Method, device and system for detecting running state of main bearing of shield tunneling machine

Abstract

The invention relates to a method, a device and a system for detecting the running state of a main bearing of a shield tunneling machine, and relates to the technical field of quality monitoring. The method comprises the following steps: acquiring an original vibration signal of a main bearing of the shield tunneling machine; performing first feature extraction processing on the original vibration signal to obtain a dimensioned time domain feature, a dimensionless time domain feature and a frequency domain feature of the original vibration signal; performing second feature extraction processing on the original vibration signal to obtain a degradation feature of the main bearing of the shield tunneling machine; carrying out fusion processing on the dimensional time domain feature, the dimensionless time domain feature, the frequency domain feature and the degradation feature to obtain a comprehensive vibration feature; and inputting the comprehensive vibration characteristics into a pre-trained operation state prediction model to obtain operation state information obtained by predicting the operation state of the main bearing of the shield tunneling machine by the operation state prediction model based on the comprehensive vibration characteristics. According to the scheme, the accuracy of judging the operation state of the shield main bearing is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of quality monitoring, and in particular to a method, device and system for detecting the operating status of a main bearing of a shield machine. Background Art

[0002] In the related art, the shield main bearing vibration signal will inevitably attenuate from generation to detection under different working conditions. The existing main bearing vibration signal detection method is difficult to effectively extract the vibration signal characteristics generated by the shield main bearing, so it is difficult to accurately judge the operating state and operating conditions of the shield main bearing. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present disclosure provides a method, device and system for detecting the operating status of a main bearing of a shield machine.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for detecting an operating state of a main bearing of a shield machine is provided, comprising:

[0005] Obtain the original vibration signal of the shield machine main bearing;

[0006] Performing a first feature extraction process on the original vibration signal to obtain a dimensional time domain feature, a dimensionless time domain feature, and a frequency domain feature of the original vibration signal;

[0007] Obtain the total usage time of the main bearing of the shield machine;

[0008] When the total duration is greater than a preset duration, performing a second feature extraction process on the original vibration signal to obtain a degradation feature of the shield machine main bearing;

[0009] The dimensional time domain features, the dimensionless time domain features, the frequency domain features and the degradation features are fused to obtain a comprehensive vibration feature;

[0010] The comprehensive vibration feature is input into a pre-trained operation status prediction model to obtain operation status information obtained by the operation status prediction model by predicting the operation status of the main bearing of the shield machine based on the comprehensive vibration feature.

[0011] In some embodiments of the present disclosure, the dimensioned time-domain feature includes at least one or more of the following items:

[0012] Mean, absolute mean, square root value, root mean square value, variance, standard deviation

[0013] The dimensionless time domain feature includes at least one or more of the following items:

[0014] Waveform characteristics, peak characteristics, pulse characteristics, margin characteristics, skewness characteristics, kurtosis characteristics;

[0015] The frequency domain features include frequency band energy.

[0016] In some embodiments of the present disclosure, the dimensional time domain features, dimensionless time domain features, frequency domain features and degradation features are fused to obtain comprehensive vibration features, including:

[0017] According to the degradation feature, a feature associated with the degradation feature is selected from the dimensional time domain feature, the dimensionless time domain feature, and the frequency domain feature to obtain an intermediate feature;

[0018] The degenerate feature and the intermediate feature are spliced ​​to obtain the comprehensive vibration feature.

[0019] In some embodiments of the present disclosure, the step of performing splicing processing on the degenerate feature and the intermediate feature to obtain the comprehensive vibration feature includes:

[0020] Obtaining a first feature vector according to the degradation feature;

[0021] Obtaining a second feature vector according to the intermediate feature;

[0022] Concatenating the first feature vector and the second feature vector to obtain a concatenated feature vector;

[0023] The concatenated feature vector is normalized to obtain the comprehensive vibration feature.

