CN107806690B - A kind of domestic air conditioning method for diagnosing faults based on Bayesian network - Google Patents
A kind of domestic air conditioning method for diagnosing faults based on Bayesian network Download PDFInfo
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Abstract
The domestic air conditioner fault detection and diagnosis method based on Bayesian network that the present invention provides a kind of.The Bayesian network describes most of domestic air conditioner typical fault and its causality for relying on the complexity between occurrence condition and failure symptom structure-characterizedly；Conditional probability table representated by the directed edge of the Bayesian network quantitatively describes the probability value in causality.This method can merge etiologic diagnosis information and quantitative data, it makes full use of the knowledge experience of industry specialists and diagnoses the additional information of object, the efficiency and accuracy of fault diagnosis are improved, is realized diagnostic message is imperfect, carries out accurate fault diagnosis in uncertain situation.Bayesian network provided by the present invention being capable of effective checkout and diagnosis major part domestic air conditioner failure.
Description
Technical field
The invention belongs to air-conditioning system fault detection and diagnosis and artificial intelligence field, it is related to that information is uncertain, not comprehensive feelings
Knowledge reasoning under condition, more particularly to a kind of domestic air conditioner method for diagnosing faults and technology based on Bayesian network.
Background technique
With the generally raising of people's living standard, the usage amount of domestic air conditioner increases year by year, it has also become uses at present
Most commonly used air conditioning form.Since domestic air conditioner is in actual moving process, various failures may be generated, interior is influenced
Hot comfort simultaneously brings energy waste.Traditional air conditioner fault diagnosis scheme is exactly the expertise by engineer, to special
Industry competency profiling is higher, and time-consuming and laborious.Developing intellectual resource diagnostic tool is to field diagnostic efficiency is improved, and based on cloud
Domestic air conditioner fault detection and diagnosis, has great importance.
The operation of air-conditioning is dynamic, a time-varying, nonlinear process, and the form of expression of failure has uncertain
Property.Domestic air conditioner does not often record operation data, is not available physical modeling and carries out accurate description to its operation characteristic, needs
Novel fault detection and diagnosis algorithm is proposed in theoretic.
Bayesian network is one of current uncertain knowledge expression and the most effective theoretical model in reasoning field.It is description
Complicated influence relationship provides approach between domestic air conditioner failure and failure symptom, can describe between various stochastic variables
Relationship, pass through use for reference domain expert knowledge, practical experience and diagnostic reasoning thinking.
Summary of the invention
The present invention is intended to provide the domestic air conditioner method for diagnosing faults under a kind of information deficiency and uncertain condition.The party
Method is based on Bayes net algorithm, refines efficient Bayesian diagnostic network by building, and propose to be based on the Bayesian network
The fault detection and diagnosis process of network is realized and efficiently and rapidly carries out fault detection and diagnosis to domestic air conditioner.
In view of this, including following basic operation step the present invention is based on the domestic air conditioning method for diagnosing faults of Bayesian network
It is rapid:
S1: building is used for the Bayesian diagnostic network of domestic air conditioning fault diagnosis, and the diagnostic network is by additional information, failure
It is formed with sign three classes node, is established and contacted by directed edge according to air-conditioning failure mechanism and characteristic between three classes node, formed
Topological structure；Wherein each additional information node indicates a kind of O&M situation of domestic air conditioning；Each malfunctioning node indicates household
One potentially possible failure of air-conditioning；Each sign node indicates the sign that domestic air conditioning breaks down；
S2: the conditional probability between prior probability and node is arranged to node each in Bayesian diagnostic network；
S3: the prognostic information of on-site collection domestic air conditioner to be diagnosed is inputted Bayes when there is abnormal sign
Network；
S4: updating the posterior probability of each malfunctioning node, and finds the maximum two failure P of posterior probability1stAnd P2nd, wherein
P1st>P2nd；
S5: judge P1stAnd P2ndDifference whether be more than threshold value；If being more than, by P1stCorresponding failure output is used as should
Failure corresponding to abnormal sign；If not exceeded, then obtaining other for the attached of auxiliary judgment based on Cost Benefit Principle measurement
Add information or prognostic information, and after being re-entered Bayesian network, return step S4.
