CN108509732A - Appraisal procedure, terminal device and the storage medium of steam turbine fault severity level - Google Patents
Appraisal procedure, terminal device and the storage medium of steam turbine fault severity level Download PDFInfo
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- CN108509732A CN108509732A CN201810303637.6A CN201810303637A CN108509732A CN 108509732 A CN108509732 A CN 108509732A CN 201810303637 A CN201810303637 A CN 201810303637A CN 108509732 A CN108509732 A CN 108509732A
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Abstract
The present invention relates to a kind of appraisal procedures of steam turbine fault severity level, include the following steps：S1, it obtains according to the grouped data of steam turbine fault severity level；S2, characteristic set A is divided into two character subsets：Monotonic characteristics subset AmWith nonmonotonic character subset Anm；S3, from monotonic characteristics subset AmMiddle calculate obtains best monotonic characteristics subset Amp；S4, from nonmonotonic character subset AnmWith best monotonic characteristics subset AmpMiddle calculate obtains best character subset Ap；S5, the sample set U={ x based on steam turbine1,x2,...,xn, obtain ApAll features, with the severity D={ d of failure1,d2,...,dkIt is classification, as the training sample of grader, structure trains a disaggregated model, and training result is denoted as Me；S6, using model M e as the assessment models of fault severity level, the data for treating judge are classified, and classification results are also in D={ d1,d2,...,dkIn, the severity of this i.e. corresponding failure.
Description
Technical field
The invention belongs to the application fields of fault detect, more particularly to a kind of assessment of the severity of steam turbine failure
Method, terminal device and storage medium.
Background technology
Steam turbine is widely used in as important power-equipment in power plant, and to the basic theory of the operation of steam turbine and
Research work in terms of application technology carries out condition monitoring and fault diagnosis especially for the chief unit in production,
Grasp equipment running status promptly and accurately predicts its following development trend, has achieved significant economic benefit and society
Benefit.
Modern production requires to ensure the safety of enterprise to the greatest extent, and steam turbine is as core active force equipment, a hair
Raw failure, with regard to horse back maintenance down, this is all very big economic caused by production and safety influence.But the failure of steam turbine is
Gradually develop, there is different severity information, production division be more desirable to can be according to equipment running status exception or morning
The failure symptom of phase carries out purposive maintenance by the severity and development trend of failure.Obtain the severity letter of failure
Breath can help user to understand the development trend of equipment state, formulate rational maintenance policy and maintenance solution.In equipment state
A large amount of sensing datas in monitoring provide important scientific basis for the identification of equipment fault degree.It is examined in actual failure
In disconnected problem, in order to preferably carry out fault diagnosis research, with greater need for the severity information for considering failure.This maintenance mode
It is more scientific and reasonable, the time of maintenance of equipment can be shortened, improve utilization rate of equipment and installations, reduce equipment downtime, extend equipment
Life cycle is enhanced one's market competitiveness.
Steam turbine fault degree identification it is extremely difficult, and fault degree identification be fault diagnosis field new challenge and
One special duty.
Invention content
The present invention is intended to provide a kind of appraisal procedure of the severity of steam turbine failure, to solve effectively know at present
The problem of severity of other steam turbine failure.For this purpose, the specific technical solution that the present invention uses is as follows：
A kind of appraisal procedure of steam turbine fault severity level, includes the following steps：
S1：The grouped data according to steam turbine fault severity level is obtained, the sample set of steam turbine is denoted as U={ x1,
x2,...,xn, the characteristic set of these samples is denoted as A={ a1,a2,...,aj, sample each in this way is described by A, and therefore
The severity of barrier is D={ d1,d2,...,dk, to sample xiIn feature ajValue on ∈ A and the severity D of failure
It is denoted as v (x respectivelyi,aj) and v (xi,D)；
S2：Characteristic set A is divided into two character subsets：Monotonic characteristics subset AmWith nonmonotonic feature
Subset Anm；
S3：From monotonic characteristics subset AmMiddle calculate obtains best monotonic characteristics subset Amp；
S4：From nonmonotonic character subset AnmWith best monotonic characteristics subset AmpMiddle calculate obtains best feature
Collect Ap；
S5：Sample set U={ x based on steam turbine1,x2,...,xn, obtain ApAll features, with the serious of failure
Degree D={ d1,d2,...,dkIt is classification, as the training sample of grader, structure trains a disaggregated model, training knot
Fruit is denoted as Me；
S6：Using model M e as the assessment models of fault severity level, the data for treating judge are classified, classification results
In D={ d1,d2,...,dkIn, the severity of this i.e. corresponding failure.
