CN102176239A - Kitchenware fault diagnosing method based on case-based reasoning - Google Patents
Kitchenware fault diagnosing method based on case-based reasoning Download PDFInfo
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- CN102176239A CN102176239A CN2011100435391A CN201110043539A CN102176239A CN 102176239 A CN102176239 A CN 102176239A CN 2011100435391 A CN2011100435391 A CN 2011100435391A CN 201110043539 A CN201110043539 A CN 201110043539A CN 102176239 A CN102176239 A CN 102176239A
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Abstract
The invention discloses a kitchenware fault diagnosing method based on case-based reasoning, which comprises the following steps: a kitchenware fault problem is submitted to obtain fault symptom information, the fault symptom information is transmitted to a fault diagnosing server of an enterprise for retrieving and screening of kitchenware fault cases so as to obtain a target fault case, and the target fault case is transmitted back to a client, and a maintenance man or a user maintains the kitchenware according to a maintenance scheme in the target fault cases; if the kitchenware fault can be solved, the fault case is studied; otherwise, the step (5) is carried out to revise the fault case to obtain a new kitchenware fault case for maintenance till the kitchenware fault is solved. The kitchenware fault diagnosing method disclosed by the invention utilizes CBR (Case-Base Reasoning) to diagnose the kitchenware faults, and adopts the strategic concept of classifying the fault cases for retrieval, therefore, the method is beneficial to not only reduction of the difficulty in representing and organizing cases and creating a case base, but also great improvement of the case retrieving efficiency, and has higher adaptability compared with the traditional RBR (Rule-Based Reasoning) or MBR (Model-Based Reasoning) expert systems.
Description
Technical field
The invention belongs to artificial intelligence fault diagnosis technology field, more specifically say, relate to a kind of kitchen tools method for diagnosing faults based on reasoning by cases.
Background technology
Kitchen tools are as the essential daily necessities of family, along with the raising of people's living standard, to the also increase day by day of requirement of kitchen tools.But in today of product height homogeneity, numerous products are placed in together identically, even the product of high-quality can not form advantage.Along with improving constantly that the quality of production and technical service are required, people require also in continuous rising the selection of household cookware.The homogeneity of kitchen tools product technology, the homogeneity of image product make that enterprise can't be on product and obtain the competitive edge of differentiation.Kitchen tools enterprise wants to obtain survival advantage in intense market competition, just must set up own exclusive core competitiveness.
Set up own exclusive core competitiveness and must constantly seek the place that allows own product make new advances.The innovation of fault diagnosis technology then is one of key point of making new advances of product.At first, carry out fault diagnosis timely, can increase the serviceable life of kitchen tools; Secondly, the kitchen tools accident may cause unnecessary loss, and serious words jeopardize user's lives and properties, and it is very important that fault diagnosis then seems.
Utilize the fault diagnosis system of artificial intelligence technology to become the development trend of kitchen tools fault diagnosis, current at kitchen tools maintenance field, traditional fault diagnosis major part is to adopt RBR (rule based reasoning, rule-based reasoning), the expert system technology of MBR (model based reasoning, pattern reasoning).Because these traditional expert systems are based on (diagnostic method based on model uses structure, behavior and the functional mode etc. of diagnosis object to know very well knowledge to carry out diagnostic reasoning) that modelling drives, there is wretched insufficiency aspect the obtaining of the structure of model, information, the information processing, the shortcoming that has some to be difficult to overcome is as the Rule Extraction difficulty of system's domain knowledge; The establishment of rule base, library and complex management arduousness; Rule is difficult to accurately choose etc. with pattern in the reasoning process.
Summary of the invention
The objective of the invention is to overcome the deficiencies in the prior art, a kind of kitchen tools method for diagnosing faults based on reasoning by cases strong, that efficient is high that adapts to is provided.
