CN111026046A - Production line equipment fault diagnosis system and method based on semantics - Google Patents
Production line equipment fault diagnosis system and method based on semantics Download PDFInfo
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 48
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000012423 maintenance Methods 0.000 claims abstract description 30
- 239000011159 matrix material Substances 0.000 claims description 9
- 208000024891 symptom Diseases 0.000 claims description 9
- 239000013598 vector Substances 0.000 claims description 8
- 238000004804 winding Methods 0.000 claims description 6
- 230000006870 function Effects 0.000 claims description 4
- 238000005461 lubrication Methods 0.000 claims description 4
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 3
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000005299 abrasion Methods 0.000 claims description 3
- 238000001816 cooling Methods 0.000 claims description 3
- 238000005260 corrosion Methods 0.000 claims description 3
- 230000007797 corrosion Effects 0.000 claims description 3
- 238000010438 heat treatment Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 239000010687 lubricating oil Substances 0.000 claims description 2
- 238000004806 packaging method and process Methods 0.000 claims description 2
- 238000007781 pre-processing Methods 0.000 claims description 2
- 239000000758 substrate Substances 0.000 claims 1
- 238000007726 management method Methods 0.000 abstract description 16
- 238000005516 engineering process Methods 0.000 abstract description 6
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- 230000000694 effects Effects 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The invention relates to a production line equipment fault diagnosis system and method based on semantics, which comprises an original information layer, a semantic layer, a service layer and an application layer. And semantically describing the original fault information by using a semantic ontology technology. I.e. using classes and attributes for formal specification. And then carrying out fault diagnosis by using an inference technology on the basis of the fault diagnosis. The primary information layer collects primary fault information by gathering and investigating various data, experiences, knowledge, etc. The semantic layer is used for constructing a fault diagnosis knowledge base and a fuzzy inference system. The fault diagnosis knowledge base is used for constructing a fault ontology knowledge base and a rule base. The service layer includes query service and knowledge base management service. The application layer comprises a user login management module, a knowledge base management module, a fault diagnosis module and a maintenance scheme module. The invention can effectively check the cause of the production line equipment failure, thereby reducing the downtime caused by the production line equipment failure.
Description
Technical Field
The invention belongs to the field of combination of a semantic network and production line equipment, and relates to a production line equipment fault diagnosis mechanism research based on semantic and fuzzy reasoning.
Background
China's manufacturing industry develops rapidly, and the equipment on the production line is also various, and machinery equipment is often in high load, and in the middle of long-time operation, mechanical equipment is inevitable to break down. Due to the fact that the fault knowledge of the production line equipment is rich and scattered, such as an expert book network and the like. The knowledge is often heterogeneous, and lacks accurate semantic information among the knowledge, so that the problem of repeated construction of production line fault information is easily caused. The fault symptoms and the fault reasons are not in a one-to-one mapping relationship, but in a many-to-many relationship, namely, one fault phenomenon can have multiple fault reasons, and one fault reason can also cause multiple faults. The cause and effect relationship between the fault phenomena and the fault reasons of some equipment is difficult to determine by mutually staggering and inducing the fault phenomena and cognizing the fault phenomena.
As a novel data processing technology, the semantic web technology has unique advantages in the aspects of organizing and managing information, realizing some intelligent applications and the like. The ontology is an accurate description of concepts and relationships between the concepts, has obvious advantages in the aspects of reuse and sharing of knowledge, and is more and more concerned in recent years. The semantic network technology can be used for semantically describing heterogeneous fault information, the fault information is defined to be more complete, and better cooperation of a computer and people can be promoted.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. A production line equipment fault diagnosis system and method based on semantics are provided. The technical scheme of the invention is as follows:
a semantic-based production line equipment fault diagnosis system, comprising:
data source layer: the method comprises the steps of obtaining original information of equipment faults of the production line, including obtaining original information including fault phenomena, fault reasons and maintenance schemes and corresponding relations between the original information;
and (3) semantic layer: the fault diagnosis system comprises a fault diagnosis knowledge base and a fuzzy reasoning system, wherein the fault diagnosis knowledge base comprises a body base and a rule base, the body base stores body models and semantic data related to the faults of the production line equipment, the rule base is used for storing relevant rules required by the fault phenomena, the fault reasons and the maintenance method of the production line equipment, and the fuzzy reasoning system is used for reasoning the uncertain fault phenomena and the fault reasons; according to the acquired original information including the fault phenomenon, the fault reason, the fault source and the maintenance scheme, an ontology model is constructed under a TopBraidComperser ontology editor, then an inference rule between the fault reason and the fault phenomenon is edited by using a logic rule language prolog (programming in logic), and the fault reason, the fault phenomenon, the fault source, the maintenance scheme ontology model and the inference rule are stored in a semantic graph database;
and (3) a service layer: the system comprises an inquiry service module and a knowledge base management service module, wherein the inquiry service module is used for performing web packaging on a request of a fault diagnosis and maintenance method from an application layer for the application layer to call; the knowledge base management service module is used for managing a production line equipment fault body base and a rule base;
an application layer: the system comprises a user login management module, a knowledge base management module, a fault diagnosis module and a maintenance scheme module, wherein the user login management module is used for registration and management of personnel; the knowledge base management module is used for adding, deleting or modifying the fault diagnosis knowledge base; the fault diagnosis module is used for diagnosing the equipment fault of the production line; the maintenance scheme is used for inquiring a fault maintenance method corresponding to the fault phenomenon.
