Gearbox state detection system and gearbox state detection method

Abstract

The invention relates to the technical field of gear box state detection, in particular to a gear box state detection system and a gear box state detection method. The gear box state detection system comprises a gear box; the first detection assembly is arranged on the gear box and is used for detecting a wear degree parameter of the gear box; the wireless transmission module is arranged on the gear box; and the control terminal is provided with an abrasion analysis unit, the first detection assembly sends the abrasion degree parameters to the abrasion analysis unit through the wireless transmission module, and the abrasion analysis unit analyzes the abrasion degree of the gearbox according to the abrasion degree parameters. By means of the arrangement, online real-time detection of the abrasion degree condition of the gearbox can be achieved, it is guaranteed that a user can determine whether the gearbox is abraded or not in the first time, it is guaranteed that the gearbox stably and reliably operates, shutdown detection is not needed, it is guaranteed that the gearbox can continuously operate, manpower waste is avoided, and the working efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of gear box state detection, and particularly relates to a gear box state detection system and a gear box state detection method. BACKGROUND

[0002] The yaw device of a wind turbine generator is an important component of the wind turbine generator. The main function of the yaw gear box in the yaw device is to adjust the orientation of the nacelle by meshing the output pinion gear with the yaw bearing gear in the wind turbine, so as to maximize the use of wind power. In the related art, the use state of the gear box, such as the lubrication degree and the wear degree, needs to be detected periodically, which affects the work progress and consumes time and effort.

[0003] Therefore, it is necessary to provide a gear box state detection system and a gear box state detection method to at least partially solve the problems in the prior art. SUMMARY

[0004] The present disclosure aims to at least solve one of the technical problems in the prior art or related art.

[0005] To this end, the first aspect of the present disclosure provides a gear box state detection system;

[0006] The second aspect of the present disclosure provides a gear box state detection method for the above gear box state detection system.

[0007] Therefore, according to the first aspect of the present disclosure, a gear box state detection system is provided, which comprises:

[0008] a gear box;

[0009] a first detection component arranged in the gear box and configured to detect a wear degree parameter of the gear box;

[0010] a wireless transmission module arranged in the gear box;

[0011] a control terminal, wherein the control terminal is provided with a wear analysis unit, the first detection component sends the wear degree parameter to the wear analysis unit through the wireless transmission module, and the wear analysis unit analyzes the wear degree of the gear box according to the wear degree parameter.

[0012] In a possible implementation, the first detection component comprises:

[0013] a vibration sensor configured to detect the vibration frequency inside the gear box;

[0014] a temperature sensor configured to detect the temperature data inside the gear box;

[0015] A piezoelectric ceramic sensor is used to detect the acoustic emission frequency inside the aforementioned gearbox.

[0016] In one feasible implementation, the wear analysis unit is equipped with a wear assessment model. The vibration frequency data, temperature data, and acoustic emission frequency data are input into the wear assessment model to obtain the actual wear degree of the gearbox. If the actual wear degree is greater than or equal to a preset wear degree, it is determined that the gearbox has experienced wear.

[0017] In one feasible implementation, the gearbox condition detection system further includes:

[0018] The second detection component is installed in the gearbox and is used to detect the lubrication coefficient parameter of the gearbox.

[0019] The control terminal is equipped with a lubrication coefficient analysis unit. The second detection component sends the lubrication coefficient parameters to the lubrication coefficient analysis unit through the wireless transmission module. The lubrication coefficient analysis unit analyzes the lubrication coefficient of the gearbox based on the lubrication coefficient parameters.

[0020] In one feasible implementation, the second detection component includes:

[0021] An oil viscosity sensor is used to detect the viscosity of the oil in the aforementioned gearbox.

[0022] A ferromagnetic wear debris concentration sensor is used to detect the ferromagnetic wear debris concentration of the aforementioned gearbox.

[0023] A moisture content sensor is used to detect the moisture content of the gearbox mentioned above.

[0024] In one feasible implementation, the lubrication coefficient analysis unit is equipped with a lubrication coefficient evaluation model, which sends the oil viscosity data, the ferromagnetic wear debris concentration data, and the moisture content data to the lubrication coefficient evaluation model to obtain the actual lubrication coefficient of the gearbox. If the actual lubrication coefficient exceeds the lubrication coefficient threshold, the gearbox is determined to have lubrication failure.

