CN106870298A - 基于机器学习的叶根螺栓断裂故障检测方法 - Google Patents
基于机器学习的叶根螺栓断裂故障检测方法 Download PDFInfo
- Publication number
- CN106870298A CN106870298A CN201710191583.4A CN201710191583A CN106870298A CN 106870298 A CN106870298 A CN 106870298A CN 201710191583 A CN201710191583 A CN 201710191583A CN 106870298 A CN106870298 A CN 106870298A
- Authority
- CN
- China
- Prior art keywords
- data
- blower fan
- machine learning
- detection method
- blade root
- 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
- 238000001514 detection method Methods 0.000 title claims abstract description 31
- 238000010801 machine learning Methods 0.000 title claims abstract description 21
- 230000009467 reduction Effects 0.000 claims abstract description 17
- 230000007257 malfunction Effects 0.000 claims abstract description 8
- 238000004422 calculation algorithm Methods 0.000 claims description 10
- 238000010606 normalization Methods 0.000 claims description 5
- 238000010248 power generation Methods 0.000 abstract description 5
- 238000007689 inspection Methods 0.000 abstract description 2
- 230000009286 beneficial effect Effects 0.000 abstract 1
- 238000000034 method Methods 0.000 description 27
- 230000008569 process Effects 0.000 description 10
- 238000012360 testing method Methods 0.000 description 7
- 230000008859 change Effects 0.000 description 6
- 238000012423 maintenance Methods 0.000 description 6
- 238000013528 artificial neural network Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000012217 deletion Methods 0.000 description 3
- 230000037430 deletion Effects 0.000 description 3
- 230000005611 electricity Effects 0.000 description 3
- 238000011068 loading method Methods 0.000 description 3
- 208000037656 Respiratory Sounds Diseases 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 2
- 230000004913 activation Effects 0.000 description 2
- 238000005276 aerator Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 239000011521 glass Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 239000007769 metal material Substances 0.000 description 2
- 238000013179 statistical model Methods 0.000 description 2
- 244000098674 Pinus cembroides Species 0.000 description 1
- 235000005013 Pinus cembroides Nutrition 0.000 description 1
- 235000008591 Pinus cembroides var edulis Nutrition 0.000 description 1
- 235000015593 Pinus monophylla Nutrition 0.000 description 1
- 235000015594 Pinus parryana Nutrition 0.000 description 1
- 241000209140 Triticum Species 0.000 description 1
- 235000021307 Triticum Nutrition 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000012938 design process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000013100 final test Methods 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 230000028514 leaf abscission Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 230000007935 neutral effect Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000010791 quenching Methods 0.000 description 1
