CN112364762A - Mechanical transmission fault detection method based on step error frequency spectrum characteristics - Google Patents
Mechanical transmission fault detection method based on step error frequency spectrum characteristics Download PDFInfo
- Publication number
- CN112364762A CN112364762A CN202011247189.6A CN202011247189A CN112364762A CN 112364762 A CN112364762 A CN 112364762A CN 202011247189 A CN202011247189 A CN 202011247189A CN 112364762 A CN112364762 A CN 112364762A
- Authority
- CN
- China
- Prior art keywords
- error
- frequency spectrum
- data
- mechanical transmission
- time sequence
- 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.)
- Granted
Links
- 238000001228 spectrum Methods 0.000 title claims abstract description 27
- 230000009347 mechanical transmission Effects 0.000 title claims abstract description 17
- 238000001514 detection method Methods 0.000 title claims abstract description 12
- 238000012549 training Methods 0.000 claims abstract description 16
- 238000010801 machine learning Methods 0.000 claims abstract description 15
- 238000000034 method Methods 0.000 claims abstract description 10
- 238000013499 data model Methods 0.000 claims abstract description 9
- 238000012360 testing method Methods 0.000 claims abstract description 9
- 238000012544 monitoring process Methods 0.000 claims abstract description 7
- 238000000605 extraction Methods 0.000 claims abstract description 5
- 238000010183 spectrum analysis Methods 0.000 claims abstract description 4
- 238000013145 classification model Methods 0.000 claims description 9
- 230000005540 biological transmission Effects 0.000 claims description 7
- 238000013480 data collection Methods 0.000 claims description 7
- 208000032369 Primary transmission Diseases 0.000 claims description 4
- 238000012163 sequencing technique Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 abstract description 2
- 230000009466 transformation Effects 0.000 abstract 1
- 238000005034 decoration Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
- G06F2218/10—Feature extraction by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
- G01M13/045—Acoustic or vibration analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Acoustics & Sound (AREA)
- Signal Processing (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention discloses a mechanical transmission fault detection method based on step error frequency spectrum characteristics, which comprises a data acquisition step, a step error characteristic extraction step, a machine learning model training step and a new data model testing step; firstly, collecting time sequence signals of component operation through sensors at monitoring points of gears, bearings and the like; then, carrying out transformation processing on the signals by a spectrum analysis method, and extracting the step error spectrum characteristics; and finally, performing machine learning model training and new data model testing through the extracted features. The fault signal can be effectively captured through the extracted step error characteristics, the performance and the efficiency of the machine learning model are greatly improved, the deployment is easy to realize, and the applicability is strong.
Description
Technical Field
The invention relates to a mechanical transmission fault detection method based on step error frequency spectrum characteristics, and belongs to the field of transmission mechanical fault detection.
Background
Wind power generation has received a great deal of attention worldwide due to its clean and renewable advantages. However, wind turbines often work in harsh environments and are extremely labor intensive. On one hand, the damage of the fan blades, the tower frame, the power generation box, even the bearings and the gears can bring huge safety threat and economic loss to the production process; on the other hand, the manual judgment of whether a fan fails by a failure diagnosis expert has high time and labor costs. These are the difficulties of fan fault detection and maintenance, so a way of detecting and analyzing fan faults by using a machine learning technology appears.
Machine learning techniques require a large amount of data as training support, especially deep learning techniques. In addition, the noise tolerance of the model to the data and the label information is low, and when the data changes slightly, the model prediction changes greatly. On one hand, however, the training data in the field of fan fault detection is relatively small and has much noise (such as improper installation position of a sensor); on the other hand, the fan fault detection is a time sequence signal, the input dimensionality is high, the requirement on the capacity of the model is high, and overfitting of the model is easily caused. The prior machine learning technology needs to pass through characteristics designed by experts and assist shallow models when processing mechanical transmission time sequence signals, and the method meets the performance bottleneck, so an intelligent solution is needed.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the mechanical transmission fault detection method based on the step error frequency spectrum characteristics, the fault signal can be effectively captured through the extracted step error characteristics, the performance and the efficiency of the machine learning model are greatly improved, the deployment is easy to realize, and the applicability is strong.
