WO2011023596A1 - Procédé et système pour contrôler des vibrations de générateur d'éolienne - Google Patents

Procédé et système pour contrôler des vibrations de générateur d'éolienne Download PDF

Info

Publication number
WO2011023596A1
WO2011023596A1 PCT/EP2010/061972 EP2010061972W WO2011023596A1 WO 2011023596 A1 WO2011023596 A1 WO 2011023596A1 EP 2010061972 W EP2010061972 W EP 2010061972W WO 2011023596 A1 WO2011023596 A1 WO 2011023596A1
Authority
WO
WIPO (PCT)
Prior art keywords
vibration characteristic
wind turbine
data
turbine generator
characteristic values
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.)
Ceased
Application number
PCT/EP2010/061972
Other languages
English (en)
Inventor
Wen Gang Shi
Jian Hui Xing
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens AG
Siemens Corp
Original Assignee
Siemens AG
Siemens Corp
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Siemens AG, Siemens Corp filed Critical Siemens AG
Publication of WO2011023596A1 publication Critical patent/WO2011023596A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0262—Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
    • G01H1/003—Measuring characteristics of vibrations in solids by using direct conduction to the detector of rotating machines
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
    • G05B23/0235—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold

Definitions

  • the present invention relates to a method and system for monitoring vibration and, particularly, to a method and system for monitoring vibrations in a wind turbine generator.
  • Condition monitoring and fault diagnosis of wind turbine generators are generally focused on transmission systems, and such a transmission system comprises a main shaft, a main gearbox and a generator and so on, with such components being assembled by corresponding bearings, and faults in the mechanical transmission components being mainly caused by inadequate lubrication, contamination, overload or inherent defects.
  • the vibration signals obtained from the transmission system are very useful in determining the condition changes of a wind turbine generator, while the traditional vibration monitoring systems in wind turbine generators produce too many false alarms, the main reason is that the behavior pattern of a wind turbine generator is extremely unstable in comparison with gas/steam turbines, centrifugal compressors, fans and other rotary machines, and therefore it leads to questions of the reliability of the vibration monitoring systems for wind turbine generators currently available.
  • the rotary speed of the wind turbine generator rotor and the load of the transmission system are both unstable, and in terms of the rotary speed of the rotor, the pneumatic torque on the transmission system is related to the blade's tip-speed ratio, blade design, wind speed, blade pitch angle, yaw errors and any additional resistance on the blades, whereas the loads on the traditional system are also affected by many factors, for example random factors such as the stator/rotor currents, the wind turbulence, etc.
  • some peculiar conditions, such as braking events can generate random extra-large torques within a short time. In this way, even if the wind turbine generator is operating under the same wind speed and power, its normal vibration values will change when other operation data (such as wind deviation or the generator's stator current) change.
  • the definition of the vibration threshold will still lead to high false alarm rate in vibration monitoring if various possible combinations of operation data are not taken into consideration.
  • the object of the present invention is to provide a method and system for monitoring vibrations in a wind turbine generator, which can determine adaptively the thresholds of the monitored parameters by taking into consideration various possible combinations of operation data, so as to reduce the false alarm rate, and at the same time to improve their operability and interpretability, and reduce the difficulty of maintenance.
  • Another object of the present invention is to provide a vibration monitoring system for realizing the method of the present invention for monitoring vibrations in a wind turbine generator, which system is capable of comprehensively taking the influences of various factors on the wind turbine generator' s vibrations into consideration, so as to reduce the false alarm rate and the maintenance costs.
  • the present invention proposes a method for monitoring vibrations in a wind turbine generator, which method comprises:
  • the above historical data of the various operation parameters include the
  • SCADA supervisory control and data acquisition
  • the method for carrying out data mining based on the rough set is applied to carry out attribute reduction to a attribute set constituted by the various operation parameters of the wind turbine generator, so as to determine the minimum attribute set, thus simplifying the knowledge presentation and increasing the system processing efficiency and
  • the present invention also proposes a system for using the method of the present invention for monitoring vibrations in a wind turbine generator, which system comprises:
  • a rule base for the range of the vibration characteristic values comprising rules for the range of the vibration characteristic values