[0024] According to a second aspect of an embodiment of the present disclosure, there is provided a device for detecting the operating status of a main bearing of a shield machine, comprising:

[0025] A first acquisition unit is used to acquire an original vibration signal of a main bearing of a shield machine;

[0026] A first feature extraction unit, configured to perform a first feature extraction process on the original vibration signal to obtain a dimensional time domain feature, a dimensionless time domain feature, and a frequency domain feature of the original vibration signal;

[0027] A second acquisition unit is used to acquire the total usage time of the main bearing of the shield machine;

[0028] A second feature extraction unit is used to perform a second feature extraction process on the original vibration signal to obtain a degradation feature of the main bearing of the shield machine when the total duration is greater than a preset duration;

[0029] A fusion unit, used for fusing the dimensioned time domain features, dimensionless time domain features, frequency domain features and degradation features to obtain a comprehensive vibration feature;

[0030] The prediction unit is used to input the comprehensive vibration characteristics into a pre-trained operation status prediction model to obtain operation status information obtained by the operation status prediction model based on the comprehensive vibration characteristics to predict the operation status of the main bearing of the shield machine.

[0031] In some embodiments of the present disclosure, the dimensioned time-domain feature includes at least one or more of the following items:

[0032] Mean, absolute mean, square root value, root mean square value, variance, standard deviation

[0033] The dimensionless time domain feature includes at least one or more of the following items:

[0034] Waveform characteristics, peak characteristics, pulse characteristics, margin characteristics, skewness characteristics, kurtosis characteristics;

[0035] The frequency domain features include frequency band energy.

[0036] In some embodiments of the present disclosure, the fusion unit is specifically used for:

[0037] According to the degradation feature, a feature associated with the degradation feature is selected from the dimensional time domain feature, the dimensionless time domain feature, and the frequency domain feature to obtain an intermediate feature;

[0038] The degenerate feature and the intermediate feature are spliced ​​to obtain the comprehensive vibration feature.

[0039] According to a third aspect of an embodiment of the present disclosure, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in any one of the first aspects is implemented.

[0040] According to a fourth aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the first aspects is implemented.

[0041] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method as described in any one of the first aspects when executed by a processor.

[0042] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: obtaining the original vibration signal of the main bearing of the shield machine; performing a first feature extraction process on the original vibration signal to obtain the dimensional time domain features, dimensionless time domain features and frequency domain features of the original vibration signal; obtaining the total usage time of the main bearing of the shield machine; when the total usage time is greater than the preset usage time, performing a second feature extraction process on the original vibration signal to obtain the degradation features of the main bearing of the shield machine; fusing the dimensional time domain features, dimensionless time domain features, frequency domain features and degradation features to obtain comprehensive vibration features; inputting the comprehensive vibration features into a pre-trained operation status prediction model to obtain the operation status information obtained by the operation status prediction model based on the comprehensive vibration features to predict the operation status of the main bearing of the shield machine, thereby being able to effectively extract the required features and improving the accuracy of judging the operation status of the main bearing of the shield machine.

[0043] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0045] Figure 1 It is a flow chart of a method for detecting the operating status of a main bearing of a shield machine according to an exemplary embodiment.

[0046] Figure 2 It is a block diagram of a device for detecting the operating status of a main bearing of a shield machine according to an exemplary embodiment.

[0047] Figure 3 It is a block diagram of a device for detecting the operating status of a main bearing of a shield machine according to an exemplary embodiment. DETAILED DESCRIPTION

[0048] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0049] The terms used in the disclosed embodiments are only for the purpose of describing specific embodiments, and are not intended to limit the disclosed embodiments. The singular forms "a", "an" and "the" used in the disclosed embodiments and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0050] It should be understood that although the terms first, second, third, etc. may be used to describe various information in the disclosed embodiments, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the disclosed embodiments, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at" or "when" or "in response to determination".

[0051] In addition, various forms of processes shown in the embodiments of the present disclosure may be used to reorder, add or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present disclosure can be achieved, and this document does not limit this.

