Method, device and equipment for monitoring state of main bearing of shield tunneling machine and storage medium

Abstract

The invention provides a method, device and equipment for monitoring the state of a main bearing of a shield tunneling machine and a storage medium, and the method comprises the steps: obtaining the dynamic response characteristics of the main bearing of the shield tunneling machine under different working conditions; obtaining time sequence monitoring data of a plurality of preset measuring points in the main bearing of the shield tunneling machine; performing time-frequency domain analysis and acceleration envelope analysis on the time sequence monitoring data to obtain time sequence monitoring data features; and determining the working condition state of the main bearing of the shield tunneling machine based on the time sequence monitoring data characteristics and the dynamic response characteristics. According to the technical scheme, the state of the main bearing of the shield tunneling machine can be effectively monitored.

Description

Technical Field

[0001] The present application relates to the technical field of shield machine bearings, and in particular to a method, device, equipment and storage medium for monitoring the status of a main bearing of a shield machine. Background Art

[0002] In the related art, it is necessary to perform relatively complex data processing and analysis methods on the monitoring data of the shield machine main bearing to determine the health status of the bearing, resulting in poor monitoring efficiency and accuracy. Summary of the invention

[0003] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0004] In the first aspect, the present application proposes a method for monitoring the status of a main bearing of a shield machine, the method comprising: obtaining the dynamic response characteristics of the main bearing of the shield machine under different working conditions; obtaining the time series monitoring data of multiple preset measuring points in the main bearing of the shield machine; performing time-frequency domain analysis and acceleration envelope analysis on the time series monitoring data to obtain the characteristics of the time series monitoring data; and determining the working status of the main bearing of the shield machine based on the characteristics of the time series monitoring data and the dynamic response characteristics.

[0005] In one implementation, the method of obtaining the dynamic response characteristics of the main bearing of the shield machine under different working conditions includes: establishing a dynamic model of the main bearing of the shield machine; obtaining working conditions corresponding to different working conditions; respectively introducing the working conditions corresponding to different working conditions into the dynamic model; respectively solving the dynamic model based on the working conditions corresponding to each working condition, and obtaining the dynamic response characteristics corresponding to each working condition.

[0006] In one implementation, determining the operating state of the shield machine main bearing based on the time series monitoring data characteristics and the dynamic response characteristics includes: inputting the time series monitoring data characteristics into a pre-trained state judgment model to obtain the operating state output by the state judgment model; wherein the state judgment model has been trained by the dynamic response characteristics corresponding to different working conditions to obtain the ability to judge the state of the shield machine main bearing.

[0007] In an optional implementation, the method further includes: using the time series monitoring data features and the operating status as training data to train the state judgment model to update model parameters of the state judgment model.

[0008] In one implementation, the operating state of the shield machine main bearing is determined based on the time series monitoring data characteristics and the dynamic response characteristics, including: calculating the similarity between the time series monitoring data characteristics and the dynamic response characteristics corresponding to each of the operating conditions, and obtaining the similarity between the time series monitoring data characteristics and the dynamic response characteristics corresponding to each of the operating conditions; obtaining the target operating condition corresponding to the maximum similarity among the similarities as the operating state of the shield machine main bearing.

[0009] In one implementation, the time series monitoring data includes at least one of the following: axial vibration data; radial vibration data; rotation speed data.

[0010] In the second aspect, the present application proposes a monitoring device for the main bearing status of a shield machine, the device comprising: a first acquisition module, used to obtain the dynamic response characteristics of the main bearing of the shield machine under different working conditions; a second acquisition module, used to obtain the time series monitoring data of multiple preset measuring points in the main bearing of the shield machine; a first processing module, used to perform time-frequency domain analysis and acceleration envelope analysis on the time series monitoring data to obtain the characteristics of the time series monitoring data; a second processing module, used to determine the working condition of the main bearing of the shield machine based on the characteristics of the time series monitoring data and the dynamic response characteristics.

