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
- Publication number
- 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
- Authority
- CN
- China
- Prior art keywords
- severity
- fault
- feature
- subset
- steam turbine
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000003860 storage Methods 0.000 title claims description 10
- 230000003121 nonmonotonic effect Effects 0.000 claims abstract description 14
- 238000012549 training Methods 0.000 claims abstract description 9
- 238000004590 computer program Methods 0.000 claims description 19
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000013145 classification model Methods 0.000 claims description 6
- 238000003066 decision tree Methods 0.000 claims description 3
- 238000013210 evaluation model Methods 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims 1
- 238000012423 maintenance Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 4
- 238000003745 diagnosis Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Geometry (AREA)
- Computer Hardware Design (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Testing And Monitoring For Control Systems (AREA)
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 field of application of fault detection, and particularly relates to a method for evaluating severity of steam turbine fault, terminal equipment and a storage medium.
Background
The steam turbine is widely used in a power plant as important power equipment, and research works on basic theories and application technologies of the operation of the steam turbine are particularly carried out on main key units in production to carry out state monitoring and fault diagnosis, timely and accurately grasp the operation state of the equipment, predict the future development trend of the equipment and obtain remarkable economic and social benefits.
The safety of an enterprise is guaranteed to the greatest extent by modern production requirements, a steam turbine serves as a core main power device, and the steam turbine is stopped to be overhauled immediately when a fault occurs, so that the economic and safety of production are greatly influenced. However, the failure of the steam turbine is gradually developed and has information of different severity, and the production department preferably can carry out purposeful maintenance according to the severity and development trend of the failure according to the abnormal or early failure symptoms of the operation state of the equipment. The severity information of the fault is acquired, so that a user can know the development trend of the equipment state, and a reasonable maintenance strategy and a maintenance scheme are formulated. A large amount of sensor data in equipment state monitoring provides important scientific basis for equipment fault degree identification. In the actual fault diagnosis problem, in order to better perform fault diagnosis research, the severity information of the fault needs to be considered. The maintenance mode is more scientific and reasonable, the equipment maintenance time can be shortened, the equipment utilization rate is improved, the equipment downtime is reduced, the equipment life cycle is prolonged, and the market competitiveness is enhanced.
The identification of the degree of failure of a steam turbine is extremely difficult, and is a new challenge and a special task in the field of failure diagnosis.
Disclosure of Invention
The invention aims to provide a method for evaluating the severity of a steam turbine fault so as to solve the problem that the severity of the steam turbine fault cannot be effectively identified at present. Therefore, the invention adopts the following specific technical scheme:
a method for evaluating the severity of a steam turbine fault, comprising the steps of:
s1: obtaining classification data according to the severity of the steam turbine fault, and recording a sample set of the steam turbine as U ═ x1,x2,...,xnThe feature set of these samples is denoted as a ═ a }1,a2,...,ajThus each sample is described by a and the severity of the fault is D ═ D1,d2,...,dkThus, sample xiIn the feature ajValues of the e A and the severity D of the fault are respectively recorded as v (x)i,aj) And v (x)i,D)；
S2: the feature set a is divided into two feature subsets: monotonic feature subset AmAnd non-monotonic characteristics
Subset Anm；
S3: from the monotonic feature subset AmThe best monotone feature subset A is obtained by the middle calculationmp；
S4: from a non-monotonic subset A of featuresnmWith the best monotonic feature subset ampThe best feature subset a is obtained by the middle calculationp；
S5: steam turbine based sample set U ═ x1,x2,...,xnGet ApAll features of, or fail toD ═ D1,d2,...,dkThe classification is used as a training sample of a classifier, a classification model is constructed and trained, and a training result is recorded as Me;
s6: using the model Me as an evaluation model of the fault severity, classifying the data to be evaluated, wherein the classification result is also represented by D ═ D { (D)1,d2,...,dkThis corresponds to the severity of the fault.