[0052] In the related art, the shield main bearing vibration signal will inevitably attenuate from generation to detection under different working conditions. The existing main bearing vibration signal detection method is difficult to effectively extract the vibration signal characteristics generated by the shield main bearing, so it is difficult to accurately judge the operating state and operating conditions of the shield main bearing.

[0053] In order to solve the above problems, the present disclosure provides a method, device and system for detecting the operating status of a main bearing of a shield machine, which obtains the original vibration signal of the main bearing of the shield machine; performs a first feature extraction process on the original vibration signal to obtain dimensional time domain features, dimensionless time domain features and frequency domain features of the original vibration signal; obtains the total usage time of the main bearing of the shield machine; when the total usage time is greater than a preset time, performs a second feature extraction process on the original vibration signal to obtain the degradation features of the main bearing of the shield machine; fuses the dimensional time domain features, dimensionless time domain features, frequency domain features and degradation features to obtain comprehensive vibration features; inputs the comprehensive vibration features into a pre-trained operating status prediction model to obtain operating status information obtained by the operating status prediction model by predicting the operating status of the main bearing of the shield machine based on the comprehensive vibration features, thereby being able to effectively extract the required features and improving the accuracy of judging the operating status of the main bearing of the shield machine.

[0054] Figure 1 is a flow chart of a method for detecting the operating status of a main bearing of a shield machine according to an exemplary embodiment. Figure 1 As shown, it should be noted that the operating state detection method of the shield machine main bearing of the embodiment of the present disclosure is applied to the operating state detection of the shield machine main bearing. Figure 1 As shown, the method may include the following steps:

[0055] Step 101, obtaining the original vibration signal of the main bearing of the shield machine.

[0056] In one embodiment, after the original vibration signal is collected, the original vibration signal may be subjected to denoising processing to obtain the denoised original vibration signal.

[0057] Step 102 : performing a first feature extraction process on the original vibration signal to obtain a dimensional time domain feature, a dimensionless time domain feature, and a frequency domain feature of the original vibration signal.

[0058] It can be understood that the time domain and frequency domain are two different ways of reflecting the characteristics of the same vibration signal. Relatively speaking, the frequency domain responds to the signal characteristics more clearly and can reflect more details.

[0059] In some embodiments of the present disclosure, the dimensioned time domain feature includes at least one or more of the following items:

[0060] Mean, absolute mean, square root value, root mean square value, variance, standard deviation

[0061] The dimensionless time domain characteristics include at least one or more of the following items:

[0062] Waveform characteristics, peak characteristics, pulse characteristics, margin characteristics, skewness characteristics, kurtosis characteristics;

[0063] Frequency domain features include frequency band energy.

[0064] Step 103, obtaining the total usage time of the main bearing of the shield machine.

[0065] Step 104: When the total duration is greater than the preset duration, a second feature extraction process is performed on the original vibration signal to obtain degradation features of the shield machine main bearing.

[0066] In some embodiments of the present disclosure, a pre-trained degradation feature extraction model may be used to extract degradation features of a shield machine main bearing from an original vibration signal.

[0067] In one example, the degradation feature extraction model may be a fuzzy neural network.

[0068] Step 105 , fusing the dimensional time domain features, the dimensionless time domain features, the frequency domain features and the degradation features to obtain a comprehensive vibration feature.

[0069] In some embodiments of the present disclosure, step 105 may specifically include:

[0070] According to the degradation feature, features associated with the degradation feature are selected from the dimensional time domain feature, the dimensionless time domain feature, and the frequency domain feature to obtain the intermediate feature;

[0071] The degenerate features and intermediate features are spliced ​​to obtain the comprehensive vibration features.

[0072] In some embodiments of the present disclosure, the degenerate features and the intermediate features are spliced ​​to obtain the comprehensive vibration features, which may specifically include the following steps:

[0073] Obtaining a first eigenvector according to the degradation feature;

[0074] Obtain a second eigenvector based on the intermediate features;

[0075] Concatenate the first eigenvector and the second eigenvector to obtain a concatenated eigenvector;

[0076] The concatenated feature vectors are normalized to obtain the comprehensive vibration features.

