888results about "Machine bearings testing" patented technology

The invention relates to the technical field of intelligent manufacturing equipment fault diagnosis, and particularly discloses a Meta-Transform driven multi-working-condition equipment bearing fault diagnosis method and system. The method aims at bearing fatigue damage risks caused by dynamic adjustment of technological parameters of a numerical control machine tool in the aerospace manufacturing process and challenges such as feature distribution offset and fault sample scarcity caused by variable working conditions. The diagnosis system is constructed through three core modules. The method comprises the following steps: firstly, reconstructing an original bearing signal into a multi-scale time-frequency feature space by adopting continuous wavelet transform; then designing a causal Transform architecture with a strict lower triangle attention mask, and realizing feature extraction and classification according to a physical causal law of fault propagation; and finally, integrating the mechanisms into a model-independent element learning framework, and realizing cross-working-condition rapid self-adaption through a self-adaption gradient pruning strategy. The bearing fault diagnosis accuracy under the condition of few samples is improved, the interpretability and generalization ability of the model are enhanced, and the industrial application practicability of bearing fault diagnosis is improved.

The invention belongs to the technical field of vibration analysis and testing of bearings, and particularly relates to a cam driven bearing wear degree monitoring method and system, and the method comprises the steps: carrying out the equal-angle resampling processing of a vibration signal through a rotating speed signal, decomposing an obtained angular domain vibration signal into a plurality of mode components through a variational mode decomposition algorithm, and carrying out the measurement of the vibration signal; according to the kurtosis value of each modal component and the correlation coefficient of each modal component and the original vibration signal, evaluating the impact saliency weight of each modal component, and performing weighted summation on the energy of each modal component to obtain comprehensive impact energy; calculating to obtain a speed decoupling wear index without the influence of the rotating speed by utilizing the comprehensive impact energy and the vibration energy calculated by the physical mapping model; and the speed decoupling wear index is compared with a preset self-adaptive alarm threshold value, and the wear state of the cam driven bearing is judged according to a comparison result. According to the invention, the problems of false alarm and missing alarm under the variable-speed working condition are solved.

The invention relates to the technical field of data processing and mode recognition, in particular to a bearing fault diagnosis method based on multi-scale feature fusion, which comprises the following steps: fusing multi-source data such as vibration, acoustic emission and rotating speed, performing angle domain resampling by using rotating speed data, generating a two-dimensional order spectrogram, and stacking to construct a three-dimensional working condition information tensor; a master-slave modulation heterogeneous neural network is adopted, high-dimensional spatial-temporal features are extracted through a main branch three-dimensional convolutional network, time sequence details are extracted from an original sequence through an auxiliary branch one-dimensional convolutional network, affine transformation parameters are generated, and dynamic modulation is achieved on the high-dimensional features; and the output state vector is mapped to a fault evolution knowledge graph, probability prediction is carried out through a graph attention network and by introducing a Monte Carlo discarding mechanism, a probability mean value is calculated as a fault classification result, and the diagnosis confidence is quantified by a probability variance. According to the invention, through multi-scale feature fusion and dynamic modulation, the problem of insufficient feature discrimination caused by scale mismatch under variable working conditions is solved.

The invention discloses a traceable self-evolution multi-agent high-speed train axle box bearing fault diagnosis method, which comprises the following steps of: firstly, acquiring a bearing working condition parameter and a vibration signal, processing to generate an envelope spectrum and characteristic data, and packaging the envelope spectrum and the characteristic data; five types of agents are initialized, data are distributed by a coordinator agent, and each agent outputs a standardized evidence unit containing information such as a unique identifier and a reasoning track under the constraint of a cue word; the coordinator constructs an evidence graph, processes the confidence coefficient of the fault component through a weighted average or specific fusion strategy, and inquires a contradictory conclusion to obtain supplementary reasoning; generating a hierarchical structured detection report based on the multi-source information; and receiving engineer recheck information, updating the case knowledge base, and dynamically optimizing cue word and agent weight. According to the method, multi-source information is fused through multi-agent cooperation, so that diagnosis conclusion traceability and system self-evolution are realized, and the accuracy, adaptability and reliability of fault diagnosis under complex working conditions are improved.

