Load self-sensing tunneling cutter and cutter monitoring method

Abstract

The embodiment of the invention discloses a load self-sensing tunneling cutter and a cutter monitoring method, belongs to the technical field of cutter monitoring, and solves the problem that the actual working condition of the tunneling cutter is difficult to accurately analyze since a non-integrated packaged sensing device cannot be provided with various types of sensing elements due to spatial size limitation and can only monitor a single parameter. The tool comprises a tool body, a multi-source sensor integrated module and an upper computer. An axial center blind hole is formed in the axis of a cutter shaft of the cutter body; a protective shell of the multi-source sensor integrated module is attached to the inner wall of the axial center blind hole, and the multi-source sensor integrated module comprises a multi-source sensor module, a self-generating module and a signal transmission module; the multi-source sensor integrated module is in wireless connection with the upper computer, and the multi-source sensor integrated module is used for transmitting the collected cutter multi-source state data to the upper computer, so that the upper computer analyzes the cutter multi-source state data, and monitoring of the load self-sensing tunneling cutter is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of tool monitoring, and in particular to a load self-sensing tunneling tool and a tool monitoring method. Background Art

[0002] With the rapid development of urban underground facilities, the demand for tunneling equipment is also growing. Tunneling equipment mainly relies on the tunneling tools on the cutterhead to break rocks. Currently, the existing equipment for monitoring the working condition of the cutter is externally mounted on the cutter box seat, and the complex and harsh tunneling environment leads to poor stability.

[0003] In the prior art, non-integrated packaged sensor devices are usually used to monitor the cutting tool. Non-integrated packaged sensor devices have high requirements for the working environment and are easily affected by external factors such as dust, high temperature and strong vibration during operation, which makes data collection difficult and even equipment damage. Secondly, non-integrated packaged sensors require more connections and occupy more space. In applications where the internal space of the cutting tool is limited, the difficulty of assembly and layout is increased, limiting the flexibility of equipment design. At the same time, due to space size limitations, non-integrated packaged sensor devices cannot be installed with multiple types of sensing elements and can only monitor a single parameter, making it difficult to accurately analyze the actual working conditions of the tunneling tool. Summary of the invention

[0004] The embodiments of the present application provide a load self-sensing tunneling tool and a tool monitoring method, which are used to solve the following technical problems: non-integrated packaged sensor equipment cannot be installed with multiple types of sensing elements due to space size limitations, and can only monitor a single parameter, making it difficult to accurately analyze the actual working condition of the tunneling tool.

[0005] The present application embodiment adopts the following technical solutions:

[0006] An embodiment of the present application provides a load self-sensing tunneling tool, characterized in that the tool includes: a tool body, a multi-source sensor integrated module and a host computer; an axial center blind hole is provided at the axis of the tool shaft of the tool body; a protective shell of the multi-source sensor integrated module is fitted with the inner wall of the axial center blind hole, and the multi-source sensor integrated module includes a multi-source sensor module, a self-generating module and a signal transmission module; the multi-source sensor integrated module is wirelessly connected to the host computer, and the multi-source sensor integrated module is used to transmit the collected multi-source status data of the tool to the host computer, so that the host computer can analyze the multi-source status data of the tool to realize the monitoring of the load self-sensing tunneling tool.

[0007] The embodiment of the present application solves the problem of difficult wiring in the tunneling cutter disc tool by installing highly integrated sensors and self-generating modules on the basis of ensuring the overall rigidity and strength of the tool. At the same time, the front-end integrated module of the embodiment of the present application is an integrated design. By machining corresponding blind holes at the axial lifting holes on one side of the cutter shaft, tunneling tools of various types and sizes can be converted into intelligent tools with sensing functions, which does not affect the normal operation of the tools and improves the applicability of the tools. In addition, the embodiment of the present application uses multi-source sensors to monitor the various status parameters of the tunneling tools in real time, so that users can obtain the operating status of a single tool in real time. At the same time, the overall working condition and potential faults of the cutter disc can be evaluated by comprehensive comparative analysis of the operating conditions of the tool array.

