CN108519769A - A kind of rule-based flight control system method for diagnosing faults - Google Patents
A kind of rule-based flight control system method for diagnosing faults Download PDFInfo
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- CN108519769A CN108519769A CN201810311603.1A CN201810311603A CN108519769A CN 108519769 A CN108519769 A CN 108519769A CN 201810311603 A CN201810311603 A CN 201810311603A CN 108519769 A CN108519769 A CN 108519769A
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- knowledge
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Classifications
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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
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
- G05B23/0229—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions knowledge based, e.g. expert systems; genetic algorithms
-
- 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
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
- G05B23/0245—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a qualitative model, e.g. rule based; if-then decisions
-
- 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
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
- G05B23/0278—Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA
Abstract
The invention belongs to aircraft fault diagnosis technology fields, and in particular to a kind of rule-based flight control system method for diagnosing faults.The knowledge representation method using production rule tree+frame of the present invention indicates fault diagnosis knowledge, the learning machine as knowledge is combined using graphical rule tree modeling tool and Characters tool, the method of the present invention improves expression ability of the rule-based method for diagnosing faults to complicated knowledge, the structure speed of knowledge base is accelerated, and then enhances performance of fault diagnosis.
Description
Technical field
The invention belongs to aircraft fault diagnosis technology fields, and in particular to a kind of rule-based flight control system failure is examined
Disconnected method.
Background technology
The failure of core system of the flight control system as aircraft, system unit not only affects the property of flight control system
Can, can also be that the flight safety of aircraft brings great threat, intelligent Fault Diagnosis Technique, which is applied to aircraft, flies control system
In the fault diagnosis of system, auxiliary flight crew excludes flight control system failure in time, improves craft preservation efficiency, ensures aircraft
Safe flight, be currently have there is an urgent need to research contents.
The common method of fault diagnosis of flight control system has the fault diagnosis based on model, and the failure based on signal processing is examined
Disconnected and Knowledge based engineering fault diagnosis.
Fault diagnosis based on model is a kind of mathematical model diagnosing object by foundation, is output in by model practical defeated
Go out to calculate system residual error, by analyzing residual error to make a kind of method for diagnosing faults of level diagnosis to failure.But fly
The row device dynamical system complicated as one, many relevant mathematical models of component are difficult often to establish, and residual error amount is also
It can be interfered by many noises so that the application range of such method is limited by very large.
Method based on signal processing is that a kind of signal model by analyzing measurand completes the side of fault diagnosis
Method, commonly the method based on signal model have wavelet analysis method, principle component analysis etc..But the failure letter that such methods utilize
It ceases relatively simple, the uncertain problem in fault diagnosis can not be solved, cause to the diagnosis capability of complex fault relatively
It is weak.
Knowledge based engineering method for diagnosing faults is a kind of independent of mathematical model, and fault diagnosis knowledge is used by study
The method for completing fault diagnosis.Common Knowledge based engineering method for diagnosing faults has：Method based on expert system, based on fuzzy
The method of logic, the method based on grey relational grade, the method for case-based reasioning, is based on information at the method based on rough set
The method of fusion, the method based on artificial neural network, the method based on Bayesian network, the method based on fault tree and base
In the method etc. of support vector machines.Knowledge based engineering method for diagnosing faults since it does not depend on accurate mathematical model, so its
The scope of application is wider, but the acquisition of knowledge often influences the key factor of its performance of fault diagnosis.
Rule-based method for diagnosing faults is a kind of specific implementation form of expert system, be one kind in fault diagnosis
In apply to obtain very extensive method for diagnosing faults, the failure by the way that expert to be obtained to fault diagnosis Heuristics and many is examined
Disconnected factual knowledge is stored in the form of production rule in knowledge base, imitate the thinking of expert to the failure of flight control system into
Expression, acquisition and the utilization of row diagnostic knowledge are three key elements of knowledge processing.Common rule-based failure is examined
Disconnected method is often using the representation method of production rule in the representation of knowledge, but production rule is indicating there is knot
Exist when the fact that structure knowledge greatly difficult.Its common knowledge acquisition method is by knowledge engineer by using solid
The table for the formula that fixes or the mode typing filled a vacancy, this strong influence knowledge base establishes speed, and some complexity are known
The ability to express of knowledge is weaker.
Invention content
The purpose of the present invention is indicated aiming at the above problem using the knowledge representation method of production rule tree+frame
Fault diagnosis knowledge is combined the learning machine as knowledge using graphical rule tree modeling tool and Characters tool, carries
Go out a kind of rule-based flight control system method for diagnosing faults.