For achieving the above object, the present invention is based on the kitchen tools method for diagnosing faults of reasoning by cases, it is characterized in that may further comprise the steps:
(1), the kitchen tools failure problems is submitted to
Maintenance personal or user are according to the disease million that shows when the prior fault kitchen tools, and input fault sign information on client computer is as the input of kitchen tools failure problems; Failure symptom information by internet transmission to the fault diagnosis server of enterprise;
(2), kitchen tools fault case retrieval
Fault diagnosis server calculates the secondary grading search method of similarity by the nearest neighbor method that adopts grey relational grade from its kitchen tools fault case storehouse, retrieve the candidate casebook similar to failure symptom information:
(3), kitchen tools fault case screening
If the similarity through a certain case in (2) candidate's casebook of obtaining of step is enough high, and other case similarities are all lower, then need not carry out the case screening, directly with it as the target faults case, entered for (4) step; Otherwise call case screening module, carry out the case screening, obtain the target faults case, entered for (4) step again by case screening module;
(4), maintenance program is reused
Fault diagnosis server is passed the target faults case on the client computer back by the internet, and maintenance personal or user keep in repair kitchen tools according to the maintenance program in the target faults case, if can solve the kitchen tools fault, entering for (6) step carries out fault case study; Otherwise changing for (5) step over to carries out the fault case correction;
(5), fault case correction
Fault diagnosis server calls case correction backup system, pass through interactive means, the technician of enterprise carries out the fault case correction, obtain new kitchen tools fault case, and then by passing back on the client computer by the internet, maintenance personal or user keep in repair kitchen tools according to the maintenance program in the target faults case, and till solving the kitchen tools fault, changing for (6) step over to carries out fault case study;
(6), fault case study
Fault diagnosis server joins new kitchen tools fault case in the kitchen tools fault case storehouse; At the case assisted learning system ratio that the kitchen tools fault case solves physical fault is judged, if less than certain value, then with its deletion.
The object of the present invention is achieved like this.
Owing to concern that complexity, fault type are many between many, phenomenon of the failure of kitchen tools model and the failure cause, based on the method for case because of its knowledge acquisition easily, easy to understand, develop the mind, characteristics such as adaptive ability is strong, knowledge base maintenance is convenient, have stronger adaptability than the expert system of traditional RBR, MBR.Utilize CBR (Case-Base Reasoning is based on reasoning by cases) to carry out the kitchen tools fault diagnosis,, avoided the imperfect, inconsistent of structural description kitchen tools faults by semi-structured description to the kitchen tools fault.Analyzing and studying on kitchen tools fault diagnosis field knowledge characteristics and a large amount of breakdown maintenance daily record basis, inquired into gordian technique based on the reasoning by cases method, the present invention proposes the tactful thought that fault case is classified and then retrieved, this method has not only reduced the structure difficulty of case representation, tissue and case library, and has greatly improved the case effectiveness of retrieval.Simultaneously, the present invention is based on reasoning by cases kitchen tools diagnostic method and also have following characteristics:
(1), kitchen tools diagnosis becomes and is more prone to, and still can not carry out fault diagnosis when having the kitchen tools model; Because kitchen tools fault case storehouse is ever-increasing, so even just begun only to have the reasoning by cases system of a small amount of case also can move; Kitchen tools diagnostic method based on reasoning by cases can provide kitchen tools fault solution fast and needn't all from the beginning carry out reasoning at every turn; What offer the user based on reasoning by cases is concrete kitchen tools fault case, understands easily; By obtaining new case, new knowledge can be learnt in the fault case storehouse from different fields, and safeguards easily.
(2), the present invention proposes to adopt grey relational grade to weigh the similarity of case based on case 2-level search in the reasoning by cases method for diagnosing faults, uses grey Advantage Analysis and obtains candidate's casebook, the candidate's case that obtains is comparatively accurate.
Description of drawings
Fig. 1 is the kitchen tools method for diagnosing faults process flow diagram that the present invention is based on reasoning by cases;
Fig. 2 is gas-cooker fault case storehouse taxonomic structure figure;
Fig. 3 is the secondary index structural drawing in gas-cooker fault case storehouse.
Embodiment
Below in conjunction with accompanying drawing the specific embodiment of the present invention is described, so that those skilled in the art understands the present invention better.What need point out especially is that in the following description, when perhaps the detailed description of known function and design can desalinate main contents of the present invention, these were described in here and will be left in the basket.