[0011] In one implementation, the first acquisition module can be used to: establish a dynamic model of the main bearing of the shield machine; obtain the operating conditions corresponding to different operating conditions; introduce the operating conditions corresponding to different operating conditions into the dynamic model respectively; solve the dynamic model based on the operating conditions corresponding to each operating condition, and obtain the dynamic response characteristics corresponding to each operating condition.

[0012] In one implementation, the second processing module can be used to: input the time series monitoring data features into a pre-trained state judgment model to obtain the operating state output by the state judgment model; wherein the state judgment model has been trained through the corresponding dynamic response characteristics under different working conditions to obtain the ability to judge the state of the main bearing of the shield machine.

[0013] In an optional implementation, the second processing module may also be used to: use the time series monitoring data features and the operating status as training data to train the state judgment model to update model parameters of the state judgment model.

[0014] In one implementation, the second processing module can be used to: calculate the similarity between the time series monitoring data features and the dynamic response features corresponding to each of the operating conditions, and obtain the similarity between the time series monitoring data features and the dynamic response features corresponding to each of the operating conditions; obtain the target operating condition corresponding to the maximum similarity among the similarities as the operating condition of the main bearing of the shield machine.

[0015] In one implementation, the time series monitoring data includes at least one of the following: axial vibration data; radial vibration data; rotation speed data.

[0016] In a third aspect, the present application proposes an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for monitoring the main bearing status of a shield machine as described in the first aspect.

[0017] In a fourth aspect, the present application proposes a computer-readable storage medium for storing instructions, which, when executed, enables the method described in the first aspect to be implemented.

[0018] In a fifth aspect, the present application proposes a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method for monitoring the status of a main bearing of a shield machine as described in the first aspect.

[0019] The shield machine main bearing status monitoring method, device, equipment and storage medium provided in the present application can obtain the time series data characteristics of the shield machine main bearing monitoring data, thereby determining the working condition of the shield machine main bearing according to the time series data characteristics and the dynamic response characteristics of different working conditions, and realize effective monitoring of the working condition of the shield machine main bearing.

[0020] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 It is a flow chart of a method for monitoring the main bearing status of a shield machine provided in an embodiment of the present application;

[0023] Figure 2 It is a schematic diagram of the position of radial measuring points of a shield machine main bearing provided in an embodiment of the present application;

[0024] Figure 3 It is a schematic diagram of the position of the axial measuring points of the main bearing of a shield machine provided in an embodiment of the present application;

[0025] Figure 4 It is a flow chart of another method for monitoring the main bearing status of a shield machine provided in an embodiment of the present application;

[0026] Figure 5 It is a flow chart of another method for monitoring the status of the main bearing of a shield machine provided in an embodiment of the present application;

[0027] Figure 6 It is a flow chart of another method for monitoring the status of the main bearing of a shield machine provided in an embodiment of the present application;

[0028] Figure 7 It is a structural schematic diagram of a monitoring device for the main bearing status of a shield machine provided in an embodiment of the present application;

[0029] Figure 8 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0031] The following describes a method and device for monitoring the main bearing status of a shield machine according to an embodiment of the present application with reference to the accompanying drawings.

[0032] Figure 1 Schematic diagram of a method for monitoring the main bearing status of a shield machine provided in an embodiment of the present application. Figure 1 As shown, the method may include but is not limited to the following steps:

[0033] Step S101: Obtain the dynamic response characteristics of the shield machine main bearing under different working conditions.

[0034] Exemplarily, monitoring data of the main bearing of the shield machine under different working conditions are obtained, and then based on the monitoring data under different working conditions, the dynamic response characteristics of the main bearing of the shield machine under different working conditions are obtained.

[0035] Step S102: Acquire time-series monitoring data of multiple preset measuring points in the main bearing of the shield machine.

