CN104486096A - Inference method based on decision tress of industrial Ethernet fault diagnosis method - Google Patents
Inference method based on decision tress of industrial Ethernet fault diagnosis method Download PDFInfo
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Abstract
The invention discloses an inference method based on a decision tress of an industrial Ethernet fault diagnosis method. The decision tree comprises a decision attribute node, an attribute value branch and a leaf node, wherein the decision attribute node is a set of classified decision attributes, the attribute value branch is a set of the attribute values of which the value using characteristics are further divided according to the decision attributes, and the leaf node is a set of decisions or classification results. The method has the advantages that the effective inference decision is provided for the industrial Ethernet fault diagnosis, and the accurate implementation and working of the industrial Ethernet fault diagnosis are guaranteed.
Description
Technical field
The present invention relates to Industrial Ethernet Control System technical field, particularly relate to a kind of inference method based on decision tree in Industrial Ethernet method for diagnosing faults.
Background technology
PROFINET is released by PROFIBUS international organization (PROFIBUS International, PI), is the automation bus standard of a new generation based on industrial Ethernet technology.As a strategic technological innovation; PROFINET is that automated communication field provides a complete Networking Solutions & provisioned; enumerate the much-talked-about topic of the current automatic fields such as such as real-time ethernet, motion control, distributed automatization, failure safe and network security; and; as the technology across supplier; can complete compatible Industrial Ethernet and existing fieldbus (as PROFIBUS) technology, protection existing investment.
Between in the past several years, the scale of industrial machine network experienced by explosive growth.The application of network has been deep into each corner of people's production, becomes requisite infrastructure.Along with the reinforcement to mesh dependence, the reliability of people to network it is also proposed higher requirement: the first, has stable, efficient, safe network environment: the second, when network failure, can detect failure cause timely and repair.Can find out, network fault diagnosis has great importance to keeping the health status of network.But under current network environment, network fault diagnosis encounters unprecedented difficulty, and it is mainly manifested in the following aspects; Controller network no matter from scale, or has had huge development from network complexity and business diversity.The fault relationship of large scale network is intricate, and the corresponding relation between failure cause and phenomenon of the failure is fuzzy, substantially increases the difficulty of failure diagnosis.
The complexity of the network equipment also improves the difficulty of failure diagnosis.The complexity of the network equipment has two implications: first is that the new network equipment is constantly released, and function gets more and more, and becomes increasingly complex; Second is equipment supplier's One's name is legion, product specification and standard disunity.
Along with the extensive use of PROFINET, controller technology and the network communications technology are developed rapidly, and new digital communications network not only has multiple business flow, and have employed the network transmission technology of multiple fusion.The new network of continuous employing proposes more and more higher requirement to network fault diagnosis; Just because of the existence of above-mentioned difficulties, the failure diagnosis that traditional dependence digerait manual type is carried out can not have been satisfied the demand.Modern Network calls intelligentized fault diagnosis technology, to realize the automation of network fault diagnosis, people is freed from heavy diagnostic work.
Intelligent network fault diagnosis technology has the difficult point of following four aspects:
The first, the dynamic change of the uncertainty that fault discovery network failure occurs and network hardware and Software Architecture, makes the knowledge comprising expert receive limitation.
The second, fault location equipment produces fault can affect the equipment or subsystem that are much connected with it, and even can cause the paralysis of network, this phenomenon is just called fault correlation.
3rd, the fault detection method of fault detect routine needs founding mathematical models, and the complexity of Mathematical Modeling and accuracy are difficult to the real-time requirement meeting express network; The Mathematical Modeling simplified causes again Actual Control Effect of Strong can not be satisfactory.
4th, representation for fault, due to the diversity of network application and continuous renewal, can't find a clear and definite function can represent all application layer faults now.
Summary of the invention
The object of this invention is to provide a kind of inference method based on decision tree in Industrial Ethernet method for diagnosing faults, effective inductive decision can be provided for Industrial Ethernet failure diagnosis, ensure accurate enforcement and the work of Industrial Ethernet failure diagnosis.
