Method and system for monitoring and identifying loose bolts of fans based on acoustic emission technology

Abstract

The present application provides a method and system for monitoring and identifying the loosening of fan bolts based on acoustic emission technology. The method includes: collecting the operation monitoring information of the target fan bolts at multiple continuous monitoring time nodes within a preset time period, including acoustic emission signal monitoring information, fan operation condition monitoring information and operation environment monitoring information, extracting signal change characteristic data according to the acoustic emission signal monitoring information, processing to obtain the signal theoretical change index, extracting fan condition change characteristic data according to the fan operation condition monitoring information, processing to obtain the fan operation dynamic quantity index, extracting environmental characteristic data according to the operation environment monitoring information, processing to obtain the environmental interference compensation coefficient, processing according to the signal theoretical change quantity index, the fan operation dynamic quantity index and the environmental interference compensation coefficient, obtaining the signal actual change quantity index, and judging the loosening of the target fan bolts, thereby realizing the technology of monitoring and identifying the loosening of fan bolts based on acoustic emission technology.

Description

Technical Field

[0001] The present application relates to the technical field of fan bolt loosening monitoring, and in particular, to a fan bolt loosening monitoring and identification method and system based on acoustic emission technology. Background Art

[0002] During the operation of the fan, loose bolts are one of the common faults, which can lead to structural instability, increased vibration and even safety accidents. Traditional bolt loosening monitoring methods mainly rely on manual inspections and regular maintenance, which are inefficient and difficult to detect bolt loosening problems in real time.

[0003] How to monitor and identify loose bolts in fans based on acoustic emission technology and improve detection accuracy and real-time performance has become an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present application is to provide a method and system for monitoring and identifying loose fan bolts based on acoustic emission technology. The method and system can collect operation monitoring information of target fan bolts at multiple continuous monitoring time nodes within a preset time period, extract signal change characteristic data according to the acoustic emission signal monitoring information, process to obtain the signal theoretical change index, extract fan condition change characteristic data according to the fan operation condition monitoring information, process to obtain the fan operation dynamic index, extract environmental characteristic data according to the operation environment monitoring information, process to obtain the environmental interference compensation coefficient, process according to the signal theoretical change index, the fan operation dynamic index and the environmental interference compensation coefficient, obtain the signal actual change index, and judge the looseness of the target fan bolts, thereby realizing the technology of monitoring and identifying loose fan bolts based on acoustic emission technology.

[0005] The present application also provides a method for monitoring and identifying loose bolts of a fan based on acoustic emission technology, comprising the following steps: Collect the operation monitoring information of the target fan bolts at multiple continuous monitoring time nodes within a preset time period, including acoustic emission signal monitoring information, fan operation condition monitoring information and operation environment monitoring information; Extracting signal change characteristic data based on the acoustic emission signal monitoring information, and further processing to obtain the signal theoretical change index; Extracting the fan operating condition change characteristic data according to the fan operating condition monitoring information, and further processing to obtain the fan operating dynamic quantity index; Extracting environmental characteristic data according to the operating environment monitoring information, and further processing to obtain an environmental interference compensation coefficient; The signal actual variation index is obtained by processing the signal theoretical variation index, the fan operation dynamics index and the environmental interference compensation coefficient, and the looseness of the target fan bolts is determined.

[0006] Optionally, in the method for monitoring and identifying loose bolts of a fan based on acoustic emission technology described in the present application, extracting signal change characteristic data according to the acoustic emission signal monitoring information, and further processing to obtain a signal theoretical variation index, includes: The acoustic emission signal monitoring information includes acoustic emission signal amplitude monitoring information, acoustic emission signal frequency monitoring information and acoustic emission signal capability monitoring information; Extracting the signal amplitude change rate data corresponding to each adjacent time node according to the acoustic emission signal amplitude monitoring information; Extracting the signal frequency change rate data corresponding to each adjacent time node according to the acoustic emission signal frequency monitoring information; Extracting the signal energy change rate data corresponding to each adjacent time node according to the acoustic emission signal energy monitoring information; The signal theoretical variation index is obtained by processing the signal amplitude variation rate data, the signal frequency variation rate data and the signal energy variation rate data.

[0007] Optionally, in the method for monitoring and identifying loose bolts of a fan based on acoustic emission technology described in the present application, extracting characteristic data of fan operating condition changes according to the fan operating condition monitoring information, and further processing to obtain a fan operating dynamic quantity index, includes: The fan operating condition monitoring information includes fan speed monitoring information and fan load monitoring information; Extracting the fan speed change rate data corresponding to each adjacent time node according to the fan speed monitoring information; Extracting the wind turbine load change rate data corresponding to each adjacent time node according to the wind turbine load monitoring information; The fan speed change rate data and the fan load change rate data are processed to obtain a fan operation dynamic quantity index.

[0008] Optionally, in the method for monitoring and identifying loose bolts of a fan based on acoustic emission technology described in the present application, extracting environmental feature data according to the operating environment monitoring information and further processing to obtain an environmental interference compensation coefficient includes: Extracting environmental characteristic data according to the operating environment monitoring information, including noise characteristic data, temperature data and humidity data; The noise characteristic data includes mechanical vibration noise data, aerodynamic noise data and electromagnetic interference noise data; The environmental interference compensation coefficient is obtained by processing the mechanical vibration noise data, the aerodynamic noise data and the electromagnetic interference noise data in combination with the temperature data and the humidity data.

