691 results about "Vibration sensing" patented technology

The invention discloses a subway tunnel intrusion monitoring and early warning system and method, and relates to the technical field of subway safety monitoring. The system comprises a vibration monitoring module, a stress monitoring module, a signal analysis unit, an alarm unit and a database module, wherein the vibration monitoring module adopts a distributed vibration sensing optical cable to collect tunnel wall surface vibration signals in real time; the stress monitoring module monitors the stress change of the tunnel structure through a spirally wound and pre-embedded fiber grating array; the signal analysis unit decomposes vibration frequency band energy through wavelet packet transformation, counts a stress standard deviation in combination with a sliding time window, and performs multi-modal feature matching with an abnormal signal database by using a dynamic time warping algorithm; and the alarm unit triggers multi-stage response according to a feature matching degree threshold value or vibration-stress space-time correlation abnormity, and train speed limiting control is linked. The problems that the single-mode false alarm rate is high, multi-source interference cannot be decoupled and stress monitoring coverage is insufficient are solved, recognition and early warning of construction intrusion events are achieved, and tunnel structure safety is improved.

The invention relates to the technical field of intelligent equipment monitoring, and discloses a sandstone separator monitoring control method and system based on multi-source data processing. According to the method, equipment operation parameters, real-time image data and vibration sensing data of the sandstone separator are collected in real time, data preprocessing and feature extraction are carried out, a monitoring model based on a multi-modal deep learning framework is constructed, and accurate joint characterization of the equipment state is achieved. The system can dynamically generate a control instruction according to a monitoring result, adjust equipment operation parameters in time, and avoid production interruption and quality problems. Besides, the multi-modal data fusion technology and the self-adaptive PID control algorithm are adopted, so that the operation efficiency and stability of the sandstone separator are further improved, and powerful technical support is provided for the field of intelligent manufacturing.

The invention discloses a road network abnormal event identification and risk evaluation early warning method based on vibration perception, and relates to the technical field of urban road safety monitoring. Comprising the steps of collecting road vibration data of a road network through a sensor; preprocessing the road vibration data through Kalman filtering and multi-dimensional feature extraction; abnormal event recognition is carried out on the processed road vibration data through a differentiation algorithm, wherein abnormal events comprise vehicle overload, illegal construction and road structure damage; and carrying out risk scoring on the abnormal events through dynamic weight distribution, dividing the risk scores according to the risk levels, and carrying out graded response. According to the invention, through differential algorithm design, a mixed model architecture and a hierarchical response mechanism are adopted, abnormal event identification, risk level quantitative evaluation and differential early warning processing are realized, the safety state of an urban road network is monitored in real time, and early warning information is provided.

The invention discloses a multi-scene positioning and intelligent warning system and method for industrial equipment, and the system comprises a track analysis module which obtains the radar signal data of the industrial equipment, carries out the denoising, extracts a signal intensity change feature, and generates a human body dynamic track when the matching degree of the feature and a preset metal reflection model exceeds a threshold value; the abnormity judgment module is used for acquiring the vibration sensing data of the key position, generating a vibration frequency spectrum by applying wavelet decomposition, and generating abnormal vibration alarm information if the vibration frequency spectrum exceeds a range; the intrusion alarm module is used for acquiring node signal sources, performing weighted fusion to obtain an initial positioning coordinate group, inputting the initial positioning coordinate group into a three-dimensional map model, obtaining a final coordinate result through multi-source fusion, and sending out intrusion alarm information if the result falls into a dangerous fence boundary; and the safety evaluation module is used for performing time alignment and deep fusion on the human body dynamic track, the abnormal vibration alarm information and the intrusion alarm information to generate a safety evaluation report. According to the invention, omnibearing safety monitoring and management of industrial equipment are realized.

A distributed optical fiber sensing event classification and identification method based on mask auto-encoder self-supervised learning belongs to the technical field of distributed optical fiber sound and vibration sensing, on one hand, a pre-training network does not need label data, a downstream classification and identification network only needs a small amount of labeled data, and the demand of the labeled data is reduced; and on the other hand, in the pre-training process, the model extracts DAS signal features and compresses the calculation amount of the DAS signals, so that pixel-level reconstruction of DAS signal reconstruction is changed into feature reconstruction, a large amount of time is saved, and the accuracy is ensured.

The invention discloses an electromechanical fusion intelligent operation and maintenance elevator integrated control system which comprises a multi-mode sensing fusion system, an edge computing control center and a cloud intelligent decision platform. The multi-mode sensing system collects elevator physical state data in real time through a high-precision electromagnetic field sensor, a vibration sensing network and an infrared-optical fiber fusion temperature measurement module. The edge computing center adopts an FPGA accelerated signal processor to realize multi-source data feature fusion, and the multi-source data feature fusion is uploaded to the cloud platform through an encrypted data link; and the cloud digital twin engine constructs a multi-body dynamics and electromagnetic field coupling model, generates an optimization control instruction in combination with a deep learning algorithm, and feeds back the optimization control instruction to the edge layer for execution. According to the system, a sensing-edge-cloud three-level collaborative architecture is innovatively provided, millisecond-level sensing, minute-level decision making and microsecond-level control closed loop of the elevator running state are achieved, and the fault early warning accuracy and the operation and maintenance response efficiency are further improved.