Embodiment
Fig. 1 is the kitchen tools method for diagnosing faults process flow diagram that the present invention is based on reasoning by cases.
1. the kitchen tools failure problems is submitted to
As shown in Figure 1, in the present embodiment, in the kitchen tools method for diagnosing faults based on reasoning by cases, at first be that the kitchen tools failure problems is submitted to, be step (1): maintenance personal or user are according to the disease million that shows when the prior fault kitchen tools, input fault sign information on client computer is as the input of kitchen tools failure problems.Failure symptom information is to select to obtain according to the failure symptom option that fault diagnosis server provides, and can obtain the input fault sign information of a standard like this, is convenient to the retrieval of kitchen tools fault case.Can certainly discern automatically and produce by fault diagnosis server by maintenance personal or user's input fault phenomenon.
The failure symptom information of maintenance personal or user input by internet transmission to the fault diagnosis server of enterprise.
2. kitchen tools fault case retrieval
Then, carry out the retrieval of kitchen tools fault case, be step (2): fault diagnosis server calculates the secondary grading search method of similarity by the nearest neighbor method that adopts grey relational grade from its kitchen tools fault case storehouse, retrieve the candidate casebook similar to failure symptom information.
In the present embodiment, the expression of kitchen tools fault case represents with a four-tuple, promptly kitchen tools fault case C=(E, S, A, P).
Wherein: kitchen tools fault explanation tuple E={e
1, e
2..., e
rBe that a finite nonempty set is closed e
j(j=1,2 ..., r) descriptive information of expression, for example: case numbering, kitchen tools model, trouble location, maintenance personal, maintenance date and effect assessment.
The disease million tuple S={s of kitchen tools fault case
1, s
2..., s
mBe that a finite nonempty set is closed, fault disease million is divided into qualitative fault disease million and quantitative fault disease million, fault disease million s
j={ f
j, d
j(j=1,2 ..., m), f
jBe qualitative fault disease million, as: during fuzzy concepts such as gas combustion range ignition difficulty, gas leakage or frowziness, d
jThe degree of confidence of expression fault disease million is used for illustrating the order of severity of fault disease million facts; d
jBe quantitative fault disease million, as combustion gas, temperature etc., d
jThe actual measured value of representing these parameters is through the value after changing, d
jBetween interval [0,1]; In the kitchen tools fault diagnosis field, some qualitative fault disease million is clear and definite amounts, promptly only show as " have " or " not having ", as fault, can handle with the method for two-valued function, " having " then is expressed as " 1 ", then is not expressed as " 0 ".But how qualitative fault disease million is some fuzzy quantities often, as ignition difficulties " seriously " and with " very serious ", steam line gas leakage " have a few " and " seriously " shakiness etc.Adopt the two-valued function method to handle and have certain deficiency, for this reason, can adopt the method for the fault order of severity to portray, and represent the degree that it is serious by giving a value, as shown in table 1:
| The fault order of severity | Fault disease million values |
| Very serious | 0.9 |
| Seriously | 0.7 |
| Medium | 0.5 |
| Generally | 0.3 |
| Slightly | 0.1 |
| Normally | 0 |
Table 1
The origin cause of formation tuple A={a of kitchen tools fault
1, a
2, a
lBe that a finite nonempty set is closed a
1, a
2..., a
rBe a certain origin cause of formation wherein, a certain fault can be by l fault cause;
The maintenance program tuple P={p of kitchen tools fault case
1, p
2..., p
oBe that a finite nonempty set is closed; p
1, p
2..., p
rBe a certain solution wherein.
In the present embodiment, the retrieval of kitchen tools fault case comprises, comprises two subprocess:
A. first order retrieval: determine the similar cases collection.At first, determine abstract case under it according to fault disease million information of kitchen tools fault target case, by fault disease million information of kitchen tools fault target case and fault disease million information in each abstract case are compared, size according to similarity, obtain concrete case with the abstract case representative of the fault disease million information similarity maximums of kitchen tools fault target case, finish first order index, if there is not similar abstract case, then directly retrieve the case in the abstract case of the 0th class (the 0th class case is meant that other case of case that peels off does not have similar place, independently becomes a case); Secondly, keywords such as the kitchen tools failure system by user's input, critical failure feature retrieve the case that meets the keyword condition again from the result of first order index, obtain the similar cases collection, finish second level index.
