Shield machine main bearing state monitoring method, device and equipment and storage medium

Abstract

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

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of shield machine bearing, and particularly relates to a shield machine main bearing state monitoring method, device and equipment and a storage medium. BACKGROUND

[0002] In the related art, a relatively complex data processing and analysis method needs to be used to determine the health status of the shield machine main bearing based on the monitoring data of the shield machine main bearing, which leads to poor monitoring efficiency and accuracy. SUMMARY

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

[0004] In a first aspect, the present application provides a shield machine main bearing state monitoring method, which comprises: acquiring dynamic response characteristics of a shield machine main bearing under different working conditions; acquiring time sequence monitoring data of a plurality of preset measuring points in the shield machine main bearing; performing time-frequency domain analysis and acceleration envelope analysis on the time sequence monitoring data to obtain time sequence monitoring data characteristics; and determining a working condition state of the shield machine main bearing based on the time sequence monitoring data characteristics and the dynamic response characteristics.

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

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

[0007] In an optional implementation manner, the method further comprises: taking the time sequence monitoring data characteristics and the working condition state as training data to train the state judgment model to update model parameters of the state judgment model.

[0008] In an implementation manner, the determining the working condition state of the main bearing of the shield tunneling machine based on the time sequence monitoring data feature and the dynamic response feature comprises: performing similarity calculation on the time sequence monitoring data feature and the dynamic response feature corresponding to each working condition respectively to obtain a similarity between the time sequence monitoring data feature and the dynamic response feature corresponding to each working condition; and obtaining a target working condition corresponding to a maximum similarity in the similarities as the working condition state of the main bearing of the shield tunneling machine.

[0009] In an implementation manner, the time sequence monitoring data comprises at least one of the following: axial vibration data; radial vibration data; and rotation speed data.

[0010] In a second aspect, the application provides a device for monitoring a state of a main bearing of a shield tunneling machine, the device comprising: a first obtaining module configured to obtain dynamic response features of the main bearing of the shield tunneling machine under different working conditions; a second obtaining module configured to obtain time sequence monitoring data of a plurality of preset monitoring points in the main bearing of the shield tunneling machine; a first processing module configured to perform time-frequency domain analysis and acceleration envelope analysis on the time sequence monitoring data to obtain time sequence monitoring data features; and a second processing module configured to determine a working condition state of the main bearing of the shield tunneling machine based on the time sequence monitoring data features and the dynamic response features.

[0011] In an implementation manner, the first obtaining module can be configured to: establish a dynamic model of the main bearing of the shield tunneling machine; obtain working condition conditions corresponding to different working conditions; introduce the working condition conditions corresponding to the different working conditions into the dynamic model respectively; and solve the dynamic model based on the working condition conditions corresponding to each working condition respectively to obtain the dynamic response features corresponding to each working condition.

[0012] In an implementation manner, the second processing module can be configured to: input the time sequence monitoring data features into a pre-trained state judgment model to obtain the working condition state output by the state judgment model; and wherein the state judgment model has been trained by the dynamic response features corresponding to different working conditions to obtain an ability of judging the state of the main bearing of the shield tunneling machine.

[0013] In an optional implementation manner, the second processing module can be further configured to: take the time sequence monitoring data features and the working condition state as training data to train the state judgment model to update model parameters of the state judgment model.

[0014] In an implementation manner, the second processing module can be configured to: perform similarity calculation on the time sequence monitoring data features and the dynamic response features corresponding to each of the working conditions respectively, to obtain similarities between the time sequence monitoring data features and the dynamic response features corresponding to each of the working conditions; and obtain a target working condition corresponding to a maximum similarity in the similarities as the working condition state of the main bearing of the shield tunneling machine.

[0015] In an implementation manner, the time sequence monitoring data comprises at least one of: axial vibration data; radial vibration data; and rotation speed data.

[0016] In a third aspect, the present application provides an electronic device, comprising: at least one processor; and a memory in communication connection with 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 to enable the at least one processor to perform the method for monitoring the state of the main bearing of the shield tunneling machine according to the first aspect.

[0017] In a fourth aspect, the present application provides a computer readable storage medium for storing instructions, which, when executed, cause the method according to the first aspect to be implemented.

[0018] In a fifth aspect, the present application provides a computer program product comprising a computer program, which, when executed by a processor, implements the steps of the method for monitoring the state of the main bearing of the shield tunneling machine according to the first aspect.