[0025] A gearbox condition detection method is provided according to a second aspect embodiment of this disclosure, used in a gearbox condition detection system as described in any of the above technical solutions, comprising:

[0026] Obtain wear parameters of gearboxes with different wear levels, and construct a wear assessment model based on the above wear parameters;

[0027] Obtain the actual wear parameters of the target gearbox and send the actual wear parameters to the wear assessment model to obtain the actual wear of the target gearbox.

[0028] Specifically, if the actual wear level is greater than or equal to the preset wear level, the target gearbox is determined to be worn.

[0029] In one feasible implementation, the steps of obtaining wear parameters of gearboxes with different wear levels and constructing a wear assessment model based on the wear parameters include:

[0030] The wear parameters of the gearboxes with different wear levels are obtained, and the wear parameters are used as data samples to construct a dataset, wherein the wear parameters include vibration frequency data, temperature data and acoustic emission frequency data;

[0031] The dataset is divided into a training set and a validation set. The training set is used to train a machine learning model, and the validation set is used to evaluate the performance and accuracy of the machine learning model. The best-performing machine learning model is selected to construct a wear assessment model.

[0032] In one feasible implementation, the aforementioned machine learning model includes at least one of the following: logistic regression model, linear discriminant analysis model, K-nearest neighbor model, Naive Bayes model, support vector machine model, random forest model, and neural network model.

[0033] In one feasible implementation, the above gearbox condition detection method further includes:

[0034] The optimal viscosity value of the gears, the critical ferromagnetic wear debris concentration value and the critical moisture content value for lubrication failure of the gearbox were obtained to construct the lubrication coefficient model of the gearbox.

[0035] Obtain the lubrication coefficient parameters of the target gearbox and input them into the lubrication coefficient model to obtain the actual lubrication coefficient of the target gearbox.

[0036] If the actual lubrication coefficient exceeds the threshold, the target gearbox is determined to have lubrication failure.

[0037] Compared to existing technologies, this disclosure offers at least the following advantages: The gearbox condition detection system provided in this embodiment includes a gearbox, a first detection component, a wireless transmission module, and a control terminal. The first detection component is disposed within the gearbox, specifically embedded in the top of the gearbox, and detects wear parameters of the gearbox. A wireless transmission module is located on the side wall of the gearbox, and the control terminal may include a wear analysis unit. The first detection component can remotely transmit the detected wear parameter data to the wear analysis unit via the wireless transmission module, and the wear analysis unit analyzes the wear of the gearbox, thereby achieving online real-time detection of the gearbox's wear status. This ensures that users can determine whether the gearbox is worn immediately, guaranteeing stable and reliable gearbox operation without downtime for inspection, ensuring continuous gearbox operation, avoiding wasted manpower, and improving work efficiency. Attached Figure Description

[0038] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of exemplary embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this disclosure. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0039] Figure 1 This is a schematic structural diagram of a gearbox condition detection system according to an embodiment of the present disclosure;

[0040] Figure 2 This is a schematic cross-sectional view of a gearbox condition detection system according to an embodiment of the present disclosure;

[0041] Figure 3 This is a schematic flowchart illustrating a gearbox condition detection method according to an embodiment of this disclosure.

[0042] in, Figure 1 and Figure 2 The correspondence between the reference numerals and component names in the attached drawings is as follows:

[0043] 100 Gearbox, 200 Integrated Sensor, 300 Wireless Transmission Module, 400 Control Terminal. Detailed Implementation

[0044] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be noted that the description of these embodiments is intended to aid in understanding the invention, but does not constitute a limitation thereof. The specific structural and functional details disclosed herein are merely for describing exemplary embodiments of the invention. However, the invention can be embodied in many alternative forms and should not be construed as being limited to the embodiments described herein.

[0045] like Figure 1 and Figure 2 As shown, a gearbox condition detection system is proposed according to a first aspect embodiment of the present disclosure, comprising: a gearbox 100; a first detection component disposed in the gearbox 100 for detecting wear parameters of the gearbox 100; a wireless transmission module 300 disposed in the gearbox 100; and a control terminal 400, wherein the control terminal 400 is provided with a wear analysis unit, the first detection component transmitting the wear parameters to the wear analysis unit via the wireless transmission module 300, and the wear analysis unit analyzing the wear of the gearbox 100 based on the wear parameters.