[0077] Step 106, inputting the comprehensive vibration characteristics into the pre-trained operation status prediction model, and obtaining the operation status information obtained by the operation status prediction model by predicting the operation status of the main bearing of the shield machine based on the comprehensive vibration characteristics.

[0078] In some embodiments of the present disclosure, the operation state prediction model can be a long-short time neural network prediction model. The full life cycle signals of the main bearing at different speeds are collected respectively, and the full life cycle signals are decomposed into horizontal vibration signals and vertical vibration signals to obtain a training data set; the signals in the training data set are processed by continuous wavelet transform, and an operation state label and a speed label are set for each training data; the prediction model is trained by the processed training data set, the operation state label and the speed label; the trained prediction model is verified by selecting test data, and the obtained model prediction value is smoothed, and the model prediction error is calculated according to the actual life corresponding to the test data, and the model parameters are adjusted according to the model prediction error until the model converges; the learning rate of the converged prediction model is adaptively optimized by the Adam optimization algorithm to obtain the operation state prediction model.

[0079] According to the operating status detection method of the shield machine main bearing proposed in the embodiment of the present disclosure, the original vibration signal of the shield machine main bearing is obtained; the first feature extraction process is performed on the original vibration signal to obtain the dimensional time domain feature, dimensionless time domain feature and frequency domain feature of the original vibration signal; the total usage time of the shield machine main bearing is obtained; when the total usage time is greater than the preset time, the second feature extraction process is performed on the original vibration signal to obtain the degradation feature of the shield machine main bearing; the dimensional time domain feature, dimensionless time domain feature, frequency domain feature and degradation feature are fused to obtain the comprehensive vibration feature; the comprehensive vibration feature is input into a pre-trained operating status prediction model to obtain the operating status information obtained by the operating status prediction model by predicting the operating status of the shield machine main bearing based on the comprehensive vibration feature, thereby being able to effectively extract the required features and improving the accuracy of judging the operating status of the shield machine main bearing.

[0080] Figure 2 1 is a block diagram of a device for detecting the operating status of a main bearing of a shield machine according to an exemplary embodiment. Figure 2 The device includes a detection module 121, a determination module 122 and a conversion module 123.

[0081] Wherein, the first acquisition unit 201 is used to acquire the original vibration signal of the main bearing of the shield machine;

[0082] A first feature extraction unit 202 is used to perform a first feature extraction process on the original vibration signal to obtain a dimensional time domain feature, a dimensionless time domain feature and a frequency domain feature of the original vibration signal;

[0083] The second acquisition unit 203 is used to acquire the total usage time of the main bearing of the shield machine;

[0084] The second feature extraction unit 204 is used to perform a second feature extraction process on the original vibration signal to obtain the degradation feature of the shield machine main bearing when the total duration is greater than the preset duration;

[0085] A fusion unit 205 is used to fuse the dimensioned time domain features, the dimensionless time domain features, the frequency domain features and the degradation features to obtain a comprehensive vibration feature;

[0086] The prediction unit 206 is used to input the comprehensive vibration characteristics into the pre-trained operation status prediction model to obtain the operation status information obtained by the operation status prediction model based on the comprehensive vibration characteristics to predict the operation status of the main bearing of the shield machine.

[0087] In some embodiments of the present disclosure, the dimensioned time domain feature includes at least one or more of the following items:

[0088] Mean, absolute mean, square root value, root mean square value, variance, standard deviation

[0089] The dimensionless time domain characteristics include at least one or more of the following items:

[0090] Waveform characteristics, peak characteristics, pulse characteristics, margin characteristics, skewness characteristics, kurtosis characteristics;

[0091] Frequency domain features include frequency band energy.