The technical scheme adopted by the invention is as follows:
a mechanical transmission fault detection method based on step error frequency spectrum characteristics comprises the following steps:
step one, data collection: collecting time sequence characteristics of each monitoring point through a sensor;
step two, extracting the step error frequency spectrum characteristics: extracting step error frequency spectrum characteristics through frequency spectrum analysis;
step three, training a machine learning model;
and step four, testing a new data model.
Preferably, the data collection in the first step comprises the following steps:
step 100: determining a transmission mechanical fault monitoring point;
step 101: deploying a signal collection sensor;
step 102: collecting a time sequence signal of the rotating speed of the transmission machinery;
step 103: the collected data are combined into a plurality of groups of data in the forms of 'timing signal failure' and 'timing signal failure-free'.
Further preferably, the step two step error spectrum feature extraction includes the following steps:
step 200: converting the primary transmission mechanical rotating speed time sequence signal T into frequency domain data F0, and extracting a spectrogram F1 in a fixed frequency domain range;
step 201: traversing different window sizes W1, W2, …, WN in proportion;
step 202: for a given window size Wi, sequentially traversing each window in the spectrogram to obtain a maximum value, and removing a side 'burr';
step 203: taking the index value of the obtained peak;
step 204: calculating first, second and third differences of the index values, and sequencing all the difference values to obtain a high-peak index value difference curve C;
step 205: performing linear fitting on the high-peak index value difference curve C, and taking an Error value of the curve C as a step Error characteristic, namely a Stage Error, se characteristic;
step 206: repeating the steps 202 to 205 for all window sizes W1, W2, … and WN, and extracting step error features se1, se2, … and seN under each window;
step 207: and calculating the mean value, the variance, the maximum value and the minimum value of all the step error characteristics, and putting all the step error characteristics together to serve as the finally extracted step error characteristics.
Further preferably, the training of the machine learning model in the third step comprises the following steps:
step 300: organizing the step error features extracted from each section of time sequence data into a vector Vi;
step 301: a component corresponding to the time series data has no fault, and Yi =0 is set; otherwise, if the component corresponding to the time series data is faulty, setting Yi = 1;
step 302: the classification model M is trained on the training data "Vi, Yi".
Further preferably, the new data model test in step four includes the following steps:
step 400: collecting mechanical transmission time sequence characteristic data of a part to be predicted;
step 401: extracting step error frequency spectrum characteristics;
step 402: predicting by using the trained classification model M;
step 403: if the predicted value is less than or equal to 0.5, outputting no fault;
step 404: if the predicted value is greater than 0.5, the output is faulty.
The invention has the beneficial effects that:
the method has the advantages that the burr phenomenon can be effectively removed, the robustness of the model is guaranteed by adopting the sliding windows, the fault signal can be effectively captured through the extracted step error characteristics, the performance and efficiency of the machine learning model are greatly improved, the deployment is easy to realize, and the applicability is strong.
Detailed Description
The present invention will be described in detail with reference to the following examples.
Example 1: the embodiment is a mechanical transmission fault detection method based on step error frequency spectrum characteristics, which comprises the following steps:
step one, data collection: collecting time sequence characteristics of each monitoring point through a sensor;
step two, extracting the step error frequency spectrum characteristics: extracting step error frequency spectrum characteristics through frequency spectrum analysis;
step three, training a machine learning model;
and step four, testing a new data model.
Wherein, the data collection in the first step comprises the following steps:
step 100: determining a transmission mechanical fault monitoring point;
step 101: deploying a signal collection sensor;
step 102: collecting a time sequence signal of the rotating speed of the transmission machinery;
step 103: the collected data are combined into a plurality of groups of data in the forms of 'timing signal failure' and 'timing signal failure-free'.