[0009] Optionally, in the method for monitoring and identifying loose bolts of a fan based on acoustic emission technology described in the present application, the signal theoretical variation index, the fan operation dynamics index and the environmental interference compensation coefficient are processed to obtain the actual variation index of the signal, and the looseness of the target fan bolt is judged, including: According to the signal theoretical variation index, the fan operation dynamic index and the environmental interference compensation coefficient, the signal actual variation index is obtained by processing through a preset fan bolt loosening assessment model; Comparing the actual signal change index with a preset signal actual change threshold to obtain a threshold comparison result; Determining the looseness of the target fan bolts according to the threshold comparison result; If the threshold comparison result is greater than the preset threshold, it indicates that the bolts of the target fan are loose.

[0010] Optionally, the method for monitoring and identifying loose bolts of a fan based on acoustic emission technology described in the present application further includes: If the target fan bolts are already loose, collecting the count monitoring information of the target fan bolts within the preset time period; Extracting count change feature data according to the count monitoring information, including ring count change rate data and event count change rate data; Processing the ring count change rate data and the event count change rate data to obtain a bolt loosening speed assessment index; Comparing the bolt loosening speed assessment index with a preset bolt loosening speed assessment threshold to obtain a threshold comparison result; The loosening development speed of the target fan bolts is determined according to the threshold comparison result, and corresponding measures are taken.

[0011] In a second aspect, the present application provides a fan bolt loosening monitoring and identification system based on acoustic emission technology, the system comprising: a memory and a processor, the memory comprising a program of a fan bolt loosening monitoring and identification method based on acoustic emission technology, and the program of the fan bolt loosening monitoring and identification method based on acoustic emission technology is executed by the processor to implement the following steps: Collect the operation monitoring information of the target fan bolts at multiple continuous monitoring time nodes within a preset time period, including acoustic emission signal monitoring information, fan operation condition monitoring information and operation environment monitoring information; Extracting signal change characteristic data based on the acoustic emission signal monitoring information, and further processing to obtain the signal theoretical change index; Extracting the fan operating condition change characteristic data according to the fan operating condition monitoring information, and further processing to obtain the fan operating dynamic quantity index; Extracting environmental characteristic data according to the operating environment monitoring information, and further processing to obtain an environmental interference compensation coefficient; The signal actual variation index is obtained by processing the signal theoretical variation index, the fan operation dynamics index and the environmental interference compensation coefficient, and the looseness of the target fan bolts is determined.

[0012] Optionally, in the wind turbine bolt loosening monitoring and identification system based on acoustic emission technology described in the present application, the signal change characteristic data is extracted according to the acoustic emission signal monitoring information, and further processed to obtain the signal theoretical change index, including: The acoustic emission signal monitoring information includes acoustic emission signal amplitude monitoring information, acoustic emission signal frequency monitoring information and acoustic emission signal capability monitoring information; Extracting the signal amplitude change rate data corresponding to each adjacent time node according to the acoustic emission signal amplitude monitoring information; Extracting the signal frequency change rate data corresponding to each adjacent time node according to the acoustic emission signal frequency monitoring information; Extracting the signal energy change rate data corresponding to each adjacent time node according to the acoustic emission signal energy monitoring information; The signal theoretical variation index is obtained by processing the signal amplitude variation rate data, the signal frequency variation rate data and the signal energy variation rate data.

[0013] Optionally, in the wind turbine bolt loosening monitoring and identification system based on acoustic emission technology described in the present application, the extracting of wind turbine operating condition change characteristic data according to the wind turbine operating condition monitoring information, and further processing to obtain the wind turbine operating dynamic quantity index, includes: The fan operating condition monitoring information includes fan speed monitoring information and fan load monitoring information; Extracting the fan speed change rate data corresponding to each adjacent time node according to the fan speed monitoring information; Extracting the wind turbine load change rate data corresponding to each adjacent time node according to the wind turbine load monitoring information; The fan speed change rate data and the fan load change rate data are processed to obtain a fan operation dynamic quantity index.

[0014] Optionally, in the wind turbine bolt loosening monitoring and identification system based on acoustic emission technology described in the present application, extracting environmental feature data according to the operating environment monitoring information and further processing to obtain an environmental interference compensation coefficient includes: Extracting environmental characteristic data according to the operating environment monitoring information, including noise characteristic data, temperature data and humidity data; The noise characteristic data includes mechanical vibration noise data, aerodynamic noise data and electromagnetic interference noise data; The environmental interference compensation coefficient is obtained by processing the mechanical vibration noise data, the aerodynamic noise data and the electromagnetic interference noise data in combination with the temperature data and the humidity data.

[0015] From the above, it can be seen that the method and system for monitoring and identifying loose fan bolts based on acoustic emission technology provided by the present application collects operation monitoring information of the target fan bolts at multiple continuous monitoring time nodes within a preset time period, including acoustic emission signal monitoring information, fan operation condition monitoring information and operation environment monitoring information, extracts signal change characteristic data according to the acoustic emission signal monitoring information, and further processes to obtain the signal theoretical change index, extracts fan condition change characteristic data according to the fan operation condition monitoring information, and further processes to obtain the fan operation dynamic quantity index, extracts environmental characteristic data according to the operation environment monitoring information, and further processes to obtain the environmental interference compensation coefficient, processes according to the signal theoretical change quantity index, the fan operation dynamic quantity index and the environmental interference compensation coefficient, obtains the signal actual change quantity index, and judges the looseness of the target fan bolts, thereby realizing the technology of fan bolt loosening monitoring and identification based on acoustic emission technology.