[0036] In an embodiment of the present application, the above-mentioned time series monitoring data includes at least one of the following: axial vibration data; radial vibration data; rotation speed data.

[0037] Exemplarily, vibration acceleration sensors are arranged in the axial and radial directions of the main bearing of the shield machine, so that axial vibration data and radial vibration data are acquired based on the vibration acceleration sensors, and rotation speed data is acquired from the control system of the shield machine.

[0038] As an example, see Figure 2 , Figure 2 Schematic diagram of the position of radial measuring points of a shield machine main bearing provided in an embodiment of the present application. Figure 2 As shown, three vibration sensors can be arranged in the radial direction of the main bearing to monitor whether the radial rolling elements and the segmented cage of the main bearing are damaged, and the sensors are distributed at equal intervals.

[0039] As an example, see Figure 3 , Figure 3 Schematic diagram of the position of the axial measuring points of the main bearing of a shield machine provided in an embodiment of the present application. Figure 3 As shown in the figure, five vibration sensors can be set at equal intervals in the axial position of the main bearing to monitor whether there is damage on the main thrust end of the bearing.

[0040] Step S103: Perform time-frequency domain analysis and acceleration envelope analysis on the time series monitoring data to obtain the characteristics of the time series monitoring data.

[0041] Exemplarily, time domain analysis is performed on the time series monitoring data to obtain time domain characteristic data; frequency domain analysis is performed on the time series monitoring data to obtain frequency domain characteristic data; acceleration envelope analysis is performed on the time series monitoring data to obtain envelope spectrum characteristics.

[0042] Step S104: Determine the working state of the shield machine main bearing based on the time series monitoring data characteristics and the dynamic response characteristics.

[0043] Exemplarily, the time-series monitoring data characteristics of the shield machine main bearing are compared with the dynamic response characteristics corresponding to different working conditions to obtain the target dynamic response characteristics corresponding to the time-series monitoring data characteristics, so as to determine the working state of the shield machine main bearing according to the working condition corresponding to the target dynamic response characteristics.

[0044] By implementing the embodiments of the present application, the time series data characteristics of the shield machine main bearing monitoring data can be obtained, so as to determine the working state of the shield machine main bearing according to the time series data characteristics and the dynamic response characteristics of different working conditions, thereby realizing effective monitoring of the working state of the shield machine main bearing.

[0045] In some embodiments, the dynamic response characteristics of the shield machine main bearing under different working conditions can be obtained based on the simulation model of the shield machine main bearing. As an example, see Figure 4 , Figure 4 FIG. 1 is a flow chart of another method for monitoring the main bearing status of a shield machine provided in an embodiment of the present application. Figure 4 As shown, the method may include but is not limited to the following steps:

[0046] Step S401: Establish a dynamic model of the shield machine main bearing.

[0047] Exemplarily, a model is established according to the actual structural dimensions of the shield machine main bearing using three-dimensional modeling software, and interference checks are performed. The three-dimensional model is imported into multi-body dynamics analysis software (e.g., Adams) to create a rigid body model of the shield machine main bearing. Finite element analysis is performed on the inner ring, outer ring and retaining frame of the main bearing, and a flexible model thereof is obtained using software (e.g., ANSYS). The flexible model is imported into the rigid body model, and the corresponding rigid components are replaced to obtain a rigid-flexible coupling model of the shield machine main bearing. The material properties of each component are defined in the rigid-flexible coupling model, and the connection relationship between the components in the model is set to obtain the dynamic model of the shield machine main bearing.

[0048] Step S402: Obtain operating conditions corresponding to different operating conditions.

[0049] Among them, the above operating conditions include at least one of the following: normal operating state; overload condition; high-speed operating condition; low-speed operating condition; unbalanced condition; wear and fatigue condition.

[0050] The above operating conditions include at least one of the following: load conditions; rotation speed; operating temperature; bearing material and manufacturing process; and bearing size.