Further, the specific process of step S2 is as follows:
s21: calculating a ranked mutual information value between each feature and the severity of the fault:
wherein,refers to the attribute a, x or lessiA sample set of (i.e.) Indicating that the decision value is less than xiA sample set of (i.e.)The larger the ranking mutual information value is, the stronger the monotonous relation between the characteristic and the severity of the fault is;
s22: setting a threshold valueIf it isThe feature a is judged to be a monotonic feature, belonging to the monotonic feature subset amOtherwise, to non-monotonic characteristicsSet Anm。
Further, the specific process of step S3 is as follows:
s31: obtaining a monotonic feature subset AmIn various possible feature subsets: combining all the characteristics into a combination of all possible characteristic subsets in a form of permutation and combination;
s32: calculating the fault severity support judgment degree of each feature subset obtained in the step S31 to obtain Amp, wherein if a single subset is B, any sample x is subjected toiFind the monotonic feature subset BmRatio of xiGood, non-monotonic feature subset BnmTo xiSimilar sample sets:
wherein,representation collectionThe best B is Amp。
Further, the specific process of step S4 is as follows:
s41: obtaining AnmFeature (A) andmpcombinations of various possible feature subsets that can be combined;
s42: calculating the fault severity support judgment degree of each feature subset obtained in step S41 to obtain the best feature subset apI.e. for a certain feature subset B, the number of sample pairs with monotonously uniform mixture is calculatedThe larger the number of consistent sample pairs, the capability of classifying the severity D of the fault by the feature subset B is representedThe stronger the best B is Ap。
Further, the classifier in step S5 includes: decision trees, support vector machines, or bayesian classifiers.
The invention also provides a terminal device for evaluating the severity of a steam turbine fault, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method as described above when executing the computer program.
Furthermore, the present invention also provides a computer-readable storage medium, in which a computer program is stored, which is characterized in that the computer program, when being executed by a processor, implements the steps of the method as described above.
By adopting the technical scheme, the invention has the beneficial effects that: the invention can realize automatic identification of the fault degree of the steam turbine and provide powerful support for the overhaul of the steam turbine. In addition, the algorithm process of the invention is simple and clear, the calculation speed is high, the requirement on the configuration of the computer is relatively low, and the method has good popularization.
Drawings
FIG. 1 is a flow chart of a method of evaluating the severity of a steam turbine fault according to the present invention.
Detailed Description
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures. Elements in the figures are not drawn to scale and like reference numerals are generally used to indicate like elements.
The invention will now be further described with reference to the accompanying drawings and detailed description.
As shown in fig. 1, a method for evaluating the severity of a steam turbine fault includes the steps of:
s1: obtaining classification data according to the severity of the steam turbine fault, and recording a sample set of the steam turbine as U ═ x1,x2,...,xnThe feature set of these samples is denoted as a ═ a }1,a2,...,ajThus each sample is described by a and the severity of the fault is D ═ D1,d2,...,dkThus, sample xiIn the feature ajValues of the e A and the severity D of the fault are respectively recorded as v (x)i,aj) And v (x)i,D)；
S2: the feature set a is divided into two feature subsets: monotonic feature subset AmAnd a non-monotonic subset of features AnmThe specific process is as follows:
s21: calculating a ranked mutual information value between each feature and the severity of the fault:
wherein,refers to the attribute a, x or lessiA sample set of (i.e.) Indicating that the decision value is less than xiA sample set of (i.e.)The larger the ranking mutual information value is, the stronger the monotonous relation between the characteristic and the severity of the fault is;
s22: setting a threshold valueIf it isThe feature a is judged to be a monotonic feature, belonging to the monotonic feature subset amOtherwise, it falls on the non-monotonic feature subset Anm；
S3: from the monotonic feature subset AmThe best monotone feature subset A is obtained by the middle calculationmpThe specific process is as follows:
s31: obtaining a monotonic feature subset AmIn various possible feature subsets: combining all the characteristics into a combination of all possible characteristic subsets in a form of permutation and combination;
s32: calculating the fault severity support judgment degree of each feature subset obtained in the step S31 to obtain AmpWhere a single subset is B, then for any sample xiFind the monotonic feature subset BmRatio of xiGood, non-monotonic feature subset BnmTo xiSimilar sample sets:
wherein,representation collectionThe best B is Amp；