[0092] In some embodiments of the present disclosure, the fusion unit 205 is specifically used to:

[0093] According to the degradation feature, features associated with the degradation feature are selected from the dimensional time domain feature, the dimensionless time domain feature, and the frequency domain feature to obtain the intermediate feature;

[0094] The degenerate features and intermediate features are spliced ​​to obtain the comprehensive vibration features.

[0095] In some embodiments of the present disclosure, the fusion unit 205 is specifically used to:

[0096] Obtaining a first eigenvector according to the degradation feature;

[0097] Obtain a second eigenvector based on the intermediate features;

[0098] Concatenate the first eigenvector and the second eigenvector to obtain a concatenated eigenvector;

[0099] The concatenated feature vectors are normalized to obtain the comprehensive vibration features.

[0100] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0101] According to the operating status detection device for the main bearing of a shield machine proposed in the embodiment of the present disclosure, the original vibration signal of the main bearing of the shield machine is obtained; a first feature extraction process is performed on the original vibration signal to obtain dimensional time domain features, dimensionless time domain features and frequency domain features of the original vibration signal; the total usage time of the main bearing of the shield machine is obtained; when the total usage time is greater than a preset time, a second feature extraction process is performed on the original vibration signal to obtain a degradation feature of the main bearing of the shield machine; the dimensional time domain features, dimensionless time domain features, frequency domain features and degradation features are fused to obtain a comprehensive vibration feature; the comprehensive vibration feature is input into a pre-trained operating status prediction model to obtain operating status information obtained by the operating status prediction model by predicting the operating status of the main bearing of the shield machine based on the comprehensive vibration feature, thereby being able to effectively extract the required features and improving the accuracy of judging the operating status of the main bearing of the shield machine.

[0102] Figure 33 is a block diagram of a device for detecting the operating state of a main bearing of a shield machine according to an exemplary embodiment. For example, the device 300 may be an electronic device, such as a mobile phone, a computer, a digital broadcast terminal, a message transceiver device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0103] Reference Figure 3 , the device 300 may include one or more of the following components: a processing component 302 , a memory 304 , a power component 306 , a multimedia component 308 , an audio component 310 , an input / output (I / O) interface 312 , a sensor component 314 , and a communication component 316 .

[0104] The processing component 302 generally controls the overall operation of the device 300, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 302 may include one or more processors 320 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 302 may include one or more modules to facilitate the interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate the interaction between the multimedia component 308 and the processing component 302.

[0105] The memory 304 is configured to store various types of data to support operations on the device 300. Examples of such data include instructions for any application or method operating on the device 300, contact data, phone book data, messages, pictures, videos, etc. The memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0106] The power component 306 provides power to the various components of the device 300. The power component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 300.

[0107] The multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 includes a front camera and / or a rear camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0108] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC), and when the device 300 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 304 or sent via the communication component 316. In some embodiments, the audio component 310 also includes a speaker for outputting audio signals.

[0109] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0110] The sensor assembly 314 includes one or more sensors for providing various aspects of status assessment for the device 300. For example, the sensor assembly 314 can detect the open / closed state of the device 300, the relative positioning of components, such as the display and keypad of the device 300, the sensor assembly 314 can also detect the position change of the device 300 or a component of the device 300, the presence or absence of user contact with the device 300, the orientation or acceleration / deceleration of the device 300, and the temperature change of the device 300. The sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 314 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0111] The communication component 316 is configured to facilitate wired or wireless communication between the device 300 and other devices. The device 300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0112] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0113] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, and the instructions can be executed by the processor 320 of the device 300 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0114] In an exemplary embodiment, a computer program product is also provided, comprising a computer program, which implements the above method when executed by the processor 320 of the device 300 .

