CN112699598A - Intelligent diagnosis method and device for abnormal oil temperature of gear box - Google Patents
Intelligent diagnosis method and device for abnormal oil temperature of gear box Download PDFInfo
- Publication number
- CN112699598A CN112699598A CN202011467739.5A CN202011467739A CN112699598A CN 112699598 A CN112699598 A CN 112699598A CN 202011467739 A CN202011467739 A CN 202011467739A CN 112699598 A CN112699598 A CN 112699598A
- Authority
- CN
- China
- Prior art keywords
- oil temperature
- wind turbine
- gearbox
- intelligent diagnosis
- turbine generator
- 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 52
- 238000003745 diagnosis Methods 0.000 title claims abstract description 32
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 27
- 238000011156 evaluation Methods 0.000 claims abstract description 10
- 238000010977 unit operation Methods 0.000 claims abstract description 10
- 238000005516 engineering process Methods 0.000 claims abstract description 7
- 238000007637 random forest analysis Methods 0.000 claims abstract description 7
- 230000005856 abnormality Effects 0.000 claims description 10
- 230000007246 mechanism Effects 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 8
- 238000013461 design Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 6
- 230000001364 causal effect Effects 0.000 claims description 6
- 238000003672 processing method Methods 0.000 claims description 6
- 238000010220 Pearson correlation analysis Methods 0.000 claims description 5
- 238000012423 maintenance Methods 0.000 claims description 5
- 238000012549 training Methods 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 4
- 238000005520 cutting process Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 2
- 238000002405 diagnostic procedure Methods 0.000 claims 1
- 230000008859 change Effects 0.000 abstract description 8
- 230000036541 health Effects 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 6
- 238000002360 preparation method Methods 0.000 description 4
- 238000002790 cross-validation Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000007418 data mining Methods 0.000 description 2
- 238000003066 decision tree Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 241000269627 Amphiuma means Species 0.000 description 1
- 241000879217 Apocynum androsaemifolium Species 0.000 description 1
- 238000005299 abrasion Methods 0.000 description 1
- 239000000872 buffer Substances 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000006116 polymerization reaction Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Images
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
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/06—Wind turbines or wind farms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Wind Motors (AREA)
Abstract
The invention provides an intelligent diagnosis method and device for abnormal oil temperature of a gearbox. The method comprises the following steps: dividing the operation working conditions of the wind turbine generator into different working conditions according to the unit operation data; selecting Random Forest, Adaboost, GBDT and KNN regression prediction models as candidate models; evaluating the candidate models under different working conditions through the selected evaluation indexes, and selecting a prediction model from the candidate models according to an evaluation result; and deploying a prediction model by using a container technology, and predicting the oil temperature abnormity by using the deployed prediction model. The intelligent diagnosis method and device for the abnormal oil temperature of the gearbox can accurately judge the health state of the fan according to the change of the oil temperature.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to an intelligent diagnosis method and device for abnormal oil temperature of a gear box.
Background
With the development of economy, the global energy crisis environment crisis increasingly prominent, and wind energy is more and more emphasized by countries in the world due to the advantages of low cost, cleanness, safety, renewability and the like, and becomes the fastest renewable energy in the world at present. The wind turbine generator gearbox serving as a speed change mechanism is subjected to alternating load and impact load for a long time, and is easy to cause faults such as gear abrasion, pitting corrosion and bearing surface damage, so that the gear box fault accounts for a higher proportion in mechanical faults of the wind turbine generator. The problems of unit limited power operation, fault shutdown and the like caused by high oil temperature of the gear box are obvious, each wind power plant is disturbed, and the generated energy of the unit and the income of the wind power plant are seriously influenced.
Therefore, the gearbox oil temperature is analyzed, the abnormal change of the gearbox oil temperature is identified, and early signs of the wind turbine generator gearbox fault, particularly the fault of accessory components, can be sensed in advance. The temperature signal indicates whether all the components of the wind turbine generator are healthy or not, the temperature and the temperature rise of all the components and subsystems are regular and recyclable, and the temperature change of the fan components can be used for judging the health state of the fan.