S4: from non-monotonic characteristicsToken subset AnmWith the best monotonic feature subset ampThe best feature subset a is obtained by the middle calculationpThe specific process is as follows:
s41: obtaining AnmFeature (A) andmpcombinations of various possible feature subsets that can be combined;
s42: calculating the fault severity support judgment degree of each feature subset obtained in step S41 to obtain the best feature subset apI.e. for a certain feature subset B, the number of sample pairs with monotonously uniform mixture is calculatedThe larger the number of the consistent sample pairs is, the stronger the classification capability of the characteristic subset B on the severity D of the fault is, and the best B is Ap；
S5: steam turbine based sample set U ═ x1,x2,...,xnGet ApIn the severity of the fault D ═ D1,d2,...,dkThe classification is used as a training sample of a classifier, a classification model is constructed and trained, the training result is marked as Me, and the classification model can be any classifier, such as a decision tree, a support vector machine, a Bayesian classifier and the like;
s6: using the model Me as an evaluation model of the fault severity, classifying the data to be evaluated, wherein the classification result is also represented by D ═ D { (D)1,d2,...,dkThis corresponds to the severity of the fault.
According to the invention, a fault severity classification model is obtained by performing classification training on a large amount of turbine fault data, and the fault severity classification model can realize automatic identification of the turbine fault severity and provide powerful support for overhaul of the turbine. In addition, from the recognition of the fault, the characteristics which have main influence on the severity of the fault can be deeply excavated, so that personnel can conveniently have preliminary describable recognition on the development of the fault; in the aspect of troubleshooting, the technical scheme is based on automatic data calculation, can be separated from the dependence on the operating characteristics of the on-site steam turbine to a certain extent, and has strong popularization.
In an embodiment of the present invention, there is also provided a terminal device for evaluating severity of steam turbine fault, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method as described above when executing the computer program.
Further, the terminal device may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor, a memory. It will be understood by those skilled in the art that the above-mentioned structure of the terminal device is only an example of the terminal device for evaluating the severity of the steam turbine fault, and does not constitute a limitation to the terminal device for evaluating the severity of the steam turbine fault, and may include more or less components than the above, or combine some components, or different components, for example, the terminal device for evaluating the severity of the steam turbine fault may further include an input/output device, a network access device, a bus, etc., which is not limited in this embodiment of the present invention.
Further, the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being a control center of the terminal equipment for evaluating the severity of the steam turbine fault, various interfaces and lines connecting the various parts of the overall terminal equipment for evaluating the severity of the steam turbine fault.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the terminal equipment for assessing the severity of a turbine fault by operating or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps of the above method according to the embodiment of the present invention.
The integrated terminal equipment modules/units for evaluating the severity of turbine faults, if implemented as software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A method for evaluating the severity of a steam turbine fault, comprising: the evaluation method comprises the following steps:
s1: obtaining classification data according to the severity of the steam turbine fault, and recording a sample set of the steam turbine as U ═ x1,x2,...,xnThe feature set of these samples is denoted as a ═ a }1,a2,...,ajThus each sample is described by a and the severity of the fault is D ═ D1,d2,...,dkThus, sample xiIn the feature ajValues of the e A and the severity D of the fault are respectively recorded as v (x)i,aj) And v (x)i,D)；
S2: the feature set a is divided into two feature subsets: monotonic feature subset AmAnd a non-monotonic subset of features Anm；
S3: from the monotonic feature subset AmThe best monotone feature subset A is obtained by the middle calculationmp；
S4: from a non-monotonic subset A of featuresnmWith the best monotonic feature subset ampThe best feature subset a is obtained by the middle calculationp；
S5: steam turbine based sample set U ═ x1,x2,...,xnGet ApIn the severity of the fault D ═ D1,d2,...,dkThe classification is used as a training sample of a classifier, a classification model is constructed and trained, and a training result is recorded as Me;
s6: using the model Me as an evaluation model of the fault severity, classifying the data to be evaluated, wherein the classification result is also represented by D ═ D { (D)1,d2,...,dkThis corresponds to the severity of the fault.