[0046] It is understood that the gearbox condition detection system provided in this embodiment includes a gearbox 100, a first detection component, a wireless transmission module 300, and a control terminal 400. The first detection component is disposed in the gearbox 100, specifically embedded in the top of the gearbox 100, and can detect the wear parameters of the gearbox 100. The wireless transmission module 300 is disposed on the side wall of the gearbox 100, and the control terminal 400 may be equipped with a wear analysis unit. The first detection component can remotely transmit the detected wear parameter data to the wear analysis unit via the wireless transmission module 300, and the wear analysis unit analyzes the wear of the gearbox 100, thereby achieving online real-time detection of the wear status of the gearbox 100. This ensures that the user can determine whether the gearbox 100 has experienced wear in a timely manner, ensuring stable and reliable operation of the gearbox 100 without requiring downtime for inspection, ensuring continuous operation of the gearbox 100, avoiding wasted manpower, and improving work efficiency.

[0047] In some examples, the first detection component includes: a vibration sensor for detecting the vibration frequency inside the gearbox 100; a temperature sensor for detecting temperature data inside the gearbox 100; and a piezoelectric ceramic sensor for detecting the acoustic emission frequency inside the gearbox 100.

[0048] Understandably, the first detection component may include a vibration sensor, a temperature sensor, and a piezoelectric ceramic sensor. These sensors are integrated and mounted on the top of the gearbox 100. The vibration sensor detects the vibration frequency inside the gearbox 100; the temperature sensor detects the temperature inside the gearbox 100; and the piezoelectric ceramic sensor detects the acoustic emission frequency of the gearbox 100 to capture the microcrack propagation signal on the gear teeth, thereby obtaining three wear parameters: vibration frequency, temperature, and acoustic emission frequency.

[0049] In some examples, the wear analysis unit is equipped with a wear assessment model. The vibration frequency data, temperature data, and acoustic emission frequency data are input into the wear assessment model to obtain the actual wear degree of the gearbox 100. If the actual wear degree is greater than or equal to the preset wear degree, it is determined that the gearbox 100 has worn.

[0050] Understandably, the wear analysis unit can be equipped with a wear assessment model. Vibration frequency data detected by the vibration sensor, temperature data detected by the temperature sensor, and acoustic emission frequency data detected by the piezoelectric ceramic sensor can be transmitted to the wear assessment model via a wireless transmission unit. The wear assessment model can obtain the actual wear degree of the gearbox 100 using these three wear parameters: vibration frequency, temperature, and acoustic emission frequency. It then compares the actual wear degree with a preset wear degree. If the actual wear degree is greater than or equal to the preset wear degree, it determines that the gearbox 100 has experienced wear and requires downtime maintenance or replacement.

[0051] It should be noted that the wear assessment model is as follows:

[0052]

[0053] Where P represents the actual wear degree, F represents the vibration frequency, T represents the temperature, A represents the acoustic emission frequency, and W1, W2, and W3 are the weighting coefficients for the vibration frequency, temperature, and acoustic emission frequency, respectively. The preset wear degree is 0.5. When P ≥ 0.5, the gearbox 100 is judged to have experienced wear; when P < 0.5, the gearbox 100 is judged to have experienced no wear or a low degree of wear.

[0054] In some examples, such as Figure 1 and Figure 2 As shown, the gearbox condition detection system further includes: a second detection component, disposed in the gearbox 100, for detecting the lubrication coefficient parameter of the gearbox 100; the control terminal 400 is provided with a lubrication coefficient analysis unit, the second detection component sends the lubrication coefficient parameter to the lubrication coefficient analysis unit through the wireless transmission module 300, and the lubrication coefficient analysis unit analyzes the lubrication coefficient of the gearbox 100 based on the lubrication coefficient parameter.

[0055] Understandably, the gearbox condition monitoring system also includes a second detection component. Specifically, the second detection component can be integrated with the first detection component, both being embedded in the top of the gearbox 100. The second detection component detects the lubrication coefficient parameter of the gearbox 100. The control terminal 400 is equipped with a lubrication coefficient analysis unit. The second detection component can wirelessly transmit the detected lubrication coefficient parameter to the lubrication coefficient analysis unit via the wireless transmission module 300. The lubrication coefficient analysis unit then analyzes the lubrication coefficient, thereby enabling real-time online detection of lubrication failure in the gearbox 100. This ensures that the user can be aware of and address lubrication failure immediately, preventing the gearbox 100 from continuing to operate under lubrication failure conditions, which could lead to severe wear and improve reliability.