Further, other additional informations or prognostic information for being used for auxiliary judgment are obtained based on Cost Benefit Principle measurement
The specific method is as follows:
With failure P1stThe collection of relevant sign and additional information is combined into E1st, with failure P2ndRelevant sign and additional letter
The collection of breath is combined into E2nd, U=E1st∪E2nd, T=E1st∩E2nd, the supplementary set of subset T is C in UUT, to supplementary set CUAll indications in T
It obtains complexity with additional information to be ranked up, in-site measurement is easiest to the sign or additional information obtained, sentences for assisting
Disconnected domestic air conditioning failure.
Further, the threshold value is set as 30%.
Further, the posterior probability is calculated by prior probability and conditional probability by Bayesian formula.
Domestic air conditioner method for diagnosing faults based on Bayesian network of the invention, at least has the advantages that
(1) it is set out based on graph theory thought, constructs domestic air conditioner fault diagnosis Bayesian network, describe to qualitative and quantitative
Failure and its rely on causality and conditional relationship complicated between condition and sign performance；
(2) Bayesian network has incorporated priori knowledge and additional information carries out Multi-source Information Fusion, has diagnostic message
It is capable of the ability of efficiently fault diagnosis in imperfect uncertain situation；
(3) algorithm provided by the present invention can carry out Efficient fault diagnosis in information deficiency situation, can reduce air-conditioning
The cost of device fault diagnosis can be used in developing live auxiliary diagnostic tool and the remote diagnosis tool based on cloud.
Detailed description of the invention
Fig. 1 is the domestic air conditioner fault diagnosis network topological diagram established using Bayesian network.
Fig. 2 is the process for using figure of Bayes's fault detection and diagnosis algorithmic tool.
Specific embodiment
Further explaination and explanation are made to the present invention with reference to the accompanying drawings and examples.
Domestic air conditioning method for diagnosing faults based on Bayesian network the following steps are included:
(1) it by carrying out going deep into anatomy to mechanism such as domestic air-conditioning system failure, signs, is tieed up by expertise, scene
Personnel's experience, historic survey result etc. are repaired, typical fault list, the failure symptom list of domestic air conditioner are summarized.Sufficiently obtain
The O&M information for taking domestic air conditioner to be diagnosed, including the current operating status of air conditioner, air-conditioner repair record and operation note
Record etc., forms the additional information list of auxiliary diagnosis.
(2) tissue is carried out to additional information, fault message, prognostic information, is determined according to air-conditioning failure mechanism and characteristic attached
Add the causality between information and failure, failure and sign, above three list is abstracted as malfunctioning node, sign node respectively
Pass through directed edge according to the causality of aforementioned determination between three classes node with additional information node and establish connection, form topology
Structure.Wherein additional information node is indicated with C={ C1, C2 ... ..., Ct }, illustrates the O&M situation of domestic air conditioning；Each
Additional information node indicates a kind of O&M situation of domestic air conditioning, such as the filling maintenance event and domestic air conditioning of refrigerant
Maintenance record, such as maintenance of valve and the cleaning treatment of replacement, filter screen and heat exchanger.Malfunctioning node with F=F1,
F2 ... ..., Fn } to indicate, it expresses and diagnoses the potential possible breakdown of object, each malfunctioning node indicates one of domestic air conditioning
Potentially possible failure, such as lack of refrigerant, incoagulable gas, fouling of evaporator.Sign node with E=E1, E2 ... ...,
Em } it indicates, describe the common sign of domestic air conditioning failure for identification, evident information when for diagnosing, each sign
Node indicates that the sign that domestic air conditioning breaks down, such as evaporating temperature, condensation temperature, frozen air import and export the temperature difference.
