confidence interval or not, and if not, updating the recommended maintenance means of the fault type in the fault knowledge base. The fault rate monitoring module is used for monitoring the effectiveness of recommended maintenance means associated with the fault type in the fault knowledge base according to the fault rate of the historical fault type of the equipment. If the currently associated recommended service means is valid, the failure rate of the historical failure type should be kept within a low value interval, i.e. within a confidence interval. If the fault rate of the historical fault type is kept in a higher value interval, the effectiveness of the recommended maintenance means associated with the corresponding fault type in the fault knowledge base is lower, and the recommended maintenance means associated with the fault type should be considered to be adjusted. Specifically, the statistical method and the confidence interval of the fault rate may be set according to the actual situation of the industrial field, for example, the fault rate of a certain type of historical fault may be set as follows: for a plurality of devices of the same device type, the total downtime of the historical fault type accounts for the percentage of the total load time of the plurality of devices; the fault rate of a certain historical fault type may be set for a certain device as follows: the total downtime of the apparatus, in percent of the total load time of the apparatus, for which the historical fault type occurred. Correspondingly, the confidence intervals of the fault rate are set to be different numerical intervals according to different statistical methods of the fault rate and different requirements on the reliability of equipment.
In a preferred implementation of this embodiment, the recommended maintenance means associated with each fault type in the fault knowledge base is a plurality of, and the plurality of recommended maintenance means are arranged according to a preset priority order. In the early warning module, the recommended maintenance means for acquiring the association from the fault knowledge base are as follows: and acquiring the associated recommended maintenance means with the highest priority from the fault knowledge base. In the fault rate monitoring module, the recommended maintenance means for updating the fault type in the fault knowledge base comprises: adjusting the priority sequence of the recommended maintenance means associated with the fault type, and adjusting the recommended maintenance means with the highest priority sequence to the lowest priority sequence; and/or when receiving the adjustment operation of the priority order of the recommended maintenance means by the user, adjusting the priority order of the recommended maintenance means.
Based on the fault rate monitoring module, the equipment operation and maintenance system provided by the invention can update the recommended maintenance means associated with the equipment types in the fault knowledge base according to the change condition of the fault rate of the historical fault types, so that the maintenance means recommended by the equipment operation and maintenance system for each fault type gradually tend to be accurate, and the fault rate of the equipment is effectively maintained at a lower level. The longer the equipment operation and maintenance system provided by the invention is used, the more data and experiences are accumulated, and the more accurate the maintenance means recommended by the fault knowledge base for each fault type are. Therefore, when the predicted fault time of the equipment operation and maintenance system and the recommended maintenance means have considerable accuracy, even if operation and maintenance personnel change, new operation and maintenance personnel can quickly grasp the whole condition of equipment based on the equipment operation and maintenance system, and can quickly grasp the whole condition of the equipment in the transition period of work handover, so that the equipment fault rate in the transition period of work handover is reduced.
The equipment operation and maintenance system provided by the embodiment of the invention predicts the predicted fault type and the predicted fault time point of the target equipment according to the historical fault time point and the historical fault type in the fault record based on the fault prediction module, and outputs the recommended maintenance means, the predicted fault time point and the predicted fault type as the early warning information of the target equipment based on the early warning module, so that a reference is provided for an operation and maintenance person to formulate a preventive maintenance plan of the target equipment. Based on the predicted fault time point obtained by the equipment operation and maintenance system, operation and maintenance personnel can more reasonably formulate a preventive maintenance plan with periodic dynamic changes, so that the risk of equipment fault shutdown is reduced, and the damage to equipment caused by frequent maintenance is reduced.
Example two
In order to better understand the device operation and maintenance system in the first embodiment, the present embodiment is described in detail with reference to a specific architecture of the device operation and maintenance system.
As shown in fig. 2, the equipment operation and maintenance system provided in this embodiment includes a fault knowledge base, an equipment maintenance database, a fault prediction module, an early warning module, a fault rate monitoring module, a fault knowledge base updating module, and a retrieving module, specifically:
the fault knowledge base comprises a plurality of equipment types, fault types associated with each equipment type and recommended maintenance means associated with each fault type.
And the equipment maintenance database comprises equipment identification codes of each equipment in the industrial field, and equipment types and fault records which are associated with the equipment identification codes, wherein the fault records comprise historical fault time points, historical fault types and historical maintenance means of the equipment.
The fault prediction module is used for predicting any target equipment in the industrial field based on a fault prediction algorithm to obtain a prediction result, wherein the prediction result comprises a prediction fault type and a prediction fault time point of the target equipment.