[0019] The method, device, equipment and storage medium for monitoring the state of the main bearing of the shield tunneling machine provided by the present application can obtain time sequence data features of the main bearing monitoring data of the shield tunneling machine, so as to determine the working condition state of the main bearing of the shield tunneling machine according to the time sequence data features and dynamic response features of different working conditions. Effective monitoring of the working condition state of the main bearing of the shield tunneling machine is realized.

[0020] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0021] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the accompanying drawings, wherein:

[0022] Figure 1 is a flowchart of a method for monitoring the state of the main bearing of the shield tunneling machine provided by an embodiment of the present application;

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

[0024] Figure 3 is a position diagram of an axial measuring point of a main bearing of a shield tunneling machine provided by an embodiment of the present application.

[0025] Figure 4 is a flow diagram of another method for monitoring a state of a main bearing of a shield tunneling machine provided by an embodiment of the present application.

[0026] Figure 5 is a flow diagram of another method for monitoring a state of a main bearing of a shield tunneling machine provided by an embodiment of the present application.

[0027] Figure 6 is a flow diagram of another method for monitoring a state of a main bearing of a shield tunneling machine provided by an embodiment of the present application.

[0028] Figure 7 is a structural diagram of a device for monitoring a state of a main bearing of a shield tunneling machine provided by an embodiment of the present application.

[0029] Figure 8 is a structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

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

[0031] A method and a device for monitoring a state of a main bearing of a shield tunneling machine are described below with reference to the accompanying drawings.

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

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

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

[0035] Step S102: Obtain time sequence monitoring data of a plurality of preset measuring points in the main bearing of the shield tunneling machine.

[0036] In an embodiment of the present application, the time sequence monitoring data includes at least one of the following: axial vibration data; radial vibration data; and rotational speed data.

[0037] Exemplarily, the vibration acceleration sensors are arranged in the axial direction and the radial direction of the main bearing of the shield tunneling machine, so as to obtain axial vibration data and radial vibration data based on the vibration acceleration sensors, and obtain rotation speed data from a control system of the shield tunneling machine.

[0038] As an example, please refer to Figure 2 , Figure 2 is a schematic diagram of the position of a radial measuring point of a main bearing of a shield tunneling machine provided by an embodiment of the present application. As shown in Figure 2 , three vibration sensors can be arranged in the radial direction of the main bearing, for monitoring whether the segmented cage and the radial rolling elements of the main bearing are damaged, and the sensors are distributed at equal intervals.

[0039] As an example, please refer to Figure 3 , Figure 3 is a schematic diagram of the position of an axial measuring point of a main bearing of a shield tunneling machine provided by an embodiment of the present application. As shown in Figure 3 , five vibration sensors can be arranged at the axial position of the main bearing, and are placed at equal intervals. The sensors are used to monitor whether the main thrust end of the bearing is damaged.

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

[0041] Exemplarily, time domain analysis is performed on the time series monitoring data to obtain time domain feature data, frequency domain analysis is performed on the time series monitoring data to obtain frequency domain feature data, and acceleration envelope analysis is performed on the time series monitoring data to obtain envelope spectrum features.

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

[0043] Exemplarily, the time series monitoring data features of the main bearing of the shield tunneling machine are compared with the dynamic response features corresponding to different working conditions, target dynamic response features corresponding to the time series monitoring data features are obtained, and the working condition state of the main bearing of the shield tunneling machine is determined according to the working condition corresponding to the target dynamic response features.

[0044] By implementing the embodiments of the present application, time series data features of the monitoring data of the main bearing of the shield tunneling machine can be obtained, so as to determine the working condition state of the main bearing of the shield tunneling machine according to the time series data features and the dynamic response features of different working conditions. Effective monitoring of the working condition state of the main bearing of the shield tunneling machine is realized.

[0045] In some embodiments, the dynamic response features of the main bearing of the shield tunneling machine in different working conditions can be obtained based on a simulation model of the main bearing of the shield tunneling machine. As an example, please refer to Figure 4 ,Figure 4 is a flowchart of another method for monitoring the state of the main bearing of a shield tunneling machine provided by an embodiment of the present application. As shown in Figure 4 , the method can include but is not limited to the following steps:

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

[0047] Exemplarily, a three-dimensional modeling software is used to establish a model according to the actual structural dimensions of the main bearing of the shield tunneling machine, and an interference check is performed, the three-dimensional model is imported into a multi-body dynamics analysis software (for example, Adams) to create a rigid body model of the main bearing of the shield tunneling machine; finite element analysis is performed on the inner ring, outer ring and retainer of the main bearing, and a software (for example, ANSYS) is used to obtain a flexible model thereof; the flexible model is imported into the rigid body model and replaces the corresponding rigid components, thereby obtaining a rigid-flexible coupling model of the main bearing of the shield tunneling machine; the material properties of each component in the rigid-flexible coupling model are defined, and the connection relationship of each component in the model is set, thereby obtaining the dynamic model of the main bearing of the shield tunneling machine.