B. second level retrieval: determine candidate's casebook.Concentrate in similar cases, adopt nearest neighbor method,, find out and the most similar candidate's casebook of kitchen tools fault target case by the similarity that fault disease million information and the similar cases of calculating target case are concentrated fault disease million information of case based on grey relational grade.
The a kind of of the present invention's proposition is the similarity of calculating the kitchen tools fault case based on the nearest neighbor method of grey relational grade by adopting based on case 2-level search in the reasoning by cases kitchen tools method for diagnosing faults.The grey incidence coefficient of corresponding disease million indexs is the local similar degree in the fault characteristic information of the similarity calculating method of grey relational grade by calculating kitchen tools fault target case and the candidate's casebook, again the employing method calculated population similarity of getting local similar degree mean value.
Fig. 2 is gas-cooker fault case storehouse taxonomic structure figure.
In the present embodiment, when organizing gas-cooker fault case storehouse, the present invention adopts hierarchical structure, as gas-cooker fault case storehouse being divided into ignition failure case library, principal fault case library, the unusual case library of flame, each part can be divided into the experimental process part again again, can also be divided into supply air line case library, lighter case library, fire hole fault case storehouse as the ignition failure case library, as shown in Figure 4.Wherein, the case library of the bottom (as the supply air line case library) is only the concrete case of storage.
Fig. 3 is the secondary index structural drawing in gas-cooker fault case storehouse.
In the present embodiment, case in each case library is gathered into each classification in advance according to the size of similarity, case all has higher similarity in each classification, and the case similarity between different classes of is then less, and this division methods makes the case quantity in each classification less relatively.For one of each category construction can be represented the abstract case of such all cases, and the case in each classification is set up index, be used to identify these concrete cases and belong to which abstract case.
Suppose total M case in the gas-cooker fault case storehouse,, can be divided into M cluster, be i.e. M abstract case by cluster analysis.Comprise several concrete gas-cooker cases in each cluster, represent the characteristic distributions of case characteristic in each cluster respectively with abstract case, this M abstract case is as first order index.For each concrete case, set up index according to a certain or multinomial characteristic attribute (gas-cooker trouble location, critical failure feature) of gas-cooker fault case, thereby form second level index, as shown in Figure 5.
Abstract case is a case of representing the feature situation of case in this classification, and it was not present in the gas-cooker fault case storehouse originally, neither a complete case, and have only gas-cooker fault disease million collection, and do not have the solution collection.
In the present embodiment, described grading search is
(1), determines the abstract casebook similar:, determine and the most similar abstract casebook of gas-cooker fault target case fault disease million information according to the input of user to target case fault disease million information to fault disease million information of gas-cooker fault target case.
(2), determine the concrete case library of best abstract case representative: gas-cooker fault target case fault disease million information and disease million information in the abstract casebook that obtains are carried out the similarity coupling calculate, according to a certain minimum similarity threshold values, find one to the most similar abstract case of gas-cooker fault target case, and retrieve the concrete case library of this abstract case representative; If the similarity of all abstract cases all is lower than this threshold values, then retrieve the concrete case of the abstract case representative of the 0th class.
(3) determine the similar cases collection: the key feature attribute information according to user's input retrieves the similar cases collection that meets the demands again in the concrete case library that obtains from above.
(4) determine candidate's casebook, by the similarity of concentrating all case disease million information based on nearest neighbor method calculating gas-cooker fault target case fault disease million information and the similar cases of grey relational grade, determine and the most similar candidate's casebook of gas-cooker fault target case.
Illustrate: the 0th class case is meant the case that peels off herein, and the case that peels off is the core case often, and they can not be dropped, should be separately as a class (the 0th class).The case that peels off and other case dissmilarity.
3. kitchen tools fault case screening
Carry out the screening of kitchen tools fault case then, be step (3): if enough high through the similarity of a certain case in (2) candidate's casebook of obtaining of step, and other case similarities are all lower, then need not carry out the case screening, directly with it as the target faults case, enter (4) step; Otherwise call case screening module, carry out the case screening, obtain the target faults case, entered for (4) step again by case screening module.