And the early warning module is used for acquiring the associated recommended maintenance means from a fault knowledge base based on the predicted fault type of the target equipment and the equipment type of the target equipment, and outputting the recommended maintenance means, the predicted fault time point and the predicted fault type as early warning information of the target equipment.
The fault rate monitoring module is used for counting the fault rate of each historical fault type based on the fault record in the equipment maintenance database, judging whether the fault rate is in the confidence interval or not, and if not, updating the recommended maintenance means of the fault type in the fault knowledge base.
The fault knowledge base updating module is used for extracting all the historical fault types associated with each equipment type and all the historical maintenance means associated with each historical fault type based on fault records in the equipment maintenance database; comparing the historical fault type corresponding to the equipment type with the fault types in a fault knowledge base, judging whether the historical fault type is a newly added fault type, and if so, adding the newly added fault type to the equipment type in the fault knowledge base; comparing the historical maintenance means corresponding to the historical fault type with the recommended maintenance means in the fault knowledge base, judging whether the historical maintenance means is a newly added maintenance means, and if so, adding the newly added maintenance means to the fault knowledge base under the fault type. The fault knowledge base updating module can continuously update and enrich the fault knowledge base according to fault records in the equipment maintenance database, and adds fault types and maintenance means which are not recorded in the fault knowledge base into the fault knowledge base, so that the equipment operation and maintenance system can provide richer and more reliable support for equipment operation and maintenance personnel.
And the retrieval module is used for extracting the fault type with the highest matching degree from the fault knowledge base based on a cosine similarity algorithm according to the keywords about the fault information input by the user, and outputting the fault type and the recommended maintenance means related to the fault type. The retrieval module is used for outputting possible fault types and recommended maintenance means associated with each fault type to the user according to the keywords input by the user so as to meet the autonomous query requirement of the user. The retrieval module can realize the retrieval function based on the existing cosine similarity algorithm. Specifically, each fault type in the fault knowledge base may include a description of fault details, and a first feature vector is constructed in advance based on the description of the fault type and the fault details; when the retrieval module receives keywords which are input by a user and related to fault information, extracting features such as word segmentation, word frequency and the like from the keywords which are input by the user to form a second feature vector, traversing the first feature vector in a fault knowledge base by utilizing a cosine similarity algorithm based on the second feature vector to obtain a fault type corresponding to one or more first feature vectors with the highest matching degree with the second feature vector, and outputting the fault type and recommended maintenance means associated with the fault type as retrieval results.
Specifically, in the above-mentioned fault prediction module, the fault prediction algorithm is a gray prediction algorithm, and the fault prediction module predicts the predicted fault time and the predicted fault type of the target device according to the historical fault record of the target device in the device maintenance database. The fault prediction module comprises a data extraction unit, a prediction unit, a sorting unit and other subunits, and specifically:
the data extraction unit is used for obtaining the fault record of the target equipment from the equipment maintenance database; extracting original sequence data of the target equipment based on the fault record; the raw sequence data includes all of the historical fault types of the target device, and a time sequence consisting of a plurality of historical fault time points corresponding to each of the historical fault types. In the data extraction unit, the time series is in the format of:
wherein ,x(0) A time series representing a certain historical fault type of the target device,
indicating the historical fault time point for the nth time of the fault type.
The prediction unit is used for predicting the next fault time point of each historical fault type of the target equipment according to the original sequence data based on a gray prediction algorithm, outputting the historical fault type as a predicted fault type, and outputting the next fault time point corresponding to the fault type as a predicted fault time point.
The sorting unit is used for sorting the priorities according to the predicted fault time points corresponding to each predicted fault type and the sequence of the predicted fault time points, wherein the earlier the predicted fault time point is, the higher the predicted fault type priority is, and all the predicted fault types and the corresponding predicted fault time points are arranged according to the sequence of the priorities and are output as a predicted result.
More specifically, in the prediction unit, the predicting, based on the gray prediction algorithm, the next failure time point of each historical failure type of the target device according to the original sequence data may be performed according to the following specific method:
a1, traversing all the historical fault types of the target equipment, accumulating according to a formula (1) based on a time sequence of the historical fault types to obtain A1-AGO sequence,
the formula (1) is:
wherein ,
a value representing the kth element in the 1-AGO sequence;
the 1-AGO sequence is expressed as:
a2, constructing a data vector Y based on the time sequence of the historical fault type n Constructing a data matrix B based on the 1-AGO sequence of the historical fault type; wherein,
a3, the data matrix B and the data vector Y are processed n Substituting the parameter matrix into the formula (2) to calculate to obtain the parameter matrix
From a parameter matrix
Extracting to obtain parameters a and b;
the formula (2) is:
a4, obtaining the next fault time point of the historical fault type based on the formula (3)
The formula (3) is:
in the formula (3),
and />
The result is obtained by the formula (4),
the formula (4) is:
the fault prediction module predicts the time point of possible next fault of a fault type according to the historical fault time point of the fault type of one device based on a gray prediction algorithm; traversing all the historical fault types of the equipment to obtain a time point when the next possible fault of all the historical fault types occurs; and sequencing all the time points according to the time sequence, namely obtaining the time point of possible faults of the equipment in the future and the corresponding fault type, wherein the earlier the time point is, the greater the possibility of the fault type is. Based on the fault prediction module, the equipment operation and maintenance system can predict the type of the fault and the fault time point of the next occurrence of the equipment and pre-warn operation and maintenance personnel, so that reliable references are provided for the operation and maintenance personnel when a reasonable and dynamic maintenance plan is formulated.
Example III
The present embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the computer program when executed by the processor implements the device operation and maintenance system based on a fault knowledge base according to the first to third embodiments.
The present embodiment also provides a computer readable storage medium, where a computer program is stored, where the computer program when executed by a processor implements the device operation and maintenance system based on a fault knowledge base according to the first to third embodiments.
Since the system/device described in the foregoing embodiments of the present invention is a system/device used for implementing the method of the foregoing embodiments of the present invention, those skilled in the art will be able to understand the specific structure and modification of the system/device based on the method of the foregoing embodiments of the present invention, and thus will not be described in detail herein. All systems/devices used in the methods of the above embodiments of the present invention are within the scope of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the terms first, second, third, etc. are for convenience of description only and do not denote any order. These terms may be understood as part of the component name.
Furthermore, it should be noted that in the description of the present specification, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with the embodiment or example being included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art upon learning the basic inventive concepts. Therefore, the appended claims should be construed to include preferred embodiments and all such variations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, the present invention should also include such modifications and variations provided that they come within the scope of the following claims and their equivalents.
Claims (9)
1. A device operation and maintenance system based on a fault knowledge base, comprising:
the fault knowledge base comprises a plurality of equipment types, fault types related to each equipment type and recommended maintenance means related to each fault type;
the equipment maintenance database comprises an equipment identification code of each equipment in the industrial field, and equipment types and fault records associated with the equipment identification codes, wherein the fault records comprise historical fault time points, historical fault types and historical maintenance means of the equipment;
the fault prediction module is used for predicting any target equipment in the industrial field based on a fault prediction algorithm to obtain a prediction result, wherein the prediction result comprises a prediction fault type and a prediction fault time point of the target equipment;
the early warning module is used for acquiring related recommended maintenance means from a fault knowledge base based on the predicted fault type of the target equipment and the equipment type of the target equipment, and outputting the recommended maintenance means, the predicted fault time point and the predicted fault type as early warning information of the target equipment;
the fault rate monitoring module is used for counting the fault rate of each historical fault type based on the fault record in the equipment maintenance database, judging whether the fault rate is in the confidence interval or not, and if not, updating the recommended maintenance means of the fault type in the fault knowledge base.
2. The equipment operation and maintenance system according to claim 1, wherein in the fault prediction module, the fault prediction algorithm is a gray prediction algorithm, and the fault prediction module predicts a predicted fault time and a predicted fault type of the target equipment according to a historical fault record of the target equipment in an equipment maintenance database.
3. The equipment operation and maintenance system according to claim 2, wherein the failure prediction module comprises:
the data extraction unit is used for obtaining the fault record of the target equipment from the equipment maintenance database; extracting original sequence data of the target equipment based on the fault record; the original sequence data comprises all historical fault types of the target equipment and a time sequence which corresponds to each historical fault type and consists of a plurality of historical fault time points;
the prediction unit is used for predicting the next fault time point of each historical fault type of the target equipment according to the original sequence data based on a gray prediction algorithm, outputting the historical fault type as a predicted fault type, and outputting the next fault time point corresponding to the fault type as a predicted fault time point;
the sorting unit is used for sorting the priorities according to the predicted fault time points corresponding to each predicted fault type and the sequence of the predicted fault time points, wherein the earlier the predicted fault time point is, the higher the predicted fault type priority is, and all the predicted fault types and the corresponding predicted fault time points are arranged according to the sequence of the priorities and are output as a predicted result.
4. The equipment operation and maintenance system according to claim 3, wherein,
in the data extraction unit, the time series is in the format of:
wherein ,x(0) A time series representing a certain historical fault type of the target device,
a historical fault time point representing the nth time of the fault type;
in the prediction unit, the predicting, based on the gray prediction algorithm, a next fault time point of each historical fault type of the target device according to the original sequence data includes:
traversing all the historical fault types of the target equipment, accumulating according to a formula (1) based on the time sequence of the historical fault types to obtain a 1-AGO sequence,
the formula (1) is:
wherein ,
a value representing the kth element in the 1-AGO sequence;
the 1-AGO sequence is expressed as:
constructing a data vector Y based on a time series of the historical fault types n Constructing a data matrix B based on the 1-AGO sequence of the historical fault type; wherein,
the data matrix B and the data vector Y are processed n Substituting the parameter matrix into the formula (2) to calculate to obtain the parameter matrix
From the parameter matrix->
Extracting to obtain parameters a and b;
the formula (2) is:
obtaining the next fault time point of the historical fault type based on a formula (3)
The formula (3) is:
in the formula (3),
and />
The result is obtained by the formula (4),
the formula (4) is:
5. the equipment operation and maintenance system according to claim 1, wherein in the fault knowledge base, a plurality of recommended maintenance means are associated with each fault type; the plurality of recommended maintenance means are arranged according to a preset priority sequence;
in the early warning module, the recommended maintenance means for acquiring the association from the fault knowledge base are as follows: acquiring the associated recommended maintenance means with the highest priority from a fault knowledge base;
in the fault rate monitoring module, the recommended maintenance means for updating the fault type in the fault knowledge base comprises: adjusting the priority sequence of the recommended maintenance means associated with the fault type, and adjusting the recommended maintenance means with the highest priority sequence to the lowest priority sequence; and/or when receiving the adjustment operation of the priority order of the recommended maintenance means by the user, adjusting the priority order of the recommended maintenance means.
6. The device operation and maintenance system according to claim 1, wherein the device operation and maintenance system further comprises:
the fault knowledge base updating module is used for extracting all the historical fault types associated with each equipment type and all the historical maintenance means associated with each historical fault type based on fault records in the equipment maintenance database; comparing the historical fault type corresponding to the equipment type with the fault types in a fault knowledge base, judging whether the historical fault type is a newly added fault type, and if so, adding the newly added fault type to the equipment type in the fault knowledge base; comparing the historical maintenance means corresponding to the historical fault type with the recommended maintenance means in the fault knowledge base, judging whether the historical maintenance means is a newly added maintenance means, and if so, adding the newly added maintenance means to the fault knowledge base under the fault type.
7. The equipment operation and maintenance system according to claim 1, wherein the equipment operation and maintenance system further comprises:
and the retrieval module is used for extracting the fault type with the highest matching degree from the fault knowledge base based on a cosine similarity algorithm according to the keywords about the fault information input by the user, and outputting the fault type and the recommended maintenance means related to the fault type.
8. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor implements the fault knowledge base based device operation and maintenance system of any one of claims 1 to 7.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the fault knowledge base based device operation and maintenance system of any of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117273709A (en) * | 2023-11-20 | 2023-12-22 | 中况检测技术(南京)有限公司 | Equipment operation and maintenance and fault monitoring on-line evaluation system and method |
| CN117369392A (en) * | 2023-11-17 | 2024-01-09 | 岳阳长炼机电工程技术有限公司 | Equipment fault intelligent early warning method based on multiparameter logic relation |
| CN117436181A (en) * | 2023-12-19 | 2024-01-23 | 中冶南方工程技术有限公司 | Intelligent evaluation method for heat insulation and heat tracing of pipeline in metallurgical engineering process |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117369392A (en) * | 2023-11-17 | 2024-01-09 | 岳阳长炼机电工程技术有限公司 | Equipment fault intelligent early warning method based on multiparameter logic relation |
| CN117369392B (en) * | 2023-11-17 | 2024-04-16 | 岳阳长炼机电工程技术有限公司 | Equipment fault intelligent early warning method based on multiparameter logic relation |
| CN117273709A (en) * | 2023-11-20 | 2023-12-22 | 中况检测技术(南京)有限公司 | Equipment operation and maintenance and fault monitoring on-line evaluation system and method |
| CN117273709B (en) * | 2023-11-20 | 2024-01-26 | 中况检测技术(南京)有限公司 | Equipment operation and maintenance and fault monitoring on-line evaluation system and method |
| CN117436181A (en) * | 2023-12-19 | 2024-01-23 | 中冶南方工程技术有限公司 | Intelligent evaluation method for heat insulation and heat tracing of pipeline in metallurgical engineering process |
| CN117436181B (en) * | 2023-12-19 | 2024-03-22 | 中冶南方工程技术有限公司 | Intelligent evaluation method for heat insulation and heat tracing of pipeline in metallurgical engineering process |
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