[0115] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0116] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for detecting the operating status of a main bearing of a shield machine, characterized in that: include: Obtain the original vibration signal of the shield machine main bearing; Performing a first feature extraction process on the original vibration signal to obtain a dimensional time domain feature, a dimensionless time domain feature, and a frequency domain feature of the original vibration signal; Obtain the total usage time of the main bearing of the shield machine; When the total duration is greater than a preset duration, performing a second feature extraction process on the original vibration signal to obtain a degradation feature of the shield machine main bearing; The dimensional time domain features, the dimensionless time domain features, the frequency domain features and the degradation features are fused to obtain a comprehensive vibration feature; The comprehensive vibration feature is input into a pre-trained operation status prediction model to obtain operation status information obtained by the operation status prediction model by predicting the operation status of the main bearing of the shield machine based on the comprehensive vibration feature.

2. The method for detecting the operating status of the main bearing of a shield machine according to claim 1, characterized in that: in: The dimensioned time domain features include at least one or more of the following items: Mean, absolute mean, square root value, root mean square value, variance, standard deviation The dimensionless time domain feature includes at least one or more of the following items: Waveform characteristics, peak characteristics, pulse characteristics, margin characteristics, skewness characteristics, kurtosis characteristics; The frequency domain features include frequency band energy.

3. The method for detecting the operating status of the main bearing of a shield machine according to claim 1, characterized in that: The dimensional time domain features, dimensionless time domain features, frequency domain features and degradation features are fused to obtain comprehensive vibration features, including: According to the degradation feature, a feature associated with the degradation feature is selected from the dimensional time domain feature, the dimensionless time domain feature, and the frequency domain feature to obtain an intermediate feature; The degenerate feature and the intermediate feature are spliced ​​to obtain the comprehensive vibration feature.

4. The method for detecting the operating status of the main bearing of a shield machine according to claim 3, characterized in that: The step of performing splicing processing on the degenerate feature and the intermediate feature to obtain the comprehensive vibration feature includes: Obtaining a first feature vector according to the degradation feature; Obtaining a second feature vector according to the intermediate feature; Concatenating the first feature vector and the second feature vector to obtain a concatenated feature vector; The concatenated feature vector is normalized to obtain the comprehensive vibration feature.

5. A device for detecting the operating status of a main bearing of a shield machine, characterized in that: include: A first acquisition unit is used to acquire an original vibration signal of a main bearing of a shield machine; A first feature extraction unit, configured to perform a first feature extraction process on the original vibration signal to obtain a dimensional time domain feature, a dimensionless time domain feature, and a frequency domain feature of the original vibration signal; A second acquisition unit is used to acquire the total usage time of the main bearing of the shield machine; A second feature extraction unit is used to perform a second feature extraction process on the original vibration signal to obtain a degradation feature of the main bearing of the shield machine when the total duration is greater than a preset duration; A fusion unit, used for fusing the dimensioned time domain features, dimensionless time domain features, frequency domain features and degradation features to obtain a comprehensive vibration feature; The prediction unit is used to input the comprehensive vibration characteristics into a pre-trained operation status prediction model to obtain operation status information obtained by the operation status prediction model based on the comprehensive vibration characteristics to predict the operation status of the main bearing of the shield machine.

6. The operating status detection device for the main bearing of a shield machine according to claim 1, characterized in that: in: The dimensioned time domain features include at least one or more of the following items: Mean, absolute mean, square root value, root mean square value, variance, standard deviation The dimensionless time domain feature includes at least one or more of the following items: Waveform characteristics, peak characteristics, pulse characteristics, margin characteristics, skewness characteristics, kurtosis characteristics; The frequency domain features include frequency band energy.

7. The operating status detection device for the main bearing of a shield machine according to claim 1, characterized in that: The fusion unit is specifically used for: According to the degradation feature, a feature associated with the degradation feature is selected from the dimensional time domain feature, the dimensionless time domain feature, and the frequency domain feature to obtain an intermediate feature; The degenerate feature and the intermediate feature are spliced ​​to obtain the comprehensive vibration feature.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

10. A computer program product, comprising a computer program, characterized in that The computer program implements the method according to any one of claims 1 to 4 when executed by a processor.

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