[0008] In one implementation of the present application, the multi-source sensor module includes a force sensor, a vibration sensor and a temperature sensor; the force sensor is attached to the inner wall flexible structure of the multi-source sensor integrated module, and the force sensor is used to obtain the force data of the load self-sensing tool; the vibration sensor is arranged on the central axis of the multi-source sensor integrated module, and the vibration sensor is a MEMS accelerometer; the vibration sensor is used to obtain the vibration data of the load self-sensing tool; the temperature sensor is arranged on the inner side of the multi-source sensor integrated module and is adjacent to the inner ring of the tunneling tool mounting bearing. The assembled temperature sensor is located at the bottom of the axial center blind hole and is used to obtain the temperature data of the load self-sensing tool.

[0009] In one implementation of the present application, the vibration sensor also includes a three-way vibration sensing element, a thin film resistor layer and a signal conditioning circuit; the three-way vibration sensing element is used to measure the vibration amplitude and frequency of the load self-sensing tunneling tool in three orthogonal directions; the thin film resistor layer is used to measure the vibration intensity by detecting the strain changes caused by the vibration; the signal conditioning circuit is used to amplify, filter and convert the acquired vibration signal to generate a digital signal corresponding to the vibration signal.

[0010] In one implementation of the present application, the temperature sensor is provided with a temperature sensing element; the temperature sensing element is arranged at the side opening of the protective shell of the multi-source sensor integrated module; the temperature sensing element is provided with a temperature sensing probe; the temperature sensing probe is exposed and placed, and the exposed height of the temperature sensing probe is lower than the surface of the protective shell of the multi-source sensor integrated module.

[0011] In one implementation of the present application, the self-generating module is connected to the signal transmission module; the self-generating module is an annular thin film piezoelectric self-generating module, and the self-generating module is arranged on the outer wall of the surface of the multi-source sensor integrated module; the self-generating module supplies power to the multi-source sensor module through the data line of the signal transmission module.

[0012] In one implementation of the present application, the signal transmission module is connected to the multi-source sensor module; the signal transmission module is arranged at the bottom of the multi-source sensor integrated module and is located at the top of the axial center blind hole after assembly.

[0013] In one implementation of the present application, the size of the axial center blind hole matches the size of the protective shell of the multi-source sensor integrated module; a sealing support mechanism is provided at the open end of the axial center blind hole; and shock-absorbing sealant is filled between the multi-source sensor integrated module and the inner wall of the axial center blind hole.

[0014] The embodiment of the present application provides a tool monitoring method, which includes: a host computer receives a tool multi-source signal uploaded by a multi-source sensor integration module; the host computer preprocesses and extracts features of the tool multi-source signal, and generates a feature matrix based on the extracted feature data; the host computer compares each feature data with a corresponding preset threshold value to determine an abnormal point, and determines the abnormal state of the tool based on the abnormal point; the host computer determines the mutual correlation coefficient between the tool multi-source signals based on a preset function, and determines the correlation of the tool multi-source signals in the time domain and the frequency domain based on the mutual correlation coefficient; the host computer determines the working condition type of the tool based on the correlation, generates a working condition warning signal based on the working condition type, and feeds back the working condition type and the tool multi-source data to the tool management system to realize monitoring of the tool.

[0015] In one implementation of the present application, the host computer determines the mutual correlation coefficient between the tool multi-source signals based on a preset function, and determines the correlation of the tool multi-source signals in the time domain and the frequency domain based on the mutual correlation coefficient, specifically including: the host computer determines the mutual correlation coefficient between the tool multi-source signals based on the time domain characteristics corresponding to the tool multi-source signals and the preset function, so as to quantify the time domain correlation of the tool multi-source signals; wherein the tool multi-source signals include pressure signals, vibration signals and temperature signals; the host computer converts the tool multi-source signals from the time domain to the frequency domain through fast Fourier transform, obtains the frequency spectrum corresponding to the tool multi-source signals, so as to extract the frequency domain features of the tool multi-source signals; the host computer determines the time domain signal correlation corresponding to the tool multi-source signals based on the synchronization of the tool multi-source signals in the same time period; the host computer determines the frequency domain signal correlation corresponding to the tool multi-source signals based on the consistency of the tool multi-source signals in different frequency ranges.