2. The method of evaluating the severity of a steam turbine fault as recited in claim 1 wherein: the specific process of step S2 is as follows:
s21: calculating a ranked mutual information value between each feature and the severity of the fault:wherein,refers to the attribute a, x or lessiA sample set of (i.e.) Indicating that the decision value is less than xiA sample set of (i.e.)The larger the ranking mutual information value is, the stronger the monotonous relation between the characteristic and the severity of the fault is;
s22: setting a threshold valueIf it isThe feature a is judged to be a monotonic feature, belonging to the monotonic feature subset amOtherwise, it falls on the non-monotonic feature subset Anm。
3. The method of evaluating the severity of a steam turbine fault as recited in claim 2 wherein: the specific process of step S3 is as follows:
s31: obtaining a monotonic feature subset AmIn various possible feature subsets: combining all the characteristics into a combination of all possible characteristic subsets in a form of permutation and combination;
s32: calculating the fault severity support judgment degree of each feature subset obtained in the step S31 to obtain Amp: let a single subset be B, then for an arbitrary sample xiFind the monotonic feature subset BmRatio of xiGood, non-monotonic feature subset BnmTo xiSimilar sample sets:
wherein,representation collectionThe best B is Amp。
4. The method of evaluating the severity of a steam turbine fault as recited in claim 3 wherein: the specific process of step S4 is as follows:
s41: obtaining AnmFeature (A) andmpcombinations of various possible feature subsets that can be combined;
s42: calculating the fault severity support judgment degree of each feature subset obtained in step S41 to obtain the best feature subset apI.e. for a certain feature subset B, the number of sample pairs with monotonously uniform mixture is calculatedThe larger the number of the consistent sample pairs is, the stronger the classification capability of the characteristic subset B on the severity D of the fault is, and the best B is Ap。
5. The method of evaluating the severity of a steam turbine fault as recited in claim 1 wherein: the classifier in step S5 includes: decision trees, support vector machines, or bayesian classifiers.
6. Terminal device for assessing the severity of a steam turbine fault, comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor, when executing said computer program, carries out the steps of the method according to any one of claims 1 to 5.
7. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810303637.6A CN108509732A (en) | 2018-04-03 | 2018-04-03 | Appraisal procedure, terminal device and the storage medium of steam turbine fault severity level |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810303637.6A CN108509732A (en) | 2018-04-03 | 2018-04-03 | Appraisal procedure, terminal device and the storage medium of steam turbine fault severity level |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108509732A true CN108509732A (en) | 2018-09-07 |
Family
ID=63380561
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810303637.6A Pending CN108509732A (en) | 2018-04-03 | 2018-04-03 | Appraisal procedure, terminal device and the storage medium of steam turbine fault severity level |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108509732A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109855879A (en) * | 2019-01-26 | 2019-06-07 | 厦门华夏国际电力发展有限公司 | A kind of steam turbine servo mechanism On-line Fault Detection method for early warning and system |
| 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 |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030045992A1 (en) * | 2001-08-31 | 2003-03-06 | Humerickhouse Charles Edward | Diagnostic method and system for turbine engines |
| US20070124113A1 (en) * | 2005-11-28 | 2007-05-31 | Honeywell International, Inc. | Fault detection system and method using multiway principal component analysis |
| CN101783578A (en) * | 2010-02-03 | 2010-07-21 | 北京奥福瑞科技有限公司 | Intelligent online detection optimizing management control method of high-frequency switch power supply and device thereof |
| CN105814583A (en) * | 2013-12-13 | 2016-07-27 | 沃尔沃卡车集团 | A method for monitoring the operation of a component |