The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster monitoring method and system based on a distributed optical fiber vibration sensing system, and aims to solve the problem of how to solve the interference of the change of an optical fiber and the change of an external environment on a geological disaster prediction result in optical fiber transformation. In order to solve the problem, the geological disaster monitoring method provided by the invention comprises the following steps: determining an area to be expanded according to a monitoring demand and a system distribution path of an original optical fiber; determining an actual distribution path of newly added optical fibers according to the landform characteristics and the geological risk level of the to-be-expanded area; after the newly-added optical fiber is installed, correcting the characteristic data according to the loss condition of the original optical fiber and the transmission characteristics of the newly-added optical fiber, and constructing a prediction model according to the corrected data; and combining the prediction model with the environment information, and determining the type of a disaster occurring in the target area.

The present invention discloses a laser metal curved surface automatic focus processing system and method thereof, including: a multimodal data fusion device, a curved surface detection module with a secondary tracking structure, integrated 3D structured light vision and vibration sensing equipment; a dynamic focus correction module, configured with an adaptive energy control unit and a beam shaping component; a nonlinear motion control console, including a six-degree-of-freedom parallel mechanism motion platform, and its servo control system integrates a time-space phase compensation algorithm; a core control unit, the core control unit has an embedded fusion algorithm model, and establishes a nonlinear mapping between material removal efficiency η and multivariable dynamic parameters. The processing quality level of the present invention is improved through the multi-source data fusion of 3D structured light and vibration sensing, the curvature recognition accuracy is improved, and the focus offset is reduced in combination with the axial vibration compensation mechanism, thereby effectively improving the processing quality.

The invention provides a road vehicle monitoring method and system based on a distributed optical fiber vibration sensing technology. The method comprises the following steps: acquiring a vibration signal; drawing a vehicle space-time trajectory diagram based on the vibration signal; extracting a vehicle trajectory based on the vehicle space-time trajectory diagram; and obtaining vehicle information according to the vehicle trajectory. According to the road vehicle monitoring method, the vehicle vibration signals are collected based on the DAS technology, the vehicle tracks are extracted from the vehicle space-time track diagram through the image processing algorithm, the traffic flow and the vehicle speed are monitored, long-distance and long-time-period dense monitoring can be achieved without deploying extra equipment, the processing process is convenient and visual, the monitoring result is efficiently obtained, and the monitoring efficiency is improved. In combination with an image processing technology, traffic flow information and vehicle speed information at any time and place on the road within a long distance and a long time period can be obtained without processing a large amount of data, and all-directional monitoring of the road is realized.

The invention discloses a distributed sound wave sensing monitoring device and method for sea wave impact force. The device comprises a vibration sensing optical fiber, an anchoring unit, a distributed optical fiber sound wave sensing demodulator, a vibration signal processing unit and a sea wave impact force inversion unit. The method comprises the following steps: fixing the vibration sensing optical fiber on a coast impacted by sea waves through the anchoring unit; the distributed optical fiber sound wave sensing demodulator transmits a monitored sea wave vibration signal to the vibration signal processing unit for preprocessing; the vibration signal processing unit transmits the processed optical fiber vibration amplitude signal to the sea wave impact force inversion unit; obtaining an optical fiber vibration amplitude signal and sea wave impact force training data set through a calibration experiment, and constructing an attention convolutional neural network model for training; and the sea wave impact force inversion unit establishes a corresponding relation between the optical fiber vibration amplitude signal and the sea wave impact force by using an attention convolutional neural network model to obtain the real impact force of the coast sea wave. According to the invention, accurate monitoring of the coastal wave impact force is realized.

The invention relates to the technical field of optical cable line external force damage monitoring, and discloses an optical cable external force damage intelligent identification system. The system comprises an optical fiber vibration sensing module, a multi-source feature fusion analysis module, an abnormal event association positioning module and an intelligent checking strategy generation module. The optical fiber vibration sensing module constructs a model based on historical data and outputs a reference vibration characteristic value; the multi-source feature fusion analysis module performs three-dimensional fusion analysis of time domain, frequency domain and phase on the reference value and the measured value to generate a feature difference matrix; the abnormal event association positioning module is used for generating an abnormal probability distribution cloud picture and positioning an abnormal area in combination with the topological parameters and the environment information; and the intelligent checking strategy generation module configures checking parameters according to the cloud picture, starts high-frequency acquisition for a high-probability region, and applies an environment disturbance test to adjacent lines. The system can accurately identify and position optical cable external force damage, and the monitoring efficiency is improved.