Further, the detailed process of step S2 is as follows：
S21：Calculate the sequence association relationship between each feature and the severity of failure:
Wherein,Refer to about attribute a, is less than or equal to xiSample
This set, i.e., Indicate that decision value is less than xiSample set, i.e.,
The association relationship that sorts is bigger to indicate that the monotonic relationshi between this feature and the severity of failure is stronger；
S22：Threshold value is setIfThen feature a is judged as monotonic characteristics, is grouped into monotonic characteristics
Collect Am, otherwise it is grouped into non-monotonic character subset Anm。
Further, the detailed process of step S3 is as follows：
S31：Obtain monotonic characteristics subset AmIn various possible character subsets combination：By all features, pass through row
The form for arranging combination, is combined into the combination of all possible character subset；
S32：The failure for calculating each character subset that step S31 is obtained seriously supports judgement degree, Amp is obtained, wherein setting
Single subset is B, then to arbitrary sample xi, find with regard to monotonic characteristics subset BmFor compare xiIt is good, with regard to non-monotonic character subset Bnm
For with xiSimilar sample set：
Wherein,Indicate setRadix, best B is Amp。
Further, the detailed process of step S4 is as follows：
S41：Obtain AnmIn feature and AmpThe combination for the various possible character subsets that can be combined；
S42：The failure for calculating each character subset that step S41 is obtained seriously supports judgement degree, obtains best feature
Subset Ap, i.e., to a certain character subset B, the consistent sample of Mixed Monotony is calculated to quantityConsistent sample gets over quantity
Greatly, indicate that character subset B is stronger to the classification capacity of the severity D of failure, best B is Ap。
Further, the grader in step S5 includes：Decision tree, support vector machines or Bayes classifier.
The present invention also provides a kind of terminal devices for assessing steam turbine fault severity level, including memory, place
It manages device and is stored in the computer program that can be run in the memory and on the processor, which is characterized in that is described
The step of processor realizes method as described above when executing the computer program.
In addition, the present invention also provides a kind of computer readable storage medium, the computer-readable recording medium storage
There is computer program, which is characterized in that the step of computer program realizes method as described above when being executed by processor.
The present invention uses above-mentioned technical proposal, has an advantageous effect in that：The present invention can be realized to steam turbine failure journey
The automatic identification of degree provides strong support for the maintenance of steam turbine.It is illustrated in addition, the algorithmic procedure of the present invention is fairly simple, meter
It is fast to calculate speed, it is relatively low to allocation of computer requirement, there is good generalization.
Description of the drawings
Fig. 1 is the flow chart of the appraisal procedure of the steam turbine fault severity level of the present invention.
Specific implementation mode
To further illustrate that each embodiment, the present invention are provided with attached drawing.These attached drawings are that the invention discloses one of content
Point, mainly to illustrate embodiment, and the associated description of specification can be coordinated to explain the operation principles of embodiment.Cooperation ginseng
These contents are examined, those of ordinary skill in the art will be understood that other possible embodiments and advantages of the present invention.In figure
Component be not necessarily to scale, and similar component symbol is conventionally used to indicate similar component.
In conjunction with the drawings and specific embodiments, the present invention is further described.
As shown in Figure 1, a kind of appraisal procedure of steam turbine fault severity level, includes the following steps：
S1：The grouped data according to steam turbine fault severity level is obtained, the sample set of steam turbine is denoted as U={ x1,
x2,...,xn, the characteristic set of these samples is denoted as A={ a1,a2,...,aj, sample each in this way is described by A, and therefore
The severity of barrier is D={ d1,d2,...,dk, to sample xiIn feature ajValue on ∈ A and the severity D of failure
It is denoted as v (x respectivelyi,aj) and v (xi,D)；
S2：Characteristic set A is divided into two character subsets：Monotonic characteristics subset AmWith nonmonotonic character subset Anm,
Detailed process is as follows：
S21：Calculate the sequence association relationship between each feature and the severity of failure:
Wherein,Refer to about attribute a, is less than or equal to xiSample
Set, i.e., Indicate that decision value is less than xiSample set, i.e.,
The association relationship that sorts is bigger to indicate that the monotonic relationshi between this feature and the severity of failure is stronger；
S22：Threshold value is setIfThen feature a is judged as monotonic characteristics, is grouped into monotonic characteristics
Collect Am, otherwise it is grouped into non-monotonic character subset Anm；
S3：From monotonic characteristics subset AmMiddle calculate obtains best monotonic characteristics subset Amp, detailed process is as follows：
S31：Obtain monotonic characteristics subset AmIn various possible character subsets combination：By all features, pass through row
The form for arranging combination, is combined into the combination of all possible character subset；
S32：The failure for calculating each character subset that step S31 is obtained seriously supports judgement degree, obtains Amp, wherein setting list
A subset is B, then to arbitrary sample xi, find with regard to monotonic characteristics subset BmFor compare xiIt is good, with regard to non-monotonic character subset BnmAnd
Speech and xiSimilar sample set：
Wherein,Indicate setRadix, best B is Amp；
S4：From nonmonotonic character subset AnmWith best monotonic characteristics subset AmpMiddle calculate obtains best feature
Collect Ap, detailed process is as follows：
S41：Obtain AnmIn feature and AmpThe combination for the various possible character subsets that can be combined；
S42：The failure for calculating each character subset that step S41 is obtained seriously supports judgement degree, obtains best feature
Subset Ap, i.e., to a certain character subset B, the consistent sample of Mixed Monotony is calculated to quantityConsistent sample is to quantity
It is bigger, indicate that character subset B is stronger to the classification capacity of the severity D of failure, best B is Ap；
S5：Sample set U={ x based on steam turbine1,x2,...,xn, obtain ApAll features, with the serious of failure
Degree D={ d1,d2,...,dkIt is classification, as the training sample of grader, structure trains a disaggregated model, training knot
Fruit is denoted as Me, this disaggregated model can be any one grader, such as decision tree, support vector machines, Bayes classifier；
S6：Using model M e as the assessment models of fault severity level, the data for treating judge are classified, classification results
In D={ d1,d2,...,dkIn, the severity of this i.e. corresponding failure.