Disclosure of Invention
The invention aims to provide an intelligent diagnosis method and device for abnormal oil temperature of a gearbox, which can accurately judge the health state of a fan according to the change of the oil temperature.
In order to solve the technical problem, the invention provides an intelligent diagnosis method for abnormal oil temperature of a gearbox, which comprises the following steps: dividing the operation working conditions of the wind turbine generator into different working conditions according to the unit operation data; selecting Random Forest, Adaboost, GBDT and KNN regression prediction models as candidate models; evaluating the candidate models under different working conditions through the selected evaluation indexes, and selecting a prediction model from the candidate models according to an evaluation result; and deploying a prediction model by using a container technology, and predicting the oil temperature abnormity by using the deployed prediction model.
In some embodiments, further comprising: the method comprises the steps of preparing unit operation data before dividing operation conditions of the wind turbine into different conditions according to the unit operation data.
In some embodiments, the unit operational data includes: SCADA operation data, fault maintenance records and unit technical specification documents.
In some embodiments, further comprising: before the operation working conditions of the wind turbine generator are divided into different working conditions according to the operation data of the wind turbine generator, after the operation data of the wind turbine generator are prepared, indexes of which the correlation is larger than a preset threshold value are selected by adopting a pearson correlation analysis method and a wind turbine generator design mechanism causal analysis combination method.
In some embodiments, further comprising: before the operation working conditions of the wind turbine generator are divided into different working conditions according to the operation data of the wind turbine generator, an abnormal value processing method, a missing value processing method and a repeated value processing method are carried out after an index with the correlation larger than a preset threshold value is selected by adopting a pearson correlation analysis method and a wind turbine generator design mechanism causal analysis combination method.
In some embodiments, the outlier processing comprises: and identifying the threshold value according to the technical specification document of the wind turbine generator and the normal value range of the index provided by the maintenance experience of the fan.
In some embodiments, the different operating conditions include: the system comprises a region to be started, a maximum wind energy capture region, a constant power operation region and a strong wind cutting region.
In some embodiments, evaluating the indicator comprises: the absolute error is averaged.
In some embodiments, predicting the oil temperature anomaly with the deployed prediction model comprises: and determining a residual error threshold value for residual errors obtained by a test set in the model training process by using a 3 sigma method.
In addition, the invention also provides an intelligent diagnosis device for abnormal oil temperature of the gearbox, which comprises: one or more processors; a storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the intelligent diagnosis method for gearbox oil temperature abnormality according to the foregoing description.
After adopting such design, the invention has at least the following advantages:
according to the method, the machine learning regression model is innovatively applied to oil temperature abnormity diagnosis, meanwhile, a designated early warning strategy is divided according to the working conditions of the wind turbine generator based on the design and the operation mechanism of the wind turbine generator, and finally, model deployment and management are performed by adopting a container technology, so that the accurate judgment of the running health state of the fan is completed.
Drawings
The foregoing is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and the detailed description.
FIG. 1 is a schematic view of wind turbine operating conditions;
FIG. 2 is a schematic diagram of a gearbox oil temperature distribution situation of a maximum wind energy capture area in a wind power plant in Hebei;
FIG. 3 is a schematic diagram of the distribution of the oil temperature of a gearbox in a constant power operation area in a wind farm in Hebei;
FIG. 4 is a schematic diagram of a trend of oil temperature change of a gearbox of a certain wind turbine;
FIG. 5 is a schematic flow chart diagram of a method for intelligently diagnosing abnormal oil temperature of a gearbox;
FIG. 6 is a schematic structural diagram of an intelligent diagnosis device for abnormal oil temperature of a gearbox.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The intelligent diagnosis method for the abnormal oil temperature of the gearbox of the wind turbine generator mainly comprises the following four steps: 1. preparing data; 2. establishing a model; 3. deploying a model; 4. and (5) early warning strategy. The data preparation method mainly comprises the steps of selecting effective data according to wind turbine SCADA operation data, fault overhaul records and turbine technical specification documents; the model establishment is divided into: data processing, model construction and model evaluation; the models are deployed and managed by using a container technology, so that the models are isolated from each other; and the early warning strategy determines a residual error threshold value through residual errors obtained in model training and gives fault early warning according to the accumulated time length.