[0016] In one implementation of the present application, the upper computer determines the working condition type of the tool based on the correlation, specifically including: the correlation between the mean and root mean square values ​​of the vibration signal and the pressure signal in the time domain is greater than a preset first correlation threshold, and the energy proportion corresponding to the low frequency band in the frequency domain characteristics is greater than the preset energy proportion value, determining that the tool is in a normal working condition; the correlation between the vibration signal and the temperature signal in the time domain characteristics is greater than a preset second correlation threshold, and a new high-frequency component appears in the temperature signal in the frequency domain characteristics, determining that the tool is in a wear condition; the fluctuation value of the pressure signal in the time domain characteristics is greater than the preset fluctuation threshold, and the main frequency distribution of the vibration signal in the frequency domain characteristics is inconsistent with the main frequency distribution of the pressure signal, determining that the tool is in an abnormal vibration condition.

[0017] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: On the basis of ensuring the overall rigidity and strength of the tool, the embodiments of the present application solve the problem of difficult wiring in the tunneling cutter disc tool by installing highly integrated sensors and self-generating modules. At the same time, the front-end integrated module of the embodiment of the present application is an integrated design. By machining corresponding blind holes at the axial lifting holes on one side of the cutter shaft, tunneling tools of various types and sizes can be converted into intelligent tools with sensing functions, which does not affect the normal operation of the tool and improves the applicability of the tool. In addition, the embodiment of the present application monitors the various status parameters of the tunneling tool in real time through multi-source sensors, and the user obtains the operating status of a single tool in real time. At the same time, the overall working condition and potential faults of the cutter disc can be evaluated by comprehensive comparative analysis of the operating conditions of the tool array. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0019] In the figure:

[0020] Figure 1 A schematic diagram of the structure of a load self-sensing tunneling tool provided in an embodiment of the present application;

[0021] Figure 2 A structural cross-sectional diagram of a load self-sensing tunneling tool provided in an embodiment of the present application;

[0022] Figure 3 A flow chart of a tool monitoring method provided in an embodiment of the present application.

[0023] Figure numerals: 1, tool body; 2, axial center blind hole; 3, temperature sensor; 4, pressure sensor; 5, vibration sensor; 6, self-generating module; 7, signal transmission module; 8, tool bearing; 9, tool shaft. DETAILED DESCRIPTION

[0024] The embodiments of the present application provide a load self-sensing tunneling tool and a tool monitoring method.

[0025] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0026] The technical solution proposed in the embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0027] Figure 1 A schematic diagram of the structure of a load self-sensing tunneling tool provided in an embodiment of the present application, Figure 2 This is a cross-sectional view of a load-sensing tunneling tool provided in an embodiment of the present application. Figure 1 and Figure 2 As shown, the load self-sensing tunneling tool includes a tool body 1, a multi-source sensor integrated module and a host computer. An axial center blind hole 2 is provided at the axis of the tool shaft 9 of the tool body 1. The protective shell of the multi-source sensor integrated module is fitted with the inner wall of the axial center blind hole 2. The multi-source sensor integrated module includes a multi-source sensor module, a self-generating module 6 and a signal transmission module 7. The multi-source sensor integrated module is wirelessly connected to the host computer. The multi-source sensor integrated module is used to transmit the collected tool multi-source status data to the host computer, so that the host computer can analyze the tool multi-source status data and realize the monitoring of the load self-sensing tunneling tool.

[0028] Specifically, the load self-sensing tunneling tool includes a tool body 1, a multi-source sensor integrated module and a host computer. The multi-source sensor integrated module includes a multi-source sensor module, a self-generating module 6, a signal transmission module 7 and a tool bearing 8. Each module is encapsulated in a protective shell and installed in a blind hole on one side of the tunneling tool shaft 9. The multi-source sensor module is connected to the signal transmission module 7 to wirelessly transmit the collected data to the host computer in the operating room.

[0029] Furthermore, the self-generating module 6 supplies power to the main control panel and the monitoring assembly, thereby eliminating the need for wiring outside the hob, reducing changes to the hob and the tool holder, lowering the difficulty of processing, and having good applicability to different types of tools.