Thus domestic air conditioner fault diagnosis bayesian network structure is established.Bayesian network is by directed acyclic graph and condition
What probability distribution table two parts were constituted, this two parts has respectively represented the structure and parameter of Bayesian network.Wherein, oriented no circle
Figure is its qualitative part, and conditional probability is its dosing section, is the premise and basis that data reasoning calculates.Directed acyclic graph is by saving
Point and directed edge composition.Node variable can be the abstract of any problem, can be discrete or continuous.Discrete Bayesian network
Each node is there are different values, the referred to as state of node in network, the most commonly used is two state of value (it is normal and abnormal, or
Be with it is no), also there are three above state.In our current research, mainly additional information, failure and failure symptom that node refers to,
Each node has different status and appearances, these status and appearances all can be considered an event；Directed edge between node represents
Cross correlation between node, and conditional probability can be used for describing the relationship strength between node.
(3) conditional probability in Bayesian diagnostic network between the prior probability and node of each node is determined.The present embodiment
In, the setting method of prior probability and conditional probability is as follows:
Firstly, can determine bayesian network structure by mechanism study and specificity analysis, opening up for network node is being determined
It flutters after sequence, it is thus necessary to determine that the conditional probability between the prior probability and node of each node.For setting for these probability numbers
It is fixed, according to the degree that people can distinguish, 7 grades are tentatively set by it: 90%, 75%, 60%, 50%, 25%, 10% and
1%, expert, domestic air conditioner operation maintenance personnel and the measuring technology personnel etc. in HVAC field are seeked advice from, it is allowed to select from above-mentioned 7 values
Most suitable probability numbers are selected, multiple expertises are integrated, preliminary assignment is carried out to all prior probabilities and conditional probability.
Then, by various air-conditioning system fault condition findings and practical operation situation of research object etc., to elder generation
Probability is tested further to be corrected.For example, the dirty stifled, fouling of evaporator of strainer, condenser fouling probability of happening 10%
Left and right, still, since air conditioner filter gauze is directly contacted with room air, and since its mesh is fine and closely woven, a possibility that blocking
Maximum, therefore 12% is set by its prior probability；And since the air that evaporator touches is to filter it by air conditioner filter gauze
Air afterwards, dust impurity is less, and the air that condenser touches is the outdoor air without any processing, and dust is more,
So the probability of condenser fouling is set as 11%, the probability of fouling of evaporator is set as 6%.
Finally, for the amendment and adjustment of conditional probability numerical value, need to carry out in terms of two, first is that utilizing various air-conditioning systems
The history data and investigation statistics data of system determine that the relationship between node is strong and weak；Second is that by specificity analysis, according to certain
On direct/indirect influence of different signs and the difference of influence sequence when failure occurs, relative size relationship is determined.For example,
Indoor fan blocks or when the failure such as belt slippage, will lead to the increase and evaporating temperature of the frozen air inlet and outlet temperature difference
Reduction, according to expertise, the two probability are all between 75% and 60%.But due to indoor fan once occur it is above-mentioned
Failure, most direct consequence are exactly that wind pressure reduces, and air-supply air quantity reduces, at this time evaporating temperature or normal value, evaporation and heat-exchange amount
Also normal, therefore the frozen air inlet and outlet temperature difference increases, to guarantee wind side heat exchange amount (cpM Δ t) is equal to evaporation and heat-exchange amount.When cold
When jelly air ports temperature difference increase is also not enough to make up heat exchange amount reduction in wind side caused by air-supply air quantity is reduced, evaporation and heat-exchange amount
It reduces, evaporating temperature reduces.So the conditional probability for causing the frozen air temperature difference to increase is wanted when fan trouble causes revolving speed to reduce
The slightly above conditional probability of evaporating temperature reduction.Certainly, the determination of prior probability and conditional probability can also be using others
Research method above are only the implementation in the present embodiment, be not intended to limit the present invention.