The invention relates to a sliding wear performance evaluation method of a sliding bearing, a sliding wear testing machine and application. The method comprises the following steps: acquiring initial-state benchmark characterization of a sliding bearing sample, placing the sliding bearing sample at a test station of the sliding wear testing machine, outputting lubricating oil, collecting a process oil sample, and acquiring final-state characterization of the sliding bearing sample after the test is completed; on the basis of initial state benchmark characterization, process oil sample analysis data, final state characterization and working condition parameters applied by a sliding wear testing machine, the wear stage and the wear mechanism are analyzed, and the sliding wear performance of the sliding bearing is evaluated; a sliding shaft supporting module, a rotating power module, an oil supply lubricating module and a radial load pressurizing module are arranged on the testing machine matching platform; the method is applied to wear resistance evaluation of sliding bearings prepared by adopting different processes. According to the invention, the core working condition of the actual dynamic pressure bearing is accurately reproduced, the stability and durability of the test system are improved, and the occurrence, development and evolution process of wear is visualized and quantifiable.

The invention discloses a deep learning method for realizing mechanical fault diagnosis, and belongs to the technical field of intelligent manufacturing fault prediction and diagnosis. Aiming at the problem that the diagnosis precision is sharply reduced along with the improvement of noise due to insufficient front-end feature extraction and mutual superposition of time domain limitation of a self-attention mechanism, a fault diagnosis classification model composed of three levels of feature processing layers is constructed; each stage comprises a wavelet-guided adaptive multi-scale convolution module and a frequency domain enhanced self-attention module; the wavelet-guided adaptive multi-scale convolution module can extract abundant multi-scale features under high noise; the frequency domain enhanced self-attention module carries out global modeling in the frequency domain, and the influence of noise on the overall recognition precision is reduced. The method has strong multi-scale feature extraction capability and anti-noise interference capability, effectively solves the problem of inaccurate diagnosis precision caused by a high-noise environment under an actual industrial condition, and is suitable for fault diagnosis of rotating mechanical equipment such as bearings and gears.

A method of detecting a bearing fault of a rolling element bearing mounted around a shaft, using a shaft displacement sensor providing a shaft displacement signal to detect shaft displacement, the method including: a) subtracting a modelled shaft displacement reference signal from the shaft displacement signal to obtain a residual vector, b) estimating for at least one bearing fault type a respective bearing fault frequency, and estimating, for each bearing fault type, an amplitude of the residual vector in a frequency range containing a bearing fault frequency of the associated bearing fault type, and d) determining whether a bearing fault is present based on the at least one estimated amplitude.

The invention relates to the technical field of fault diagnosis, in particular to an inductance winding equipment fault detection method and system based on multi-sensor fusion, and the method comprises the following steps: injecting a high-frequency sinusoidal voltage signal into a servo motor stator winding of inductance winding equipment, and synchronously collecting a motor end voltage, a three-phase current and a rotor position encoder signal; and constructing an original multi-dimensional signal matrix. According to the invention, the high-frequency sinusoidal voltage signal is actively injected into the stator winding of the servo motor of the inductance winding equipment, and the motor end voltage, the three-phase current and the rotor position encoder signal are synchronously acquired, so that the active excitation response capture of the operation state is realized, and compared with a passive monitoring mode, the signal controllability and the response accuracy are better; in an original multi-dimensional signal matrix, sequence calibration is carried out through a unified timestamp standard, time alignment is carried out on electrical signals and motion signals, and the collaboration and effective association of multi-source data are enhanced.

The invention discloses a motor bearing fault diagnosis method and system based on multi-modal fusion and few-sample learning, and relates to the field of motor bearing fault diagnosis, and the method comprises the steps: constructing a multi-modal time sequence signal sample set and a motor bearing fault diagnosis model based on multi-modal fusion and few-sample learning; performing Riemannian metric-based quality weighted element training on the motor bearing fault diagnosis model by using the multi-modal time sequence signal sample set; performing rapid fine adjustment on the trained motor bearing fault diagnosis model by using a small number of marked multi-mode time sequence signals of the target motor bearing; and performing fault diagnosis on the to-be-detected multi-mode time sequence signal of the target motor bearing by using the finely-adjusted motor bearing fault diagnosis model. The method can effectively improve the diagnosis accuracy and robustness.