[0016] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a method for monitoring and identifying loose bolts of a fan based on acoustic emission technology provided in an embodiment of the present application; Figure 2 A flow chart of obtaining a signal theoretical variation index for a method for monitoring and identifying loose bolts of a fan based on acoustic emission technology provided in an embodiment of the present application; Figure 3 A flow chart of obtaining a wind turbine operating dynamic index for a wind turbine bolt loosening monitoring and identification method based on acoustic emission technology provided in an embodiment of the present application; Figure 4A flow chart of obtaining an environmental interference compensation coefficient for a method for monitoring and identifying loose bolts in a fan based on acoustic emission technology provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 The following is a flow chart of a method for monitoring and identifying loose fan bolts based on acoustic emission technology in some embodiments of the present application. The method for monitoring and identifying loose fan bolts based on acoustic emission technology is used in terminal devices, such as computers, mobile phone terminals, etc. The method for monitoring and identifying loose fan bolts based on acoustic emission technology includes the following steps: S11, collecting operation monitoring information of target fan bolts at multiple continuous monitoring time nodes within a preset time period, including acoustic emission signal monitoring information, fan operation condition monitoring information, and operation environment monitoring information; S12, extracting signal change characteristic data according to the acoustic emission signal monitoring information, and further processing to obtain a signal theoretical change index; S13, extracting the fan operating condition change characteristic data according to the fan operating condition monitoring information, and further processing to obtain the fan operating dynamic quantity index; S14, extracting environmental characteristic data according to the operating environment monitoring information, and further processing to obtain an environmental interference compensation coefficient; S15. Process the signal according to the signal theoretical variation index, the fan operation dynamic index and the environmental interference compensation coefficient to obtain the signal actual variation index, and determine the looseness of the target fan bolts.

[0022] It should be noted that during the operation of the fan, bolt loosening is one of the common faults, which will lead to unstable fan structure, increased vibration and even safety accidents. The traditional bolt loosening monitoring method mainly relies on manual inspection and regular maintenance, which is inefficient and difficult to detect bolt loosening problems in real time. Acoustic emission technology, as an advanced non-destructive testing technology, can effectively monitor the acoustic emission signals generated during the bolt loosening process, and provides a reliable means for monitoring the loosening of fan bolts. Therefore, in the embodiment of the present invention, firstly, the operation monitoring information of the target fan bolts at multiple continuous monitoring time nodes within a preset time period is collected, including acoustic emission signal monitoring information, fan operation The operating condition monitoring information and the operating environment monitoring information are used to extract the signal change characteristic data according to the acoustic emission signal monitoring information, and further process it to obtain the signal theoretical change index. The fan operating condition change characteristic data are extracted according to the fan operating condition monitoring information, and further process it to obtain the fan operation dynamic index. The environmental characteristic data are extracted according to the operating environment monitoring information, and further process it to obtain the environmental interference compensation coefficient. The actual signal change index is obtained according to the signal theoretical change index, the fan operation dynamic index and the environmental interference compensation coefficient, and the looseness of the target fan bolts is judged, thereby realizing the technology of fan bolt loosening monitoring and identification based on acoustic emission technology.

[0023] Please refer to Figure 2 , Figure 2 The present invention is a flowchart of obtaining a signal theoretical variation index of a method for monitoring and identifying loose bolts of a fan based on acoustic emission technology in some embodiments of the present invention. According to an embodiment of the present invention, extracting signal variation characteristic data according to the acoustic emission signal monitoring information and further processing to obtain a signal theoretical variation index includes: S21, the acoustic emission signal monitoring information includes acoustic emission signal amplitude monitoring information, acoustic emission signal frequency monitoring information and acoustic emission signal capacity monitoring information; S22, extracting the signal amplitude change rate data corresponding to each adjacent time node according to the acoustic emission signal amplitude monitoring information; S23, extracting the signal frequency change rate data corresponding to each adjacent time node according to the acoustic emission signal frequency monitoring information; S24, extracting the signal energy change rate data corresponding to each adjacent time node according to the acoustic emission signal energy monitoring information; S25. Process the signal amplitude change rate data, the signal frequency change rate data, and the signal energy change rate data to obtain a signal theoretical variation index.

[0024] It should be noted that when the bolts begin to loosen, the amplitude of the acoustic emission signal will change. Under normal circumstances, the bolts are tightly connected and the amplitude of the acoustic emission signal is low. As the degree of bolt loosening increases, due to the intensified friction and collision between the contact surfaces, an acoustic emission signal with a large amplitude will be generated. According to the acoustic emission signal amplitude monitoring information, the signal amplitude change rate data corresponding to each adjacent time node in multiple continuous monitoring time nodes within a preset time period is extracted. In addition, the acoustic emission signal generated by bolt loosening has a specific frequency range. Different loosening stages and different types of bolt loosening will produce signals of different frequencies. According to the acoustic emission signal frequency monitoring information, the signal frequency change rate data corresponding to each adjacent time node in multiple continuous monitoring time nodes within a preset time period is extracted. In addition, the energy of the acoustic emission signal can also be used as an important indicator for judging the loosening of the bolt. During the loosening of the bolt, the energy will increase with the increase in the degree of loosening. According to the acoustic emission signal energy monitoring information, the signal energy change rate data corresponding to each adjacent time node in multiple continuous monitoring time nodes within a preset time period is extracted. Finally, the signal amplitude change rate data, signal frequency change rate data and signal energy change rate data obtained by extraction are processed to obtain the signal theoretical variation index; The calculation formula of the signal theoretical variation index is: ; in, is the signal theory change index, , , are the signal amplitude change rate data, signal frequency change rate data, and signal energy change rate data corresponding to the i-th adjacent time node among the n-1 adjacent time nodes, , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset fan bolt loosening monitoring database).

[0025] Please refer to Figure 3 , Figure 3 The present invention is a flowchart of obtaining a fan operation dynamic quantity index of a fan bolt loosening monitoring and identification method based on acoustic emission technology in some embodiments of the present application. According to an embodiment of the present invention, the fan operating condition change characteristic data is extracted according to the fan operating condition monitoring information, and further processed to obtain the fan operating dynamic quantity index, including: S31, the fan operating condition monitoring information includes fan speed monitoring information and fan load monitoring information; S32, extracting the fan speed change rate data corresponding to each adjacent time node according to the fan speed monitoring information; S33, extracting the wind turbine load change rate data corresponding to each adjacent time node according to the wind turbine load monitoring information; S34, processing the fan speed change rate data and the fan load change rate data to obtain a fan operation dynamic quantity index.