[0048] Step S402: Obtain the working condition corresponding to each working condition.

[0049] The working 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 working conditions include at least one of the following: load condition; rotational speed; working temperature; bearing material and manufacturing process; bearing size.

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

[0052] Exemplarily, the working condition corresponding to each working condition is introduced into the dynamic model respectively, and a preset numerical solver is used to solve the dynamic model, thereby obtaining the dynamic response characteristics corresponding to each working condition.

[0053] Step S404: Obtain the time series monitoring data of a plurality of preset monitoring points in the main bearing of the shield tunneling machine.

[0054] In an embodiment of the present application, step S404 can be implemented by any one of the embodiments of the present application, and the present application does not limit this and will not be repeated.

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

[0056] In the embodiments of the present application, step S405 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 repeated here.

[0057] Step S406: determining the working condition state of the main bearing of the shield machine based on the time sequence monitoring data features and the dynamic response features.

[0058] In the embodiments of the present application, step S406 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 repeated here.

[0059] By implementing the embodiments of the present application, the dynamic response features 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 condition corresponding to different working conditions. Thus, the working condition state of the main bearing of the shield machine is determined based on the dynamic response features corresponding to different working conditions and the time sequence monitoring data. Thus, the effective judgment of the state of the main bearing of the shield machine is realized.

[0060] In some embodiments, the working condition state of the main bearing of the shield machine can be determined based on a pre-trained neural network model. As an example, please refer to Figure 5 , Figure 5 is another flowchart of a method for monitoring the state of the main bearing of the shield machine provided by the embodiments of the present application. As shown in Figure 5 , the method can include but is not limited to the following steps:

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

[0062] In the embodiments of the present application, step S501 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 repeated here.

[0063] Step S502: obtaining the time sequence monitoring data of a plurality of preset measuring points in the main bearing of the shield machine.

[0064] In the embodiments of the present application, step S502 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 repeated here.

[0065] Step S503: performing time-frequency domain analysis and acceleration envelope analysis on the time sequence monitoring data to obtain time sequence monitoring data features.

[0066] In the embodiments of the present application, step S503 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 repeated here.

[0067] Step S504: inputting the time sequence monitoring data features into the pre-trained state judgment model to obtain a working condition state output by the state judgment model.

[0068] The state judgment model has been trained to judge the working condition state of the main bearing of the shield machine corresponding to the monitoring data based on the dynamic response features corresponding to different working conditions.

[0069] For example, the dynamic response features corresponding to different working conditions can be used as training data to output an initial state judgment model, and a predicted state output by the initial state judgment model is obtained. Then, based on the difference between the predicted state and the working condition, a loss value is calculated by combining a pre-set loss function, the model parameters of the initial state judgment model are optimized based on the loss value, and the state judgment model is obtained based on the optimized model parameters.

[0070] In some embodiments, the time sequence monitoring data features and the working condition state can also be used as training data to train the state judgment model, so as 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 dynamic response features corresponding to different working conditions can be used in combination with the time sequence monitoring data features of the main bearing of the shield machine to determine the working condition state of the main bearing of the shield machine. Thus, the state of the main bearing of the shield machine can be effectively judged.

[0072] In some embodiments, the working condition state of the main bearing of the shield machine can be determined based on the similarity between the time sequence monitoring data features and the dynamic response features corresponding to different working conditions. As an example, please refer to Figure 6 , Figure 6 is another flowchart of a method for monitoring the state of the main bearing of the shield machine provided by the embodiments of the present application. As shown in Figure 6 , the method can include but is not limited to the following steps:

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

[0074] In the embodiments of the present application, step S601 can be implemented by any of the embodiments of the present application, and the present application does not limit this and will not be repeated.

[0075] Step S602: obtaining the time sequence monitoring data of a plurality of preset monitoring points in the main bearing of the shield machine.

[0076] In the embodiments of the present application, step S602 can be implemented by any of the embodiments of the present application, and the present application does not limit this and will not be repeated.