The present invention obtains a fault severity level classification mould by carrying out classification based training to a large amount of steam turbine fault datas
Type can realize the automatic identification to steam turbine fault degree by the fault severity level disaggregated model, be the inspection of steam turbine
Offer strong support is provided.In addition, from the understanding to failure, can deeply excavate mainly influences having for fault severity level
Feature facilitates personnel to have preliminary describable understanding to the development of failure；In the investigation to failure, the technical program whole base
It is calculated in automatic data, the dependence of the operation characteristic to live steam turbine can be detached to a certain degree, there is very strong generalization.
In an embodiment of the present invention, a kind of terminal device for assessing steam turbine fault severity level is additionally provided,
Including memory, processor and it is stored in the computer program that can be run in the memory and on the processor,
In, the step of processor realizes method as described above when executing the computer program.
Further, which can be the meters such as desktop PC, notebook, palm PC and cloud server
Calculate equipment.The terminal device may include, but be not limited only to, processor, memory.It will be understood by those skilled in the art that above-mentioned
The composed structure of terminal device is only used for assessing the example of the terminal device of steam turbine fault severity level, composition pair
Restriction for the terminal device for assessing steam turbine fault severity level, may include than above-mentioned more or fewer components, or
Person combines certain components or different components, such as the terminal device for assessing steam turbine fault severity level can be with
Including input-output equipment, network access equipment, bus etc., it is not limited in the embodiment of the present invention.
Further, alleged processor can be central processing unit (Central Processing Unit, CPU), also
Can be other general processors, digital signal processor (Digital Signal Processor, DSP), special integrated electricity
Road (Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor can also be any conventional processor
Deng, the processor is the control centre of the terminal device for assessing steam turbine fault severity level, using various interfaces and
Connection entirely is used to assess the various pieces of the terminal device of steam turbine fault severity level.
The memory can be used for storing the computer program and/or module, and the processor is by running or executing
Computer program in the memory and/or module are stored, and calls the data being stored in memory, described in realization
Various functions for the terminal device for assessing steam turbine fault severity level.The memory can include mainly storing program area
And storage data field, wherein storing program area can storage program area, the application program etc. needed at least one function.In addition,
Memory may include high-speed random access memory, can also include nonvolatile memory, such as hard disk, memory, grafting
Formula hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash card
(Flash Card), at least one disk memory, flush memory device or other volatile solid-state parts.
The embodiment of the present invention additionally provides a kind of computer readable storage medium, the computer-readable recording medium storage
The step of having computer program, the above method of the embodiment of the present invention is realized when the computer program is executed by processor.
If module/unit that the terminal device for assessing steam turbine fault severity level integrates is with SFU software functional unit
Form realize and when sold or used as an independent product, can be stored in a computer read/write memory medium.
Based on this understanding, the present invention realizes all or part of flow in above-described embodiment method, can also pass through computer journey
Sequence is completed to instruct relevant hardware, and the computer program can be stored in a computer readable storage medium, the meter
Calculation machine program is when being executed by processor, it can be achieved that the step of above-mentioned each embodiment of the method.Wherein, the computer program packet
Include computer program code, the computer program code can be source code form, object identification code form, executable file or
Certain intermediate forms etc..The computer-readable medium may include：Any reality of the computer program code can be carried
Body or device, recording medium, USB flash disk, mobile hard disk, magnetic disc, CD, computer storage, read-only memory (ROM, Read-
Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and
Software distribution medium etc..It should be noted that the content that the computer-readable medium includes can be according in jurisdiction
Legislation and the requirement of patent practice carry out increase and decrease appropriate, such as in certain jurisdictions, according to legislation and patent practice, meter
Calculation machine readable medium does not include electric carrier signal and telecommunication signal.