  • a real-time data acquiring unit for acquiring the SCADA data and control variables of the wind turbine generator in operation
  • a vibration characteristic value range predicting unit for predicting a range of the vibration characteristic values according to the rules in the rule base for the range of the vibration characteristic values and the data acquired in real-time operation
  • a vibration characteristic threshold calculating unit for calculating the threshold of the vibration characteristic values of the wind turbine generator according to the predicted range of the vibration characteristic values
  • a vibration characteristic value monitoring unit for monitoring the corresponding vibration characteristic value of the wind turbine generator and acquiring the real-time data of the vibration characteristic value
  • a comparison unit for comparing the threshold of said vibration characteristic value and the real-time data of said vibration characteristic value
  • an alarm unit for sending out a corresponding alarm signal when said real-time data of said vibration
  • the threshold of the vibration characteristic values of the wind turbine generator is calculated on the basis of the mutual relationships of the automatically selected operation data, which can greatly reduce the false alarms under various normal operation conditions.
  • the rule base for the range of the vibration characteristic values is
  • the threshold of the vibration characteristic values is calculated under various normal operation
  • the rule base for the range of the vibration characteristic values is represented as explicit and
  • Fig. 1 is a flow chart for establishing a rule base of a wind turbine generator under normal operation conditions in the present invention
  • Fig. 2 is a flow chart of the method of the present invention for monitoring vibrations in a wind turbine generator
  • Fig. 3 is a block diagram of the system of the present invention for monitoring vibrations in a wind turbine generator.
  • Fig. 1 shows the process of establishing a rule base for the range of vibration characteristic values.
  • step SlO is carried out within a particular normal operation period of a wind turbine
  • Such data include the data of normal vibration characteristic values, the historical data of supervisory control and data acquisition (SCADA), known normal operation conditions and control variables of the wind turbine generator and so on, and such data are historical data for data mining of the operating conditions of the wind turbine generator.
  • SCADA supervisory control and data acquisition
  • the data of vibration characteristic values can be statistical parameters of vibration signals in the time domain, frequency domain or other domains, such as the virtual values of the vibration speed and time signals (10- 1000Hz) acquired at the high-speed end of the main gearbox.
  • Table 1 shows a set of attributes of the collected historical data, and of course this attribute set is also the set of attributes of the conditional data to be mentioned below.
  • Table 2 shows a set of decision attributes of the range of the vibration
  • the ranges of the vibration characteristic values can be determined by current standards (such as German Standard VDI 3834) or by the operation experience of the wind turbine generator, for example, each normal vibration speed range in the standard VDI 3834 can be equally divided into 5 sections, and each section can be defined as a decision attribute in Table 2.
  • step S12 data mining based on a rough set is carried out on the data collected at step SlO in the data mining unit in Fig. 1 to construct a knowledge system. From the wind turbine generator' s operation data and the vibration
  • the knowledge system is defined as follows, i.e. the knowledge system is the decision table, which can be used for rule extraction:
  • U is a finite set of N objects, e.g. the sample data obtained for N times from the wind turbine generator;
  • C is a conditional attribute set, e.g. the various wind turbine generator operation data as shown in Table 1;
  • D is a decision attribute set, e.g. the ranges of the vibration characteristic values as shown in Table 2;
  • V is defined as follows:
  • V q is the domain set of the attribute g, ge ( CU D ) ;
  • f is defined as follows:
  • attribute reduction can determine a smaller attribute set for a knowledge system, and the reduced attribute set contains the knowledge which is the same as or similar to the original attribute set.
  • a reduced set (RED(A)) of an original attribute set A can be defined as:
  • IND ( ) represents the irresolvable relationship generated by the attribute set.
  • the rules for the range of the vibration characteristic values of the wind turbine generator under normal conditions are extracted from the knowledge system according to Table 3, and, with reference to Table 4, these rules provide conditions for monitoring vibrations in a wind turbine generator.
  • the rule base for the range of the vibration characteristic values of the wind turbine generator can be established by attribute reduction, the rules are represented as a compact set of the automatically selected conditional attributes, and apparently the