[0051] Step S403: Solve the dynamic model based on the working conditions corresponding to each working condition to obtain the dynamic response characteristics corresponding to each working condition.

[0052] Exemplarily, operating conditions corresponding to different operating conditions are introduced into the dynamic model respectively, and the dynamic model is solved using a preset numerical solver to obtain the dynamic response characteristics corresponding to each operating condition.

[0053] Step S404: Acquire time-series monitoring data of multiple preset measuring points in the main bearing of the shield machine.

[0054] In the embodiments of the present application, step S404 can be implemented by any of the methods in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0055] Step S405: Perform time-frequency domain analysis and acceleration envelope analysis on the time series monitoring data to obtain time series monitoring data features.

[0056] In the embodiments of the present application, step S405 can be implemented in any of the ways in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0057] Step S406: Determine the working state of the shield machine main bearing based on the time series monitoring data characteristics and the dynamic response characteristics.

[0058] In the embodiments of the present application, step S406 can be implemented in any of the ways in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0059] By implementing the embodiments of the present application, the dynamic response characteristics corresponding to the working conditions of the main bearing of the shield machine can be obtained based on the dynamic model corresponding to the main bearing of the shield machine and the working conditions corresponding to different working conditions. Therefore, the working condition of the main bearing of the shield machine can be determined based on the dynamic response characteristics corresponding to different working conditions and the time series monitoring data. Thus, the effective judgment of the state of the main bearing of the shield machine can be achieved.

[0060] In some embodiments, the operating state of the shield machine main bearing can be determined based on a pre-trained neural network model. As an example, see Figure 5 , Figure 5 FIG. 1 is a flow chart of another method for monitoring the main bearing status of a shield machine provided in an embodiment of the present application. Figure 5 As shown, the method may include but is not limited to the following steps:

[0061] Step S501: Obtain the dynamic response characteristics of the shield machine main bearing under different working conditions.

[0062] In the embodiments of the present application, step S501 can be implemented by any of the methods in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0063] Step S502: Acquire time-series monitoring data of multiple preset measuring points in the main bearing of the shield machine.

[0064] In the embodiments of the present application, step S502 can be implemented by any of the methods in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0065] Step S503: Perform time-frequency domain analysis and acceleration envelope analysis on the time series monitoring data to obtain the characteristics of the time series monitoring data.

[0066] In the embodiments of the present application, step S503 can be implemented by any of the methods in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0067] Step S504: input the time series monitoring data features into a pre-trained state judgment model to obtain the operating state output by the state judgment model.

[0068] Among them, the state judgment model has been trained through the corresponding dynamic response characteristics under different working conditions to gain the ability to judge the working state of the main bearing of the shield machine corresponding to different monitoring data.

[0069] Exemplarily, the corresponding dynamic response characteristics under different working conditions can be used as training data to output the initial state judgment model to obtain the predicted state output by the initial state judgment model; thus, based on the difference between the predicted state and the working condition, the loss value is calculated in combination with a preset loss function to optimize the model parameters of the initial state judgment model based on the loss value, and the state judgment model is obtained based on the optimized model parameters.

[0070] In some embodiments, the time series monitoring data features and operating conditions may be used as training data to train the state judgment model to update the model parameters of the state judgment model.

[0071] By implementing the embodiments of the present application, the neural network model trained based on the corresponding dynamic response characteristics under different working conditions can be used in combination with the time series monitoring data characteristics of the main bearing of the shield machine to determine the working state of the main bearing of the shield machine, thereby achieving effective judgment of the state of the main bearing of the shield machine.

[0072] In some embodiments, the operating state of the shield machine main bearing can be determined based on the similarity between the time series monitoring data characteristics and the dynamic response characteristics corresponding to different working conditions. As an example, see Figure 6 , Figure 6 FIG. 1 is a flow chart of another method for monitoring the main bearing status of a shield machine provided in an embodiment of the present application. Figure 6 As shown, the method may include but is not limited to the following steps:

[0073] Step S601: Obtain the dynamic response characteristics of the shield machine main bearing under different working conditions.