The technical solution adopted in the present invention is：
A kind of rule-based flight control system method for diagnosing faults, which is characterized in that include the following steps：
A, knowledge base is built：
The procedural knowledge of fault diagnosis, i.e. patrolling between failure symptom and failure are indicated in the form of production rule tree
The relationship of collecting；
With the fact that frame representation failure symptom and failure sex knowledge, the definition institute to failure symptom, failure is included at least
The fact that need sex knowledge；
Based on production rule tree, frame is that daughter element builds knowledge base, forms the relational database of stored knowledge
Database model；
B, the knowledge learning machine that knowledge base matches in foundation and step a, i.e. knowledge learning machine have graphical regular number
Modeling and Characters function, the graphical regular number modeling are used for the logical relation between typing failure symptom and failure,
Generate production rule number；The Characters are used for the fact that typing fault diagnosis sex knowledge, that is, generate frame；
C, flight control system fault diagnosis knowledge is entered into knowledge base using knowledge learning machine, flight control system event
Barrier diagnostic knowledge includes the empirical knowledge and other factual knowledges of expert；
D, it is made inferences by the inference machine based on logical relation between failure symptom and failure, obtains fault diagnosis knot
By.
Further, the specific method of the step d is：
The inference mode of inference machine is searched out using mixed inference mode that is positive and being inversely combined, first forward reasoning
The production rule for needing further reasoning to confirm, then by way of backward inference according to Strategy of Conflict Resolution in conjunction with known to
Failure symptom completes entire reasoning process；
The Strategy of Conflict Resolution is the mode in conjunction with subregion and priority, i.e., in subregion of the failure symptom where it
Fault diagnosis reasoning is carried out, the fault diagnosis reasoning between different subregions is independent of each other, and in same subregion, priority is higher
Rule initially enter reasoning process, combine existing fault diagnosis to enter next priority using the new fact of its generation
Reasoning.
Further, further include step：
E, using the explanation engine based on text interpretation method and tracing, by text interpretation method using in knowledge base
Factual knowledge, the definition, description of the fact used, pass through tracing and track inference machine in explaining the process of reasoning to user
Reasoning process generates fault reasoning decision tree, the reasoning process of current conclusion is explained to user.
Beneficial effects of the present invention are that method of the invention improves rule-based method for diagnosing faults to complicated knowledge
Expression ability, accelerate the structure speed of knowledge base, and then enhance performance of fault diagnosis.
Description of the drawings
Fig. 1 is the fault diagnosis system structure chart of embodiment；
Fig. 2 is that flow is embodied in the method for diagnosing faults of embodiment；
Fig. 3 is the production rule tree representation of the knowledge of embodiment.
Specific implementation mode
With reference to embodiment and attached drawing, detailed description of the present invention technical solution：
Embodiment
As shown in Figure 1, for the system framework figure of this example, wherein：
Human-computer interaction interface：It is responsible for system and the human-computer interaction before expert, engineering, expert will by human-computer interaction interface
Fault diagnosis knowledge is entered among system, and engineering completes fault diagnosis by using human-computer interaction interface calling system function,
Fault diagnosis conclusion is fed back to engineering by system by human-computer interaction interface.
Knowledge base：Fault diagnosis knowledge of the knowledge base for storing flight control system fault diagnosis, including expert's are empirical
Knowledge and other factual knowledges.Knowledge is indicated in knowledge base in the form of production rule tree+frame.
Learning machine：Learning machine is responsible for the Heuristics of expert and other fault diagnosis factual knowledges being entered into knowledge base
In.
Inference machine：Inference machine is responsible for carrying out fault diagnosis reasoning under the control of the inference strategy set, finally obtains
Fault diagnosis conclusion.
Explanation engine：Explanation engine is responsible for solving the reasoning process of release system to user, makes its reasoning process transparence.
The workflow of this example is：
Step 1：Indicate the procedural knowledge of fault diagnosis in the form of production rule tree, i.e., failure symptom and failure it
Between logical relation.With the fact that frame representation failure symptom and failure sex knowledge, determine including to failure symptom, failure
True sex knowledge needed for a series of fault diagnosises such as justice, description, as shown in table 1.Based on production rule tree, frame is
Daughter element builds knowledge base, forms the database model of the relational database of stored knowledge, as shown in Figure 3.