Furthermore, in the fault diagnosis module, accurate reasoning can be carried out on fault diagnosis in an accurate reasoning mode, fuzzy reasoning is carried out if the fault diagnosis cannot be accurately inferred, and finally a fault reason set ordered by the membership degree is output.
Further, the fuzzy inference system comprises three processes of information fuzzification, fuzzy inference and defuzzification, wherein the information fuzzification is to carry out fuzzification preprocessing on system input, real definite quantity input is converted into fuzzy input, a fuzzy set represented by fuzzy linguistic variables and corresponding membership degrees of the fuzzy linguistic variables is used for fuzzification processing on the input information by using a triangular membership function to obtain fuzzy characteristic vectors, and a fuzzy inference machine is the core of the fuzzy inference system and is used for converting fuzzy rules of a model IF. … THEN. … in a fuzzy rule base into fuzzy rules of a model x < 78 > THEN. … according to operation rules of fuzzy logic1×x2×x3×…×xnMapping relation of the fuzzy sets to the fuzzy sets on Y, namely according to received fuzzy input information x including fault symptoms, (x)1,x2,…xn) Generating corresponding fuzzy output variable Y including fault reasons according to the existing rules including fault reasoning rules in the fuzzy rule base; defuzzification is to defuzzify the obtained fuzzy output variable Y into an accurate output.
Further, the fault diagnosis module is characterized in that a fault matrix R is constructed, the fault matrix R is a fuzzy relation between fault phenomena and fault reasons, and then a fuzzy relation matrix is established according to fuzzy generator rules and membership functions, wherein R isijRepresenting the membership degree of a certain fault phenomenon corresponding to the fault reason, m representing the number of the fault phenomena, and n representing the number of the fault reasons.
Further, the fault reason, fault phenomenon, fault source and maintenance scheme body model is divided into the following parts:
the fault phenomena comprise faults including slip ring faults, stator faults and sliding bearing faults, wherein the stator faults comprise abnormal stator sound and stator rotation, and the bearing faults comprise bearing heating, bearing corrosion and bearing abrasion;
the fault reasons comprise the reasons that the bearing clearance is too small, the bearing load is too large, the bearing lubrication is not timely, the gear is abraded, and the cooling is not timely;
the fault source comprises a stator, a bearing, a centrifugal pump and motor equipment, wherein the stator comprises a stator winding, a stator iron core and a machine shell;
the maintenance scheme comprises the scheme of replacing a stator winding, reducing load and adding lubricating oil to replace a gear.
A device fault diagnosis method based on the system comprises the following steps:
firstly, inputting fault symptom information;
judging whether accurate reasoning can be carried out, if the accurate reasoning can be carried out, outputting the fault reason, and if the accurate reasoning cannot be carried out, entering the next step;
fuzzifying input fault symptom information to obtain a fuzzy vector, starting a fault matrix R to obtain a fuzzy output vector, and then performing defuzzification;
judging whether a plurality of output results are available, if only one result is available, directly outputting the fault reason, if so, directly outputting the fault reason
And if the number of the results is multiple, sorting the results, and finally outputting the fault reasons sorted by the membership degree. The invention has the following advantages and beneficial effects:
the invention has wide future sources and produces line equipment fault information which is lack of accurate semantic information, and utilizes the heterogeneous fault information of the semantic network technology to carry out semantic description, thereby avoiding the problem of repeated construction of the production line equipment fault information. Through regular reasoning, fault diagnosis can be divided into precise reasoning and fuzzy reasoning. And (3) directly outputting fault reasons capable of carrying out accurate reasoning, starting fuzzy reasoning if the fault reasoning cannot be carried out, and finally outputting fault reasons sorted by the membership degree. By the method, the cause of the production line equipment failure can be effectively checked, so that the down time caused by the production line equipment failure is reduced.