[0074] In the embodiments of the present application, step S601 can be implemented in any of the ways in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0075] Step S602: Acquire time-series monitoring data of multiple preset measuring points in the main bearing of the shield machine.

[0076] In the embodiments of the present application, step S602 can be implemented in any of the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0077] Step S603: Perform time-frequency domain analysis and acceleration envelope analysis on the time series monitoring data to obtain the characteristics of the time series monitoring data.

[0078] In the embodiments of the present application, step S603 can be implemented by any of the methods in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

[0079] Step S604: Calculate the similarity between the time series monitoring data features and the dynamic response features corresponding to each working condition, and obtain the similarity between the time series monitoring data features and the dynamic response features corresponding to each working condition.

[0080] Exemplarily, the Euclidean distance between the acquired time series feature data and the dynamic response feature corresponding to each working condition is calculated as the similarity.

[0081] Step S605: Obtain the target working condition corresponding to the maximum similarity among the similarities as the working condition of the main bearing of the shield machine.

[0082] Exemplarily, the working condition corresponding to the maximum similarity is selected as the target working condition, and the target working condition is used as the working condition state of the main bearing of the shield machine.

[0083] By implementing the embodiment of the present application, the similarity between the time series monitoring data characteristics and the dynamic response characteristics corresponding to each working condition can be obtained, and the target working condition corresponding to the maximum similarity can be selected as the working condition of the shield machine main bearing, thereby achieving effective judgment of the shield machine main bearing state.

[0084] See also Figure 7 , Figure 7 Schematic diagram of the structure of a monitoring device for the main bearing status of a shield machine provided in an embodiment of the present application. Figure 7 As shown, the device 700 includes: a first acquisition module 701, used to obtain the dynamic response characteristics of the main bearing of the shield machine under different working conditions; a second acquisition module 702, used to obtain the time series monitoring data of multiple preset measuring points in the main bearing of the shield machine; a first processing module 703, used to perform time-frequency domain analysis and acceleration envelope analysis on the time series monitoring data to obtain the characteristics of the time series monitoring data; a second processing module 704, used to determine the working state of the main bearing of the shield machine based on the characteristics of the time series monitoring data and the dynamic response characteristics.

[0085] In one implementation, the first acquisition module 701 can be used to: establish a dynamic model of the main bearing of the shield machine; obtain the working conditions corresponding to different working conditions; introduce the working conditions corresponding to different working conditions into the dynamic model respectively; solve the dynamic model based on the working conditions corresponding to each working condition, and obtain the dynamic response characteristics corresponding to each working condition.

[0086] In one implementation, the second processing module 704 can be used to: input the time series monitoring data features into a pre-trained state judgment model to obtain the operating state output by the state judgment model; wherein the state judgment model has been trained through the corresponding dynamic response characteristics under different working conditions to obtain the ability to judge the state of the main bearing of the shield machine.

[0087] In an optional implementation, the second processing module 704 may also be used to: use the time series monitoring data features and the operating status as training data to train the state judgment model to update the model parameters of the state judgment model.

[0088] In one implementation, the second processing module 704 can be used to: calculate the similarity between the time series monitoring data features and the dynamic response features corresponding to each operating condition, and obtain the similarity between the time series monitoring data features and the dynamic response features corresponding to each operating condition; obtain the target operating condition corresponding to the maximum similarity in the similarity as the operating condition of the main bearing of the shield machine; obtain the target operating condition corresponding to the maximum similarity in the similarity as the operating condition of the main bearing of the shield machine.

[0089] In one implementation, the time series monitoring data includes at least one of the following: axial vibration data; radial vibration data; rotation speed data.