The case screening is after retrieving one group of gas-cooker candidate casebook, and by case screening backup system, the user further checks and confirms the gas-cooker failure symptom in user's presence, excludes undesirable candidate's case.The strategy of case screening:, then do not carry out the case screening if the similarity of candidate's casebook is enough high; If the similarity of candidate's casebook and target case is generally lower, by the mode of man-machine combination, screening falls not meet the case of user's alternative condition from candidate's casebook.This causes the case of loss of ignition a lot of as gas-cooker, in candidate's casebook, whole cases can be shown, as battery case, high pressure output lead case, electrode needle case, electrode needle and some fire support apart from case, ignitor circuit plate case, the user gets rid of according to the feature that gas-cooker fault target case shows, such as determining that battery has electricity, just the battery case that the gas combustion range ignition that causes because of the cell voltage deficiency is failed can be screened.
4. maintenance program is reused and is revised
Maintenance program is reused: fault diagnosis server is passed the target faults case on the client computer back by the internet, maintenance personal or user keep in repair kitchen tools according to the maintenance program in the target faults case, if can solve the kitchen tools fault, entering for (6) step carries out fault case study; Otherwise changing for (5) step over to carries out the fault case correction.
In the present embodiment, maintenance program is reused the experience that is exactly with solution source case and is solved gas-cooker fault target case.The present invention adopts gas-cooker row barrier method to reuse.Again use the method for dealing with problems in the gas-cooker fault case storehouse in gas-cooker fault target case.As gas-cooker fault loss of ignition case, having been got rid of by the case screening is the reason of dead battery, and then adopt other fault solution of preserving in the gas-cooker fault case storehouse, as the lead that more renews fix a breakdown, the cleaning electrode pin, with electrode needle with some fire support distance adjustment to 3～4mm, in time change the ignitor circuit plate.
5. fault case correction
The fault case correction: fault diagnosis server calls case correction backup system, pass through interactive means, the technician of enterprise carries out the fault case correction, obtain new kitchen tools fault case, and then by passing back on the client computer by the internet, maintenance personal or user keep in repair kitchen tools according to the maintenance program in the target faults case, and till solving the kitchen tools fault, changing for (6) step over to carries out fault case study.
Sometimes loss of ignition is not to have a fault to cause, can not solve fault so a kind of row hinders method, and this just uses the case correcting module row's barrier scheme is made amendment, and hinders scheme in conjunction with two kinds or two or more rows, solves up to fault.
6. fault case correction
Fault case study: fault diagnosis server joins new kitchen tools fault case in the kitchen tools fault case storehouse; At the case assisted learning system ratio that the kitchen tools fault case solves physical fault is judged, if less than certain value, then with its deletion.
Through the retrieving of case, the CBR system select one the case of approximate coupling as the solution of system recommendation.Under normal conditions, this suggested design is fit to.If but case that retrieves and gas-cooker fault target case are approached inadequately, can not satisfy gas-cooker fault target case find the solution demand the time, just need carry out the correction of case, be template promptly, revise to adapt to the situation of gas-cooker fault target case with the gas-cooker fault candidate case that retrieves.The problem characteristic that gas-cooker fault target case is described is adjusted, revised, make its essential characteristic that more can reflect problem exactly, and approach to actual conditions.It is to case and the process of New understanding again of finding the solution problem in fact.The adjustment more complicated of case will be finished under expert's guidance in conjunction with domain knowledge and expert's experience, and reaches the correction effect of expection.
Case study is after new case produces, and it is estimated screening, otherwise the quality of case can reduce just in the case library, and the scale of case library can expand rapidly simultaneously, thereby reduces the Reasoning Efficiency of system.Can adopt following strategy that its learning behavior is controlled: the gas-cooker new case that forms in the diagnostic procedure is carried out value analysis, promptly calculate it with corresponding to the similarity of all court case of long standing examples in the case library.Have only when all similarities during all less than a certain given threshold value, new case just has been considered to have higher-value, allows that joining gas-cooker fault case falls in the storehouse: otherwise will not add.For the case that has existed in the gas-cooker fault case storehouse, can calculate the use value of each case, promptly add up the number of times that each case is cited, time that exists in conjunction with case again, obtain the reference frequency of each case, when this is worth less than certain threshold value, promptly represent not too big meaning of this case, this case can be deleted, thereby case library can be simplified.So just can guarantee that each case in the case library can both satisfy specific user's needs, and keep certain utilization rate.