1. Data preparation
The method comprises the following steps of (1) performing fault early warning model data characteristic engineering on accessory parts of a gearbox, wherein required data are mainly selected according to causal analysis of a wind turbine mechanism, and mechanism related data comprise: the method comprises the steps of wind turbine SCADA operation data, fault overhaul records and turbine technical specification documents.
1.1, preparing fan operation data: and selecting the SCADA system 10 min data as target data. And selecting indexes such as a fan ID, a wind field ID, a power factor, the active power of a generator, the rotating speed of the generator, the wind speed, the oil temperature of a gear box, the temperature of a main bearing and the like by taking a single type of wind turbine generator as a unit.
1.2 fault record table preparation: and selecting and storing historical fault records of all fans of the wind field. The data includes information such as wind farm, wind turbine I D, model, start time, end time, fault message, shutdown reason, shutdown type, etc.
1.3 preparing technical specification documents of the unit: the selected unit technical specification document comprises unit operation parameter conditions. The core parameter description comprises: power factor, wind speed, generator speed, wind wheel speed, generator power, etc.
2. Model building
The model establishment is divided into 3 steps by referring to a data mining standard process CRISP-DM (cross-index standard process for data mining): 1) preparing data; 2) establishing a model; 3) and (6) evaluating the model.
2.1 data processing: the data preparation process mainly comprises the processes of abnormal value processing, feature selection, normal operation data definition and the like.
a. Abnormal value processing: the outlier identification strategy includes: threshold identification and data statistics observation identification. And identifying the threshold value according to the technical specification document of the wind turbine generator and the normal value range of the index provided by the maintenance experience of the fan. Replace the outliers with NaN values.
b. Missing value processing: deleting the data of the whole record if the data of the whole column is missing, and filling the data if the missing value exists in the individual record in the data.
c. Repeated value processing: and if all index column values of any two or more rows are equal, deleting all the two or more rows.
d. Selecting characteristics: and filtering out low-correlation and lacking indexes and columns with empty index values by adopting a pearson correlation analysis method and a wind generating set design mechanism causal analysis combined method, and reserving indexes with correlation values larger than 0.5.
TABLE 1 Primary selection index List of Fault early warning data set of gearbox body
e. Dividing working conditions: firstly, normal operation data must meet the parameter requirements of wind turbine technical specification, and secondly, the wind turbine is divided into four regions according to different operation modes of the wind turbine under different wind speed conditions: the system comprises a region to be started, a maximum wind energy capture region, a constant power operation region and a high wind cutting region, and a working condition schematic diagram is shown in figure 1.
In the research, working conditions are distinguished according to wind speeds, a wind turbine generator is divided into a maximum wind energy capture area and a constant power operation area in an operation state, and upper and lower limit values of the oil temperature of a gear box are respectively determined by adopting a 2 sigma method. Specifically, as shown in fig. 2 and 3, μ represents an oil temperature mean value, and σ represents a variance.
2.2 model building
Selecting Random Forest, Adaboost, GBDT and KNN regression prediction models as candidate models, determining the optimal parameters of each model through a grid search method, and selecting the optimal regression model.
2.3 model evaluation
The model evaluation indexes of the regression model of the research mainly comprise:
a. mean Square Error (MSE)
In the formula: y isiIn order to be the true value of the value,
for the prediction value, m is the length of the sequence.
b. Root Mean Square Error (RMSE)
In the formula: y isiIn order to be the true value of the value,
for the prediction value, m is the length of the sequence.
c. Mean Absolute Error (MAE)
In the formula: y isiIn order to be the true value of the value,
for the prediction value, m is the length of the sequence.
And training and testing the four regression models by adopting a K-Fold cross validation method, so that the classification model with the minimum MAE value is selected as the identification model finally applied to the unit operation fault data. Specific results are shown in tables 2 and 3 (wherein K is 5), and the default parameters of the random forest regression model are selected to have a good prediction effect in the maximum wind energy tracking and constant power region.