[0030] Furthermore, the signal transmission module 7 is wirelessly connected to the receiving device behind the cutterhead through an antenna, and transmits the collected multi-source status data of the tool to the host computer through wireless transmission, thereby realizing real-time monitoring of the state of the excavation tool. At the same time, the host computer obtains the working state of the tool array by analyzing these parameters, judges the condition of the working face of the cutterhead in front, and guides the operator to replace the tool, maintain the equipment and optimize the excavation parameters.

[0031] like Figure 1 and Figure 2 As shown, the multi-source sensor module includes a force sensor, a vibration sensor 5 and a temperature sensor 3. The force sensor is attached to the inner wall flexible structure of the multi-source sensor integrated module, and the force sensor is used to obtain the force data of the load self-sensing tool. The vibration sensor 5 is set on the central axis of the multi-source sensor integrated module, and the vibration sensor 5 is a MEMS accelerometer; the vibration sensor 5 is used to obtain the vibration data of the load self-sensing tool. The temperature sensor 3 is set on the inner side of the multi-source sensor integrated module and is adjacent to the inner ring of the excavation tool mounting bearing. The assembled temperature sensor 3 is located at the bottom of the axial center blind hole 2, and is used to obtain the temperature data of the load self-sensing tool.

[0032] Specifically, the force sensor of the multi-source sensor integrated module is attached to the flexible structure of the inner wall of the module, and detects the external force by sensing the slight deformation of the flexible structure. The arrangement of the force sensor can enhance the sensitivity of the internal and external force distribution of the module, and realize the accurate monitoring and evaluation of the force state of the tool during the excavation process. The vibration sensor 5 of the multi-source sensor integrated module adopts the MEMS accelerometer design, which is set in the middle of the integrated module to detect the vibration signal generated by the excavation tool during operation. The vibration sensor 5 is installed on the central axis of the module, which can effectively sense the vibration characteristics of the tool in all directions during the rock breaking process, and realize accurate monitoring of the tool state. The temperature sensor 3 of the multi-source sensor integrated module adopts a micro-thermocouple design and is set on the inner side of the integrated module near the inner ring of the excavation tool mounting bearing; after the module is assembled in the hob, the temperature sensor 3 contacts the bottom of the central blind hole of the cutter shaft 9 to monitor the temperature change inside the tool.

[0033] like Figure 1 and Figure 2 As shown, the vibration sensor 5 also includes a three-dimensional vibration sensing element, a thin film resistor layer and a signal conditioning circuit. The three-dimensional vibration sensing element is used to measure the vibration amplitude and frequency of the load self-sensing tunneling tool in three orthogonal directions. The thin film resistor layer is used to measure the vibration intensity by detecting the strain change caused by the vibration. The signal conditioning circuit is used to amplify, filter and convert the acquired vibration signal to generate a digital signal corresponding to the vibration signal.

[0034] Specifically, the vibration sensor 5 includes a three-dimensional vibration sensing element, a thin film resistor layer and a signal conditioning circuit. These sensors can accurately capture the vibration data of the tool in the three directions of X, Y and Z, thereby providing detailed information about the working state of the tool. Specifically, the three-dimensional vibration sensing element is used to measure the vibration amplitude and frequency of the tool in three orthogonal directions, the thin film resistor layer measures the vibration intensity by detecting the strain change caused by the vibration, and the signal conditioning circuit amplifies, filters and converts these raw signals to generate digital signals for further processing.

[0035] like Figure 1 and Figure 2 As shown, the temperature sensor 3 is provided with a temperature sensing element, and the temperature sensing element is provided at the side opening of the protective shell of the multi-source sensor integrated module. The temperature sensing element is provided with a temperature sensing probe. The temperature sensing probe is exposed and placed, and the exposed height of the temperature sensing probe is lower than the surface of the protective shell of the multi-source sensor integrated module.

[0036] Specifically, the temperature sensing element of the temperature sensor 3 is installed at the side opening of the integrated module protective shell, and the probe is partially exposed, with its exposed height slightly lower than the outer surface of the protective shell to ensure the sensitivity and response speed of temperature detection, while preventing the probe from being damaged externally during excavation.

[0037] like Figure 1 and Figure 2 As shown, the self-generating module 6 is connected to the signal transmission module 7. The self-generating module 6 is an annular thin film piezoelectric self-generating module 6, and the self-generating module 6 is arranged on the outer wall of the surface of the multi-source sensor integrated module. The self-generating module 6 supplies power to the multi-source sensor module through the data line of the signal transmission module 7.