  • the rule base mines the important relationships between the vibration characteristic values and the operation data of the wind turbine generator, and then the range of the vibration characteristic values can be predicted by using the rule base in combination with the real-time operation data of the wind turbine generator.
  • Fig. 2 shows a flow chart of the method of the present invention for monitoring vibrations.
  • step S20 various real-time data representing the performance of the wind turbine generator are acquired, which data mainly include the supervisory control and data acquisition data and the control variables used in the data mining step S12 shown in Fig. 1 ;
  • corresponding rules for the range of the vibration characteristic values are extracted from the rule base for the range of the vibration characteristic values of the wind turbine generator established at step S14 shown in Fig. 1, and according to the rules, real-time operation data are used to predict the range of the vibration characteristic values of the wind turbine generator;
  • the corresponding threshold of the vibration characteristic values is calculated from the predicted range of the vibration characteristic values, and, for example, the alarm threshold can be defined as the predicted virtual value of the vibration speed upper limit multiplied by various weight factors;
  • the corresponding vibration characteristic values of the actual operating wind turbine generator are monitored and the real-time data acquired; at step S26, the threshold of the vibration characteristic values calculated at step S24 is compared with the real-time data of the vibration characteristic values measured at step S25. If the real-time data of the vibration characteristic values are within a normal range, the
  • Fig. 3 shows a block diagram of a system of the present invention for monitoring vibration in a wind turbine
  • the vibration monitoring system comprises: a real-time data acquiring unit 30 for acquiring the SCADA data and control variables of the wind turbine
  • rule base 31 is the rule base for the range of the vibration characteristic values of the wind turbine generator established in the vibration monitoring method of the present invention as shown in Fig. 1, formed by rules for the vibration characteristic value ranges;
  • a vibration characteristic value range predicting unit 32 for predicting the range of the vibration characteristic values
  • a vibration characteristic value threshold calculating unit 34 for calculating the threshold of the vibration characteristic values
  • a vibration characteristic value monitoring unit 35 for monitoring the corresponding vibration characteristic values of the wind turbine generator and acquiring the real-time data of the vibration characteristic values
  • a comparison unit 36 for comparing the threshold of the vibration characteristic values and the real-time data of the vibration characteristic value
  • an alarm unit 38 for sending out a corresponding fault alarm when the particular data of the vibration characteristic values are larger than the threshold of the vibration characteristic values.
  • the system for monitoring vibration in the wind turbine generator acquires various real-time data of the wind turbine generator by the real-time data acquiring unit 30, including the SCADA data and the control variables in Table 1.
  • the system extracts the corresponding rules for the range of the vibration characteristic values from the vibration characteristic value range rule base 31, and the vibration characteristic value range predicting unit 32 predicts the range of the vibration characteristic values of the wind turbine generator according to the extracted rules and the real-time operation data.
  • characteristic value threshold calculating unit 34 calculates the threshold of the vibration characteristic values of the wind turbine generator from the predicted range of the vibration characteristic values.
  • the vibration characteristic value monitoring unit 35 monitors the corresponding vibration characteristic values of the wind turbine generator and acquires the real-time data of the vibration characteristic values. When the real-time data of the wind turbine
  • the comparison unit 36 compares the threshold of the vibration characteristic values and the real-time data of the vibration characteristic values.
  • the alarm unit 38 sends out a corresponding fault alarm in a certain form if the real-time data of the vibration
  • the rule base for the range of the vibration characteristic values is established by way of the data mining method (such as rough set calculation) on the basis of a large quantity of
  • the threshold of the vibration characteristic values under any operation status can be calculated by means of the rule base mentioned above by taking into consideration various
  • the threshold is calculated on the basis of the relationships between the automatically selected operation data, it can significantly reduce the false alarms in the system.
  • the rule base for the range of the vibration characteristic values is represented in the form of explicit and explainable multiple rules, which is easily understandable and convenient for automatic or manual maintenance, and the rules obtained from tests, standards or maintenance experiences of other similar wind turbine generators can be easily added into the existing rule base by the maintenance personnel. Furthermore, by way of an

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
  • Wind Motors (AREA)

Abstract

La présente invention porte sur un procédé et sur un système pour contrôler des vibrations dans un générateur d'éolienne, et dans le procédé, une base de règles pour la plage de valeurs caractéristiques de vibration est établie sur la base d'une quantité importante de données d'historique par une méthode d'exploration de données basée sur un ensemble brute, puis la plage de valeurs caractéristiques de vibration est prédite (S22) à partir des données de fonctionnement en temps réel du générateur d'éolienne selon les règles extraites de la base de règles, le seuil des valeurs caractéristiques de vibration est calculé (S24) et finalement une estimation pour une alerte de défaillance est réalisée par comparaison des données en temps réel des valeurs caractéristiques de fonctionnement du générateur d'éolienne avec le seuil mentionné ci-dessus des valeurs caractéristiques de vibration (S26).
PCT/EP2010/061972 2009-08-28 2010-08-17 Procédé et système pour contrôler des vibrations de générateur d'éolienne Ceased WO2011023596A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN200910167594.4 2009-08-28
CN 200910167594 CN101995290B (zh) 2009-08-28 2009-08-28 风力发电机振动监测的方法和系统

Publications (1)

Publication Number Publication Date
WO2011023596A1 true WO2011023596A1 (fr) 2011-03-03

Family

ID=43015054

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2010/061972 Ceased WO2011023596A1 (fr) 2009-08-28 2010-08-17 Procédé et système pour contrôler des vibrations de générateur d'éolienne

Country Status (2)

Country Link
CN (1) CN101995290B (fr)
WO (1) WO2011023596A1 (fr)

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102829941A (zh) * 2012-08-16 2012-12-19 杭州亿恒科技有限公司 一种振动系统检定的自动化方法
CN104729865A (zh) * 2013-12-19 2015-06-24 广州市地下铁道总公司 一种电机丝杆驱动车门的故障诊断方法
EP2824324A4 (fr) * 2012-03-08 2015-10-21 Ntn Toyo Bearing Co Ltd Système de surveillance d'état
CN105139021A (zh) * 2015-07-08 2015-12-09 Tcl集团股份有限公司 一种基于粗糙集理论实现电视用户快速分类的方法及系统
EP2538184A3 (fr) * 2011-06-22 2016-05-11 Honeywell International Inc. Appareil et procédé diagnostique à base de règles pour machine rotative
CN107239660A (zh) * 2017-06-02 2017-10-10 北京航空航天大学 基于混合整数线性规划的粗糙集模型建立方法和装置
CN107256444A (zh) * 2017-04-24 2017-10-17 中国电力科学研究院 一种配电网故障风险模糊综合评价方法及装置
CN108664752A (zh) * 2018-05-23 2018-10-16 同济大学 一种基于工艺规则和大数据分析技术的工艺参数优化方法
CN110765177A (zh) * 2019-10-17 2020-02-07 大连理工大学 一种基于粗糙集理论的航空发动机故障规则生成方法
EP2581724B1 (fr) 2011-10-13 2020-03-25 Moventas Gears Oy Système et procédé pour suivre l'état de boîtes de vitesses
CN111400961A (zh) * 2020-02-17 2020-07-10 通标标准技术服务有限公司 风力发电机组叶片故障判断方法及装置
EP3671175A4 (fr) * 2018-02-27 2020-12-16 Mitsubishi Heavy Industries Marine Machinery & Equipment Co., Ltd. Dispositif de diagnostic d'état, procédé de diagnostic d'état et programme de diagnostic d'état
CN112130067A (zh) * 2020-09-02 2020-12-25 国网天津市电力公司电力科学研究院 一种大型发电机漏磁故障在线监测系统及监测辨识方法
EP3828407A1 (fr) * 2019-11-28 2021-06-02 Siemens Gamesa Renewable Energy A/S Éolienne et procédé
CN115539327A (zh) * 2022-10-13 2022-12-30 沈阳嘉越电力科技有限公司 风电机组监测预警方法、监测预警系统及可读存储介质
EP4464997A1 (fr) 2023-05-15 2024-11-20 Airbus Helicopters Procédé et dispositif de surveillance de santé d'un système mécanique d'un véhicule en utilisant des seuils variables selon des paramètres de fonctionnement du systeme
US12188812B2 (en) 2018-09-18 2025-01-07 Stmicroelectronics International N.V. Method for monitoring the operation of a machine generating vibrations and device for the implementation of such a method

Families Citing this family (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103512651B (zh) * 2012-06-26 2016-08-24 华锐风电科技(集团)股份有限公司 基于振动突变报警的风电机组状态监测方法及装置
CN103234753A (zh) * 2013-04-11 2013-08-07 华北电力大学 基于高斯过程建模的风电机组轴系状态监测方法
CN103439109B (zh) * 2013-09-12 2016-09-07 华北电力大学（保定） 一种风力发电机组传动系统故障预警的方法
CN103674234B (zh) * 2013-12-23 2017-01-18 北京天源科创风电技术有限责任公司 一种风力发电机组振动异常的状态预警方法及系统
CN103714383B (zh) * 2014-01-09 2017-03-22 北京泰乐德信息技术有限公司 一种基于粗糙集的轨道交通故障诊断方法和系统
CN105606205B (zh) * 2015-12-23 2018-06-08 南京南瑞继保电气有限公司 一种旋转设备的振动越限判别装置及方法
CN105928611A (zh) * 2016-04-14 2016-09-07 中国神华能源股份有限公司 机械设备故障预警系统及方法
CN105928710B (zh) * 2016-04-15 2019-02-12 中国船舶工业系统工程研究院 一种柴油机故障监测方法
CN107831024B (zh) * 2017-04-11 2020-06-16 上海发电设备成套设计研究院 基于多点振动信号特征值的风机振动故障状态诊断方法
CN107218997A (zh) * 2017-05-16 2017-09-29 南京南瑞继保电气有限公司 一种基于工况识别的水电机组振动监测通道异常检测方法
CN108760018B (zh) * 2018-04-19 2024-03-08 国网湖北省电力有限公司经济技术研究院 一种基于激光测距的变电联合构架风振系数在线监测系统及方法
CN110412966B (zh) * 2018-04-27 2021-05-25 新疆金风科技股份有限公司 监测变桨电机温度异常的方法和装置
CN108872852A (zh) * 2018-05-04 2018-11-23 上海交通大学 一种风力发电机故障诊断系统及方法
CN110697075B (zh) * 2019-09-29 2022-11-25 中国直升机设计研究所 一种直升机hums振动阈值生成方法
CN110657885B (zh) * 2019-10-14 2022-03-11 深圳市蓝度汽车电控技术有限公司 一种震动传感器的震动报警方法、系统及终端
CN111122096B (zh) * 2020-01-21 2021-06-18 浙江巨力电机成套设备有限公司 一种电机震动校正设备
CN111413096B (zh) * 2020-04-20 2021-12-10 厦门华夏国际电力发展有限公司 一种带高温阻断的振动保护系统及方法
CN111504450A (zh) * 2020-04-28 2020-08-07 郑州恩普特科技股份有限公司 一种设备故障报警方法及装置
CN112633583B (zh) * 2020-12-29 2023-07-11 南方电网调峰调频发电有限公司 发电机组振动预测方法、装置、计算机设备和存储介质
CN113327341A (zh) * 2021-04-29 2021-08-31 平顶山聚新网络科技有限公司 一种基于网络技术的设备预警系统、方法及存储介质
CN114215705B (zh) * 2021-12-15 2023-08-29 广东省风力发电有限公司 风电机组故障预警方法和系统
CN115655731B (zh) * 2022-10-21 2025-10-21 中国船舶集团有限公司第七一一研究所 柴油机状态监测方法、装置及存储介质

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080243344A1 (en) * 2004-12-20 2008-10-02 Caterpillar Inc. Vibration management system

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101187593A (zh) * 2007-12-18 2008-05-28 华南理工大学 电站汽轮机转子振动故障的柯尔莫果洛夫熵诊断方法
CN101266279B (zh) * 2008-05-09 2011-03-16 东北大学 一种电网故障诊断装置及方法
CN101393049B (zh) * 2008-08-25 2010-09-08 北京天源科创风电技术有限责任公司 风力发电机组振动监测及故障诊断的方法

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080243344A1 (en) * 2004-12-20 2008-10-02 Caterpillar Inc. Vibration management system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
YANG PING ET AL: "A Fault Diagnosis System for Turbo-Generator Set by Data Mining", COMPUTATIONAL INTELLIGENCE AND SECURITY, 2006 INTERNATIONAL CONFERENCE ON, IEEE, PI, 1 November 2006 (2006-11-01), pages 801 - 804, XP031012908, ISBN: 978-1-4244-0604-3 *

Cited By (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2538184A3 (fr) * 2011-06-22 2016-05-11 Honeywell International Inc. Appareil et procédé diagnostique à base de règles pour machine rotative
EP2581724B1 (fr) 2011-10-13 2020-03-25 Moventas Gears Oy Système et procédé pour suivre l'état de boîtes de vitesses
EP2824324A4 (fr) * 2012-03-08 2015-10-21 Ntn Toyo Bearing Co Ltd Système de surveillance d'état
US9458835B2 (en) 2012-03-08 2016-10-04 Ntn Corporation Condition monitoring system
EP2824324B1 (fr) 2012-03-08 2018-05-02 NTN Corporation Système de surveillance d'état
CN102829941A (zh) * 2012-08-16 2012-12-19 杭州亿恒科技有限公司 一种振动系统检定的自动化方法
CN104729865A (zh) * 2013-12-19 2015-06-24 广州市地下铁道总公司 一种电机丝杆驱动车门的故障诊断方法
CN105139021B (zh) * 2015-07-08 2019-09-10 Tcl集团股份有限公司 一种基于粗糙集理论实现电视用户快速分类的方法及系统
CN105139021A (zh) * 2015-07-08 2015-12-09 Tcl集团股份有限公司 一种基于粗糙集理论实现电视用户快速分类的方法及系统
CN107256444A (zh) * 2017-04-24 2017-10-17 中国电力科学研究院 一种配电网故障风险模糊综合评价方法及装置
CN107239660A (zh) * 2017-06-02 2017-10-10 北京航空航天大学 基于混合整数线性规划的粗糙集模型建立方法和装置
EP3671175A4 (fr) * 2018-02-27 2020-12-16 Mitsubishi Heavy Industries Marine Machinery & Equipment Co., Ltd. Dispositif de diagnostic d'état, procédé de diagnostic d'état et programme de diagnostic d'état
CN108664752A (zh) * 2018-05-23 2018-10-16 同济大学 一种基于工艺规则和大数据分析技术的工艺参数优化方法
US12188812B2 (en) 2018-09-18 2025-01-07 Stmicroelectronics International N.V. Method for monitoring the operation of a machine generating vibrations and device for the implementation of such a method
CN110765177A (zh) * 2019-10-17 2020-02-07 大连理工大学 一种基于粗糙集理论的航空发动机故障规则生成方法
CN110765177B (zh) * 2019-10-17 2020-12-11 大连理工大学 一种基于粗糙集理论的航空发动机故障规则生成方法
EP3828407A1 (fr) * 2019-11-28 2021-06-02 Siemens Gamesa Renewable Energy A/S Éolienne et procédé
CN111400961B (zh) * 2020-02-17 2023-09-19 通标标准技术服务有限公司 风力发电机组叶片故障判断方法及装置
CN111400961A (zh) * 2020-02-17 2020-07-10 通标标准技术服务有限公司 风力发电机组叶片故障判断方法及装置
CN112130067A (zh) * 2020-09-02 2020-12-25 国网天津市电力公司电力科学研究院 一种大型发电机漏磁故障在线监测系统及监测辨识方法
CN112130067B (zh) * 2020-09-02 2024-04-23 国网天津市电力公司电力科学研究院 一种大型发电机漏磁故障在线监测系统及监测辨识方法
CN115539327A (zh) * 2022-10-13 2022-12-30 沈阳嘉越电力科技有限公司 风电机组监测预警方法、监测预警系统及可读存储介质
EP4464997A1 (fr) 2023-05-15 2024-11-20 Airbus Helicopters Procédé et dispositif de surveillance de santé d'un système mécanique d'un véhicule en utilisant des seuils variables selon des paramètres de fonctionnement du systeme
FR3148842A1 (fr) 2023-05-15 2024-11-22 Airbus Helicopters Procédé et dispositif de surveillance de santé d’un système mécanique d’un véhicule en utilisant des seuils variables selon des paramètres de fonctionnement du système

Also Published As

Publication number Publication date
CN101995290A (zh) 2011-03-30
CN101995290B (zh) 2013-04-24

Similar Documents

Publication Publication Date Title
WO2011023596A1 (fr) Procédé et système pour contrôler des vibrations de générateur d'éolienne
Saidi et al. Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR
Zappalá et al. Side‐band algorithm for automatic wind turbine gearbox fault detection and diagnosis
JP2019128704A (ja) 設備状態監視装置および設備状態監視方法
Elforjani et al. Analysis of extremely modulated faulty wind turbine data using spectral kurtosis and signal intensity estimator
Butler et al. A feasibility study into prognostics for the main bearing of a wind turbine
CN109840666B (zh) 一种预测未来风电机组发生故障的模型构建方法及系统
Márquez et al. An overview of wind turbine maintenance management
CN103808405A (zh) 烟气发电机组振动故障的自动诊断方法和装置
Merizalde et al. Diagnosis of wind turbine faults using generator current signature analysis: A review
CN110794683A (zh) 基于深度神经网络和峭度特征的风电齿轮箱状态评估方法
CN120684364A (zh) 基于多参数融合的新能源风电系统故障检测方法、系统、设备及介质
Chouidira et al. Fuzzy logic based broken bar fault diagnosis and behavior study of induction machine
Tchakoua et al. New trends and future challenges for wind turbines condition monitoring
CN113408199A (zh) 一种基于多层感知神经网络的齿轮箱油温故障预警方法
Gong et al. Current-based online bearing fault diagnosis for direct-drive wind turbines via spectrum analysis and impulse detection
CN119982378B (zh) 一种海上风电机组变桨系统的故障预警方法及装置
Karadayi et al. Fault-related alarm detection of a wind turbine SCADA system
Song et al. Framework of designing an adaptive and multi-regime prognostics and health management for wind turbine reliability and efficiency improvement
CN116537965B (zh) 一种柴油机在线监测和故障诊断装置
Fu et al. Machine learning-based adaptive fault detection method for wind turbine gearboxes with imbalanced data through an IIoT platform
Bilendo et al. Wind turbine anomaly detection based on SCADA data
Entezami et al. Wind turbine condition monitoring system
He et al. Remote monitoring and diagnostics of blade health in commercial MW-scale wind turbines using electrical signature analysis (ESA)
CN118208374B (zh) 用于海上风电的工况外载加载一体化监测系统与方法

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 10751854

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 10751854

Country of ref document: EP

Kind code of ref document: A1