The technical solution used in the present invention is:
A kind of inference method based on decision tree in Industrial Ethernet method for diagnosing faults, described decision tree comprises decision attribute node, property value branch and leaf node, decision attribute node is the set carrying out the decision attribute of classifying, property value branch is the set of the property value of value characteristic according to decision attribute Further Division, and leaf node is the set of decision-making or classification results;
The inference method of decision tree comprises the following steps:
A: first, root node carries out the comparison of decision attribute and property value thereof;
A1: when the decision attribute on certain decision attribute of the known fact and property value and root node thereof and certain property value thereof match, then search for downward-extension along this property value branch, arrive next node;
A2: if all properties of own county magistrate's reality does not all mate with the attribute on root node, then reasoning process terminates, provides known true deficiency, cannot carry out reasoning;
A3: if own county magistrate real in decision attribute mate with the decision attribute on root node, but own county magistrate real in property value and the property value Incomplete matching on root node, then basis closes on principle most, and the maximum branch of selection matching degree is searched for downwards, arrival new node;
B: if present node is the leaf node of decision tree, then provide the conclusion comprised in leaf node, decision tree reasoning process terminates;
C: if present node is not leaf node, then carry out the comparison of decision attribute and property value thereof thereon, to determine the branch to downward-extension, has following 3 kinds of situations usually:
(1) when on certain decision attribute of own county magistrate's reality and property value and present node thereof, decision attribute and certain property value thereof match, the branch just along this property value is searched for downward-extension, thus arrives next node;
(2) if having the attribute on attribute and present node to match in own county magistrate reality, but their property value does not mate, then according to closing on most principle, selecting the maximum branch of matching degree to search for downwards, arriving new node;
(3) if all properties of own county magistrate's reality does not all mate with the attribute on present node, then provide known true deficiency, cannot carry out reasoning, reasoning process terminates;
D: recurrence carry out above-mentioned steps A and step C, until all properties has mated.
The present invention carries out reasoning according to the decision tree of setting up, decision tree comprises decision attribute node, property value branch and leaf node, decision attribute node is the set carrying out the decision attribute of classifying, property value branch is the set of the property value of value characteristic according to decision attribute Further Division, and leaf node is the set of decision-making or classification results; Mated with the decision attribute on root node and certain property value thereof by certain decision attribute of the known fact and property value thereof, provide the conclusion comprised in leaf node after the match is successful, decision tree reasoning process terminates; If present node is not leaf node, then carry out the comparison of decision attribute and property value thereof thereon, to determine the branch to downward-extension, usually have following 3 kinds of situations:
(1) when on certain decision attribute of own county magistrate's reality and property value and present node thereof, decision attribute and certain property value thereof match, the branch just along this property value is searched for downward-extension, thus arrives next node;
(2) if having the attribute on attribute and present node to match in own county magistrate reality, but their property value does not mate, then according to closing on most principle, selecting the maximum branch of matching degree to search for downwards, arriving new node;
(3) if all properties of own county magistrate's reality does not all mate with the attribute on present node, then provide known true deficiency, cannot carry out reasoning, reasoning process terminates.
Utilize this method can provide effective inductive decision for Industrial Ethernet failure diagnosis, ensure accurate enforcement and the work of Industrial Ethernet failure diagnosis.
Accompanying drawing explanation
Fig. 1 is FB(flow block) of the present invention;
Fig. 2 is flow chart of the present invention.
Embodiment
As shown in Figure 1, decision tree of the present invention comprises decision attribute node, property value branch and leaf node, decision attribute node is the set carrying out the decision attribute of classifying, property value branch is the set of the property value of value characteristic according to decision attribute Further Division, and leaf node is the set of decision-making or classification results;
The inference method of decision tree comprises the following steps:
A: first, root node carries out the comparison of decision attribute and property value thereof;
A1: when the decision attribute on certain decision attribute of the known fact and property value and root node thereof and certain property value thereof match, then search for downward-extension along this property value branch, arrive next node;
A2: if all properties of own county magistrate's reality does not all mate with the attribute on root node, then reasoning process terminates, provides known true deficiency, cannot carry out reasoning;
A3: if own county magistrate real in decision attribute mate with the decision attribute on root node, but own county magistrate real in property value and the property value Incomplete matching on root node, then basis closes on principle most, and the maximum branch of selection matching degree is searched for downwards, arrival new node;
B: if present node is the leaf node of decision tree, then provide the conclusion comprised in leaf node, decision tree reasoning process terminates;
C: if present node is not leaf node, then carry out the comparison of decision attribute and property value thereof thereon, to determine the branch to downward-extension, has following 3 kinds of situations usually:
(1) when on certain decision attribute of own county magistrate's reality and property value and present node thereof, decision attribute and certain property value thereof match, the branch just along this property value is searched for downward-extension, thus arrives next node;
(2) if having the attribute on attribute and present node to match in own county magistrate reality, but their property value does not mate, then according to closing on most principle, selecting the maximum branch of matching degree to search for downwards, arriving new node;
(3) if all properties of own county magistrate's reality does not all mate with the attribute on present node, then provide known true deficiency, cannot carry out reasoning, reasoning process terminates;
D: recurrence carry out above-mentioned steps A and step C, until all properties has mated.