This disclosure provides a method for assessing the health status of bearings. The method includes: receiving multimodal data and prompting information about the bearing; performing feature extraction on the multimodal data using a first set of agents to generate a feature set about the multimodal data; performing a health assessment on the bearing using a second set of agents based on the feature set and prompting information about the multimodal data to generate multiple health assessment results about the bearing; and combining the multiple health assessment results using a third set of agents to generate a health assessment report about the bearing. According to this bearing health status assessment method, users can input multimodal data to perform bearing health status assessments, thereby improving the flexibility of the bearing health status assessment method. Furthermore, the bearing health status assessment method of this disclosure, by leveraging the reasoning capabilities of a large language model, can better understand user questions, thereby providing better maintenance suggestions and other outputs.

The invention discloses a testing device, which comprises a platform, a supporting seat, a pressure applying rotating shaft and a driving part, and is characterized in that the platform is provided with a fixing part, and the fixing part is used for fixing an axial air foil bearing; the supporting seat is located above the platform and installed on the platform, a shaft hole is formed in the supporting seat, and the shaft hole is located over the fixing part and penetrates through the supporting seat in the vertical direction; the pressing rotating shaft is movably arranged in the shaft hole in a penetrating mode in the vertical direction, and the bottom of the pressing rotating shaft is used for being attached to the axial air foil bearing in a normal state; the driving part is in driving connection with the pressure-applying rotating shaft to drive the pressure-applying rotating shaft to rotate. According to the testing device, the axial air foil bearing can be rapidly tested, and the production requirement of large-scale mass production is met.

The embodiment of the invention provides a health assessment method for train bearings. The health assessment method comprises the steps that a bearing temperature data set collected from a plurality of bearings of a train during the travel of the train and an environment temperature data set around the train during the travel of the train are obtained; performing feature extraction on the bearing temperature data set to generate a first temperature feature set; calculating a difference value between each piece of bearing temperature data in the bearing temperature data set and corresponding environment temperature data in the environment temperature data set to generate a first temperature difference value set, and executing feature extraction on the first temperature difference value set to generate a second temperature feature set; and based on at least one of the first temperature feature set and the second temperature feature set, generating an evaluation result of the health state of each bearing in the plurality of bearings.

The invention relates to the technical field of bearing fault diagnosis, in particular to a bearing fault diagnosis method based on a neural network and multi-criterion preference consensus, and the method comprises the steps: data collection and feature extraction, data preprocessing, fault diagnosis model matching, preliminary diagnosis and probability generation, final diagnosis and probability generation, and bearing state decision making. According to the method, a multivariate decision-making auxiliary model fusing a physical marginal value function and a neural network attention mechanism is provided, and a reinforcement learning driven consensus achievement process is combined, so that adaptive optimization of a multi-expert fault diagnosis result is realized; the problems that an existing bearing fault diagnosis method cannot deal with complex working conditions, is insufficient in physical interpretability, depends on a single decision mode and mostly adopts static weight distribution are solved, the accuracy, robustness and interpretability of bearing fault diagnosis are improved, and high-precision and low-round group fault diagnosis is achieved.

The invention provides a multi-modal intelligent diagnosis method and system for temperature vibration of a high-speed rail bearing based on a working condition spectrogram, and relates to the technical field of intelligent diagnosis, and the method comprises the steps: synchronously collecting vibration, temperature distribution and phase angle signals of a bearing, and extracting a self-adaptive reference baseline in combination with an operation working condition; and obtaining a temperature vibration joint sequence through phase alignment, calculating a thermal vibration coupling intensity spectrum, and comparing the thermal vibration coupling intensity spectrum with a baseline to obtain circumferential deviation distribution. And after dynamic threshold correction, mapping the over-limit phase interval into a fault space coordinate on the bearing raceway, and determining a fault type according to frequency spectrum characteristics. According to the invention, accurate space positioning and type identification of high-speed rail bearing faults are realized, and the adaptability and accuracy of diagnosis are improved.