In the field of fault diagnosis of production line equipment, the traditional semantic rule reasoning can only carry out logical reasoning of 0 quantity and 1 quantity, and can not meet the fault reasoning of ambiguity and uncertainty.
Drawings
FIG. 1 is a preferred embodiment of the present invention providing a semantic based production line equipment fault diagnosis system;
FIG. 2 is a diagram of a fault diagnosis ontology model according to the present invention;
fig. 3 is a flow chart of fault diagnosis in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
as shown in FIG. 1, the architecture diagram of the semantic-based fault diagnosis mechanism for production line equipment comprises the following parts:
(1) an original data layer. The original information source of the production line equipment failure is wide, and the production line equipment failure information can be deeply known by production line equipment maintenance manuals, experience of maintenance masters, network knowledge and the like. And further acquiring original information such as fault phenomena, fault reasons, maintenance schemes and the like and corresponding relations among the original information and the original information.
(2) And (5) a semantic layer. And constructing an ontology model under a TopBraidComperser ontology editor according to the acquired original information such as the fault phenomenon, the fault reason, the fault source, the maintenance scheme and the like. And then compiling inference rules between the failure reasons and failure phenomena under relevant environments. And storing the fault reason, the fault phenomenon, the fault source, the maintenance scheme body model and the inference rule in a semantic graph database.
(3) And (4) a service layer. The service layer includes query service and knowledge base management service. Wherein the query service is a web package of requests for troubleshooting and repair methods from the application layer for invocation by the application layer.
(4) And (5) an application layer. The application layer comprises a user login management module, a knowledge base management module, a fault diagnosis module and a maintenance scheme module. The user login management module is used for registration and management of personnel. The knowledge base management module is used for adding, deleting or modifying the fault diagnosis knowledge base. The fault diagnosis module is used for diagnosing the equipment fault of the production line. The maintenance scheme is used for inquiring a fault maintenance method corresponding to the fault phenomenon.
FIG. 2 is a diagram of the knowledge ontology model of the equipment failure of the production line, which is divided into the following parts.
(1) The fault phenomena include slip ring faults, stator faults, sliding bearing faults and the like, wherein the stator faults include abnormal stator sound and stator rotation. Bearing failures include bearing heating, bearing corrosion, and bearing wear.
(2) The failure reasons include too small bearing clearance, too large bearing load, untimely bearing lubrication, gear abrasion, untimely cooling and the like
(3) Sources of failure include stators, bearings, centrifugal pumps, electromechanical devices, and the like. The stator comprises a stator winding, a stator iron core, a machine shell and the like.
(4) The maintenance scheme includes replacing the stator windings, reducing the load, adding lubrication to replace the gears, and the like.
As shown in fig. 3, the process of diagnosing the faults of the production line equipment is as follows:
(1) firstly, inputting fault symptom information;
(2) and judging whether accurate reasoning can be carried out or not, and if the accurate reasoning can be carried out, outputting the fault reason.
If the accurate reasoning can not be carried out, entering the step (3);
(3) and fuzzifying the input fault symptom information to obtain a fuzzy vector, and starting a fault matrix R to obtain a fuzzy output vector. Then defuzzification is performed.