Need given expert system in Industrial Ethernet method for diagnosing faults, expert system comprises knowledge base, inference machine, KBM module, knowledge acquisition module, explanation engine and control centre.Wherein, the operation principle of inference machine completes, so the work of inference machine is the reasoning process of decision tree, complements each other between the reasoning of decision tree and knowledge base based on the supposition of decision tree.
Expert system comprises two essential parts: knowledge base and inference machine, and knowledge base is made up of global data base and rule base.Global data base is one group of set describing the symbol of process handling object.When processing particular problem, it describes for problem and environment describes, and comprises the various temporary informations relevant with particular problem.Usually global data base is called short-term memory device.To global data base tissue, data presentation technique etc., system does not have concrete regulation, generally selects suitable method for expressing according to the feature of problem domain, as set, linear list, chained list, tree structure, figure etc. can be used for representing the data in global data base.When setting up global data base, should note making data in storehouse be convenient to retrieval.Rule base is made up of one group of diagnostic rule.Based in the fault diagnosis system of decision tree, regular condition part asserting normally about some data in global data base, and conclusion part is generally the reason that causes this prerequisite to assert or this prerequisite asserts that the follow-up similar prerequisite that will occur is different from global data base, knowledge in rule base is not about a certain concrete particular problem, but for whole field question.Compared with global data base, rule base is relatively stable, so claim rule base to be long-term memory device.In general, when the method for expressing of selective rule, should note, if possible, data representation format in the representation of condition part and conclusion part and global data base is consistent, the content being convenient to condition part and global data base like this compares, and whether criterion part is set up.Under the prerequisite of effective expression problem domain knowledge, make the expression of condition part and conclusion part simplify as far as possible, be convenient to inference machine processing rule.Inference machine is responsible for that the condition part of rule base and global data base content are compared one and is commonly referred to and mates, if the match is successful, and display conclusion part.Specifically, inference machine is according to the current information of global data base, determine the strictly all rules that can match under current state, these rules are claimed to be triggering rule, a rule is selected again from the rule be triggered, become and enable rule, inference machine performs and enables rule, and according to enabling the action part amendment global data base of rule, global data base through changing can trigger new rule again, thus problem solving proceeds to NextState, so repeatedly, to realize finally solving of a problem.Due under each state of problem solving, by more than one of the rule possibility that global data base is mated, need inference machine to adopt suitable control strategy to be activated to select which bar triggering rule actually, this process is called conflict resolution.Namely the work of inference machine operate, till dealing with problems with three loop cycle of this " mating conflict resolution one operation ".
The built-in attribute node of decision tree, property value branch and leaf node constitute a kind of tree form data structure.Generate a decision tree by the learning algorithm of decision tree, it just can realize classifying to unknown example or carrying out Analysis of Policy Making.Therefore can think and contain certain knowledge in the decision tree that a study completes, that is, decision tree has the ability expressing knowledge.The representation of knowledge is a kind of agreement describing expertise, so that the data structure becoming machine to process the representation of knowledge of the mankind.Good representation of knowledge form not only can improve validity and the operation efficiency of knowledge store, and can improve the Reasoning Efficiency of intelligent system.The internal node of decision tree is the set of attribute, and branch is the set of property value, and leaf node is the set of decision-making or classification results.Decision tree is exactly utilize attribute and value thereof to represent the condition part of knowledge, and represents the conclusion part of knowledge with leaf node, thus expertise is showed with the form of decision tree.Article one, decision tree classification rule is exactly determine a knowledge of failure modes decision-making, that is can judge the type of fault based on this knowledge.This utilizes decision tree to set up the basic foundation of Network Fault Diagnosis Expert System knowledge base just.
The limit that decision tree is made up of the attribute node of inside, property value and forming for the leaf node of saving result, so its reasoning process is exactly the process that the knowledge that utilizes its inside to contain carries out problem solving.That is, decision tree reasoning is exactly from root node, by the comparison carrying out attribute and value thereof on internal node repeated, determines the branch of decision tree to downward-extension (namely searching for), final arrival leaf node, obtains desired conclusion (reasoning process of decision tree terminates).In fact, the reasoning process of decision tree is exactly the process traveled through by depth-first strategy decision tree, as long as arrive leaf node, and its reasoning of constipation bundle (or traversal) process.