The utility model discloses a ball roller pre-tightening force torque dynamic detection device, which belongs to the technical field of bearing detection and comprises a detection table, a central through hole is arranged on the upper surface of the detection table, a positioning mechanism is mounted in the central through hole, a bearing body is arranged on the positioning mechanism, and an adaptive device is slidably clamped at the bottom of the detection table. A load assembly is installed on one side of the detection table, the adaptive device comprises a movable bottom plate, a lug plate is fixed to the bottom of one end of the movable bottom plate, a movable telescopic rod is fixed to the side wall of the lug plate, one end of the movable telescopic rod is fixed to the detection table, and a torque detection device is arranged on the movable bottom plate. The detection device can carry out adaptability detection on bearings with different inner diameters and outer diameters, the detection assembly is simple, the contact structure located in the bearing body can well stabilize the bearing body, dynamic detection can be carried out through the torque detection device, and the detection efficiency is improved. The load assembly on the outer side can apply uniform external pressure to the bearings with different outer diameters and drive the outer rings of the bearings to rotate.

The invention discloses a gas film flow field pressure testing device, and particularly relates to the technical field of gas bearing detection, the gas film flow field pressure testing device comprises a rack, a testing assembly is arranged on the rack, the testing assembly comprises a testing table arranged on the rack, and two bases are arranged at the bottom of an inner cavity of the testing table; and two ends of each base are respectively provided with a pressurizing block for testing a test piece. According to the invention, the deflection angle of the articulation piece is adjusted through the screw rod, and the arc-shaped constraint groove and the self-weight resetting structure of the pressurizing block are matched, so that gas bearing test pieces with different outer diameters and different groove type parameters can be flexibly adapted; the adaptability depends on standardized test logic formed by collaborative research and development, and on the contrary, the test capability of accurate data and panoramic working conditions covers more application scenes, so that an accurate, comprehensive, efficient and practical closed-loop advantage is formed, and the industrial application value of the device is remarkably improved.

The invention provides a rolling bearing fault fusion diagnosis method based on tendency score matching causal inference, which comprises the following steps: firstly, acquiring running state time sequence data of a rolling bearing in different states by a multi-source sensor, performing filtering denoising and feature extraction, and then forming a causal analysis data feature matrix by using features; calculating tendency scores of different state samples of the rolling bearing in the causal analysis data feature matrix; matching the fault state sample and the normal state sample by using the tendency score, and carrying out multi-dimensional balance verification on a matched pair; training a rolling bearing fault recognition model by taking the verified rolling bearing state sample as a training sample; and finally, after feature extraction is carried out on actually-collected rolling bearing data, obtained feature data are input into the trained rolling bearing fault recognition model, and a fault recognition result is obtained. According to the invention, the interpretability and adaptability of the fault identification model under complex working conditions are improved.

Disclosed in the present invention is a cloud-edge collaborative digital twin-based state monitoring method for idler bearings of a belt conveyor. The method comprises: acquiring operation data of an idler bearing of an underground belt conveyor; transmitting the operation data to an edge device and a cloud-based central server; constructing a geometric model and a three-dimensional simulation model in the cloud-based central server, constructing a fault diagnosis model on the edge device, and using the geometric model, the three-dimensional simulation model and the fault diagnosis model to form a digital twin; and on the basis of measured data, updating the three-dimensional simulation model in the digital twin in real time, using incremental learning to update the fault diagnosis model in the digital twin in real time, and using the fault diagnosis model to determine an operation state of the idler bearing of the belt conveyor. In the present invention, computing resources are allocated on the basis of a cloud-edge collaborative algorithm to realize the rapid computation of a digital twin, thereby improving the virtual-physical interaction capability of the digital twin, and ultimately realizing the digital twin-based state monitoring, early-warning and visual analysis of idler bearings of a belt conveyor.

A water meter bearing wear state monitoring method and system based on a flow rate working condition relates to the field of electric digital data processing, and the method comprises the following steps: monitoring the generation frequency of a metering pulse on a target water meter, and determining whether the current metering working condition of the target water meter is a high flow rate working condition or a low flow rate working condition based on the generation frequency; counting a first pulse time interval sequence under the low flow speed working condition, and calculating a dispersion characteristic value of the first pulse time interval sequence as a first state characteristic value; counting a second pulse time interval sequence under the high flow rate working condition, and calculating a dispersion characteristic value of the second pulse time interval sequence as a second state characteristic value; based on the first state characteristic value and the second state characteristic value, calculating a working condition response index representing the wear degradation effect of the water meter bearing on the target water meter; when the working condition response index is larger than a preset abrasion judgment threshold value, abrasion early warning information corresponding to the target water meter is generated. By implementing the application, the water meter maintenance cost can be reduced.