[0038] Specifically, the self-generating module 6 of the multi-source sensor integrated module is an annular thin film piezoelectric self-generating module 6, which is placed on the outer wall of the module surface. It is connected to the signal transmission module 7, and the sensor is powered by the data line of the signal transmission module 7. The self-generating module 6 supplies power to the main control board and the monitoring component, so there is no need to wire outside the hob, which reduces the changes to the hob and the tool holder, reduces the difficulty of processing, and has good applicability to different types of tools.

[0039] like Figure 1 and Figure 2 As shown, the signal transmission module 7 is connected to the multi-source sensor module. The signal transmission module 7 is arranged at the bottom of the multi-source sensor integrated module and is located at the top of the axial center blind hole 2 after assembly.

[0040] Specifically, the multi-source sensor is connected to the signal transmission module 7, and the collected data is transmitted to the host computer in the tunneling equipment operation room through the signal transmission module 7. The signal transmission module 7 is wirelessly connected to the receiving device behind the cutter head through the antenna, and transmits the collected multi-source status data of the tool to the host computer by wireless transmission.

[0041] like Figure 1 and Figure 2 As shown, the size of the axial center blind hole 2 matches the size of the protective shell of the multi-source sensor integrated module. A sealing support mechanism is provided at the open end of the axial center blind hole 2. A shock-absorbing sealant is filled between the multi-source sensor integrated module and the inner wall of the axial center blind hole 2.

[0042] Specifically, the size of the axial center blind hole 2 opened at the axis of the tool shaft 9 of the tool body 1 matches the protective shell of the multi-source sensor integrated module, the protective shell fits the inner wall of the blind hole, a sealing support mechanism is set at the open end, and a shock-absorbing sealant is filled between the integrated module and the inner wall of the blind hole. The integrated sensor module is compact after packaging and does not interfere with other original parts.

[0043] Figure 3 A flow chart of a tool monitoring method provided in an embodiment of the present application. Figure 3 As shown, the tool monitoring method includes the following steps:

[0044] S101, the host computer receives the tool multi-source signal uploaded by the multi-source sensor integration module.

[0045] In one embodiment of the present application, the host computer receives tool multi-source signals uploaded by the multi-source sensor integration module, including the pressure signal sent by the pressure sensor 4 , the vibration signal sent by the vibration sensor 5 , and the temperature signal sent by the temperature sensor 3 .

[0046] S102, the host computer preprocesses and extracts features from the tool multi-source signals, and generates a feature matrix based on the extracted feature data.

[0047] In one embodiment of the present application, the pressure, vibration and temperature signals are subjected to low-pass filtering, wavelet denoising and normalization, and features such as the mean value, standard deviation, peak value, root mean square value, and spectrum energy of the signals are extracted to form a feature matrix X = {x1, x2, ..., x n}.

[0048] Specifically, the host computer preprocesses the received vibration data, including noise filtering and data smoothing. Noise filtering uses a low-pass filter to remove high-frequency interference signals, and data smoothing uses a sliding average or exponentially weighted moving average method to reduce short-term fluctuations in the data.

[0049] Specifically, the host computer first preprocesses the temperature data, including noise filtering and data smoothing, to ensure the stability and accuracy of the data. Noise filtering uses a low-pass filter to remove high-frequency noise, while data smoothing uses a sliding average or exponentially weighted moving average method.

[0050] Temperature sensor 3 can promptly identify overheating problems caused by tool wear, jamming and eccentric wear. By continuously monitoring the tool temperature, the host computer can capture early signs of temperature anomalies. Temperature trend analysis and statistical anomaly detection algorithms are used to identify abnormal changes in temperature and promptly warn of possible tool failures. These algorithms compare real-time temperature data with historical temperature data to determine if there is a significant deviation and issue an alarm when an anomaly is detected.

[0051] Through continuous monitoring of these temperature data, operators can promptly identify potential problems and take preventive measures, such as adjusting excavation parameters or suspending work for inspection and maintenance. In addition, the embodiment of the present application can also identify the temperature variation pattern under different operating conditions through long-term temperature data analysis to help optimize tool design and selection. The temperature data combined with the vibration sensor 5 data can provide more comprehensive tool status information and further improve the accuracy of fault prediction.