Secondly, the step-two step error frequency spectrum feature extraction comprises the following steps:
step 200: converting the primary transmission mechanical rotating speed time sequence signal T into frequency domain data F0, and extracting a spectrogram F1 in a fixed frequency domain range;
step 201: traversing different window sizes W1, W2, …, WN in proportion;
step 202: for a given window size Wi, sequentially traversing each window in the spectrogram to obtain a maximum value, and removing a side 'burr';
step 203: taking the index value of the obtained peak;
step 204: calculating first, second and third differences of the index values, and sequencing all the difference values to obtain a high-peak index value difference curve C;
step 205: performing linear fitting on the high-peak index value difference curve C, and taking an Error value of the curve C as a step Error characteristic, namely a Stage Error, se characteristic;
step 206: repeating the steps 202 to 205 for all window sizes W1, W2, … and WN, and extracting step error features se1, se2, … and seN under each window;
step 207: and calculating the mean value, the variance, the maximum value and the minimum value of all the step error characteristics, and putting all the step error characteristics together to serve as the finally extracted step error characteristics.
Then, the training of the machine learning model in the third step comprises the following steps:
step 300: organizing the step error features extracted from each section of time sequence data into a vector Vi;
step 301: a component corresponding to the time series data has no fault, and Yi =0 is set; otherwise, if the component corresponding to the time series data is faulty, setting Yi = 1;
step 302: the classification model M is trained on the training data "Vi, Yi".
Then, the new data model test in the fourth step comprises the following steps:
step 400: collecting mechanical transmission time sequence characteristic data of a part to be predicted;
step 401: extracting step error frequency spectrum characteristics;
step 402: predicting by using the trained classification model M;
step 403: if the predicted value is less than or equal to 0.5, outputting no fault;
step 404: if the predicted value is greater than 0.5, the output is faulty.
In practical application, the data collection steps are as follows in sequence: selecting a bearing as a main measuring point to deploy a rotating speed sensor, measuring low-frequency vibration in the horizontal direction, the vertical direction and the axial direction (step 100, step 101), collecting rotating speed time sequence signals corresponding to fans with faults and without faults (step 102), collecting 4s-30s of each time sequence signal, and organizing data into a time sequence signal, judging whether the time sequence signal has faults or not, and storing the time sequence signal in a form of 'time sequence signal fault' and 'time sequence signal no fault' (step 103).
The step error frequency spectrum feature extraction steps are as follows in sequence: converting the primary transmission mechanical rotating speed time sequence signal T into frequency domain data F0, and extracting a spectrogram F1 in a fixed frequency domain range (step 200); traversing different window sizes W1, W2, … and WN according to proportion (step 201), sequentially traversing each window in a spectrogram for a given window size Wi to take a maximum value, removing a side 'burr' phenomenon (step 202) to obtain a peak index value (step 203), calculating first-order, second-order and third-order difference values of the index values, sequencing all the difference values to obtain a peak index value difference curve C (step 204), performing linear fitting on the peak index value difference curve C, and taking an Error value of the peak index value difference curve C as a step Error feature, namely a Stage Error, a se feature (step 205); the steps 202 to 205 are repeated for all the window sizes W1, W2, … and WN, step 1, se2, … and seN under each window are extracted (step 206), and then the mean, variance, maximum and minimum values of all the above step error features are calculated and put together to be the final extracted step error feature (step 207).
The training steps of the machine learning model are as follows: organizing the step error features extracted from each time series data into a vector Vi (step 300), if the component corresponding to the time series data has no fault, setting Yi =0, otherwise Yi =1 (step 301), and training a classification model M according to training data "Vi, Yi" (step 302), wherein in the embodiment, the classification model M adopts a support vector machine model; in practical application, the classification model M may also adopt a random forest model.
The new data model test sequentially comprises the following steps: collecting mechanical transmission time sequence data of a component to be predicted (step 400), extracting step error frequency spectrum characteristics (step 401), predicting by using a trained model M (step 402), outputting no fault if the predicted value is less than or equal to 0.5 (step 403), and outputting fault if the predicted value is not more than 0.5 (step 404).