The invention discloses a train running gear bearing state prediction and early warning method and system, and the method comprises the steps: synchronously collecting vibration, noise and temperature data, and building a standard vibration reference based on rotation period segmentation; extracting a component characteristic frequency and confirming fault information by adopting a frequency spectrum decomposition and intermodulation analysis technology, and determining a monitoring working frequency and a component weight table; a temperature confrontation processing mechanism is introduced, decoupling parameters are generated through a heat distribution matrix and directional heat flow analysis, and accurate separation of temperature influence and mechanical wear signals is achieved; constructing an equivalent transformation matrix based on a noise dependency relationship, and performing standardized transformation on pure vibration data to generate a comparable state index; through abrasion tracking and vibration transmission analysis, path reconstruction is carried out in combination with transfer parameters, and accurate positioning and evolution prediction of faults are achieved; and finally, dynamically adjusting the component weight based on the path control signal, establishing a four-level grading early warning system, and realizing real-time monitoring and intelligent early warning of the running state of the bearing.

The invention relates to the technical field of bearings, in particular to a bearing simulation correction system and method based on actual measurement stress of a hollow roller. The control module is used for comparing the simulation stress value, calculated by the bearing digital simulation module, at the corresponding position of the hollow roller with the actually-measured stress value, output by the signal processing and stress calculation module, of the roller, performing parameter inversion and correction, and reversely adjusting key uncertain parameters in a simulation model based on a comparison result, so that a simulation result continuously approaches to actually-measured data; the simulation correction and feedback module is used for receiving and updating the corrected parameters to obtain a high-confidence simulation model verified by actually measured stress, and the high-confidence simulation model is used for deep stress analysis, life prediction and / or health state monitoring of the bearing under other working conditions. Measurement is direct, and the source is accurate; closed-loop correction is achieved, and the model is credible.

The present application relates to bolt state monitoring technical field, disclose a kind of embedded wireless passive bolt tightness state monitoring method based on surface acoustic wave technology, specifically includes the following steps: based on the real-time acquisition frequency drift amount of surface acoustic wave resonator, and the looseness risk of bolt is judged in real time, generates looseness warning signal and safety signal;Based on warning signal, the frequency drift amount before generating warning signal is analyzed at change rate, and the change rate degree is judged;Based on the change rate degree judging result, the time point when frequency drift amount reaches frequency drift amount risk value is predicted.The present application can judge bolt looseness risk in real time, generates warning signal in time when bolt appears looseness sign, reminds relevant personnel to pay attention to bolt state, avoid bolt looseness further development and not be perceived, to reduce the possibility of equipment failure, accident.

A sensorized roller for a rolling bearing includes a roller body having a central axial through bore and a device fitted in the axial central through bore. The device includes a power module having a cylindrical body having a first end cap, the cylindrical body extending into a first end of the axial through bore of the roller body. The device also includes a second end cap having a cylindrical body inserted into a second end of the axial through bore of the roller body. A first annular sealing element is interposed between the cylindrical body of the power module and the axial central through bore of roller body, and a second annular sealing element is interposed between the cylindrical body of the second end cap and the axial central through bore of the roller body.

The invention discloses a self-sensing intelligent thrust bearing based on a nano-generator and a state monitoring method of the self-sensing intelligent thrust bearing. The thrust bearing comprises a shaft ring, a seat ring, a rolling body, a retainer and a nano-generator sensing structure, wherein the rolling body and the retainer are arranged in an annular raceway between the shaft ring and the seat ring; obtaining a first voltage signal and a second voltage signal through a nano generator sensing structure; the state monitoring method comprises the following steps: acquiring a first voltage signal and a second voltage signal; respectively preprocessing the two paths of signals and then carrying out data-level fusion; dividing the fusion signal into a training set, a verification set and a test set, and training a data-level fusion 1D-CNN model; and inputting a to-be-tested signal into the trained model to realize intelligent classification of the bearing operation state. According to the invention, multi-source signal fusion sensing and real-time monitoring of the running state of the thrust bearing are realized, the structure is compact, the sensitivity is high, and the identification is accurate.