[0052] S103, the host computer compares each feature data with the corresponding preset threshold value to determine the abnormal point, and determines the abnormal state of the tool based on the abnormal point.

[0053] In one embodiment of the present application, each feature is associated with a set threshold value (such as a pressure threshold ΔP th , vibration threshold ΔV th , Temperature slope threshold ΔT / dt th ) to compare, identify the abnormal points and mark them as abnormal states {0,1}.

[0054] S104 , the host computer determines the mutual correlation coefficient between the tool multi-source signals based on the preset function, and determines the correlation between the tool multi-source signals in the time domain and the frequency domain based on the mutual correlation coefficient.

[0055] In one embodiment of the present application, the host computer determines the mutual correlation coefficient between the tool multi-source signals based on the time domain features corresponding to the tool multi-source signals and the preset function, so as to quantify the time domain correlation of the tool multi-source signals; wherein the tool multi-source signals include pressure signals, vibration signals and temperature signals. The host computer converts the tool multi-source signals from the time domain to the frequency domain through fast Fourier transform to obtain the spectrum corresponding to the tool multi-source signals, so as to extract the frequency domain features of the tool multi-source signals. The host computer determines the time domain signal correlation corresponding to the tool multi-source signals based on the synchronization of the tool multi-source signals in the same time period. The host computer determines the frequency domain signal correlation corresponding to the tool multi-source signals based on the consistency of the tool multi-source signals in different frequency ranges.

[0056] Specifically, the cross-correlation coefficients between the signals are calculated Identify the correlation of multi-source signals in the time and frequency domains, and analyze the correlation characteristics of pressure, vibration, and temperature signals;

[0057] Among them, p ij It is expressed as the correlation coefficient between signal i and signal j, and its value range is -1 to 1. The closer the value is to 1, the stronger the positive correlation between the two signals is; the closer the value is to -1, the stronger the negative correlation is; and the closer the value is to 0, the less the linear correlation is. i Indicates the sample data value of signal i. Indicates the specific value of signal i at a certain moment or a certain measurement point. The average value (mean) of signal i represents the average data value of signal i in the entire time period or sampling interval.

[0058] Specifically, when identifying the correlation between pressure, vibration and temperature signals in the time domain and frequency domain, first, the three collected signals are preprocessed to eliminate noise interference and dimensional differences. Then, each signal is segmented according to a set time window (such as 1 second), and the time domain features within each time window are extracted, such as mean, standard deviation, peak value, root mean square value, etc., to reflect the instantaneous characteristics of the signal. Then the correlation coefficient p between the signal features is calculated. ij , which is used to quantify the time domain correlation between signals. Then the signal is converted from the time domain to the frequency domain through the fast Fourier transform to obtain the signal's spectrum. Frequency domain feature extraction includes main frequency, band energy ratio and frequency center, which are used to describe the frequency distribution of the signal.

[0059] S105. The host computer determines the working condition type of the tool based on the correlation, generates a working condition warning signal based on the working condition type, and feeds back the working condition type and the tool multi-source data to the tool management system to realize monitoring of the tool.

[0060] In one embodiment of the present application, the correlation between the mean and root mean square values ​​of the vibration signal and the pressure signal in the time domain is greater than the preset first correlation threshold, and the energy proportion corresponding to the low frequency band in the frequency domain characteristics is greater than the preset energy proportion value, and it is determined that the tool is in a normal working condition. The correlation between the vibration signal and the temperature signal in the time domain characteristics is greater than the preset second correlation threshold, and a new high-frequency component appears in the temperature signal in the frequency domain characteristics, and it is determined that the tool is in a worn condition. The fluctuation value of the pressure signal in the time domain characteristics is greater than the preset fluctuation threshold, and the main frequency distribution of the vibration signal in the frequency domain characteristics is inconsistent with the main frequency distribution of the pressure signal, and it is determined that the tool is in an abnormal vibration condition.