[0026] It should be noted that changes in fan speed and load will change the stress state of the bolt connection. When the fan speed increases or the load increases, the tension, shear force, etc. on the bolts will change, which may affect the speed and method of bolt loosening, and thus change the generation law of the acoustic emission signal. Therefore, in order to more accurately judge the loosening of the fan bolts, it is necessary to monitor the changes in the fan speed and load, extract the fan speed change rate data corresponding to each adjacent time node according to the fan speed monitoring information, and extract the fan load change rate data corresponding to each adjacent time node according to the fan load monitoring information. Then, the fan speed change rate data and the fan load change rate data are processed to obtain the fan operation dynamic quantity index; The calculation formula of the fan operation dynamic index is: ; in, is the wind turbine operation dynamic index, , are the fan speed change rate data and fan load change rate data corresponding to the i-th adjacent time node among n-1 adjacent time nodes, , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset fan bolt loosening monitoring database).

[0027] Please refer to Figure 4 , Figure 4 The present invention is a flowchart of obtaining an environmental interference compensation coefficient of a method for monitoring and identifying loose bolts of a fan based on acoustic emission technology in some embodiments of the present invention. According to an embodiment of the present invention, extracting environmental feature data according to the operating environment monitoring information and further processing to obtain an environmental interference compensation coefficient include: S41, extracting environmental characteristic data according to the operating environment monitoring information, including noise characteristic data, temperature data and humidity data; S42, the noise characteristic data includes mechanical vibration noise data, aerodynamic noise data and electromagnetic interference noise data; S43, processing the mechanical vibration noise data, the aerodynamic noise data, and the electromagnetic interference noise data in combination with the temperature data and the humidity data to obtain an environmental interference compensation coefficient.

[0028] It should be noted that the fan itself will generate mechanical vibrations during operation, such as vibration noise generated by blade rotation, motor operation, gear meshing, etc. The sound waves generated by these vibrations have a wide frequency range, which may mask the acoustic emission signals generated by loose bolts. When air flows through structures such as blades and towers, aerodynamic noise will be generated, which will have a negative impact on the signal quality received by the acoustic emission sensor. There are a large number of electrical equipment inside the fan, such as generators and inverters. These devices will generate electromagnetic interference during operation. Electromagnetic interference may generate noise signals in the signal transmission line of the acoustic emission sensor in the form of electromagnetic induction. These types of noise and the temperature and humidity of the area where the fan bolts are located will affect the identification of the looseness of the fan bolts. Therefore, these factors need to be considered. To this end, the mechanical vibration noise data, aerodynamic noise data and electromagnetic interference noise data are processed in combination with the temperature data and humidity data to obtain the environmental interference compensation coefficient; Wherein, the calculation formula of the environmental interference compensation coefficient is: ; in, is the environmental interference compensation coefficient, , , They are mechanical vibration noise data, aerodynamic noise data, and electromagnetic interference noise data. , They are temperature data and humidity data respectively. , , , , , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset fan bolt loosening monitoring database).

[0029] According to an embodiment of the present invention, the signal theoretical variation index, the wind turbine operation dynamics index and the environmental interference compensation coefficient are processed to obtain the signal actual variation index, and the looseness of the target wind turbine bolts is determined, including: According to the signal theoretical variation index, the fan operation dynamic index and the environmental interference compensation coefficient, the signal actual variation index is obtained by processing through a preset fan bolt loosening assessment model; Comparing the actual signal change index with a preset signal actual change threshold to obtain a threshold comparison result; Determining the looseness of the target fan bolts according to the threshold comparison result; If the threshold comparison result is greater than the preset threshold, it indicates that the bolts of the target fan are loose.

[0030] It should be noted that the signal theoretical variation index, the fan operation dynamic index and the environmental interference compensation coefficient are processed through a preset fan bolt looseness assessment model to obtain the signal actual variation index, which is then compared with the preset signal actual variation threshold to obtain a threshold comparison result. The looseness of the target fan bolts is judged according to the threshold comparison result. If the threshold comparison result is greater than the preset threshold, it indicates that the target fan bolts are loose. The calculation formula of the actual signal change index is: ; in, is the actual change index of the signal, , , They are the signal theory variation index, the fan operation dynamic index, and the environmental interference compensation coefficient. , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset fan bolt loosening monitoring database).

[0031] According to an embodiment of the present invention, it also includes: If the target fan bolts are already loose, collecting the count monitoring information of the target fan bolts within the preset time period; Extracting count change feature data according to the count monitoring information, including ring count change rate data and event count change rate data; Processing the ring count change rate data and the event count change rate data to obtain a bolt loosening speed assessment index; Comparing the bolt loosening speed assessment index with a preset bolt loosening speed assessment threshold to obtain a threshold comparison result; The loosening development speed of the target fan bolts is determined according to the threshold comparison result, and corresponding measures are taken.

[0032] It should be noted that if the target fan bolts are already loose, then the count monitoring information of the target fan bolts within a preset time period is collected, and the count change characteristic data is further extracted, including the ring count change rate data and the event count change rate data, wherein the ring count refers to the number of times the acoustic emission signal exceeds the preset threshold, and the event count refers to the number of occurrences of the acoustic emission event. An acoustic emission event usually refers to a complete energy release process. Then, the above data is processed to obtain the bolt loosening speed assessment index, and then compared with the preset bolt loosening speed assessment threshold to obtain the threshold comparison result. The loosening development speed of the target fan bolts is judged according to the threshold comparison result. If the loosening speed is abnormal, corresponding measures must be taken immediately; The calculation formula of the bolt loosening speed evaluation index is: ; in, is the bolt loosening speed assessment index, , They are respectively the ring count change rate data and event count change rate data, , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset fan bolt loosening monitoring database).