After the conditional probability that each sign occurs when the prior probability and failure that failure occurs occur determines, Bayes
Network algorithm updates the posterior probability of the node including malfunctioning node based on Bayes' theorem, following formula:
For example, Bi can indicate that a kind of failure, such as lack of refrigerant, incoagulable gas, fouling of evaporator, A can be with tables
Show that a kind of failure symptom, such as evaporating temperature, condensation temperature, frozen air import and export the temperature difference, the prior probability P that failure Bi occurs
(Bi) and A occurs in the case where Bi occurs conditional probability P (A | Bi) is by servicing manual, historical data investigation, specially
What family's experience and air-conditioning mechanism characteristic obtained.The size that posterior probability P (Bi | A) is calculated can be indicated when observing
A possibility that a certain failure occurs when sign A.
After completing above-mentioned diagnostic network model construction, real-time diagnosis can be carried out to domestic air conditioner failure.Utilize pattra leaves
This formula, it is current in known all or part of sign node state table, the faulty probability of happening of institute can be gone out with reasoning and calculation, with
This foundation made a definite diagnosis as further fault diagnosis or failure.
(4) when carrying out domestic air conditioning fault detection and diagnosis using Bayesian network, the fault diagnosis for using for reference industry specialists is thought
Dimension, by purposefully obtaining part of nodes state, realizes accurate fault diagnosis under less amount of diagnostic information.Referring to
It is the process for using figure of Bayes's fault detection and diagnosis algorithmic tool shown in Fig. 2.Specifically, carrying out fault detection and diagnosis
When, be primarily based on can live real-time collecting domestic air conditioner diagnose prognostic information, when there is no exception sign, it is believed that
Domestic air conditioner fault-free, normal operation.When there is abnormal sign, failure symptom node and the additional information section that can be obtained
The observation of point inputs Bayesian network, updates the posterior probability for the node that remaining is not observed, and finds posterior probability maximum
Two malfunctioning node P1stAnd P2nd, wherein P1st>P2nd, the most possible failure of the exception sign be the two failures it
One.
(5) judge P1st-P2ndWhether more than 30%, if being more than, show P1stFor most probable failure, therefore by P1stIt is right
The failure answered is exported as failure corresponding to the exception sign.If being less than 30%, P cannot be distinguished1stAnd P2ndBased on whichever
Want failure, it is therefore desirable to obtain other additional informations or prognostic information, further judge for assisting.And the acquisition of this type of information
Need based on Cost Benefit Principle, guarantee while considering cost minimization it is as efficient as possible, rapidly confirm failure.This
In embodiment, the acquisition methods of other additional informations or prognostic information are as follows:
With failure P1stThe collection of relevant sign and additional information is combined into E1st, with failure P2ndRelevant sign and additional letter
The collection of breath is combined into E2nd, U=E1st∪E2nd, T=E1st∩E2nd, the supplementary set of subset T is C in UUT, the element in the supplementary set are
It is all to distinguish failure P1stAnd P2ndSign or additional information.To supplementary set CUAll indications and additional information obtain difficult in T
Easy degree is ranked up, and the information for needing air-conditioning to tear machine open and can get can generally be considered as to the information of more difficult acquisition, and incite somebody to action
The information that can be obtained without tearing machine open is considered as the information for being easier to obtain.Selection is easiest to the sign or additional information obtained, scene
The observation of the information is measured, and after being re-entered Bayesian network, updates the posterior probability and failure of each node again
Node P1stAnd P2nd, the deterministic process of this step is repeated again, until exporting failure corresponding to the exception sign.
Above-mentioned embodiment is only a preferred solution of the present invention, so it is not intended to limiting the invention.Have
The those of ordinary skill for closing technical field can also make various changes without departing from the spirit and scope of the present invention
Change and modification.Therefore all mode technical solutions obtained for taking equivalent substitution or equivalent transformation, all fall within guarantor of the invention
It protects in range.