[0061] Based on the matching results, the support vector machine algorithm is used to classify and identify the feature data, and the working condition type (such as excessive wear, eccentric wear, and jamming) is output to achieve accurate working condition judgment;

[0062] A working condition warning signal is generated based on the judgment results, and the results and characteristic data are fed back to the tool management system in real time.

[0063] Operators can view the working status of the tool in real time through the tool management system user interface, including speed, pressure, temperature, and vibration information. The data displayed in the user interface comes from the data collected in real time by the multi-source sensor integration module and transmitted to the host computer. The host computer processes and analyzes this data, allowing operators to adjust operating parameters in a timely manner. When the vibration frequency and amplitude increase abnormally, the operator can reduce the excavation speed or adjust the tool pressure; when the temperature continues to rise, the operator can suspend excavation for inspection or replace the tool. Through these real-time adjustments, operators can improve the safety and efficiency of operations.

[0064] In addition, the wear warning function provided by the system can notify operators in advance that the tool is about to reach its limit of use, preventing failures and accidents caused by excessive tool wear. This not only extends the service life of the tool, but also reduces unplanned downtime caused by emergency maintenance and tool replacement, thereby reducing construction costs. Overall, the system helps operators optimize the excavation process, improve work efficiency and safety, and reduce maintenance costs and construction risks through real-time monitoring and intelligent analysis.

[0065] Furthermore, through the correlation analysis of time domain features, the synchronization of signals in the same time period can be identified. For example, the correlation of the mean values ​​of pressure and vibration signals can be used to determine whether the stress state is consistent with the vibration response. The coherence analysis of frequency domain features can reveal the consistency of signals within a specific frequency range. For example, the vibration and temperature changes caused by rock breaking may be enhanced simultaneously in the high frequency band. Through the correlation analysis of time domain and frequency domain features, the intrinsic relationship between vibration, pressure and temperature signals can be revealed. The specific analysis is as follows: Normal working conditions: The mean and RMS values ​​of vibration and pressure signals are highly correlated in the time domain, and the energy proportion of low-frequency bands in frequency domain features is high, and the coherence is strong. Tool wear conditions: The correlation between vibration and temperature signals in the time domain is significantly improved, and new high-frequency components appear in the temperature signal spectrum. Abnormal vibration conditions: The main frequency of the vibration signal is inconsistent with the frequency distribution of the pressure signal, the coherence is reduced, and the pressure signal fluctuation in the time domain increases.

[0066] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0067] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the embodiments of the present application may have various changes and variations. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A load-sensing tunneling tool, characterized in that: The tool comprises: a tool body, a multi-source sensor integrated module and a host computer; An axial center blind hole is provided at the axis center of the tool shaft of the tool body; The protective shell of the multi-source sensor integrated module is fitted with the inner wall of the axial center blind hole, and the multi-source sensor integrated module includes a multi-source sensor module, a self-generating module and a signal transmission module; The multi-source sensor integration module is wirelessly connected to the host computer, and is used to transmit the collected multi-source status data of the tool to the host computer, so that the host computer analyzes the multi-source status data of the tool to realize the self-sensing monitoring of the load-sensing tunneling tool.

2. A load-sensing tunneling tool according to claim 1, characterized in that: The multi-source sensor module includes a force sensor, a vibration sensor and a temperature sensor; The force sensor is attached to the inner wall flexible structure of the multi-source sensor integrated module, and the force sensor is used to obtain the force data of the load self-sensing tool; The vibration sensor is arranged on the central axis of the multi-source sensor integrated module, and the vibration sensor is a MEMS accelerometer; the vibration sensor is used to obtain the vibration data of the load self-sensing tool; The temperature sensor is arranged on the inner side of the multi-source sensor integrated module and is adjacent to the inner ring of the tunneling tool mounting bearing. The assembled temperature sensor is located at the bottom of the axial center blind hole and is used to obtain the temperature data of the load self-sensing tool.

3. A load-sensing tunneling tool according to claim 2, characterized in that: The vibration sensor also includes a three-way vibration sensing element, a thin film resistor layer and a signal conditioning circuit; The three-way vibration sensing element is used to measure the vibration amplitude and frequency of the load self-sensing tunneling tool in three orthogonal directions; The thin film resistor layer is used to measure the vibration intensity by detecting the strain change caused by the vibration; The signal conditioning circuit is used to amplify, filter and convert the acquired vibration signal to generate a digital signal corresponding to the vibration signal.