- 230000000171 quenching effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000010977 unit operation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Wind Motors (AREA)
Abstract
Description
Claims (7)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710191583.4A CN106870298B (zh) | 2017-03-28 | 2017-03-28 | 基于机器学习的叶根螺栓断裂故障检测方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710191583.4A CN106870298B (zh) | 2017-03-28 | 2017-03-28 | 基于机器学习的叶根螺栓断裂故障检测方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN106870298A true CN106870298A (zh) | 2017-06-20 |
CN106870298B CN106870298B (zh) | 2020-04-07 |
Family
ID=59160088
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201710191583.4A Active CN106870298B (zh) | 2017-03-28 | 2017-03-28 | 基于机器学习的叶根螺栓断裂故障检测方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN106870298B (zh) |
Cited By (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107766879A (zh) * | 2017-09-30 | 2018-03-06 | 中国南方电网有限责任公司 | 基于特征信息抽取的mlp电网故障原因诊断方法 |
CN108644070A (zh) * | 2018-05-16 | 2018-10-12 | 浙江运达风电股份有限公司 | 一种风力发电机组桨叶叶根螺栓断裂在线定时检测方法及系统 |
CN109681391A (zh) * | 2017-10-18 | 2019-04-26 | 中车株洲电力机车研究所有限公司 | 一种叶根螺栓断裂故障检测方法及介质 |
CN110594107A (zh) * | 2019-10-24 | 2019-12-20 | 内蒙古青电云电力服务有限公司 | 一种基于快速梯度提升机的风电机组故障检测方法及装置 |
CN110672312A (zh) * | 2019-10-12 | 2020-01-10 | 北京工业大学 | 一种基于bp神经网络的预测螺栓残余夹紧力的方法 |
CN110794254A (zh) * | 2018-08-01 | 2020-02-14 | 北京映翰通网络技术股份有限公司 | 一种基于强化学习的配电网故障预测方法及系统 |
WO2020140022A1 (en) * | 2018-12-27 | 2020-07-02 | Guruprasad Srinivasan | System and method for fault detection of components using information fusion technique |
CN111852791A (zh) * | 2020-07-30 | 2020-10-30 | 国电龙源江永风力发电有限公司 | 一种风力发电机组法兰连接螺栓断裂定位预警方法 |
CN112051468A (zh) * | 2020-09-08 | 2020-12-08 | 南京航空航天大学 | 一种复杂工况下航空静止变流器健康状态评估方法 |
CN112576455A (zh) * | 2020-12-14 | 2021-03-30 | 江阴市恒润重工股份有限公司 | 风电法兰螺栓压力检测防失效装置及其检测方法 |
CN113280469A (zh) * | 2021-06-01 | 2021-08-20 | 珠海拓芯科技有限公司 | 一种风机风叶故障检测方法、空调、计算机可读存储介质 |
CN113933393A (zh) * | 2021-10-16 | 2022-01-14 | 北京创程科技有限公司 | 一种基于电磁超声与3d相控阵的螺栓监测系统及方法 |
CN113959693A (zh) * | 2021-09-30 | 2022-01-21 | 上海电气风电集团股份有限公司 | 叶根螺栓的故障检测方法、系统、设备及介质 |
CN116517790A (zh) * | 2023-05-30 | 2023-08-01 | 广州穗泰岩土工程有限公司 | 一种风力发电机叶片用螺栓紧固监控方法及系统 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7722328B2 (en) * | 2003-05-28 | 2010-05-25 | Aloys Wobben | Rotor blade connection |
CN203130381U (zh) * | 2013-01-16 | 2013-08-14 | 江苏新誉重工科技有限公司 | 一种可在线检测螺栓轴力的塔架 |
CN103380294A (zh) * | 2011-01-20 | 2013-10-30 | 维斯塔斯风力系统集团公司 | 用于诊断监视风力涡轮发电机系统的方法 |
CN106014858A (zh) * | 2016-07-21 | 2016-10-12 | 浙江运达风电股份有限公司 | 一种风电机组对风误差自动校准方法及装置 |
-
2017
- 2017-03-28 CN CN201710191583.4A patent/CN106870298B/zh active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7722328B2 (en) * | 2003-05-28 | 2010-05-25 | Aloys Wobben | Rotor blade connection |
CN103380294A (zh) * | 2011-01-20 | 2013-10-30 | 维斯塔斯风力系统集团公司 | 用于诊断监视风力涡轮发电机系统的方法 |
CN203130381U (zh) * | 2013-01-16 | 2013-08-14 | 江苏新誉重工科技有限公司 | 一种可在线检测螺栓轴力的塔架 |
CN106014858A (zh) * | 2016-07-21 | 2016-10-12 | 浙江运达风电股份有限公司 | 一种风电机组对风误差自动校准方法及装置 |