In conjunction with knowledge base, the reasoning process the following detailed description of decision tree:
Step one, using the current information of global data base in knowledge base as root node, determine the conditional plan in the strictly all rules storehouse that can match under current state, the conditional plan claiming these to mate is triggering rule.
Step 2, then according to control strategy, from triggering rule, select a rule, become and enable rule.
Step 3, inference machine performs and enables rule.
Step 4, according to the action of enabling rule, amendment global data base.
Step 5, the global data base through changing can trigger new rule again, thus problem solving proceeds to NextState.Step 6, so repeatedly, to realize finally solving of a problem, reasoning is complete.
As mentioned before, when finding matched rule according to the fact in the process of reasoning, if having and only have a rule the match is successful, then system can directly perform this rule; But the rule that often the match is successful more than one, at this moment can must carry out conflict resolution, chooses one and perform from many rules.Expert system due to this project adopts the knowledge acquisition mechanism based on decision tree, and each rule of acquisition correspond in a database by the frequency used.The rule that so frequency of utilization is high, the possibility existed in network event is also larger.Here it is adopts support sequence to carry out the basic thought of conflict resolution.The concrete way of conflict resolution when the match is successful for many rules, all rules that the match is successful is all arranged by by the frequency order from big to small used, choose the rule that support is the highest, sets up its rule objects to perform next step reasoning process.
Control strategy mainly solves the choice and application order of the functional module of whole problem solving process, namely determine first what does, after what does, and do different work respectively according to the current state of problem solving, can also determine once occur how abnormal conditions process.System has two flow processs: first flow process is that inference machine goes reasoning failure cause according to fault performance, if the failure cause diagnosed out is incorrect or can not diagnose reason, namely be a new failure cause, keeper oneself is so just needed to judge to solve, fault performance and failure cause can be input to the problem base in knowledge base after maintenance to be verified, then call knowledge acquisition module renewal diagnostic rule table.Second flow process be when the record in knowledge base is accumulative reach some after just can occur, at this moment have enough data to be used for producing and new to be more suitable for present rule and new and old rule and delete the data lost efficacy.System needs the record number of statistics after refreshing one's knowledge storehouse last time in knowledge base, whether reaches the number of specifying.Specify number if do not reached, so continue statistics, if reached, call knowledge acquisition module and adopt the Decision Tree Algorithm of native system proposition to excavate the diagnostic knowledge made new advances, to refresh one's knowledge storehouse, and using the out-of-service time of update time last time as next update, using the last time update time of current time as next update.
Claims (1)
1. the inference method based on decision tree in Industrial Ethernet method for diagnosing faults, it is characterized in that: described decision tree comprises decision attribute node, property value branch and leaf node, decision attribute node is the set carrying out the decision attribute of classifying, property value branch is the set of the property value of value characteristic according to decision attribute Further Division, and leaf node is the set of decision-making or classification results;
The inference method of decision tree comprises the following steps:
A: first, using the current information of global data base in knowledge base as root node, root node carries out the comparison of decision attribute and property value thereof;
A1: when the decision attribute on certain decision attribute of the known fact and property value and root node thereof and certain property value thereof match, then search for downward-extension along this property value branch, arrive next node;
A2: if all properties of own county magistrate's reality does not all mate with the attribute on root node, then reasoning process terminates, provides known true deficiency, cannot carry out reasoning;
A3: if own county magistrate real in decision attribute mate with the decision attribute on root node, but own county magistrate real in property value and the property value Incomplete matching on root node, then basis closes on principle most, and the maximum branch of selection matching degree is searched for downwards, arrival new node;
B: if present node is the leaf node of decision tree, then provide the conclusion comprised in leaf node, decision tree reasoning process terminates;
C: if present node is not leaf node, then carry out the comparison of decision attribute and property value thereof thereon, to determine the branch to downward-extension, has following 3 kinds of situations usually:
(1) when on certain decision attribute of own county magistrate's reality and property value and present node thereof, decision attribute and certain property value thereof match, the branch just along this property value is searched for downward-extension, thus arrives next node;
(2) if having the attribute on attribute and present node to match in own county magistrate reality, but their property value does not mate, then according to closing on most principle, selecting the maximum branch of matching degree to search for downwards, arriving new node;
(3) if all properties of own county magistrate's reality does not all mate with the attribute on present node, then provide known true deficiency, cannot carry out reasoning, reasoning process terminates;
D: recurrence carry out above-mentioned steps A and step C, until all properties has mated.
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