TABLE 2 results of K-Fold cross validation of MAE values for four regression models in the maximum wind energy tracking region
| Regression model | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
| Random forest regression | 0.391 | 0.394 | 0.397 | 0.394 | 0.391 |
| AdaBoost regression | 0.408 | 0.412 | 0.414 | 0.412 | 0.407 |
| Gradient boosting decision tree regression | 0.57 | 0.571 | 0.577 | 0.573 | 0.57 |
| KNN regression | 0.896 | 0.896 | 0.902 | 0.898 | 0.899 |
TABLE 3 results of K-Fold cross validation of MAE values for four regression models in constant power region
| Regression model | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
| Random forest regression | 0.466 | 0.457 | 0.475 | 0.482 | 0.478 |
| AdaBoost regression | 0.501 | 0.495 | 0.476 | 0.497 | 0.481 |
| Gradient boosting decision tree regression | 0.594 | 0.578 | 0.595 | 0.601 | 0.602 |
| KNN regression | 0.981 | 0.967 | 0.981 | 0.984 | 0.967 |
3. Model deployment
The deployment of the gearbox oil temperature abnormity model adopts a container technology, the technology uniformly encapsulates the python code, the model and a third party library on which the model depends into a portable mirror image, the mirror image can be operated on any Linux machine with a container environment, the algorithm model can be started only by deploying the mirror image, and other environments do not need to be deployed.
4. Early warning strategy
And inputting the data of different working conditions into corresponding prediction models to obtain a predicted value of the oil temperature of the gearbox, calculating a residual error between an actual value and the predicted value of the oil temperature of the gearbox on the basis, and carrying out mean value polymerization on the residual error for 1 hour. And determining a residual error threshold value by using a 3 sigma method for residual errors obtained by a test set in the process of model training, and giving an alarm if the residual error threshold value is exceeded for more than 3 hours accumulated within 7 days, thereby realizing fault early warning of the accessory parts. A gear box oil temperature change trend graph of the unit is derived by means of a visualization function of the gear box oil temperature change trend in the original data set corresponding to the prediction result, and the result is shown in figure 4 (a horizontal line represents a gear box oil temperature alarm threshold).
The specific process flow of the invention is shown in fig. 5.
Fig. 6 shows the structure of the intelligent diagnosis device for abnormal oil temperature of the gearbox. Referring to fig. 6, for example, the intelligent diagnosis device 600 for gearbox oil temperature abnormality can be used as a main intelligent diagnosis machine for gearbox oil temperature in a wind turbine system. As described herein, the intelligent diagnosis device 600 for gearbox oil temperature abnormality can be used for realizing the function of abnormality diagnosis of the gearbox oil temperature in the wind turbine system. The intelligent diagnosis device 600 for gearbox oil temperature abnormality may be implemented in a single node, or the function of the intelligent diagnosis device 600 for gearbox oil temperature abnormality may be implemented in a plurality of nodes in a network. Those skilled in the art will appreciate that the term intelligent diagnosis device for gearbox oil temperature abnormality includes a broad meaning of the apparatus, and the intelligent diagnosis device 600 for gearbox oil temperature abnormality shown in fig. 6 is only one example thereof. The intelligent diagnostic device 600 for gearbox oil temperature anomaly is included for clarity of presentation and is not intended to limit the application of the present invention to a particular intelligent diagnostic device embodiment for gearbox oil temperature anomaly or to a class of intelligent diagnostic device embodiments for gearbox oil temperature anomaly. At least some of the features/methods described herein may be implemented in a network device or component, such as the intelligent diagnostic device 600 for gearbox oil temperature anomalies. For example, the features/methods of the present invention may be implemented in hardware, firmware, and/or software running installed on hardware. The intelligent diagnosis device 600 for gearbox oil temperature abnormality can be any device that processes, stores and/or forwards data frames through a network, such as a server, a client, a data source, and the like. As shown in FIG. 6, the gearbox oil temperature anomaly intelligent diagnostic device 600 may include a transceiver (Tx/Rx)610, which may be a transmitter, a receiver, or a combination thereof. Tx/Rx 610 may be coupled to a plurality of ports 650 (e.g., an uplink interface and/or a downlink interface) for transmitting and/or receiving frames from other nodes. Processor 630 may be coupled to Tx/Rx 610 to process frames and/or determine to which nodes to send frames. Processor 630 may include one or more multi-core processors and/or memory devices 632, which may serve as data stores, buffers, and the like. The processor 630 may be implemented as a general-purpose processor, or may be part of one or more Application Specific Integrated Circuits (ASICs) and/or Digital Signal Processors (DSPs).