[0033] According to an embodiment of the present invention, it also includes: If the target fan bolt is already loose, collecting waveform monitoring information of the target fan bolt within the preset time period; Extracting waveform characteristic data according to the waveform monitoring information, including rise time data and duration data; Processing the rise time data and the duration data to obtain a bolt loosening fault assessment index; Comparing the bolt loosening fault assessment index with a preset bolt loosening fault assessment threshold to obtain a threshold comparison result; Determining the cause of the loosening of the target fan bolts according to the threshold comparison result; Wherein, the calculation formula of the bolt loosening fault assessment index is: ; in, is the bolt loosening fault assessment index, , They are rise time data and duration data respectively. , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset fan bolt loosening monitoring database).

[0034] It should be noted that if the target fan bolts are already loose, the waveform monitoring information of the target fan bolts within the preset time period is collected, and then the waveform feature data is extracted, including rise time data and duration data, wherein the rise time refers to the time required for the acoustic emission signal to rise from the initial amplitude to the peak amplitude, and the duration refers to the time experienced by the acoustic emission signal from the start to the end. Then, the rise time data and the duration data are processed to obtain a bolt loosening fault assessment index, and compared with the preset bolt loosening fault assessment threshold to obtain a threshold comparison result. The cause of the loosening of the target fan bolts is determined according to the threshold comparison result. The loosening of fan bolts caused by different reasons may produce acoustic emission signals with different waveform characteristics. For example, the loosening of bolts caused by impact may have a shorter rise time of the signal, while the loosening caused by slow processes such as fatigue may have a longer rise time. The duration can also reflect the scale and nature of the acoustic emission event.

[0035] In a second aspect, the present invention further discloses a fan bolt loosening monitoring and identification system based on acoustic emission technology, comprising a memory and a processor, wherein the memory comprises a fan bolt loosening monitoring and identification method program based on acoustic emission technology, and when the fan bolt loosening monitoring and identification method program based on acoustic emission technology is executed by the processor, the following steps are implemented: Collect the operation monitoring information of the target fan bolts at multiple continuous monitoring time nodes within a preset time period, including acoustic emission signal monitoring information, fan operation condition monitoring information and operation environment monitoring information; Extracting signal change characteristic data based on the acoustic emission signal monitoring information, and further processing to obtain the signal theoretical change index; Extracting the fan operating condition change characteristic data according to the fan operating condition monitoring information, and further processing to obtain the fan operating dynamic quantity index; Extracting environmental characteristic data according to the operating environment monitoring information, and further processing to obtain an environmental interference compensation coefficient; The signal actual variation index is obtained by processing the signal theoretical variation index, the fan operation dynamics index and the environmental interference compensation coefficient, and the looseness of the target fan bolts is determined.

[0036] It should be noted that during the operation of the fan, bolt loosening is one of the common faults, which will lead to unstable fan structure, increased vibration and even safety accidents. The traditional bolt loosening monitoring method mainly relies on manual inspection and regular maintenance, which is inefficient and difficult to detect bolt loosening problems in real time. Acoustic emission technology, as an advanced non-destructive testing technology, can effectively monitor the acoustic emission signals generated during the bolt loosening process, and provides a reliable means for monitoring the loosening of fan bolts. Therefore, in the embodiment of the present invention, firstly, the operation monitoring information of the target fan bolts at multiple continuous monitoring time nodes within a preset time period is collected, including acoustic emission signal monitoring information, fan operation The operating condition monitoring information and the operating environment monitoring information are used to extract the signal change characteristic data according to the acoustic emission signal monitoring information, and further process it to obtain the signal theoretical change index. The fan operating condition change characteristic data are extracted according to the fan operating condition monitoring information, and further process it to obtain the fan operation dynamic index. The environmental characteristic data are extracted according to the operating environment monitoring information, and further process it to obtain the environmental interference compensation coefficient. The actual signal change index is obtained according to the signal theoretical change index, the fan operation dynamic index and the environmental interference compensation coefficient, and the looseness of the target fan bolts is judged, thereby realizing the technology of fan bolt loosening monitoring and identification based on acoustic emission technology.

[0037] According to an embodiment of the present invention, the step of extracting signal change characteristic data based on the acoustic emission signal monitoring information and further processing to obtain a signal theoretical variation index includes: The acoustic emission signal monitoring information includes acoustic emission signal amplitude monitoring information, acoustic emission signal frequency monitoring information and acoustic emission signal capability monitoring information; Extracting the signal amplitude change rate data corresponding to each adjacent time node according to the acoustic emission signal amplitude monitoring information; Extracting the signal frequency change rate data corresponding to each adjacent time node according to the acoustic emission signal frequency monitoring information; Extracting the signal energy change rate data corresponding to each adjacent time node according to the acoustic emission signal energy monitoring information; The signal theoretical variation index is obtained by processing the signal amplitude variation rate data, the signal frequency variation rate data and the signal energy variation rate data.