The above description is only a preferred embodiment of the present patent, and it should be noted that, for those skilled in the art, several modifications and decorations can be made without departing from the inventive concept, and these modifications and decorations should also be regarded as the protection scope of the present patent.
Claims (5)
1. A mechanical transmission fault detection method based on step error frequency spectrum features is characterized in that: the method comprises the following steps:
step one, data collection: collecting time sequence characteristics of each monitoring point through a sensor;
step two, extracting the step error frequency spectrum characteristics: extracting step error frequency spectrum characteristics through frequency spectrum analysis;
step three, training a machine learning model;
and step four, testing a new data model.
2. The method for detecting the mechanical transmission fault based on the step error frequency spectrum characteristic as claimed in claim 1, wherein the data collection in the first step comprises the following steps:
step 100: determining a transmission mechanical fault monitoring point;
step 101: deploying a signal collection sensor;
step 102: collecting a time sequence signal of the rotating speed of the transmission machinery;
step 103: the collected data are combined into a plurality of groups of data in the forms of 'timing signal failure' and 'timing signal failure-free'.
3. The method for detecting the mechanical transmission fault based on the step error frequency spectrum characteristic as claimed in claim 2, wherein the step two step error frequency spectrum characteristic extraction comprises the following steps:
step 200: converting the primary transmission mechanical rotating speed time sequence signal T into frequency domain data F0, and extracting a spectrogram F1 in a fixed frequency domain range;
step 201: traversing different window sizes W1, W2, …, WN in proportion;
step 202: for a given window size Wi, sequentially traversing each window in the spectrogram to obtain a maximum value, and removing a side 'burr';
step 203: taking the index value of the obtained peak;
step 204: calculating first, second and third differences of the index values, and sequencing all the difference values to obtain a high-peak index value difference curve C;
step 205: performing linear fitting on the high-peak index value difference curve C, and taking an Error value of the curve C as a step Error characteristic, namely a Stage Error, se characteristic;
step 206: repeating the steps 202 to 205 for all window sizes W1, W2, … and WN, and extracting step error features se1, se2, … and seE under each window;
step 207: and calculating the mean value, the variance, the maximum value and the minimum value of all the step error characteristics, and putting all the step error characteristics together to serve as the finally extracted step error characteristics.
4. The method for detecting the mechanical transmission fault based on the step error frequency spectrum characteristic as claimed in claim 3, wherein the step three machine learning model training comprises the following steps:
step 300: organizing the step error features extracted from each section of time sequence data into a vector Vi;
step 301: a component corresponding to the time series data has no fault, and Yi =0 is set; otherwise, if the component corresponding to the time series data is faulty, setting Yi = 1;
step 302: the classification model M is trained on the training data "Vi, Yi".
5. The method for detecting mechanical transmission faults based on step error spectrum characteristics as claimed in claim 4, wherein the new data model test in the step four comprises the following steps:
step 400: collecting mechanical transmission time sequence characteristic data of a part to be predicted;
step 401: extracting step error frequency spectrum characteristics;
step 402: predicting by using the trained classification model M;
step 403: if the predicted value is less than or equal to 0.5, outputting no fault;