[0077] Step S603: performing time-frequency domain analysis and acceleration envelope analysis on the time sequence monitoring data to obtain time sequence monitoring data features.

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

[0079] Step S604: performing similarity calculation on the time sequence monitoring data features and the dynamic response features corresponding to each working condition to obtain the similarity between the time sequence monitoring data features and the dynamic response features corresponding to each working condition.

[0080] For example, the Euclidean distance between the time sequence feature data and the dynamic response features corresponding to each working condition is calculated as the similarity.

[0081] Step S605: obtaining the target working condition corresponding to the maximum similarity in the similarity as the working condition state of the main bearing of the shield machine.

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

[0083] By implementing the embodiments of the present application, the similarity between the time sequence monitoring data features and the dynamic response features corresponding to each working condition can be obtained, and the target working condition corresponding to the maximum similarity is selected as the working condition state of the main bearing of the shield machine. Thus, the effective judgment of the state of the main bearing of the shield machine is realized.

[0084] Please refer to Figure 7 , Figure 7 is a structural schematic diagram of a shield machine main bearing state monitoring device provided by the embodiments of the present application. As shown in Figure 7 the device 700 includes: a first acquisition module 701 configured to acquire dynamic response features of a main bearing of a shield machine under different working conditions; a second acquisition module 702 configured to acquire time sequence monitoring data of a plurality of preset monitoring points in the main bearing of the shield machine; a first processing module 703 configured to perform time-frequency domain analysis and acceleration envelope analysis on the time sequence monitoring data to obtain time sequence monitoring data features; and a second processing module 704 configured to determine a working condition state of the main bearing of the shield machine based on the time sequence monitoring data features and the dynamic response features.

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

[0086] In an implementation manner, the second processing module 704 can be configured to input the time sequence monitoring data feature into a pre-trained state judgment model to obtain a working condition state output by the state judgment model, wherein the state judgment model has been trained by corresponding dynamic response features in different working conditions to obtain the ability to judge the state of the main bearing of the shield machine.

[0087] In an optional implementation manner, the second processing module 704 can be further configured to train the state judgment model by taking the time sequence monitoring data feature and the working condition state as training data, so as to update the model parameters of the state judgment model.

[0088] In an implementation manner, the second processing module 704 can be configured to perform similarity calculation on the time sequence monitoring data feature and the dynamic response feature corresponding to each working condition respectively to obtain a similarity between the time sequence monitoring data feature and the dynamic response feature corresponding to each working condition, and obtain a target working condition corresponding to the maximum similarity in the similarity as the working condition state of the main bearing of the shield machine.

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

[0090] By means of the device provided in the embodiments of the present application, the time sequence data feature of the monitoring data of the main bearing of the shield machine can be obtained, so as to determine the working condition state of the main bearing of the shield machine according to the time sequence data feature and the dynamic response feature of different working conditions, and effectively monitor the working condition state of the main bearing of the shield machine.

[0091] It should be noted that the above-mentioned explanation and description of the monitoring method embodiment of the state of the main bearing of the shield machine are also applicable to the monitoring device of the state of the main bearing of the shield machine, which will not be described here.

[0092] In order to realize the above-mentioned embodiments, the present application further provides an electronic device. Please refer to Figure 8 , Figure 8 is a structural schematic diagram of the electronic device provided in the embodiments of the present application. As shown in Figure 8 , the electronic device 800 includes a processor 801 and a memory 802 connected with the processor 801 in communication; the memory 802 stores computer execution instructions; the processor 801 executes the computer execution instructions stored in the memory to realize the method provided in the above-mentioned embodiments.

[0093] In order to realize the above-mentioned embodiments, the present application further provides a computer readable storage medium, and the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to realize the method provided in the above-mentioned embodiments.

[0094] To achieve the above-mentioned embodiments, the application further provides a computer program product comprising a computer program which, when executed by a processor, implements the method provided by the above-mentioned embodiments.

[0095] In the description of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" herein is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can mean that A exists alone, A and B exist together, and B exists alone.

[0096] In the foregoing embodiment description, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" 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 the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction.

[0097] In addition, the terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "multiple" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0098] Any process or method descriptions in flow charts or otherwise described herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions (or steps) in the process, and the preferred embodiments of the application include additional implementations in which the order of steps can differ from those shown or discussed, including a step can occur at other times, including as recited in the description of the preferred embodiments. It will be understood by those skilled in the art that the scope of the preferred embodiments of the present application includes additional implementations in which the order of steps can differ from those shown or discussed, including a step can occur at other times, including as recited in the description of the preferred embodiments.