[0038] It should be noted that when the bolts begin to loosen, the amplitude of the acoustic emission signal will change. Under normal circumstances, the bolts are tightly connected and the amplitude of the acoustic emission signal is low. As the degree of bolt loosening increases, due to the intensified friction and collision between the contact surfaces, an acoustic emission signal with a large amplitude will be generated. According to the acoustic emission signal amplitude monitoring information, the signal amplitude change rate data corresponding to each adjacent time node in multiple continuous monitoring time nodes within a preset time period is extracted. In addition, the acoustic emission signal generated by bolt loosening has a specific frequency range. Different loosening stages and different types of bolt loosening will produce signals of different frequencies. According to the acoustic emission signal frequency monitoring information, the signal frequency change rate data corresponding to each adjacent time node in multiple continuous monitoring time nodes within a preset time period is extracted. In addition, the energy of the acoustic emission signal can also be used as an important indicator for judging the loosening of the bolt. During the loosening of the bolt, the energy will increase with the increase in the degree of loosening. According to the acoustic emission signal energy monitoring information, the signal energy change rate data corresponding to each adjacent time node in multiple continuous monitoring time nodes within a preset time period is extracted. Finally, the signal amplitude change rate data, signal frequency change rate data and signal energy change rate data obtained by extraction are processed to obtain the signal theoretical variation index; The calculation formula of the signal theoretical variation index is: ; in, is the signal theory change index, , , are the signal amplitude change rate data, signal frequency change rate data, and signal energy change rate data corresponding to the i-th adjacent time node among the n-1 adjacent time nodes, , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset fan bolt loosening monitoring database).

[0039] According to an embodiment of the present invention, extracting the fan operating condition change characteristic data according to the fan operating condition monitoring information, and further processing to obtain the fan operating dynamic quantity index, includes: The fan operating condition monitoring information includes fan speed monitoring information and fan load monitoring information; Extracting the fan speed change rate data corresponding to each adjacent time node according to the fan speed monitoring information; Extracting the wind turbine load change rate data corresponding to each adjacent time node according to the wind turbine load monitoring information; The fan speed change rate data and the fan load change rate data are processed to obtain a fan operation dynamic quantity index.

[0040] It should be noted that changes in fan speed and load will change the stress state of the bolt connection. When the fan speed increases or the load increases, the tension, shear force, etc. on the bolts will change, which may affect the speed and method of bolt loosening, and thus change the generation law of the acoustic emission signal. Therefore, in order to more accurately judge the loosening of the fan bolts, it is necessary to monitor the changes in the fan speed and load, extract the fan speed change rate data corresponding to each adjacent time node according to the fan speed monitoring information, and extract the fan load change rate data corresponding to each adjacent time node according to the fan load monitoring information. Then, the fan speed change rate data and the fan load change rate data are processed to obtain the fan operation dynamic quantity index; The calculation formula of the fan operation dynamic index is: ; in, is the wind turbine operation dynamic index, , are the fan speed change rate data and fan load change rate data corresponding to the i-th adjacent time node among n-1 adjacent time nodes, , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset fan bolt loosening monitoring database).

[0041] According to an embodiment of the present invention, extracting environmental feature data according to the operating environment monitoring information and further processing to obtain an environmental interference compensation coefficient includes: Extracting environmental characteristic data according to the operating environment monitoring information, including noise characteristic data, temperature data and humidity data; The noise characteristic data includes mechanical vibration noise data, aerodynamic noise data and electromagnetic interference noise data; The environmental interference compensation coefficient is obtained by processing the mechanical vibration noise data, the aerodynamic noise data and the electromagnetic interference noise data in combination with the temperature data and the humidity data.

[0042] It should be noted that the fan itself will generate mechanical vibrations during operation, such as vibration noise generated by blade rotation, motor operation, gear meshing, etc. The sound waves generated by these vibrations have a wide frequency range, which may mask the acoustic emission signals generated by loose bolts. When air flows through structures such as blades and towers, aerodynamic noise will be generated, which will have a negative impact on the signal quality received by the acoustic emission sensor. There are a large number of electrical equipment inside the fan, such as generators and inverters. These devices will generate electromagnetic interference during operation. Electromagnetic interference may generate noise signals in the signal transmission line of the acoustic emission sensor in the form of electromagnetic induction. These types of noise and the temperature and humidity of the area where the fan bolts are located will affect the identification of the looseness of the fan bolts. Therefore, these factors need to be considered. To this end, the mechanical vibration noise data, aerodynamic noise data and electromagnetic interference noise data are processed in combination with the temperature data and humidity data to obtain the environmental interference compensation coefficient; Wherein, the calculation formula of the environmental interference compensation coefficient is: ; in, is the environmental interference compensation coefficient, , , They are mechanical vibration noise data, aerodynamic noise data, and electromagnetic interference noise data. , They are temperature data and humidity data respectively. , , , , , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset fan bolt loosening monitoring database).

[0043] According to an embodiment of the present invention, the signal theoretical variation index, the wind turbine operation dynamics index and the environmental interference compensation coefficient are processed to obtain the signal actual variation index, and the looseness of the target wind turbine bolts is determined, including: According to the signal theoretical variation index, the fan operation dynamic index and the environmental interference compensation coefficient, the signal actual variation index is obtained by processing through a preset fan bolt loosening assessment model; Comparing the actual signal change index with a preset signal actual change threshold to obtain a threshold comparison result; Determining the looseness of the target fan bolts according to the threshold comparison result; If the threshold comparison result is greater than the preset threshold, it indicates that the bolts of the target fan are loose.

[0044] It should be noted that the signal theoretical variation index, the fan operation dynamic index and the environmental interference compensation coefficient are processed through a preset fan bolt looseness assessment model to obtain the signal actual variation index, which is then compared with the preset signal actual variation threshold to obtain a threshold comparison result. The looseness of the target fan bolts is judged according to the threshold comparison result. If the threshold comparison result is greater than the preset threshold, it indicates that the target fan bolts are loose. The calculation formula of the actual signal change index is: ; in, is the actual change index of the signal, , , They are the signal theory variation index, the fan operation dynamic index, and the environmental interference compensation coefficient. , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset fan bolt loosening monitoring database).