Non-Patent Citations (2)
Title |
---|
刘亚娟: "大型风机的多层感知智能故障诊断方法研究", 《黑龙江工程学院学报(自然科学版)》 * |
田录林等: "基于核主元分析与模糊神经网络的汽轮发电机振动故障诊断方法", 《大电机技术》 * |
Cited By (24)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107766879A (zh) * | 2017-09-30 | 2018-03-06 | 中国南方电网有限责任公司 | 基于特征信息抽取的mlp电网故障原因诊断方法 |
CN109681391A (zh) * | 2017-10-18 | 2019-04-26 | 中车株洲电力机车研究所有限公司 | 一种叶根螺栓断裂故障检测方法及介质 |
CN109681391B (zh) * | 2017-10-18 | 2020-09-11 | 中车株洲电力机车研究所有限公司 | 一种叶根螺栓断裂故障检测方法及介质 |
CN108644070A (zh) * | 2018-05-16 | 2018-10-12 | 浙江运达风电股份有限公司 | 一种风力发电机组桨叶叶根螺栓断裂在线定时检测方法及系统 |
CN110794254B (zh) * | 2018-08-01 | 2022-04-15 | 北京映翰通网络技术股份有限公司 | 一种基于强化学习的配电网故障预测方法及系统 |
CN110794254A (zh) * | 2018-08-01 | 2020-02-14 | 北京映翰通网络技术股份有限公司 | 一种基于强化学习的配电网故障预测方法及系统 |
US10867250B2 (en) | 2018-12-27 | 2020-12-15 | Utopus Insights, Inc. | System and method for fault detection of components using information fusion technique |
US11868906B2 (en) | 2018-12-27 | 2024-01-09 | Utopus Insights, Inc. | System and method for fault detection of components using information fusion technique |
WO2020140022A1 (en) * | 2018-12-27 | 2020-07-02 | Guruprasad Srinivasan | System and method for fault detection of components using information fusion technique |
CN110672312B (zh) * | 2019-10-12 | 2021-05-07 | 北京工业大学 | 一种基于bp神经网络的预测螺栓残余夹紧力的方法 |
CN110672312A (zh) * | 2019-10-12 | 2020-01-10 | 北京工业大学 | 一种基于bp神经网络的预测螺栓残余夹紧力的方法 |
CN110594107A (zh) * | 2019-10-24 | 2019-12-20 | 内蒙古青电云电力服务有限公司 | 一种基于快速梯度提升机的风电机组故障检测方法及装置 |
CN111852791B (zh) * | 2020-07-30 | 2022-06-03 | 国电龙源江永风力发电有限公司 | 一种风力发电机组法兰连接螺栓断裂定位预警方法 |
CN111852791A (zh) * | 2020-07-30 | 2020-10-30 | 国电龙源江永风力发电有限公司 | 一种风力发电机组法兰连接螺栓断裂定位预警方法 |
CN112051468A (zh) * | 2020-09-08 | 2020-12-08 | 南京航空航天大学 | 一种复杂工况下航空静止变流器健康状态评估方法 |
CN112576455A (zh) * | 2020-12-14 | 2021-03-30 | 江阴市恒润重工股份有限公司 | 风电法兰螺栓压力检测防失效装置及其检测方法 |
CN112576455B (zh) * | 2020-12-14 | 2022-06-28 | 江阴市恒润重工股份有限公司 | 风电法兰螺栓压力检测防失效装置及其检测方法 |
CN113280469A (zh) * | 2021-06-01 | 2021-08-20 | 珠海拓芯科技有限公司 | 一种风机风叶故障检测方法、空调、计算机可读存储介质 |
CN113959693A (zh) * | 2021-09-30 | 2022-01-21 | 上海电气风电集团股份有限公司 | 叶根螺栓的故障检测方法、系统、设备及介质 |
CN113959693B (zh) * | 2021-09-30 | 2024-03-01 | 上海电气风电集团股份有限公司 | 叶根螺栓的故障检测方法、系统、设备及介质 |
CN113933393A (zh) * | 2021-10-16 | 2022-01-14 | 北京创程科技有限公司 | 一种基于电磁超声与3d相控阵的螺栓监测系统及方法 |
CN113933393B (zh) * | 2021-10-16 | 2024-04-02 | 北京创程科技有限公司 | 一种基于电磁超声与3d相控阵的螺栓监测系统 |
CN116517790A (zh) * | 2023-05-30 | 2023-08-01 | 广州穗泰岩土工程有限公司 | 一种风力发电机叶片用螺栓紧固监控方法及系统 |
CN116517790B (zh) * | 2023-05-30 | 2024-01-26 | 广州穗泰岩土工程有限公司 | 一种风力发电机叶片用螺栓紧固监控方法及系统 |
Also Published As
Publication number | Publication date |
---|---|
CN106870298B (zh) | 2020-04-07 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN106870298A (zh) | 基于机器学习的叶根螺栓断裂故障检测方法 | |
CN110410282B (zh) | 基于som-mqe和sfcm的风电机组健康状态在线监测及故障诊断方法 | |
Hameed et al. | Practical aspects of a condition monitoring system for a wind turbine with emphasis on its design, system architecture, testing and installation | |