4. A load-sensing tunneling tool according to claim 2, characterized in that: The temperature sensor is provided with a temperature sensing element; The temperature sensing element is arranged at a side opening of the protective shell of the multi-source sensor integrated module; The temperature sensing element is provided with a temperature sensing probe; The temperature sensing probe is exposed and placed, and the exposed height of the temperature sensing probe is lower than the surface of the protective shell of the multi-source sensor integrated module.

5. A load-sensing tunneling tool according to claim 1, characterized in that: The self-generating module is connected to the signal transmission module; The self-generating module is an annular thin film piezoelectric self-generating module, and the self-generating module is arranged on the outer wall of the surface of the multi-source sensor integrated module; The self-generating module supplies power to the multi-source sensor module through the data line of the signal transmission module.

6. A load-sensing tunneling tool according to claim 1, characterized in that: The signal transmission module is connected to the multi-source sensor module; The signal transmission module is arranged at the bottom of the multi-source sensor integrated module and is located at the top of the axial center blind hole after assembly.

7. A load-sensing tunneling tool according to claim 1, characterized in that: The size of the axial center blind hole matches the size of the protective shell of the multi-source sensor integrated module; The opening end of the axial center blind hole is provided with a sealing support mechanism; The space between the multi-source sensor integrated module and the inner wall of the axial center blind hole is filled with shock-absorbing sealant.

8. A tool monitoring method, characterized in that: The method comprises: The host computer receives the tool multi-source signals uploaded by the multi-source sensor integration module; The host computer performs preprocessing and feature extraction on the tool multi-source signal, and generates a feature matrix based on the extracted feature data; The host computer compares each of the characteristic data with the corresponding preset threshold value to determine the abnormal point, and determines the abnormal state of the tool based on the abnormal point; The host computer determines the mutual correlation coefficient between the tool multi-source signals based on a preset function, and determines the correlation between the tool multi-source signals in the time domain and the frequency domain based on the mutual correlation coefficient; The host computer determines the working condition type of the tool based on the correlation, generates a working condition warning signal based on the working condition type, and feeds back the working condition type and the tool multi-source data to the tool management system to realize monitoring of the tool.

9. A tool monitoring method according to claim 8, characterized in that: The host computer determines the mutual correlation coefficient between the tool multi-source signals based on a preset function, and determines the correlation between the tool multi-source signals in the time domain and the frequency domain based on the mutual correlation coefficient, specifically including: The host computer determines the mutual correlation coefficients between the tool multi-source signals based on the time domain features corresponding to the tool multi-source signals and the preset function, so as to quantify the time domain correlation of the tool multi-source signals; wherein the tool multi-source signals include pressure signals, vibration signals and temperature signals; The host computer converts the tool multi-source signal from the time domain to the frequency domain through fast Fourier transform to obtain the frequency spectrum corresponding to the tool multi-source signal, so as to extract the frequency domain features of the tool multi-source signal; The host computer determines the time domain signal correlation corresponding to the tool multi-source signal based on the synchronization of the tool multi-source signal in the same time period; The host computer determines the frequency domain signal correlation corresponding to the tool multi-source signal based on the consistency of the tool multi-source signal in different frequency ranges.

10. A tool monitoring method according to claim 8, characterized in that: The host computer determines the working condition type of the tool based on the correlation, specifically including: The correlation between the mean and the root mean square value of the vibration signal and the pressure signal in the time domain is greater than a preset first correlation threshold, and the energy proportion corresponding to the low frequency band in the frequency domain characteristics is greater than a preset energy proportion value, and it is determined that the tool is in a normal working condition; The correlation between the vibration signal and the temperature signal in the time domain feature is greater than a preset second correlation threshold, and a new high-frequency component appears in the temperature signal in the frequency domain feature, and it is determined that the tool is in a wear condition; The fluctuation value of the pressure signal in the time domain feature is greater than a preset fluctuation threshold, and the main frequency of the vibration signal in the frequency domain feature is inconsistent with the main frequency distribution of the pressure signal, and it is determined that the tool is in an abnormal vibration condition.

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