step 404: if the predicted value is greater than 0.5, the output is faulty.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011247189.6A CN112364762B (en) | 2020-11-10 | 2020-11-10 | Mechanical transmission fault detection method based on step error frequency spectrum characteristics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011247189.6A CN112364762B (en) | 2020-11-10 | 2020-11-10 | Mechanical transmission fault detection method based on step error frequency spectrum characteristics |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112364762A true CN112364762A (en) | 2021-02-12 |
| CN112364762B CN112364762B (en) | 2024-02-27 |
Family
ID=74508517
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011247189.6A Active CN112364762B (en) | 2020-11-10 | 2020-11-10 | Mechanical transmission fault detection method based on step error frequency spectrum characteristics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112364762B (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102095430A (en) * | 2010-11-18 | 2011-06-15 | 合肥工业大学 | Sensor dynamic error frequency-domain correction technology based on step response |
| CN103983343A (en) * | 2014-05-29 | 2014-08-13 | 武汉大学 | Satellite platform chattering detection method and system based on multispectral image |
| GB201415567D0 (en) * | 2014-09-03 | 2014-10-15 | Ucl Business Plc | Method and apparatus for the detection of faults in data computations |
| CN105628649A (en) * | 2015-12-24 | 2016-06-01 | 山东省科学院激光研究所 | Method for extracting absorption characteristic peak in gas field monitoring |
| CN108489529A (en) * | 2018-02-09 | 2018-09-04 | 中国科学院国家空间科学中心 | A kind of method for detecting weak signals based on high-order statistic |
| CN108613802A (en) * | 2018-05-10 | 2018-10-02 | 重庆大学 | A kind of mechanical failure diagnostic method based on depth mixed network structure |
| CN109102005A (en) * | 2018-07-23 | 2018-12-28 | 杭州电子科技大学 | Small sample deep learning method based on shallow Model knowledge migration |
| CN109918417A (en) * | 2019-02-28 | 2019-06-21 | 西安交通大学 | Time series data adaptive segmentation, dimensionality reduction and characterizing method and application based on wavelet transformation |
| CN110196173A (en) * | 2019-05-05 | 2019-09-03 | 成都大学 | A kind of strong noise environment machine operation troubles online test method based on self study |
| CN110598676A (en) * | 2019-09-25 | 2019-12-20 | 南京邮电大学 | Deep learning gesture electromyographic signal identification method based on confidence score model |
| US20200192338A1 (en) * | 2018-12-13 | 2020-06-18 | Analog Devices, Inc. | Sensor mounting assembly for machine condition monitoring |
| CN111537056A (en) * | 2020-07-08 | 2020-08-14 | 浙江浙能天然气运行有限公司 | Pipeline along-line third-party construction dynamic early warning method based on SVM and time-frequency domain characteristics |
-
2020
- 2020-11-10 CN CN202011247189.6A patent/CN112364762B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102095430A (en) * | 2010-11-18 | 2011-06-15 | 合肥工业大学 | Sensor dynamic error frequency-domain correction technology based on step response |
| CN103983343A (en) * | 2014-05-29 | 2014-08-13 | 武汉大学 | Satellite platform chattering detection method and system based on multispectral image |
| GB201415567D0 (en) * | 2014-09-03 | 2014-10-15 | Ucl Business Plc | Method and apparatus for the detection of faults in data computations |
| CN105628649A (en) * | 2015-12-24 | 2016-06-01 | 山东省科学院激光研究所 | Method for extracting absorption characteristic peak in gas field monitoring |
| CN108489529A (en) * | 2018-02-09 | 2018-09-04 | 中国科学院国家空间科学中心 | A kind of method for detecting weak signals based on high-order statistic |
| CN108613802A (en) * | 2018-05-10 | 2018-10-02 | 重庆大学 | A kind of mechanical failure diagnostic method based on depth mixed network structure |
| CN109102005A (en) * | 2018-07-23 | 2018-12-28 | 杭州电子科技大学 | Small sample deep learning method based on shallow Model knowledge migration |
| US20200192338A1 (en) * | 2018-12-13 | 2020-06-18 | Analog Devices, Inc. | Sensor mounting assembly for machine condition monitoring |
| CN109918417A (en) * | 2019-02-28 | 2019-06-21 | 西安交通大学 | Time series data adaptive segmentation, dimensionality reduction and characterizing method and application based on wavelet transformation |
| CN110196173A (en) * | 2019-05-05 | 2019-09-03 | 成都大学 | A kind of strong noise environment machine operation troubles online test method based on self study |
| CN110598676A (en) * | 2019-09-25 | 2019-12-20 | 南京邮电大学 | Deep learning gesture electromyographic signal identification method based on confidence score model |