Claims (3)
1. a kind of domestic air conditioning method for diagnosing faults based on Bayesian network, it is characterised in that:
S1: building is used for the Bayesian diagnostic network of domestic air conditioning fault diagnosis, and the diagnostic network is by additional information, failure and sign
Million three classes nodes form, and pass through directed edge according to air-conditioning failure mechanism and characteristic between three classes node and establish connection, form topology
Structure；Wherein each additional information node indicates a kind of O&M situation of domestic air conditioning；Each malfunctioning node indicates domestic air conditioning
A potentially possible failure；Each sign node indicates the sign that domestic air conditioning breaks down；
S2: the conditional probability between prior probability and node is arranged to node each in Bayesian diagnostic network；
S3: the prognostic information of on-site collection domestic air conditioner to be diagnosed is inputted Bayesian network when there is abnormal sign；
S4: updating the posterior probability of each malfunctioning node, and finds the maximum two failure P of posterior probability1stAnd P2nd, wherein P1st>
P2nd；
S5: judge P1stAnd P2ndDifference whether be more than threshold value；If being more than, by P1stCorresponding failure output is used as the exception
Failure corresponding to sign；If not exceeded, then obtaining other additional letters for being used for auxiliary judgment based on Cost Benefit Principle measurement
Breath or prognostic information, and after being re-entered Bayesian network, return step S4；
It is described that other additional informations or prognostic information specific method for being used for auxiliary judgment are obtained based on Cost Benefit Principle measurement
It is as follows:
With failure P1stThe collection of relevant sign and additional information is combined into E1st, with failure P2ndRelevant sign and additional information
Collection is combined into E2nd,,, the supplementary set of subset T is in U, to supplementary setMiddle all indications
It obtains complexity with additional information to be ranked up, in-site measurement is easiest to the sign or additional information obtained, sentences for assisting
Disconnected domestic air conditioning failure.
2. the domestic air conditioning method for diagnosing faults based on Bayesian network as described in claim 1, it is characterised in that: described
Threshold value is set as 30%.
3. the domestic air conditioning method for diagnosing faults based on Bayesian network as described in claim 1, it is characterised in that: described
Posterior probability is calculated by prior probability and conditional probability by Bayesian formula.
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| JP7212245B2 (en) * | 2018-09-10 | 2023-01-25 | 日本電信電話株式会社 | diagnostic equipment |
| CN109740905B (en) * | 2018-12-26 | 2021-06-15 | 国网陕西省电力公司电力科学研究院 | Multivariate power grid secondary fault probability estimation method based on Bayesian model |
| CN111207484B (en) * | 2019-12-13 | 2021-01-19 | 浙江大学 | Central air-conditioning system fault diagnosis method based on object-oriented Bayesian network |
| CN111461497A (en) * | 2020-03-12 | 2020-07-28 | 许昌许继风电科技有限公司 | Wind turbine generator early warning method and system with intelligent diagnosis function |
| CN111319425A (en) * | 2020-03-18 | 2020-06-23 | 郑州新基业汽车电子有限公司 | Vehicle-mounted air conditioning system of electric automobile |
| US11625016B2 (en) | 2020-08-21 | 2023-04-11 | Siemens Industry, Inc. | Systems and methods for HVAC equipment predictive maintenance using machine learning |
| US11531669B2 (en) | 2020-08-21 | 2022-12-20 | Siemens Industry, Inc. | Systems and methods to assess and repair data using data quality indicators |
| CN112766047B (en) * | 2020-12-29 | 2023-03-21 | 广东麦德克斯科技有限公司 | Fault diagnosis method for refrigeration system and refrigeration device |
| CN113310171B (en) * | 2021-05-24 | 2022-04-29 | 浙江大学 | Central air-conditioning system fault detection and diagnosis method based on Bayesian network unit |
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