| CN106777606A (en) * | 2016-12-02 | 2017-05-31 | 上海电机学院 | A kind of gearbox of wind turbine failure predication diagnosis algorithm |
-
2018
- 2018-04-03 CN CN201810303637.6A patent/CN108509732A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030045992A1 (en) * | 2001-08-31 | 2003-03-06 | Humerickhouse Charles Edward | Diagnostic method and system for turbine engines |
| US20070124113A1 (en) * | 2005-11-28 | 2007-05-31 | Honeywell International, Inc. | Fault detection system and method using multiway principal component analysis |
| CN101783578A (en) * | 2010-02-03 | 2010-07-21 | 北京奥福瑞科技有限公司 | Intelligent online detection optimizing management control method of high-frequency switch power supply and device thereof |
| CN105814583A (en) * | 2013-12-13 | 2016-07-27 | 沃尔沃卡车集团 | A method for monitoring the operation of a component |
| CN106777606A (en) * | 2016-12-02 | 2017-05-31 | 上海电机学院 | A kind of gearbox of wind turbine failure predication diagnosis algorithm |
Non-Patent Citations (3)
| Title |
|---|
| SONG YUHAI 等: "Fault Pattern Recognition of Turbine-Generator Set Based on Wavelet Network and Fractal Theory", 《2007 8TH INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT AND INSTRUMENTS》 * |
| 刘晓平 等: "基于进化蒙特卡洛方法的特征选择在机械故障诊断中的应用", 《振动与冲击》 * |
| 潘巍巍 等: "齿轮裂纹程度识别的有序分类算法", 《哈尔滨工业大学学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109855879A (en) * | 2019-01-26 | 2019-06-07 | 厦门华夏国际电力发展有限公司 | A kind of steam turbine servo mechanism On-line Fault Detection method for early warning and system |
| 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 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111459700B (en) | Equipment fault diagnosis method, diagnosis device, diagnosis equipment and storage medium | |
| CN111461555B (en) | Production line quality monitoring method, device and system | |
| Bordoloi et al. | Optimum multi-fault classification of gears with integration of evolutionary and SVM algorithms | |
| CN105677791B (en) | For analyzing the method and system of the operation data of wind power generating set | |
| CN108509732A (en) | Appraisal procedure, terminal device and the storage medium of steam turbine fault severity level | |
| WO2023056723A1 (en) | Fault diagnosis method and apparatus, and electronic device and storage medium | |
| CN110866502A (en) | Fault diagnosis method based on linear discriminant analysis and particle swarm optimization support vector machine | |
| CN114757468B (en) | Root cause analysis method for process execution abnormality in process mining | |
| CN102541736A (en) | Acceleration test method in software reliability execution process | |
| CN115576293B (en) | Pressure-sensitive adhesive on-line production analysis method and system based on data monitoring | |
| CN111626360A (en) | Method, device, equipment and storage medium for detecting fault type of boiler | |
| CN114356900A (en) | Power data anomaly detection method, device, equipment and medium | |
| CN112597435B (en) | Thermal power equipment quality data processing method and device based on equipment supervision | |
| CN117951646A (en) | Data fusion method and system based on edge cloud | |
| CN109559206A (en) | A kind of regional enterprises Credit Evaluation System method, apparatus and terminal device | |
| CN110347579B (en) | Deep learning test case selection method based on neuron output behavior pattern | |
| CN111523322A (en) | Requirement document quality evaluation model training method and requirement document quality evaluation method | |
| CN113360416B (en) | Test data batch generation method and device | |
| CN113987514A (en) | System safety state rating method based on deep learning | |
| CN114399407A (en) | Power dispatching monitoring data anomaly detection method based on dynamic and static selection integration | |
| CN113705920A (en) | Generation method of water data sample set for thermal power plant and terminal equipment | |
| JP2018088080A (en) | Learning device and learning method | |
| CN110490226A (en) | A kind of recognition methods and equipment | |
| CN110598760B (en) | Unsupervised feature selection method for vibration data of transformer | |
| Han et al. | Hidden features extraction and amplification based on eigenvalue imaging and gray-level grouping for bearing fault diagnosis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180907 |
|
| WD01 | Invention patent application deemed withdrawn after publication |