| CN111537056A (en) * | 2020-07-08 | 2020-08-14 | 浙江浙能天然气运行有限公司 | Pipeline along-line third-party construction dynamic early warning method based on SVM and time-frequency domain characteristics |
Non-Patent Citations (6)
| Title |
|---|
| XIWEI LI 等: "A spectral self-focusing fault diagnosis method for automotive transmissions under gear-shifting conditions", 《MECHANICAL SYSTEMS AND SIGNAL PROCESSING》, pages 1 - 18 * |
| 孙小涵: "纸质心电图数字化信息提取与分析", 《中国优秀硕士学位论文全文数据库 (医药卫生科技辑)》, pages 060 - 16 * |
| 张田 等: "基于基座多振动信号融合的转子故障诊断", 《机械工程师》, pages 14 - 15 * |
| 郑跃滨 等: "基于超声导波的航空航天结构损伤诊断成像技术研究进展", 《航空控制技术》, pages 24 - 43 * |
| 陈海涛 等: "基于NS3-gym框架的智能干扰规避系统设计与实现", 《计算机工程与应用》, pages 1 - 9 * |
| 陈润航 等: "地震和爆破事件源波形信号的卷积神经网络分类研究", 《地球物理学进展》, vol. 33, no. 4, pages 1331 - 1338 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112364762B (en) | 2024-02-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111539553B (en) | Wind turbine generator fault early warning method based on SVR algorithm and off-peak degree | |
| CN102798529A (en) | Method and system for diagnosing bearing faults of large-size wind turbine bearing | |
| CN109655266B (en) | Wind turbine generator bearing fault diagnosis method based on AVMD and spectrum correlation analysis | |
| CN106650122B (en) | A kind of equipment variable parameter operation methods of risk assessment | |
| CN107782443B (en) | Automatic extraction method for natural frequency of wind driven generator blade | |
| CN104564542B (en) | A kind of fault diagnosis system and its diagnostic method based on big data technology | |
| CN111415070A (en) | Wind turbine generator gearbox oil temperature over-temperature fault early warning method based on SCADA data | |
| CN112347944A (en) | Mechanical transmission fault detection method based on zero-value proportional spectrum characteristics | |
| CN104101652A (en) | Audio signal based wind power blade damage monitoring method and system | |
| CN107229269A (en) | A kind of wind-driven generator wheel-box method for diagnosing faults of depth belief network | |
| CN112305388B (en) | On-line monitoring and diagnosing method for insulation partial discharge faults of generator stator winding | |
| CN111931851A (en) | Fan blade icing fault diagnosis method based on one-dimensional residual error neural network | |
| CN114742111B (en) | Fault diagnosis method and system based on parameter adaptive characteristic modal decomposition | |
| CN110595778A (en) | Wind turbine generator bearing fault diagnosis method based on MMF and IGRA | |
| CN111120388A (en) | Fan state combined monitoring method and system | |
| CN112177865B (en) | Method for solving marking noise and insufficient marks in fan fault detection | |
| CN113391207A (en) | Motor fault detection method, medium and system | |
| CN111412114B (en) | Wind turbine generator impeller imbalance detection method based on stator current envelope spectrum | |
| CN114266197A (en) | Method for diagnosing equipment fault of hydropower station | |
| CN111046790A (en) | Pump bearing fault diagnosis method | |
| KR101599210B1 (en) | A method of setting alarm levels for condition monitoring and fault diagnosis of wind turbine generator | |
| CN112986821A (en) | Fault diagnosis method for broken blade of variable pitch motor rotor of offshore wind turbine generator | |
| CN112364762A (en) | Mechanical transmission fault detection method based on step error frequency spectrum characteristics | |
| CN117093938A (en) | Fan bearing fault detection method and system based on deep learning | |
| CN110441081B (en) | Intelligent diagnosis method and intelligent diagnosis system for faults of rotating machinery |
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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20221209 Address after: 210023 163 Xianlin Road, Qixia District, Nanjing, Jiangsu Applicant after: NANJING University Applicant after: Nanjing Zhigu Artificial Intelligence Research Institute Co.,Ltd. Address before: 210000 building C4, Hongfeng Science Park, Nanjing Economic and Technological Development Zone, Nanjing City, Jiangsu Province Applicant before: Nanjing Zhigu Artificial Intelligence Research Institute Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |