WO2023284127A1 - 一种基于振动信号的gil缺陷在线监测***及方法 - Google Patents
一种基于振动信号的gil缺陷在线监测***及方法 Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 23
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
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- 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
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
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- the invention belongs to the technical field of electrician detection, in particular to the defect detection technology of a gas-insulated metal-enclosed transmission line (GIL).
- GIL gas-insulated metal-enclosed transmission line
- Gas-insulated metal-enclosed transmission line is a kind of high-voltage, high-current power transmission equipment that is insulated by SF6 or SF6/N2 mixed gas, and the shell and conductor are coaxially arranged. It has the advantages of large transmission capacity, small footprint, flexible layout, high reliability, maintenance-free, long life, and small interaction with the environment.
- the use of GIL can solve the problem of erecting transmission lines in special meteorological environments or special locations. Through reasonable planning and design, not only can the system cost be greatly reduced, but also the reliability of the system can be improved.
- GIL adopts a fully sealed design, and has a large air chamber and long pipeline.
- existing methods are difficult to accurately locate the fault point.
- it is necessary to accurately and quickly identify, locate and warn GIL defects.
- the research mainly focuses on the detection technology of UHF and ultrasonic discharge signals, and there are few researches on GIL vibration technology and mechanical defects.
- the post-event positioning of GIL breakdown faults is mainly carried out, and there is a lack of research on online monitoring and pre-fault early warning technologies.
- the purpose of the present invention is to provide a GIL defect online monitoring system and method based on vibration signals, realize the online monitoring of GIL operating status, and judge in real time whether there are mechanical or discharge defects in GIL, realize early identification and early warning of faults, and reduce GIL
- the GIL defect online monitoring system based on vibration signals designed by the present invention includes a vibration signal acquisition module, a vibration signal feature extraction module, an equipment defect identification module and a data storage display module;
- the vibration signal acquisition unit is used for Collect the vibration signal of the GIL shell, and transmit the vibration signal to the vibration signal feature extraction module;
- the vibration signal feature extraction module is used to extract the characteristic parameters of the vibration signal in the time domain, frequency domain and time-frequency domain, and transmit the characteristic parameters of the vibration signal to the device defect identification module;
- the device defect recognition module normalizes the characteristic parameters of the vibration signal, and then uses the maximum correlation minimum redundancy algorithm to analyze the redundancy between each characteristic parameter and the correlation between the characteristic parameters and various types of vibration signal characteristic maps in the data storage and display module, and prioritize the characteristic parameters according to the weight of the characteristic parameters, and finally use the defect recognition algorithm to analyze the equipment status represented by each characteristic parameter, Comprehensively assessing whether the GIL has defects based on the equipment status and obtaining equipment defect type and defect level data, the equipment defect identification module
- a method for online monitoring and diagnosis of GIL defects based on vibration signals comprising the steps of:
- Step 1 vibration signal collection: the vibration signal collection unit collects the vibration signal of the GIL shell, and transmits the vibration signal to the vibration signal feature extraction module;
- Step 2 vibration signal feature extraction: the vibration signal feature extraction module extracts the characteristic parameters of the vibration signal of the GIL shell in the time domain, frequency domain and time-frequency domain, and transmits the vibration signal characteristic parameters to the equipment defect identification module ;
- Step 3 GIL equipment status diagnosis: the equipment defect identification module normalizes the characteristic parameters of the vibration signal, and then uses the maximum correlation minimum redundancy algorithm to analyze the redundancy between the characteristic parameters and the characteristic parameters and data storage display The correlation between various vibration signal characteristic maps in the module, and the characteristic parameters are optimized and sorted according to the weight of the characteristic parameters. Finally, the defect recognition algorithm is used to analyze the equipment status represented by each characteristic parameter, and the comprehensive equipment status is used to evaluate whether the GIL exists. Defects and obtain equipment defect type and defect level data, the equipment defect identification module transmits the characteristic parameters of the vibration signal, equipment status, equipment defect type and level data to the data storage display module; if the equipment defect level reaches the fault level, Then the equipment defect identification module generates an alarm signal.
- the present invention can realize the defect analysis of the equipment by collecting and analyzing the vibration signal of the GIL shell, and realize the online monitoring and electrification detection of the state of the GIL during operation without power failure;
- the present invention can analyze the running state of GIL in real time and give an alarm in real time, and update the database according to the newly added defect types in the running process of GIL, so as to improve the accuracy of GIL defect recognition;
- the present invention identifies and judges the type of equipment defect, and gives the location where the defect may occur, thereby reducing troubleshooting events and improving repair efficiency after a fault occurs.
- Fig. 1 is a schematic diagram of the system structure of the present invention
- Fig. 2 is method flowchart of the present invention
- 1-vibration signal acquisition module 2-vibration signal feature extraction module
- 3-equipment defect identification module 4-data storage display module
- 5-equipment defect alarm module 11-vibration sensor and 12-acquisition card.
- a kind of GIL defective online monitoring system based on vibration signal as shown in Figure 1, it comprises vibration signal acquisition module 1, vibration signal feature extraction module 2, equipment defect recognition module 3 and data storage display module 4; Described vibration signal acquisition Unit 1 is used to collect the vibration signal of the GIL shell, and transmits the vibration signal to the vibration signal feature extraction module 2; the vibration signal feature extraction module 2 is used to extract the vibration signal in the time domain, frequency domain and time-frequency domain characteristic parameters of each aspect, and transmit the vibration signal characteristic parameters to the equipment defect identification module 3; the equipment defect identification module 3 normalizes the vibration signal characteristic parameters, and then uses the maximum correlation minimum redundancy algorithm to analyze each The redundancy between the characteristic parameters and the correlation between the characteristic parameters and the various types of vibration signal characteristic maps in the data storage display module 4, and according to the weight of the characteristic parameters, the characteristic parameters are optimally sorted to obtain the characteristic parameter sorting results, Finally, the defect identification algorithm is used to analyze the redundancy, the correlation between the characteristic maps and the ranking results of the characteristic parameters, evaluate the equipment status represented by each characteristic parameter,
- the characteristic parameter weights are determined according to the results of defect simulation tests, and can be adjusted and updated according to the results of on-site fault detection.
- An on-line monitoring system for GIL defects based on vibration signals also includes an equipment defect alarm module 5 configured to issue the alarm signal generated by the equipment defect identification module 3 .
- the equipment defect identification module 3 if the equipment defect identification module 3 cannot identify the characteristic parameters of the vibration signal, the staff will determine the equipment status through tests and on-site inspections, and the equipment defect identification module 3 will receive the equipment status and will not be able to The identified characteristic parameters of the vibration signal and the corresponding equipment status are stored in the data storage display module 4 .
- the vibration signal collection unit 1 includes a plurality of vibration sensors 11 and a collection card 12, the vibration sensors 11 are used to collect the vibration signal of the GIL shell, and the collection card 12 is used to receive the vibration of the vibration sensor 11 signal and transmit the vibration signal to the vibration signal feature extraction module 2.
- the characteristic parameters of the vibration signal include any of the following parameters or any combination thereof: amplitude, frequency characteristics, attenuation characteristics, pulse characteristics, and marginal spectrum distribution characteristics of the vibration signal.
- the vibration sensor 11 can be fixed on the GIL shell by sticking or strapping, the base material of the vibration sensor 11 is determined by the GIL shell material, and the base curvature of the vibration sensor 11 is determined by the GIL
- the shell radius is determined to ensure that the base of the vibration sensor 11 matches the radius of the GIL shell, and the vibration sensor 11 can be firmly fixed on the surface of the GIL; the size and surface of the vibration sensor (11) are adjusted according to the GIL shell, so
- the detection frequency range of the vibration sensor 11 is 0-15kHz, and the measurement sensitivity is not lower than 0.00001g.
- a kind of GIL defects online monitoring diagnosis method based on vibration signal as shown in Figure 2, it comprises the following steps:
- Step 1 the vibration signal acquisition unit 1 collects the vibration signal of the GIL shell, and transmits the vibration signal to the vibration signal feature extraction module 2;
- Step 2 the vibration signal feature extraction module 2 extracts the characteristic parameters of the GIL shell vibration signal in the time domain, frequency domain and time-frequency domain, and transmits the vibration signal characteristic parameters to the equipment defect identification module 3;
- the equipment defect identification module 3 normalizes the characteristic parameters of the vibration signal, and then uses the maximum correlation minimum redundancy algorithm to analyze the redundancy between the characteristic parameters and the characteristic parameters and the data stored in the display module 4. According to the correlation between the characteristic maps of similar vibration signals, the characteristic parameters are optimized and sorted according to the weight of the characteristic parameters. Finally, the defect identification algorithm is used to analyze the equipment status represented by each characteristic parameter, and the equipment status is comprehensively evaluated to determine whether the GIL has defects and obtain Equipment defect type and defect level data, the device defect identification module 3 transmits the characteristic parameters, equipment status, equipment defect type and level data of the vibration signal to the data storage display module 4; if the equipment defect level reaches the failure level, then The device defect identification module 3 generates an alarm signal;
- Step 4 The equipment defect alarm module 5 is used to issue the alarm signal generated by the equipment defect identification module 3 to remind the operation and maintenance personnel to pay attention to the equipment status; if the equipment defect identification module 3 cannot identify the characteristic parameters of the vibration signal, the staff will pass The equipment status is determined by means of testing and on-site inspection and investigation.
- the equipment defect identification module 3 receives the equipment status, and stores the unidentifiable vibration signal characteristic parameters and corresponding equipment status into the data storage display module 4 to add GIL vibration defects. feature map.
- the defect identification algorithm is a BP neural network algorithm or a support vector machine algorithm.
- the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
- computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
- These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions
- the device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.
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Abstract
一种基于振动信号的GIL缺陷在线监测***及方法，监测***包括振动信号采集模块（1）、振动信号特征提取模块（2）、设备缺陷识别模块（3）和数据存储展示模块（4）；该振动信号采集模块（1）用于采集GIL壳体振动信号，该振动信号特征提取模块（2）用于提取振动信号在时域和/或频域和/或时频域三个方面的特征参数，该设备缺陷识别模块（3）用于识别GIL是否存在缺陷及缺陷类型并将无法识别的振动信号特征参数及对应设备状态存入数据存储展示模块（4）。该***不但可以通过不停电方式实现对GIL运行过程中的状态进行在线监测与带电检测，还可以分析GIL运行状态并实时报警，还可以根据GIL运行过程中的新增缺陷类型进行数据库更新，提高GIL缺陷识别准确性。
Description
本发明属于电工检测技术领域，尤其涉及气体绝缘金属封闭输电线路(GIL)缺陷检测技术。
气体绝缘金属封闭输电线路(GIL)是一种采用SF6或者SF6/N2混合气体绝缘，外壳与导体同轴布置的高电压、大电流电力传输设备。具有输电容量大、占地少、布置灵活、可靠性高、免维护、寿命长、与环境相互影响小等优点。采用GIL可解决特殊气象环境或特殊地段的输电线路架设问题，通过合理规划和设计，不但可以大大降低***造价，而且也能提高***的可靠性。
GIL采用全密封设计，且气室大、管道长，运行过程中发生放电、机械或过热缺陷时，现有的手段难以对故障点进行准确定位。为了降低GIL严重故障的发生概率，提高故障发生后的修复效率，需要对GIL进行准确、快速的缺陷识别、定位及预警。
目前针对GIL故障诊断技术，主要集中在特高频及超声放电信号的检测技术研究，对GIL振动技术及机械缺陷研究较少。同时，主要是对于GIL击穿故障进行事后定位，缺乏在线监测、故障前预警技术的研究。
发明内容
本发明的目的就是要提供一种基于振动信号的GIL缺陷在线监测***及方法，实现GIL运行状态的在线监测、并实时判断GIL是否存在机械或放电缺陷，实现 故障的提前识别及预警，降低GIL设备故障发生概率，并对设备缺陷类型进行识别判断，给出可能出现缺陷的位置，从而降低故障排查事件，提高故障发生后的修复效率。
为实现此目的，本发明所设计的基于振动信号的GIL缺陷在线监测***，包括振动信号采集模块、振动信号特征提取模块、设备缺陷识别模块和数据存储展示模块；所述振动信号采集单元用于采集GIL壳体振动信号，并将振动信号传递至所述振动信号特征提取模块；所述振动信号特征提取模块用于提取振动信号在时域、频域和时频域三个方面的特征参数，并将振动信号特征参数传输至所述设备缺陷识别模块；所述设备缺陷识别模块将振动信号特征参数进行归一化处理，再利用最大相关最小冗余算法分析各特征参数之间的冗余性以及特征参数与所述数据存储展示模块中各类振动信号特征图谱之间的相关性，并按照特征参数的权重对特征参数进行优选排序，最后运用缺陷识别算法分析各特征参数表征的设备状态，综合所述设备状态评估GIL是否存在缺陷并获得设备缺陷类型和缺陷级别数据，所述设备缺陷识别模块将振动信号的特征参数、设备状态、设备缺陷类型和级别数据传输至所述数据存储展示模块；若设备缺陷级别达到故障级别，则所述设备缺陷识别模块生成告警信号；所述数据存储展示模块用于存储和展示所述振动信号的特征参数、设备状态、设备缺陷类型和级别数据。
一种基于振动信号的GIL缺陷在线监测诊断方法，包括如下步骤：
步骤1，振动信号采集：振动信号采集单元采集GIL壳体振动信号，并将振动信号传输至振动信号特征提取模块；
步骤2，振动信号特征提取：所述振动信号特征提取模块提取GIL壳体振动信号在时域、频域和时频域三个方面的特征参数，并将振动信号特征参数传输至设备缺陷识别模块；
步骤3，GIL设备状态诊断：所述设备缺陷识别模块将振动信号特征参数进行归一化处理，再利用最大相关最小冗余算法分析各特征参数之间的冗余性以及特征参数与数据存储展示模块中各类振动信号特征图谱之间的相关性，并按照特征参数的权重对特征参数进行优选排序，最后运用缺陷识别算法分析各特征参数表征的设备状态，综合所述设备状态评估GIL是否存在缺陷并获得设备缺陷类型和缺陷级别数据，所述设备缺陷识别模块将振动信号的特征参数、设备状态、设备缺陷类型和 级别数据传输至所述数据存储展示模块；若设备缺陷级别达到故障级别，则所述设备缺陷识别模块生成告警信号。
本发明的有益效果为：
1、本发明通过采集分析GIL壳体振动信号就能实现设备的缺陷分析，在不停电方式下实现了对GIL运行过程中的状态进行在线监测和带电检测；
2、本发明可以实时分析GIL运行状态并实时报警，并根据GIL运行过程中的新增缺陷类型进行数据库更新，提高GIL缺陷识别准确性；
3、本发明对设备缺陷类型进行识别判断，给出可能出现缺陷的位置，从而降低故障排查事件，提高故障发生后的修复效率。
图1为本发明的***结构示意图；
图2为本发明的方法流程图；
其中，1-振动信号采集模块、2-振动信号特征提取模块、3-设备缺陷识别模块、4-数据存储展示模块、5-设备缺陷告警模块、11-振动传感器和12-采集卡。
以下结合附图和具体实施例对本发明作进一步的详细说明：
一种基于振动信号的GIL缺陷在线监测***，如图1所示，它包括振动信号采集模块1、振动信号特征提取模块2、设备缺陷识别模块3和数据存储展示模块4；所述振动信号采集单元1用于采集GIL壳体振动信号，并将振动信号传递至所述振动信号特征提取模块2；所述振动信号特征提取模块2用于提取振动信号在时域、频域和时频域三个方面的特征参数，并将振动信号特征参数传输至所述设备缺陷识别模块3；所述设备缺陷识别模块3将振动信号特征参数进行归一化处理，再利用最大相关最小冗余算法分析各特征参数之间的冗余性以及特征参数与所述数据存 储展示模块4中各类振动信号特征图谱之间的相关性，并按照特征参数的权重对特征参数进行优选排序得到特征参数排序结果，最后运用缺陷识别算法对所述冗余性、所述特征图谱之间的相关性及所述特征参数排序结果进行分析，评估各特征参数表征的设备状态，综合所述设备状态评估GIL是否存在缺陷并获得设备缺陷类型和缺陷级别数据，所述设备缺陷识别模块3将振动信号的特征参数、设备状态、设备缺陷类型和级别数据传输至所述数据存储展示模块4；若设备缺陷级别达到故障级别，则所述设备缺陷识别模块3生成告警信号；所述数据存储展示模块4用于存储和展示所述振动信号的特征参数、设备状态、设备缺陷类型和级别数据。
上述技术方案中，所述特征参数权重根据缺陷模拟试验结果确定，并可根据现场故障检测结果进行调整更新。
一种基于振动信号的GIL缺陷在线监测***，如图1所示，它还包括设备缺陷告警模块5，所述设备缺陷告警模块5用于发布所述设备缺陷识别模块3生成的告警信号。
上述技术方案中，若所述设备缺陷识别模块3无法识别振动信号特征参数，则工作人员通过试验及现场检测排查手段确定设备状态，所述设备缺陷识别模块3接收所述设备状态，并将无法识别的振动信号特征参数及对应设备状态存入数据存储展示模块4。
上述技术方案中，所述振动信号采集单元1包括多个振动传感器11和采集卡12，所述振动传感器11用于采集GIL壳体振动信号，所述采集卡12用于接收振动传感器11的振动信号并将振动信号传输至所述振动信号特征提取模块2。
上述技术方案中，所述振动信号特征参数包括以下任一参数或其任意组合：振动信号的幅值、频率特性、衰减特性、脉冲特性、边际谱分布特性。
上述技术方案中，所述振动传感器11可通过粘贴或绑带方式固定于GIL壳体上，所述振动传感器11的基座材质由GIL外壳材料决定，所述振动传感器11的基座曲率由GIL外壳半径决定，确保所述振动传感器11的基座与GIL外壳半径契合，且所述振动传感器11可牢靠固定于GIL表面；所述振动传感器(11)的大小与表面根据GIL外壳进行调节，所述振动传感器11的检测频率范围为0～15kHz，测量灵敏度不低于0.00001g。
一种基于振动信号的GIL缺陷在线监测诊断方法，如图2所示，它包括如下步骤：
步骤1，振动信号采集单元1采集GIL壳体振动信号，并将振动信号传输至振动信号特征提取模块2；
步骤2，所述振动信号特征提取模块2提取GIL壳体振动信号在时域、频域和时频域三个方面的特征参数，并将振动信号特征参数传输至设备缺陷识别模块3；
步骤3，所述设备缺陷识别模块3将振动信号特征参数进行归一化处理，再利用最大相关最小冗余算法分析各特征参数之间的冗余性以及特征参数与数据存储展示模块4中各类振动信号特征图谱之间的相关性，并按照特征参数的权重对特征参数进行优选排序，最后运用缺陷识别算法分析各特征参数表征的设备状态，综合所述设备状态评估GIL是否存在缺陷并获得设备缺陷类型和缺陷级别数据，所述设备缺陷识别模块3将振动信号的特征参数、设备状态、设备缺陷类型和级别数据传输至所述数据存储展示模块4；若设备缺陷级别达到故障级别，则所述设备缺陷识别模块3生成告警信号；
步骤4：设备缺陷告警模块5用于发布所述设备缺陷识别模块3生成的告警信号，提醒运维人员注意设备状态；若所述设备缺陷识别模块3无法识别振动信号特征参数，则工作人员通过试验及现场检测排查手段确定设备状态，所述设备缺陷识别模块3接收所述设备状态，并将无法识别的振动信号特征参数及对应设备状态存入数据存储展示模块4，以新增GIL振动缺陷特征图谱。
上述技术方案中，所述缺陷识别算法为BP神经网络算法或支持向量机算法。
本领域内的技术人员应明白，本申请的实施例可提供为方法、***、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(***)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
本说明书未作详细描述的内容属于本领域专业技术人员公知的现有技术。
Claims (10)
- 一种基于振动信号的GIL缺陷在线监测***，其特征在于：它包括振动信号采集模块(1)、振动信号特征提取模块(2)、设备缺陷识别模块(3)和数据存储展示模块(4)；所述振动信号采集单元(1)用于采集GIL壳体振动信号，并将振动信号传递至所述振动信号特征提取模块(2)；所述振动信号特征提取模块(2)用于提取振动信号在时域和/或频域和/或时频域三个方面的特征参数，并将振动信号特征参数传输至所述设备缺陷识别模块(3)；所述设备缺陷识别模块(3)利用最大相关最小冗余算法分析各特征参数之间的冗余性以及特征参数与所述数据存储展示模块(4)中各类振动信号特征图谱之间的相关性，并按照特征参数的权重对特征参数进行优选排序得到特征参数排序结果，最后运用缺陷识别算法对所述冗余性、所述特征图谱之间的相关性及所述特征参数排序结果进行分析，评估各特征参数表征的设备状态，综合所述设备状态评估GIL是否存在缺陷并获得设备缺陷类型和缺陷级别数据，所述设备缺陷识别模块(3)将振动信号的特征参数、设备状态、设备缺陷类型和级别数据传输至所述数据存储展示模块(4)；若设备缺陷级别达到故障级别，则所述设备缺陷识别模块(3)生成告警信号；所述数据存储展示模块(4)用于存储和展示所述振动信号的特征参数、设备状态、设备缺陷类型和级别数据。
- 基于权利要求1所述的基于振动信号的GIL缺陷在线监测***，其特征在于：所述设备缺陷识别模块(3)首先对所述振动信号特征提取模块(2)输出的振动信号特征参数进行归一化处理获得归一化处理后的振动信号特征参数，再进行后续的最大相关最小冗余算法分析。
- 基于权利要求1所述的基于振动信号的GIL缺陷在线监测***，其特征在于：它还包括设备缺陷告警模块(5)，所述设备缺陷告警模块(5)用于发布所述设备缺陷识别模块(3)生成的告警信号。
- 基于权利要求1所述的基于振动信号的GIL缺陷在线监测***，其特征在于：若所述设备缺陷识别模块(3)无法识别振动信号特征参数，则所述设备缺陷识别模块(3)接收设备状态，并将无法识别的振动信号特征参数及对应设备状态存入数据存储展示模块(4)。
- 基于权利要求1所述的基于振动信号的GIL缺陷在线监测***，其特征在于：所述振动信号采集单元(1)包括多个振动传感器(11)和采集卡(12)，所述振动传感器(11)用于采集GIL壳体振动信号，所述采集卡(12)用于接收振动传感器(11)的振动信号并将振动信号传输至所述振动信号特征提取模块(2)。
- 基于权利要求1所述的基于振动信号的GIL缺陷在线监测***，其特征在于：所述振动信号特征参数包括以下任一参数或其任意组合：振动信号的幅值、频率特性、衰减特性、脉冲特性、边际谱分布特性。
- 基于权利要求5所述的基于振动信号的GIL缺陷在线监测***，其特征在于：所述振动传感器(11)可通过粘贴或绑带方式固定于GIL壳体上，所述振动传感器(11)的基座材质由GIL外壳材料决定，所述振动传感器(11)的基座曲率由GIL外壳半径决定；所述振动传感器(11)的大小与表面根据GIL外壳进行调节，所述振动传感器(11)的检测频率范围为0～15kHz，测量灵敏度不低于0.00001g。
- 一种基于振动信号的GIL缺陷在线监测诊断方法，其特征在于：它包括如下步骤：步骤1，采集GIL壳体振动信号，并将振动信号传输至振动信号特征提取模块(2)；步骤2，提取GIL壳体振动信号在时域和/或频域和/或时频域三个方面的特征参数，并将振动信号特征参数传输至设备缺陷识别模块(3)；步骤3，将振动信号特征参数进行归一化处理，再利用最大相关最小冗余算法分析各特征参数之间的冗余性以及特征参数与数据存储展示模块(4)中各类振动信号特征图谱之间的相关性，并按照特征参数的权重对特征参数进行优选排序，最后运用缺陷识别算法分析各特征参数表征的设备状态，综合所述设备状态评估GIL是否存在缺陷并获得设备缺陷类型和缺陷级别数据，所述设备缺陷识别模块(3)将振动信号的特征参数、设备状态、设备缺陷类型和级别数据传输至所述数据存储展示模块(4)；若设备缺陷级别达到故障级别，则所述设备缺陷识别模块(3)生成告警信号。
- 基于权利要求8所述的基于振动信号的GIL缺陷在线监测方法，其特征在于：它还包括步骤4：发布所述设备缺陷识别模块(3)生成的告警信号；若所述设 备缺陷识别模块(3)无法识别振动信号特征参数，则所述设备缺陷识别模块(3)接收设备状态，并将无法识别的振动信号特征参数及对应设备状态存入数据存储展示模块(4)。
- 基于权利要求9所述的基于振动信号的GIL缺陷在线监测方法，其特征在于：所述缺陷识别算法为BP神经网络算法或支持向量机算法。
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