Although specifically showing and describing the present invention in conjunction with preferred embodiment, those skilled in the art should be bright
In vain, it is not departing from the spirit and scope of the present invention defined by the appended claims, it in the form and details can be right
The present invention makes a variety of changes, and is protection scope of the present invention.
Claims (7)
1. a kind of appraisal procedure of steam turbine fault severity level, it is characterised in that：The appraisal procedure includes the following steps：
S1：The grouped data according to steam turbine fault severity level is obtained, the sample set of steam turbine is denoted as U={ x1,x2,...,
xn, the characteristic set of these samples is denoted as A={ a1,a2,...,aj, sample each in this way is described by A, and failure is tight
Weight degree is D={ d1,d2,...,dk, to sample xiIn feature ajValue on ∈ A and the severity D of failure is remembered respectively
For v (xi,aj) and v (xi,D)；
S2：Characteristic set A is divided into two character subsets：Monotonic characteristics subset AmWith nonmonotonic character subset Anm；
S3：From monotonic characteristics subset AmMiddle calculate obtains best monotonic characteristics subset Amp；
S4：From nonmonotonic character subset AnmWith best monotonic characteristics subset AmpMiddle calculate obtains best character subset Ap；
S5：Sample set U={ x based on steam turbine1,x2,...,xn, obtain ApAll features, with the severity D of failure
={ d1,d2,...,dkIt is classification, as the training sample of grader, structure trains a disaggregated model, training result note
For Me；
S6：Using model M e as the assessment models of fault severity level, the data for treating judge are classified, and classification results are also in D
={ d1,d2,...,dkIn, the severity of this i.e. corresponding failure.
2. the appraisal procedure of steam turbine fault severity level as described in claim 1, it is characterised in that：The specific mistake of step S2
Journey is as follows：
S21：Calculate the sequence association relationship between each feature and the severity of failure:
Wherein,Refer to about attribute a, is less than or equal to xiSample set, i.e., Indicate that decision value is small
In xiSample set, i.e.,The association relationship that sorts is bigger to indicate the tight of this feature and failure
Monotonic relationshi between weight degree is stronger；
S22：Threshold value is setIfThen feature a is judged as monotonic characteristics, is grouped into monotonic characteristics subset Am,
Otherwise it is grouped into non-monotonic character subset Anm。
3. the appraisal procedure of steam turbine fault severity level as claimed in claim 2, it is characterised in that：The specific mistake of step S3
Journey is as follows：
S31：Obtain monotonic characteristics subset AmIn various possible character subsets combination：By all features, pass through permutation and combination
Form, be combined into the combination of all possible character subset；
S32：The failure for calculating each character subset that step S31 is obtained seriously supports judgement degree, obtains Amp：If single subset is
B, then to arbitrary sample xi, find with regard to monotonic characteristics subset BmFor compare xiIt is good, with regard to non-monotonic character subset BnmFor with xiPhase
As sample set：
Wherein,Indicate setRadix, best B is Amp。
4. the appraisal procedure of steam turbine fault severity level as claimed in claim 3, it is characterised in that：The specific mistake of step S4
Journey is as follows：
S41：Obtain AnmIn feature and AmpThe combination for the various possible character subsets that can be combined；
S42：The failure for calculating each character subset that step S41 is obtained seriously supports judgement degree, obtains best character subset
Ap, i.e., to a certain character subset B, the consistent sample of Mixed Monotony is calculated to quantityConsistent sample is bigger to quantity,
Indicate that character subset B is stronger to the classification capacity of the severity D of failure, best B is Ap。
5. the appraisal procedure of steam turbine fault severity level as described in claim 1, it is characterised in that：Classification in step S5
Device includes：Decision tree, support vector machines or Bayes classifier.
6. a kind of for assessing the terminal device of steam turbine fault severity level, including memory, processor and it is stored in institute
State the computer program that can be run in memory and on the processor, which is characterized in that the processor executes the meter
It is realized when calculation machine program such as the step of any one of claim 1 to 5 the method.
7. a kind of computer readable storage medium, the computer-readable recording medium storage has computer program, feature to exist
In when the computer program is executed by processor the step of realization any one of such as claim 1 to 5 the method.
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| CN109902739A (en) * | 2019-02-27 | 2019-06-18 | 厦门理工学院 | A kind of mechanical equipment fault degree recognition methods, terminal device and storage medium |
| CN110046717A (en) * | 2019-03-14 | 2019-07-23 | 南京汽轮电力科技有限公司 | A kind of steam turbine cloud service and Diagnosing System for Oil Pump are health management system arranged |
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