[0090] Through the device of the embodiment of the present application, the time series data characteristics of the shield machine main bearing monitoring data can be obtained, so as to determine the working state of the shield machine main bearing according to the time series data characteristics and the dynamic response characteristics of different working conditions, thereby realizing effective monitoring of the working state of the shield machine main bearing.

[0091] It should be noted that the above explanation of the embodiment of the method for monitoring the main bearing state of a shield machine is also applicable to the device for monitoring the main bearing state of a shield machine of this embodiment, and will not be repeated here.

[0092] In order to implement the above embodiment, the present application also proposes an electronic device. Figure 8 , Figure 8 Schematic diagram of the structure of the electronic device provided in the embodiment of the present application. Figure 8 As shown, the electronic device 800 includes: a processor 801, and a memory 802 communicatively connected to the processor 801; the memory 802 stores computer-executable instructions; the processor 801 executes the computer-executable instructions stored in the memory to implement the method provided in the aforementioned embodiment.

[0093] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0094] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0095] In the description of this application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in this article is merely a way to describe the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0096] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0097] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0098] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0099] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0100] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0101] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0102] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0103] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for monitoring the status of a main bearing of a shield machine, characterized in that: include: Obtain the dynamic response characteristics of the shield machine main bearing under different working conditions; Acquiring time-series monitoring data of a plurality of preset measuring points in the main bearing of the shield machine; Performing time-frequency domain analysis and acceleration envelope analysis on the time series monitoring data to obtain time series monitoring data characteristics; Based on the time series monitoring data characteristics and the dynamic response characteristics, the operating state of the main bearing of the shield machine is determined.

2. The method according to claim 1, characterized in that The obtaining of the dynamic response characteristics of the shield machine main bearing under different working conditions includes: Establishing a dynamic model of the main bearing of the shield machine; Obtaining working conditions corresponding to different working conditions; Introducing the operating conditions corresponding to the different operating conditions into the dynamic model respectively; The dynamic model is solved based on the operating conditions corresponding to each operating condition to obtain the dynamic response characteristics corresponding to each operating condition.

3. The method according to claim 1, characterized in that The determining the working state of the main bearing of the shield machine based on the time series monitoring data characteristics and the dynamic response characteristics includes: The time series monitoring data features are input into a pre-trained state judgment model to obtain the operating state output by the state judgment model; wherein the state judgment model has been trained through the corresponding dynamic response features under different operating conditions to obtain the ability to judge the state of the main bearing of the shield machine.

4. The method according to claim 3, characterized in that The method further comprises: The time series monitoring data features and the operating status are used as training data to train the status judgment model to update the model parameters of the status judgment model.

5. The method according to claim 1, characterized in that The determining the working state of the main bearing of the shield machine based on the time series monitoring data characteristics and the dynamic response characteristics includes: Calculating the similarity between the time series monitoring data feature and the dynamic response feature corresponding to each of the working conditions, to obtain the similarity between the time series monitoring data feature and the dynamic response feature corresponding to each of the working conditions; The target operating condition corresponding to the maximum similarity among the similarities is obtained as the operating condition of the main bearing of the shield machine.

6. The method according to any one of claims 1 to 5, characterized in that The time series monitoring data includes at least one of the following: Axial vibration data; Radial vibration data; Speed ​​data.

7. A device for monitoring the status of a main bearing of a shield machine, characterized in that: include: The first acquisition module is used to obtain the dynamic response characteristics of the main bearing of the shield machine under different working conditions; A second acquisition module is used to acquire time-series monitoring data of a plurality of preset measuring points in the main bearing of the shield machine; A first processing module is used to perform time-frequency domain analysis and acceleration envelope analysis on the time series monitoring data to obtain time series monitoring data features; The second processing module is used to determine the working state of the main bearing of the shield machine based on the time series monitoring data characteristics and the dynamic response characteristics.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the steps of the method for monitoring the state of the main bearing of a shield machine as claimed in any one of claims 1 to 6.

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