[0099] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of instructions to implement logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor- based system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a computer- readable storage medium or a computer-readable signal medium. The computer- readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection having one or more wires (electrical connections), a portable computer diskette (a magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0100] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. As such, in some embodiments, specifically configured hardware can be used to implement at least some of the functionality described herein. For example, if implemented in hardware, the hardware can include any or a combination of the following: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0101] Those of skill in the art would understand that information and signals can be represented using any of a variety of technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that can be referenced throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0102] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0103] The storage medium mentioned above can 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 should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for monitoring the condition of the main bearing of a tunnel boring machine, characterized in that, include: Obtain the dynamic response characteristics of the main bearing of the tunnel boring machine under different working conditions; Acquire timing monitoring data of multiple preset measuring points in the main bearing of the tunnel boring machine; Time-frequency domain analysis and acceleration envelope analysis are performed on the time-series monitoring data to obtain the characteristics of the time-series monitoring data; Based on the time-series monitoring data characteristics and the dynamic response characteristics, the working condition of the main bearing of the tunnel boring machine is determined; The step of determining the working condition of the main bearing of the tunnel boring machine based on the time-series monitoring data features and the dynamic response features includes: inputting the time-series monitoring data features into a pre-trained state judgment model to obtain the working condition output by the state judgment model; wherein, the state judgment model has been trained by the corresponding dynamic response features under different working conditions to obtain the ability to judge the working condition of the main bearing of the tunnel boring machine corresponding to different monitoring data. The state determination model is pre-trained through the following steps: The dynamic response characteristics corresponding to the different working conditions are used as training data and input into the initial state judgment model to obtain the predicted state output by the initial state judgment model. Based on the difference between the predicted state and the different working conditions, a loss value is calculated using a pre-set loss function. The model parameters of the initial state judgment model are then optimized based on the loss value, and the state judgment model is obtained based on the optimized model parameters.

2. The method as described in claim 1, characterized in that, The acquisition of the dynamic response characteristics of the main bearing of the tunnel boring machine under different working conditions includes: Establish a dynamic model of the main bearing of the tunnel boring machine; Obtain the operating conditions corresponding to the different operating conditions described above; The working conditions corresponding to different working conditions are respectively introduced into the dynamic model; The dynamic model is solved based on the working conditions corresponding to each working condition to obtain the dynamic response characteristics corresponding to each working condition.

3. The method as described in claim 1, characterized in that, The method further includes: The time-series monitoring data features and the operating conditions are used as training data to train the state judgment model, thereby updating the model parameters of the state judgment model.

4. The method as described in claim 1, characterized in that, The determination of the operating condition of the main bearing of the tunnel boring machine based on the time-series monitoring data characteristics and the dynamic response characteristics includes: The similarity between the time-series monitoring data features and the dynamic response features corresponding to each working condition is calculated to obtain the similarity between the time-series monitoring data features and the dynamic response features corresponding to each working condition. The target working condition corresponding to the maximum similarity among the similarities is obtained as the working condition state of the main bearing of the tunnel boring machine.

5. The method according to any one of claims 1 to 4, characterized in that, The time-series monitoring data includes at least one of the following: Axial vibration data; Radial vibration data; Rotational speed data.

6. A monitoring device for the condition of the main bearing of a tunnel boring machine, characterized in that, include: The first acquisition module is used to acquire the dynamic response characteristics of the main bearing of the tunnel boring machine under different working conditions; The second acquisition module is used to acquire time-series monitoring data of multiple preset measuring points in the main bearing of the tunnel boring machine; The first processing module is 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. The second processing module is used to determine the working condition of the main bearing of the tunnel boring machine based on the time-series monitoring data characteristics and the dynamic response characteristics. The second processing module is specifically used to: input the time-series monitoring data features into a pre-trained state judgment model to obtain the working condition state output by the state judgment model; wherein, the state judgment model has been trained by the dynamic response features corresponding to different working conditions to obtain the ability to judge the working condition state of the shield machine main bearing corresponding to different monitoring data; The state determination model is pre-trained through the following steps: The dynamic response characteristics corresponding to the different working conditions are used as training data and input into the initial state judgment model to obtain the predicted state output by the initial state judgment model. Based on the difference between the predicted state and the different working conditions, a loss value is calculated using a pre-set loss function. The model parameters of the initial state judgment model are then optimized based on the loss value, and the state judgment model is obtained based on the optimized model parameters.

7. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 5.

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