Although above the illustrative embodiment of the present invention is described; so that the technician of present technique neck understands the present invention; but should be clear; the invention is not restricted to the scope of embodiment; to those skilled in the art; as long as various variations appended claim limit and the spirit and scope of the present invention determined in, these variations are conspicuous, all utilize innovation and creation that the present invention conceives all at the row of protection.
Claims (2)
1. kitchen tools method for diagnosing faults based on reasoning by cases is characterized in that may further comprise the steps:
(1), the kitchen tools failure problems is submitted to
Maintenance personal or user are according to the disease million that shows when the prior fault kitchen tools, and input fault sign information on client computer is as the input of kitchen tools failure problems; Failure symptom information by internet transmission to the fault diagnosis server of enterprise;
(2), kitchen tools fault case retrieval
Fault diagnosis server calculates the secondary grading search method of similarity by the nearest neighbor method that adopts grey relational grade from its kitchen tools fault case storehouse, retrieve the candidate casebook similar to failure symptom information:
(3), kitchen tools fault case screening
If the similarity through a certain case in (2) candidate's casebook of obtaining of step is enough high, and other case similarities are all lower, then need not carry out the case screening, directly with it as the target faults case, entered for (4) step; Otherwise call case screening module, carry out the case screening, obtain the target faults case, entered for (4) step again by case screening module;
(4), maintenance program is reused
Fault diagnosis server is passed the target faults case on the client computer back by the internet, and maintenance personal or user keep in repair kitchen tools according to the maintenance program in the target faults case, if can solve the kitchen tools fault, entering for (6) step carries out fault case study; Otherwise changing for (5) step over to carries out the fault case correction;
(5), fault case correction
Fault diagnosis server calls case correction backup system, pass through interactive means, the technician of enterprise carries out the fault case correction, obtain new kitchen tools fault case, and then by passing back on the client computer by the internet, maintenance personal or user keep in repair kitchen tools according to the maintenance program in the target faults case, and till solving the kitchen tools fault, changing for (6) step over to carries out fault case study;
(6), fault case study
Fault diagnosis server joins new kitchen tools fault case in the kitchen tools fault case storehouse; At the case assisted learning system ratio that the kitchen tools fault case solves physical fault is judged, if less than certain value, then with its deletion.
2. the kitchen tools method for diagnosing faults based on reasoning by cases according to claim 1 is characterized in that, described kitchen tools fault case is:
Kitchen tools fault case C=(E, S, A, P), wherein: kitchen tools fault explanation tuple E={e
1, e
2..., e
rBe that a finite nonempty set is closed e
j(j=1,2 ..., r) descriptive information of expression;
The disease million tuple S={s of kitchen tools fault case
1, s
2, s
mBe that a finite nonempty set is closed, fault disease million is divided into qualitative fault disease million and quantitative fault disease million, fault disease million s
j={ f
j, d
j(j=1,2 ..., m), f
jBe qualitative fault disease million, be used for illustrating the order of severity of fault disease million facts, d
jIt is quantitative fault disease million;
The origin cause of formation tuple A={a of kitchen tools fault
1, a
2..., a
lBe that a finite nonempty set is closed a
1, a
2..., a
rBe a certain origin cause of formation wherein, a certain fault can be by l fault cause;
The maintenance program tuple P={p of kitchen tools fault case
1, p
2..., p
oBe that a finite nonempty set is closed; p
1, p
2..., p
rBe a certain solution wherein.
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| CN111967614A (en) * | 2020-09-03 | 2020-11-20 | 中国联合网络通信集团有限公司 | Artificial intelligence learning method and device |
| CN111967614B (en) * | 2020-09-03 | 2024-02-06 | 中国联合网络通信集团有限公司 | Artificial intelligence learning method and device |
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