The frame representation of 1 knowledge of table
Step 2：The knowledge learning machine that design configuration rule tree models and Characters are combined.Characters tool master
The fact that be responsible for typing fault diagnosis sex knowledge, such as the definition to failure symptom, failure.Graphical rule tree modeling tool
The logical relation i.e. procedural knowledge of diagnostic reasoning being mainly responsible between typing failure symptom and failure.
Step 3：Design the inference machine that logical relation is built based on failure symptom and failure.Using based on subregion+priority
Strategy of Conflict Resolution.Using the mode of subregion, failure symptom is made to carry out fault diagnosis reasoning in the subregion only where it, it is different
Fault diagnosis reasoning between subregion is independent of each other.In same subregion, in such a way that Different Rule priority is set, make excellent
The first higher rule of grade initially enters reasoning process, combines existing fault diagnosis to enter using the new fact of its generation next
The reasoning of priority.The inference mode of inference machine is using mixed inference mode that is positive and being inversely combined, first forward reasoning
The production rule for needing further reasoning to confirm is searched out, then according to Strategy of Conflict Resolution knot by way of backward inference
It closes known true (failure symptom) and then completes entire reasoning process.
Step 4：Design the explanation engine based on text interpretation method and tracing.Text interpretation method mainly utilizes knowledge base
In true sex knowledge, definition, the description etc. of the fact used in explaining the process of reasoning to user.Tracing be mainly responsible for
The reasoning process of track inference machine generates fault reasoning decision tree, and then the reasoning process of current conclusion is explained to user.
Step 5：Fault diagnosis knowledge is entered into system knowledge base by man-machine interface using knowledge learning machine by expert,
After completing the diagnostic knowledge typing of certain failure, engineering is interacted by man-machine interface, operating system with flight control computer, reading machine
The failure symptom that monitoring device is recorded is carried, system, which is finally fed back the diagnosis of failure using inference machine and explanation engine, to be shown
Onto man-machine interface, and then complete the diagnosis of the failure.
Claims (3)
1. a kind of rule-based flight control system method for diagnosing faults, which is characterized in that include the following steps：
A, knowledge base is built：
The procedural knowledge of fault diagnosis is indicated in the form of production rule tree, i.e., the logic between failure symptom and failure is closed
System；
With the fact that frame representation failure symptom and failure sex knowledge, include at least needed for the definition to failure symptom, failure
Factual knowledge；
Based on production rule tree, frame is that daughter element builds knowledge base, forms the number of the relational database of stored knowledge
According to library model；
B, the knowledge learning machine that knowledge base matches in foundation and step a, i.e. knowledge learning machine have graphical regular number modeling
With Characters function, the graphical regular number modeling is given birth to for the logical relation between typing failure symptom and failure
At production rule number；The Characters are used for the fact that typing fault diagnosis sex knowledge, that is, generate frame；
C, flight control system fault diagnosis knowledge is entered into knowledge base using knowledge learning machine, the flight control system failure is examined
Disconnected knowledge includes the empirical knowledge and other factual knowledges of expert；
D, it is made inferences by the inference machine based on logical relation between failure symptom and failure, obtains fault diagnosis conclusion.
2. a kind of rule-based flight control system method for diagnosing faults according to claim 1, which is characterized in that the step
Suddenly the specific method of d is：
The inference mode of inference machine searches out needs using mixed inference mode that is positive and being inversely combined, first forward reasoning
The production rule that further reasoning confirms, then according to Strategy of Conflict Resolution combination known fault by way of backward inference
Sign completes entire reasoning process；
The Strategy of Conflict Resolution is the mode in conjunction with subregion and priority, i.e., is carried out in subregion of the failure symptom where it
Fault diagnosis reasoning, the fault diagnosis reasoning between different subregions are independent of each other, and in same subregion, the higher rule of priority
Reasoning process is then initially entered, combines existing fault diagnosis to enter pushing away for next priority using the new fact of its generation
Reason.
3. a kind of rule-based flight control system method for diagnosing faults according to claim 2, which is characterized in that further include
Step：
E, using the explanation engine based on text interpretation method and tracing, the fact in knowledge base is utilized by text interpretation method
Sex knowledge, the definition, description of the fact used, pass through the reasoning that tracing tracks inference machine in explaining the process of reasoning to user
Process generates fault reasoning decision tree, the reasoning process of current conclusion is explained to user.
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| CN114545907A (en) * | 2022-03-15 | 2022-05-27 | 中南大学 | Fault detection method of flight control system based on filter |
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