[0056] In some examples, the second detection component includes: an oil viscosity sensor for detecting the oil viscosity of the gearbox 100; a ferromagnetic wear debris concentration sensor for detecting the ferromagnetic wear debris concentration of the gearbox 100; and a moisture content sensor for detecting the moisture content of the gearbox 100.

[0057] Understandably, the second detection component includes an oil viscosity sensor, a ferromagnetic wear debris concentration sensor, and a moisture content sensor. These sensors are integrated and mounted on the top of the gearbox 100, forming an integrated sensor 200. The oil viscosity sensor detects the oil viscosity in the gearbox 100; the ferromagnetic wear debris concentration sensor detects the ferromagnetic wear debris concentration in the gearbox 100; and the moisture content sensor detects the moisture content in the gearbox 100. This yields three lubrication coefficient parameters: oil viscosity, ferromagnetic wear debris concentration, and moisture content.

[0058] In some examples, the aforementioned lubrication coefficient analysis unit is equipped with a lubrication coefficient evaluation model, which sends the aforementioned oil viscosity data, the aforementioned ferromagnetic wear debris concentration data, and the aforementioned moisture content data to the aforementioned lubrication coefficient evaluation model to obtain the actual lubrication coefficient of the aforementioned gearbox 100. In cases where the aforementioned actual lubrication coefficient exceeds the lubrication coefficient threshold, the aforementioned gearbox 100 is determined to have lubrication failure.

[0059] Understandably, the lubrication coefficient analysis unit can be equipped with a lubrication coefficient evaluation model, which transmits oil viscosity data, ferromagnetic wear debris concentration data, and moisture content data to the lubrication coefficient evaluation model via the wireless transmission module 300. The lubrication coefficient evaluation model can obtain the actual lubrication coefficient of the gearbox 100 using these three lubrication coefficient parameters. If the actual lubrication coefficient exceeds the lubrication coefficient threshold, lubrication failure of the gearbox 100 can be determined.

[0060] It should be noted that the lubrication coefficient evaluation model is as follows:

[0061]

[0062] Where L is the actual lubrication coefficient of gearbox 100, E is the oil viscosity, R is the concentration of ferromagnetic wear debris, T is the moisture content, Ea is the optimal viscosity of gearbox 100, Ra is the critical concentration of ferromagnetic wear debris for lubrication failure, Ta is the critical moisture content for lubrication failure, and the lubrication coefficient threshold is 0.6. When L < 0.6, it indicates lubrication failure of gearbox 100, requiring immediate shutdown and maintenance to prevent severe wear and improve reliability.

[0063] like Figure 3 As shown, a gearbox condition detection method is provided according to a second aspect embodiment of this disclosure, used in a gearbox condition detection system as described in any of the above technical solutions, comprising:

[0064] Step S110: Obtain wear parameters of gearbox 100 with different wear levels, and construct a wear assessment model based on the above wear parameters;

[0065] Step S110 includes: step S1101 and step S1102.

[0066] Step S1101 involves: obtaining wear parameters of the gearbox 100 with different wear levels, and constructing a dataset using the wear parameters as data samples. The wear parameters include vibration frequency data, temperature data, and acoustic emission frequency data.

[0067] Understandably, the vibration frequency, temperature, and acoustic emission frequency of gearboxes 100 with different wear levels can be collected as data samples to construct a dataset.

[0068] Step S1102 is as follows: Divide the above dataset into a training set and a validation set, train a machine learning model using the training set, evaluate the performance accuracy of the machine learning model using the validation set, and select the best-performing machine learning model to construct a wear assessment model.

[0069] Understandably, the dataset can be divided into a training set and a validation set. Multiple machine learning models can be trained simultaneously using the training set, and the performance accuracy of different machine learning models can be evaluated using the validation set. In this way, the best-performing machine learning model can be selected to build a wear assessment model, thereby ensuring that the wear assessment model is accurate and reliable.

[0070] It should be noted that a wear assessment model can be obtained through a supervised learning model. Specifically, the supervised learning model is as follows:

[0071]

[0072] Where P represents the actual wear degree, F represents the vibration frequency, T represents the temperature, A represents the acoustic emission frequency, and W1, W2, and W3 are the weighting coefficients for the vibration frequency, temperature, and acoustic emission frequency, respectively. The machine learning model continuously learns and optimizes W1, W2, and W3 to improve the accuracy of the wear assessment model. The preset wear degree is 0.5. If P ≥ 0.5, the gearbox 100 is judged to have experienced wear; if P < 0.5, the gearbox 100 is judged to have experienced no wear or a low degree of wear.