[0045] According to an embodiment of the present invention, it also includes: If the target fan bolts are already loose, collecting the count monitoring information of the target fan bolts within the preset time period; Extracting count change feature data according to the count monitoring information, including ring count change rate data and event count change rate data; Processing the ring count change rate data and the event count change rate data to obtain a bolt loosening speed assessment index; Comparing the bolt loosening speed assessment index with a preset bolt loosening speed assessment threshold to obtain a threshold comparison result; The loosening development speed of the target fan bolts is determined according to the threshold comparison result, and corresponding measures are taken.

[0046] It should be noted that if the target fan bolts are already loose, then the count monitoring information of the target fan bolts within a preset time period is collected, and the count change characteristic data is further extracted, including the ring count change rate data and the event count change rate data, wherein the ring count refers to the number of times the acoustic emission signal exceeds the preset threshold, and the event count refers to the number of occurrences of the acoustic emission event. An acoustic emission event usually refers to a complete energy release process. Then, the above data is processed to obtain the bolt loosening speed assessment index, and then compared with the preset bolt loosening speed assessment threshold to obtain the threshold comparison result. The loosening development speed of the target fan bolts is judged according to the threshold comparison result. If the loosening speed is abnormal, corresponding measures must be taken immediately; The calculation formula of the bolt loosening speed evaluation index is: ; in, is the bolt loosening speed assessment index, , They are respectively the ring count change rate data and event count change rate data, , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset fan bolt loosening monitoring database).

[0047] According to an embodiment of the present invention, it also includes: If the target fan bolt is already loose, collecting waveform monitoring information of the target fan bolt within the preset time period; Extracting waveform characteristic data according to the waveform monitoring information, including rise time data and duration data; Processing the rise time data and the duration data to obtain a bolt loosening fault assessment index; Comparing the bolt loosening fault assessment index with a preset bolt loosening fault assessment threshold to obtain a threshold comparison result; Determining the cause of the loosening of the target fan bolts according to the threshold comparison result; Wherein, the calculation formula of the bolt loosening fault assessment index is: ; in, is the bolt loosening fault assessment index, , They are rise time data and duration data respectively. , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset fan bolt loosening monitoring database).

[0048] It should be noted that if the target fan bolts are already loose, the waveform monitoring information of the target fan bolts within the preset time period is collected, and then the waveform feature data is extracted, including rise time data and duration data, wherein the rise time refers to the time required for the acoustic emission signal to rise from the initial amplitude to the peak amplitude, and the duration refers to the time experienced by the acoustic emission signal from the start to the end. Then, the rise time data and the duration data are processed to obtain a bolt loosening fault assessment index, and compared with the preset bolt loosening fault assessment threshold to obtain a threshold comparison result. The cause of the loosening of the target fan bolts is determined according to the threshold comparison result. The loosening of fan bolts caused by different reasons may produce acoustic emission signals with different waveform characteristics. For example, the loosening of bolts caused by impact may have a shorter rise time of the signal, while the loosening caused by slow processes such as fatigue may have a longer rise time. The duration can also reflect the scale and nature of the acoustic emission event.

[0049] The present invention discloses a method and system for monitoring and identifying loosening of fan bolts based on acoustic emission technology. The method and system collect operation monitoring information of target fan bolts at multiple continuous monitoring time nodes within a preset time period, including acoustic emission signal monitoring information, fan operation condition monitoring information and operation environment monitoring information. Signal change characteristic data is extracted according to the acoustic emission signal monitoring information, and further processed to obtain a signal theoretical change index. Fan condition change characteristic data is extracted according to the fan operation condition monitoring information, and further processed to obtain a fan operation dynamic quantity index. Environmental characteristic data is extracted according to the operation environment monitoring information, and further processed to obtain an environmental interference compensation coefficient. The signal theoretical change quantity index, the fan operation dynamic quantity index and the environmental interference compensation coefficient are processed to obtain an actual signal change quantity index, and the loosening of the target fan bolts is judged, thereby realizing a technology for monitoring and identifying loosening of fan bolts based on acoustic emission technology.

[0050] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0051] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0052] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0053] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0054] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A method for monitoring and identifying loose bolts in a fan based on acoustic emission technology, characterized in that: The following steps are involved: Collect the operation monitoring information of the target fan bolts at multiple continuous monitoring time nodes within a preset time period, including acoustic emission signal monitoring information, fan operation condition monitoring information and operation environment monitoring information; Extracting signal change characteristic data based on the acoustic emission signal monitoring information, and further processing to obtain the signal theoretical change index; Extracting the fan operating condition change characteristic data according to the fan operating condition monitoring information, and further processing to obtain the fan operating dynamic quantity index; Extracting environmental characteristic data according to the operating environment monitoring information, and further processing to obtain an environmental interference compensation coefficient; The signal actual variation index is obtained by processing the signal theoretical variation index, the fan operation dynamics index and the environmental interference compensation coefficient, and the looseness of the target fan bolts is determined.

2. The method for monitoring and identifying loose bolts of a fan based on acoustic emission technology according to claim 1 is characterized in that: The step of extracting signal change characteristic data according to the acoustic emission signal monitoring information and further processing to obtain a signal theoretical change index includes: The acoustic emission signal monitoring information includes acoustic emission signal amplitude monitoring information, acoustic emission signal frequency monitoring information and acoustic emission signal capability monitoring information; Extracting the signal amplitude change rate data corresponding to each adjacent time node according to the acoustic emission signal amplitude monitoring information; Extracting the signal frequency change rate data corresponding to each adjacent time node according to the acoustic emission signal frequency monitoring information; Extracting the signal energy change rate data corresponding to each adjacent time node according to the acoustic emission signal energy monitoring information; The signal theoretical variation index is obtained by processing the signal amplitude variation rate data, the signal frequency variation rate data and the signal energy variation rate data.