The invention discloses a bearing fault diagnosis method based on feature mode decomposition and heterogeneous model fusion, and belongs to the technical field of mechanical fault diagnosis. An original vibration signal of the rolling bearing is collected; independent of any bearing geometric parameter or fault characteristic frequency information, a finite impulse response (FIR) filter is iteratively optimized to maximize a correlation kurtosis (CK) value, and a plurality of intrinsic mode functions (IMF) are adaptively decomposed; selecting first M IMFs with the highest CK value, extracting time domain, frequency domain and nonlinear complexity features of the IMFs, generating high-order derivative features by adopting depth feature synthesis DFS, and constructing an enhanced fusion feature vector; and finally, inputting a stacked integrated classifier consisting of six types of heterogeneous basic learners and an XGBoost element learner, and outputting a fault diagnosis result. According to the method, high-precision and high-robustness diagnosis can still be realized under the working conditions of speed change, strong noise and weak faults.

Provided is a temperature derivation method for deriving an oil film temperature of a lubricant in a device, the temperature derivation method including a measurement step of measuring a dielectric constant of the lubricant by applying an alternating voltage while changing a frequency to an electric circuit configured by the device, a derivation step of applying the dielectric constant measured in the measurement step to a theoretical formula to derive a relaxation time of the lubricant, and a calculation step of calculating the oil film temperature by using the relaxation time.

The invention discloses a bearing unbalance loading tester, relates to bearing unbalance loading testing equipment, and aims to solve the problem that the eccentric force of a bearing cannot be accurately measured by the existing mechanical gasket adjusting method. The electric main shaft and the machine body form a simply supported beam shafting structure; the motorized spindle is rigidly connected with a rotating shaft of the servo motor through a coupling; a to-be-tested bearing is nested on the outer wall of the electric spindle; the radial loading unit is used for applying radial loading force to the to-be-tested bearing; and the axial unbalance loading unit is used for applying eccentric axial loading force with adjustable magnitude, direction and action point to the to-be-tested bearing. The beneficial effects are that the axial unbalance loading force of the bearing is effectively applied, the loading is accurate and rapid, and the anti-interference capability is strong. According to the small bearing unbalance loading force tester, the load can be set according to the actual use condition of the bearing, and meanwhile, the load of the bearing can be changed in the test process.

The application provides a bearing gear fault automatic diagnosis method, system, equipment and storage medium, the method comprises the following steps: obtaining a sampling frequency through a spectrum sensing sampling frequency convex optimization algorithm; obtaining the installation number of a signal sensitive sensor through a multi-factor weighted constraint scaling algorithm; obtaining a signal to be processed according to the sampling frequency and the installation number of the signal sensitive sensor, processing the signal to be processed, and obtaining a fault diagnosis result; the bearing gear fault automatic diagnosis method solves the problem of monitoring the inventory equipment; when processing the signal, a hardware pulse is used as a sampling clock source, software interruption and the delay and jitter of calculation are avoided, high-precision synchronous sampling is realized, the non-stationary phenomenon of the signal caused by speed fluctuation is eliminated, and the signal analysis precision is improved; speed measurement and sampling frequency adjustment are completed within one pulse period, the response is fast, and high-speed and high-dynamic speed changes can be tracked; the bearing gear fault automatic diagnosis method has a wide application range and high practicability.

The application relates to a water pump testing device and method, and relates to the technical field of water pumps.The device comprises a rack, a testing box, a simulation mechanism, a testing mechanism and a driving mechanism are arranged on the rack, the testing mechanism is located in the testing box, the testing mechanism comprises a testing shaft and a fixing assembly, the fixing assembly is used for fixing a bearing, the driving mechanism is used for driving the testing shaft to rotate, and the simulation mechanism is used for providing a simulation environment in the testing box.The application has the advantages that the performance of the water pump in a hot and humid environment can be detected.

The embodiment of the invention discloses a fault detection method and device for an electric drive bearing and a vehicle. The fault detection method comprises the following steps: acquiring a current signal of the electric drive bearing; performing time-frequency processing on the current signal to determine an instantaneous frequency estimation value; performing compression change processing on the instantaneous frequency estimation value to determine a time-frequency graph of the instantaneous frequency estimation value; and performing frequency extraction on the time-frequency diagram, and determining whether the electric drive bearing has a fault or not according to the extracted frequency. According to the technical scheme provided by the embodiment of the invention, detection is carried out based on the current signal of the electric drive bearing, efficient and accurate detection of the fault of the electric drive bearing is realized, meanwhile, the current signal acquisition mode is simple, and the detection cost is reduced.