[0073] In some examples, the aforementioned machine learning models include at least one of the following: logistic regression, linear discriminant analysis, k-nearest neighbors, Naive Bayes, support vector machine, random forest, and neural network. Multiple machine learning models can be trained simultaneously on a training set, and the performance accuracy of different models can be evaluated using a validation set. The best-performing machine learning model can then be selected to construct the wear assessment model, ensuring its accuracy and reliability.

[0074] In some examples, the gearbox condition detection method further includes: obtaining the optimal viscosity value of the gear, the critical ferromagnetic wear debris concentration value for lubrication failure, and the critical moisture content value for lubrication failure of the gearbox 100 to construct a lubrication coefficient model of the gearbox 100; obtaining the lubrication coefficient parameters of the target gearbox 100 and inputting the lubrication coefficient parameters into the lubrication coefficient model to obtain the actual lubrication coefficient of the target gearbox 100; and determining that the target gearbox 100 has failed lubrication when the actual lubrication coefficient exceeds a threshold.

[0075] Understandably, the gearbox condition detection method can also construct a lubrication coefficient model for gearbox 100 by obtaining the optimal viscosity value of the gears, the critical ferromagnetic wear debris concentration value for lubrication failure, and the critical moisture content value for lubrication failure. Specifically, the lubrication coefficient model for gearbox 100 is as follows:

[0076]

[0077] Where L is the actual lubrication coefficient of gearbox 100, E is the oil viscosity, R is the ferromagnetic wear debris concentration, T is the moisture content, Ea is the optimal viscosity of gearbox 100, Ra is the critical ferromagnetic wear debris concentration for lubrication failure, Ta is the critical moisture content for lubrication failure, and the lubrication coefficient threshold is 0.6. The gearbox condition monitoring system also includes a second detection component to detect the lubrication coefficient parameters of gearbox 100. Specifically, the second detection component can detect three lubrication coefficient parameters: oil viscosity, ferromagnetic wear debris concentration, and moisture content. When these three parameters are input into the lubrication coefficient model, the actual lubrication coefficient of gearbox 100 can be obtained. If L < 0.6, it indicates lubrication failure of gearbox 100, requiring immediate shutdown and maintenance to prevent severe wear and improve reliability. The gearbox status detection method can simultaneously detect whether the gearbox 100 has lubrication failure and wear in real time, so that users can determine the status of the gearbox 100 at the first time, ensuring that the gearbox 100 operates stably and reliably without stopping the machine for inspection, ensuring that the gearbox 100 can continue to operate, avoiding waste of manpower and improving work efficiency.

[0078] Step S120: Obtain the lubrication coefficient parameters of the target gearbox, input the lubrication coefficient parameters into the lubrication coefficient model to obtain the actual lubrication coefficient of the target gearbox 100; if the actual lubrication coefficient exceeds a threshold, determine that the target gearbox has lubrication failure.

[0079] Understandably, the oil viscosity data, ferromagnetic wear debris concentration data, and moisture content data can be transmitted to the lubrication coefficient evaluation model via the wireless transmission module 300. The lubrication coefficient evaluation model can obtain the actual lubrication coefficient of the gearbox 100 using these three parameters. If the actual lubrication coefficient exceeds the lubrication coefficient threshold, lubrication failure of the gearbox 100 can be determined. This enables online real-time detection of the gearbox 100's wear status, ensuring that users can determine whether wear has occurred in the gearbox 100 immediately, guaranteeing stable and reliable operation of the gearbox 100 without requiring downtime for inspection, ensuring continuous operation of the gearbox 100, avoiding wasted manpower, and improving work efficiency.

[0080] It should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. Although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0081] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" in this article describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the related objects before and after it are in an "or" relationship.

[0082] It should be understood that in the description of this invention, the terms "upper," "vertical," "inner," "outer," etc., indicate the orientation or positional relationship as commonly placed when the disclosed product is used, or the orientation or positional relationship commonly understood by those skilled in the art. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0083] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," and "connect" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0084] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the invention. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” “containing,” and / or “including” as used herein specify the presence of the stated features, integers, steps, operations, units, and / or components, and do not exclude the presence or addition of one or more other features, quantities, steps, operations, units, components, and / or combinations thereof.

[0085] Specific details are provided in the following description to provide a complete understanding of the exemplary embodiments. However, those skilled in the art will understand that the exemplary embodiments can be implemented without these specific details. In other embodiments, well-known processes, structures, and techniques may be omitted in the depiction of non-essential details to avoid obscuring the exemplary embodiments.