CN107341349B (zh) | 风机健康评估的方法、系统、存储器及控制器 | |
Kusiak et al. | Prediction, operations, and condition monitoring in wind energy | |
Wang et al. | SCADA data based condition monitoring of wind turbines | |
CN102022264B (zh) | 用于风力涡轮机健康管理的系统和方法 | |
EP2520794B1 (en) | Monitoring apparatus for checking a wind turbine in a wind farm for a yaw misalignment | |
CN104952000A (zh) | 基于马尔科夫链的风电机组运行状态模糊综合评价方法 | |
CN108680358A (zh) | 一种基于轴承温度模型的风电机组故障预测方法 | |
CN110735769A (zh) | 一种预测风机故障的方法及装置、系统 | |
CN104018988B (zh) | 基于物理组件模型和实时数据的风力发电机组监测系统 | |
CN110907066A (zh) | 基于深度学习模型的风电机组齿轮箱轴承温度状态监测方法 | |
CN112733283A (zh) | 一种风电机组部件故障预测方法 | |
CN110006552B (zh) | 一种机组设备温度异常检测方法 | |
CN108874733A (zh) | 一种大型半直驱机组健康状态评估方法 | |
CN108252873A (zh) | 一种风力发电机组在线数据监测及其性能评估的系统 | |
CN109356798B (zh) | 一种基于协整分析的风力发电机齿轮箱状态监测方法 | |
CN113374652A (zh) | 一种风力发电机组寿命评估方法 | |
Sharma et al. | Condition monitoring of wind turbines: a review | |
CN111931851A (zh) | 一种基于一维残差神经网络的风机叶片结冰故障诊断方法 | |
CN111340307B (zh) | 预测风机风力发电功率的方法以及相关装置 | |
Baltazar et al. | A review on neurocomputing based wind turbines fault diagnosis and prognosis | |
Nuñez-Montoya et al. | Development of a wind turbine digital-twin for failure prognosis: first results | |
Mazidi et al. | Performance analysis and anomaly detection in wind turbines based on neural networks and principal component analysis |
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 | ||
CB03 | Change of inventor or designer information | ||
CB03 | Change of inventor or designer information |
Inventor after: Liu Yang Inventor after: Chen Yanan Inventor after: Han Dehai Inventor after: Yan Huili Inventor before: Liu Yang Inventor before: Dai Chuan |
|
TA01 | Transfer of patent application right |
Effective date of registration: 20171123 Address after: 412001 Tianxin Road, Zhuzhou, Hunan Applicant after: CRRC ZHUZHOU INSTITUTE CO., LTD. Applicant after: Nanjing days Mdt InfoTech Ltd Address before: 210000, Nanjing, Nanjing, Jiangsu, Yuhuatai District, No. 180, software Avenue, 7, 3 floors and 306 rooms Applicant before: Nanjing days Mdt InfoTech Ltd |
|
TA01 | Transfer of patent application right | ||
CB02 | Change of applicant information | ||
CB02 | Change of applicant information |
Address after: 412001 Tianxin Road, Zhuzhou, Hunan Applicant after: CRRC ZHUZHOU INSTITUTE CO., LTD. Applicant after: Nanjing Tian Zhi Zhi Technology Co., Ltd. Address before: 412001 Tianxin Road, Zhuzhou, Hunan Applicant before: CRRC ZHUZHOU INSTITUTE CO., LTD. Applicant before: Nanjing days Mdt InfoTech Ltd |
|
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CP01 | Change in the name or title of a patent holder | ||
CP01 | Change in the name or title of a patent holder |
Address after: 412001 Tianxin Road, Zhuzhou, Hunan Co-patentee after: Shanghai Tiantian smart core semiconductor Co., Ltd Patentee after: CRRC Zhuzhou Institute Co.,Ltd. Address before: 412001 Tianxin Road, Zhuzhou, Hunan Co-patentee before: ILUVATAR COREX Inc. Patentee before: CRRC Zhuzhou Institute Co.,Ltd. |