(4) And judging whether a plurality of output results exist, if only one result exists, directly outputting fault reasons, and if the number of the output results is a plurality of results, sorting the results, and finally outputting the fault reasons sorted by the membership degree.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (6)
1. A production line equipment fault diagnosis system based on semantics is characterized by comprising:
data source layer: the method comprises the steps of obtaining original information of equipment faults of the production line, including obtaining original information including fault phenomena, fault reasons and maintenance schemes and corresponding relations between the original information;
and (3) semantic layer: the system comprises a fault diagnosis knowledge base and a fuzzy inference system, wherein the fault diagnosis knowledge base comprises a body base and a rule base, the body base stores a body model and semantic data related to the faults of the production line equipment, the rule base is used for storing relevant rules of the fault phenomena, the fault reasons and inference required by a maintenance method of the production line equipment, the fuzzy inference system is used for inferring the uncertain fault phenomena and the fault reasons, the body model is built under a TopBraidComposer body editor according to the acquired original information including the fault phenomena, the fault reasons, the fault sources and the maintenance schemes, then the inference rules between the fault reasons and the fault phenomena are edited by using a logic rule language Prolog, and the fault reasons, the fault phenomena, the fault sources, the maintenance scheme body model and the inference rules are stored in a semantic graph database;
and (3) a service layer: the system comprises an inquiry service module and a knowledge base management service module, wherein the inquiry service module is used for performing web packaging on a request of a fault diagnosis and maintenance method from an application layer for the application layer to call; the knowledge base management service module is used for managing a production line equipment fault body base and a rule base;
an application layer: the system comprises a user login management module, a knowledge base management module, a fault diagnosis module and a maintenance scheme module, wherein the user login management module is used for registration and management of personnel; the knowledge base management module is used for adding, deleting or modifying the fault diagnosis knowledge base; the fault diagnosis module is used for diagnosing the equipment fault of the production line; the maintenance scheme is used for inquiring a fault maintenance method corresponding to the fault phenomenon.
2. The semantic-based production line equipment fault diagnosis system of claim 1, wherein in the fault diagnosis module, accurate reasoning can be carried out on fault diagnosis, fuzzy reasoning is carried out if the fault diagnosis cannot be accurately inferred, and finally a fault reason set sorted by membership degree is output.
3. The semantic-based production line equipment fault diagnosis system of claim 2, characterized in that the fuzzy inference system comprises three processes of information fuzzification, fuzzy inference and defuzzification, wherein the information fuzzification is to perform fuzzification preprocessing on system input, to convert real definite quantity input into fuzzy input, to perform fuzzy set represented by fuzzy linguistic variables and corresponding membership degrees, to perform fuzzification processing on the input information by using a triangular membership function to obtain fuzzy characteristic vectors, and the fuzzy inference engine is the core of the fuzzy inference system and is used for converting fuzzy rules of type "IF. … THEN. …" in a fuzzy rule base into fuzzy sets from x according to operation rules of fuzzy logic1×x2×x3×…×xnMapping relation of the fuzzy sets to the fuzzy sets on Y, namely according to received fuzzy input information x including fault symptoms, (x)1,x2,…xn) Generating corresponding fuzzy output variable Y including fault reasons according to the existing rules including fault reasoning rules in the fuzzy rule base; defuzzification is to defuzzify the obtained fuzzy output variable Y into an accurate output.
4. A substrate according to claim 3The semantic production line equipment fault diagnosis system is characterized in that the fault diagnosis module is characterized in that a fault matrix R is constructed, the fault matrix R is a fuzzy relation between fault phenomena and fault causes, and then a fuzzy relation matrix is established according to fuzzy production formula rules and membership functions, wherein R isijRepresenting the membership degree of a certain fault phenomenon corresponding to the fault reason, wherein m represents the number of the fault phenomena, and n represents the number of the fault reasons;
5. the semantic-based production line equipment fault diagnosis system according to one of claims 1 to 4, characterized in that the fault cause, fault phenomenon, fault source and maintenance scheme ontology model is divided into the following parts:
(1) the fault phenomena comprise slip ring faults, stator faults, sliding bearing faults, AGV faults and faults including four-axis robots, wherein the stator faults comprise abnormal stator sound and stator rotation, and the bearing faults comprise bearing heating, bearing corrosion and bearing abrasion;
(2) the fault reasons comprise the reasons that the bearing clearance is too small, the bearing load is too large, the bearing lubrication is not timely, the gear is abraded, and the cooling is not timely;
(3) the fault source comprises a stator, a bearing, a centrifugal pump and motor equipment, wherein the stator comprises a stator winding, a stator iron core and a machine shell;
(4) the maintenance scheme comprises the scheme of replacing a stator winding, reducing load and adding lubricating oil to replace a gear.
6. An equipment fault diagnosis method based on the system according to any one of claims 1 to 4, characterized by comprising the steps of:
(1) firstly, inputting fault symptom information;
(2) judging whether accurate reasoning can be carried out, if so, outputting the fault reason, and if not, entering the step (3);
(3) fuzzifying input fault symptom information to obtain a fuzzy vector, starting a fault matrix R to obtain a fuzzy output vector, and then performing defuzzification;
(4) and judging whether a plurality of output results exist, if only one result exists, directly outputting fault reasons, and if the number of the output results is a plurality of results, sorting the results, and finally outputting the fault reasons sorted by the membership degree.
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