[0086] The above are merely specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

[0087] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art.

Claims

1. A gearbox condition detection system, characterized in that, include: Gearbox; The first detection component is disposed in the gearbox and is used to detect the wear parameters of the gearbox; A wireless transmission module is installed in the gearbox; The control terminal is equipped with a wear analysis unit. The first detection component sends the wear parameters to the wear analysis unit through the wireless transmission module. The wear analysis unit analyzes the wear of the gearbox based on the wear parameters.

2. The gearbox condition detection system according to claim 1, characterized in that, The first detection component includes: A vibration sensor is used to detect the vibration frequency inside the gearbox; A temperature sensor is used to detect temperature data inside the gearbox; A piezoelectric ceramic sensor is used to detect the acoustic emission frequency inside the gearbox.

3. The gearbox condition detection system according to claim 2, characterized in that, The wear analysis unit is equipped with a wear assessment model. The vibration frequency data, temperature data, and acoustic emission frequency data are input into the wear assessment model to obtain the actual wear degree of the gearbox. If the actual wear degree is greater than or equal to the preset wear degree, it is determined that the gearbox has experienced wear.

4. The gearbox condition detection system according to claim 1, characterized in that, Also includes: The second detection component is disposed in the gearbox and is used to detect the lubrication coefficient parameter of the gearbox; The control terminal is equipped with a lubrication coefficient analysis unit. The second detection component sends the lubrication coefficient parameters to the lubrication coefficient analysis unit through the wireless transmission module. The lubrication coefficient analysis unit analyzes the lubrication coefficient of the gearbox based on the lubrication coefficient parameters.

5. The gearbox condition detection system according to claim 4, characterized in that, The second detection component includes: An oil viscosity sensor is used to detect the viscosity of the oil in the gearbox; A ferromagnetic wear debris concentration sensor is used to detect the ferromagnetic wear debris concentration of the gearbox; A moisture content sensor is used to detect the moisture content of the gearbox.

6. The gearbox condition detection system according to claim 5, characterized in that, The lubrication coefficient analysis unit is equipped with a lubrication coefficient evaluation model. The oil viscosity data, the ferromagnetic wear debris concentration data, and the moisture content data are sent to the lubrication coefficient evaluation model to obtain the actual lubrication coefficient of the gearbox. If the actual lubrication coefficient exceeds the lubrication coefficient threshold, the gearbox is determined to have lubrication failure.

7. A gearbox condition detection method, used in the gearbox condition detection system as described in any one of claims 1 to 6, characterized in that, include: Obtain wear parameters of gearboxes with different wear levels, and construct a wear assessment model based on the wear parameters; Obtain the actual wear parameters of the target gearbox and send the actual wear parameters to the wear assessment model to obtain the actual wear degree of the target gearbox; Specifically, if the actual wear level is greater than or equal to the preset wear level, the target gearbox is determined to be worn.

8. The gearbox condition detection method according to claim 7, characterized in that, The steps of obtaining wear parameters of gearboxes with different wear levels and constructing a wear assessment model based on the wear parameters include: The wear degree parameters of the gearbox with different wear degrees are obtained, and the wear degree parameters are used as data samples to construct a dataset, wherein the wear degree parameters include vibration frequency data, temperature data and acoustic emission frequency data; The dataset is divided into a training set and a validation set. A machine learning model is trained using the training set, and the performance accuracy of the machine learning model is evaluated using the validation set. The best-performing machine learning model is selected to construct a wear assessment model.

9. The gearbox condition detection method according to claim 7, characterized in that, The machine learning model includes at least one of the following: logistic regression model, linear discriminant analysis model, K-nearest neighbor model, Naive Bayes model, support vector machine model, random forest model, and neural network model.

10. The gearbox condition detection method according to claim 7, characterized in that, Also includes: The optimal viscosity value of the gears, the critical ferromagnetic wear debris concentration value for lubrication failure, and the critical moisture content value for lubrication failure of the gearbox are obtained to construct the lubrication coefficient model of the gearbox. Obtain the lubrication coefficient parameter of the target gearbox, and input the lubrication coefficient parameter into the lubrication coefficient model to obtain the actual lubrication coefficient of the target gearbox; If the actual lubrication coefficient exceeds the threshold, the target gearbox is determined to be in lubrication failure.

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