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the present invention in any way, and it will be apparent to those skilled in the art that the above description of the present invention can be applied to various modifications, equivalent variations or modifications without departing from the spirit and scope of the present invention.
Claims (10)
1. An intelligent diagnosis method for abnormal oil temperature of a gearbox is characterized by comprising the following steps:
dividing the operation working conditions of the wind turbine generator into different working conditions according to the unit operation data;
selecting Random Forest, Adaboost, GBDT and KNN regression prediction models as candidate models;
evaluating the candidate models under different working conditions through the selected evaluation indexes, and selecting a prediction model from the candidate models according to an evaluation result;
and deploying a prediction model by using a container technology, and predicting the oil temperature abnormity by using the deployed prediction model.
2. The intelligent diagnosis method for abnormal oil temperature of gearbox according to claim 1, further comprising:
the method comprises the steps of preparing unit operation data before dividing operation conditions of the wind turbine into different conditions according to the unit operation data.
3. The intelligent diagnosis method for abnormal oil temperature of the gearbox according to claim 2, wherein the unit operation data comprises: SCADA operation data, fault maintenance records and unit technical specification documents.
4. The intelligent diagnosis method for abnormal oil temperature of gearbox according to claim 2, further comprising:
before the operation working conditions of the wind turbine generator are divided into different working conditions according to the operation data of the wind turbine generator, after the operation data of the wind turbine generator are prepared, indexes of which the correlation is larger than a preset threshold value are selected by adopting a pearson correlation analysis method and a wind turbine generator design mechanism causal analysis combination method.
5. The intelligent diagnosis method for gearbox oil temperature abnormality according to claim 4, further comprising:
before the operation working conditions of the wind turbine generator are divided into different working conditions according to the operation data of the wind turbine generator, an abnormal value processing method, a missing value processing method and a repeated value processing method are carried out after an index with the correlation larger than a preset threshold value is selected by adopting a pearson correlation analysis method and a wind turbine generator design mechanism causal analysis combination method.
6. The intelligent diagnosis method for abnormal oil temperature of gearbox according to claim 5, characterized in that abnormal value processing comprises: and identifying the threshold value according to the technical specification document of the wind turbine generator and the normal value range of the index provided by the maintenance experience of the fan.
7. The intelligent diagnosis method for abnormal oil temperature of the gearbox according to claim 1, wherein different working conditions comprise: the system comprises a region to be started, a maximum wind energy capture region, a constant power operation region and a strong wind cutting region.
8. The intelligent diagnosis method for the abnormal oil temperature of the gearbox according to claim 1, wherein the evaluation index comprises: the absolute error is averaged.
9. The intelligent diagnosis method for the abnormal oil temperature of the gearbox according to claim 1, wherein the prediction of the abnormal oil temperature by using the deployed prediction model comprises the following steps:
and determining a residual error threshold value for residual errors obtained by a test set in the model training process by using a 3 sigma method.