3. The method for monitoring and identifying loose bolts of a fan based on acoustic emission technology according to claim 2 is characterized in that: The extracting of the fan operating condition change characteristic data according to the fan operating condition monitoring information and further processing to obtain the fan operating dynamic quantity index includes: The fan operating condition monitoring information includes fan speed monitoring information and fan load monitoring information; Extracting the fan speed change rate data corresponding to each adjacent time node according to the fan speed monitoring information; Extracting the wind turbine load change rate data corresponding to each adjacent time node according to the wind turbine load monitoring information; The fan speed change rate data and the fan load change rate data are processed to obtain a fan operation dynamic quantity index.

4. The method for monitoring and identifying loose bolts of a fan based on acoustic emission technology according to claim 3 is characterized in that: The step of extracting environmental characteristic data according to the operating environment monitoring information and further processing to obtain an environmental interference compensation coefficient includes: Extracting environmental characteristic data according to the operating environment monitoring information, including noise characteristic data, temperature data and humidity data; The noise characteristic data includes mechanical vibration noise data, aerodynamic noise data and electromagnetic interference noise data; The environmental interference compensation coefficient is obtained by processing the mechanical vibration noise data, the aerodynamic noise data and the electromagnetic interference noise data in combination with the temperature data and the humidity data.

5. The method for monitoring and identifying loose bolts of a fan based on acoustic emission technology according to claim 4 is characterized in that: The processing is performed according to the signal theoretical variation index, the fan operation dynamic index and the environmental interference compensation coefficient to obtain the signal actual variation index, and to judge the looseness of the target fan bolt, including: According to the signal theoretical variation index, the fan operation dynamic index and the environmental interference compensation coefficient, the signal actual variation index is obtained by processing through a preset fan bolt loosening assessment model; Comparing the actual signal change index with a preset signal actual change threshold to obtain a threshold comparison result; Determining the looseness of the target fan bolts according to the threshold comparison result; If the threshold comparison result is greater than the preset threshold, it indicates that the bolts of the target fan are loose.

6. The method for monitoring and identifying loose bolts of a fan based on acoustic emission technology according to claim 5 is characterized in that: Also includes: If the target fan bolts are already loose, collecting the count monitoring information of the target fan bolts within the preset time period; Extracting count change feature data according to the count monitoring information, including ring count change rate data and event count change rate data; Processing the ring count change rate data and the event count change rate data to obtain a bolt loosening speed assessment index; Comparing the bolt loosening speed assessment index with a preset bolt loosening speed assessment threshold to obtain a threshold comparison result; The loosening development speed of the target fan bolts is determined according to the threshold comparison result, and corresponding measures are taken.

7. The fan bolt loosening monitoring and identification system based on acoustic emission technology is characterized by: The system includes: a memory and a processor, wherein the memory includes a program of a method for monitoring and identifying the looseness of fan bolts based on acoustic emission technology, and when the program of the method for monitoring and identifying the looseness of fan bolts based on acoustic emission technology is executed by the processor, the following steps are implemented: Collect the operation monitoring information of the target fan bolts at multiple continuous monitoring time nodes within a preset time period, including acoustic emission signal monitoring information, fan operation condition monitoring information and operation environment monitoring information; Extracting signal change characteristic data based on the acoustic emission signal monitoring information, and further processing to obtain the signal theoretical change index; Extracting the fan operating condition change characteristic data according to the fan operating condition monitoring information, and further processing to obtain the fan operating dynamic quantity index; Extracting environmental characteristic data according to the operating environment monitoring information, and further processing to obtain an environmental interference compensation coefficient; The signal actual variation index is obtained by processing the signal theoretical variation index, the fan operation dynamics index and the environmental interference compensation coefficient, and the looseness of the target fan bolts is determined.

8. The wind turbine bolt loosening monitoring and identification system based on acoustic emission technology according to claim 7 is characterized in that: The step of extracting signal change characteristic data according to the acoustic emission signal monitoring information and further processing to obtain a signal theoretical change index includes: The acoustic emission signal monitoring information includes acoustic emission signal amplitude monitoring information, acoustic emission signal frequency monitoring information and acoustic emission signal capability monitoring information; Extracting the signal amplitude change rate data corresponding to each adjacent time node according to the acoustic emission signal amplitude monitoring information; Extracting the signal frequency change rate data corresponding to each adjacent time node according to the acoustic emission signal frequency monitoring information; Extracting the signal energy change rate data corresponding to each adjacent time node according to the acoustic emission signal energy monitoring information; The signal theoretical variation index is obtained by processing the signal amplitude variation rate data, the signal frequency variation rate data and the signal energy variation rate data.

9. The wind turbine bolt loosening monitoring and identification system based on acoustic emission technology according to claim 8 is characterized in that: The extracting of the fan operating condition change characteristic data according to the fan operating condition monitoring information and further processing to obtain the fan operating dynamic quantity index includes: The fan operating condition monitoring information includes fan speed monitoring information and fan load monitoring information; Extracting the fan speed change rate data corresponding to each adjacent time node according to the fan speed monitoring information; Extracting the wind turbine load change rate data corresponding to each adjacent time node according to the wind turbine load monitoring information; The fan speed change rate data and the fan load change rate data are processed to obtain a fan operation dynamic quantity index.

10. The wind turbine bolt loosening monitoring and identification system based on acoustic emission technology according to claim 9 is characterized in that: The step of extracting environmental characteristic data according to the operating environment monitoring information and further processing to obtain an environmental interference compensation coefficient includes: Extracting environmental characteristic data according to the operating environment monitoring information, including noise characteristic data, temperature data and humidity data; The noise characteristic data includes mechanical vibration noise data, aerodynamic noise data and electromagnetic interference noise data; The environmental interference compensation coefficient is obtained by processing the mechanical vibration noise data, the aerodynamic noise data and the electromagnetic interference noise data in combination with the temperature data and the humidity data.

Images