10. The utility model provides a gearbox oil temperature anomaly intelligent diagnosis device which characterized in that includes:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the gearbox oil temperature anomaly intelligent diagnostic method according to any one of claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011467739.5A CN112699598A (en) | 2020-12-14 | 2020-12-14 | Intelligent diagnosis method and device for abnormal oil temperature of gear box |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011467739.5A CN112699598A (en) | 2020-12-14 | 2020-12-14 | Intelligent diagnosis method and device for abnormal oil temperature of gear box |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112699598A true CN112699598A (en) | 2021-04-23 |
Family
ID=75507878
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011467739.5A Pending CN112699598A (en) | 2020-12-14 | 2020-12-14 | Intelligent diagnosis method and device for abnormal oil temperature of gear box |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112699598A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114295367A (en) * | 2021-11-12 | 2022-04-08 | 华能新能源股份有限公司 | Wind turbine generator gearbox working condition online monitoring method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110907066A (en) * | 2019-11-30 | 2020-03-24 | 华能如东八仙角海上风力发电有限责任公司 | Wind turbine generator gearbox bearing temperature state monitoring method based on deep learning model |
| CN110907170A (en) * | 2019-11-30 | 2020-03-24 | 华能如东八仙角海上风力发电有限责任公司 | Wind turbine generator gearbox bearing temperature state monitoring and fault diagnosis method |
-
2020
- 2020-12-14 CN CN202011467739.5A patent/CN112699598A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110907066A (en) * | 2019-11-30 | 2020-03-24 | 华能如东八仙角海上风力发电有限责任公司 | Wind turbine generator gearbox bearing temperature state monitoring method based on deep learning model |
| CN110907170A (en) * | 2019-11-30 | 2020-03-24 | 华能如东八仙角海上风力发电有限责任公司 | Wind turbine generator gearbox bearing temperature state monitoring and fault diagnosis method |
Non-Patent Citations (1)
| Title |
|---|
| 王灿;李韶武;张天阳;吕微;: "风电机组齿轮箱油温异常诊断方法与预警策略研究", 船舶工程, no. 1, pages 543 - 546 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114295367A (en) * | 2021-11-12 | 2022-04-08 | 华能新能源股份有限公司 | Wind turbine generator gearbox working condition online monitoring method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Automated on‐line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition | |
| Zaher et al. | Online wind turbine fault detection through automated SCADA data analysis | |
| CN111597682B (en) | Method for predicting remaining life of bearing of gearbox of wind turbine | |
| Teng et al. | DNN‐based approach for fault detection in a direct drive wind turbine<? show [AQ ID= Q1]?> | |
| CN110991666A (en) | Fault detection method, model training method, device, equipment and storage medium | |
| Butler et al. | A feasibility study into prognostics for the main bearing of a wind turbine | |
| Liu et al. | Research on fault diagnosis of wind turbine based on SCADA data | |
| Miele et al. | Deep anomaly detection in horizontal axis wind turbines using graph convolutional autoencoders for multivariate time series | |
| CN108460207A (en) | A kind of fault early warning method of the generating set based on operation data model | |
| CN110362045B (en) | Marine doubly-fed wind turbine generator fault discrimination method considering marine meteorological factors | |
| CN113436194B (en) | Abnormity detection method, device and equipment for wind turbine generator | |
| Du et al. | A SCADA data based anomaly detection method for wind turbines | |
| Yang et al. | Fault early warning of wind turbine gearbox based on multi‐input support vector regression and improved ant lion optimization | |
| Shi et al. | Study of wind turbine fault diagnosis and early warning based on SCADA data | |
| Leahy et al. | Cluster analysis of wind turbine alarms for characterising and classifying stoppages | |
| CN116771610A (en) | Method for adjusting fault evaluation value of variable pitch system of wind turbine | |
| CN114819225A (en) | Intelligent operation and maintenance method and system for offshore energy unit | |
| Yongjie et al. | Research on early fault diagnostic method of wind turbines | |
| CN112699598A (en) | Intelligent diagnosis method and device for abnormal oil temperature of gear box | |
| Du et al. | A SOM based Anomaly detection method for wind turbines health management through SCADA data | |
| Campoverde-Vilela et al. | Anomaly-based fault detection in wind turbine main bearings | |
| US11339763B2 (en) | Method for windmill farm monitoring | |
| CN112696481A (en) | Intelligent diagnosis method and device for shaft temperature abnormity of wind turbine generator gearbox | |
| Zhang | Comparison of data-driven and model-based methodologies of wind turbine fault detection with SCADA data | |
| CN115842408A (en) | Wind power plant operation state detection system and method based on SCADA |
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 |