The invention belongs to the technical field of signal processing, and more specifically relates to an intrusion monitoring identification and event positioning device and method based on a DAS system. According to the invention, mode identification and accurate positioning are carried out on various vibration sources around by using sensing optical cables laid in a large range based on an optical fiber distributed sound wave vibration sensing technology. According to the method, respective advantages of a direct detection method and a coherent detection method are exerted, the vibration position can be quickly demodulated by using the direct detection method, the dynamic process of vibration change can be obtained by using a digital coherent method, and then vibration event identification is realized. The method has the advantages of being wide in monitoring area range, high in recognition accuracy, high in real-time performance, high in stability and the like.

The invention provides an ocean seismic signal processing method based on distributed optical fiber vibration sensing, and belongs to the technical field of seismic signal processing. The method comprises the following steps: acquiring a marine seismic signal through a distributed optical fiber vibration sensor; performing data preprocessing on the collected seismic signals to construct a seismic oscillogram data set; according to the preprocessed data, feature extraction is carried out on the preprocessed seismic signals through a U-KAN network to obtain feature data; identifying and classifying the feature data to obtain a P wave and an S wave; and calculating the time difference between the P wave and the S wave according to the identified P wave and S wave, and estimating the seismic source distance and magnitude according to the time difference and the seismic wave propagation speed. The earthquake signal processing automation degree is improved, the earthquake wave arrival time is accurately determined, the marine earthquake monitoring response speed and accuracy are improved, and a scientific basis is provided for prevention and reduction of marine earthquake disasters.

The invention discloses a handheld integrated itinerant detector and an itinerant detection method thereof. The itinerant detector comprises a multi-source sensing unit and a magnetic attraction positioning module, the inspection method comprises the following steps: establishing a multi-dimensional calibration parameter of a target device, adsorbing on the surface of the target device based on a magnetic attraction positioning module, loading a spatial layout, and generating an environment calibration model; synchronously operating the far / close distance temperature measurement module and the vibration sensing module, and outputting multi-source fusion data; performing dynamic correlation analysis, analyzing vibration spectrum energy distribution by using a feature extraction algorithm, constructing a vibration-temperature coupling feature vector in combination with temperature gradient change, inputting a fault diagnosis result of a pre-trained fault diagnosis model, generating a visual early warning map, and feeding back the early warning map in real time through an interactive interface; according to the method, the limitation of single parameter monitoring is overcome, the detection precision is remarkably improved, and a real-time, rapid and accurate fault detection result is provided through self-adaptive switching of a temperature measurement mode and a multi-frequency-band vibration acquisition strategy.

The invention discloses a polarization-state-based optical fiber vibration sensing system and application, and relates to the technical field of optical fiber sensing, and the polarization-state-based optical fiber vibration sensing system mainly comprises a transmitting end and a receiving end. The transmitting end comprises a laser diode, a first erbium-doped optical fiber amplifier, a first circulator, a polarization scrambler, a single-mode optical fiber link, a second circulator and a second erbium-doped optical fiber amplifier which are connected in sequence; and the receiving end comprises a polarization beam splitter, a first shunt module, a second shunt module, an optical mixer, a first balanced photoelectric detector, a second balanced photoelectric detector, a third balanced photoelectric detector and a polarization analysis module. According to the optical fiber vibration sensing system based on the polarization state and the application, the vibration monitoring performance and reliability in a long-distance and high-noise environment can be improved.

The invention relates to an active and passive hybrid vibration isolation test platform and a working method thereof. The active-passive hybrid vibration isolation test platform is composed of a main body device, a vibration sensing device, a vibration control device and an active-passive hybrid vibration isolation device. The active and passive hybrid vibration isolation devices are symmetrically arranged on the two sides of the armature and comprise supports, particle dampers and electromagnets, the electromagnets are supported above the armature through the supports, the supports are of hollow structures, damping particles are filled in the supports, and coils are wound on the electromagnets. A suspension controller in the active vibration isolation module controls the current of an electromagnet coil so as to attenuate a low-frequency vibration signal and realize active vibration control; and under the combined action of particle damping in the passive vibration reduction module, medium-frequency and high-frequency vibration signals are attenuated, and passive vibration control is realized. Compared with the prior art, the device has the advantages that the adjusting range of elasticity and damping parameters is expanded, and the vibration isolation stability is improved.

The invention relates to the technical field of automatic control, and discloses a cable winding and unwinding speed self-adaptive control system under a dynamic working condition, which comprises an endogenous vibration sensing module configured on a cable reel supporting bearing and a system state decoding module, and the decoding module can decode a single original electric signal from a single original electric signal converted by the endogenous vibration sensing module. According to the invention, the core bearing structure of the reel is converted into an information source of a self-state, and multi-dimensional state information of real-time tension, overall rotational inertia of the system, abrupt change of friction and the like after temperature self-calibration is decoded in parallel, so that the self-adaptive control module is driven to match and execute a corresponding speed control strategy. Signal delay and system vulnerability caused by traditional multi-sensor layout are avoided, a control decision is made according to information reflecting the current system state, and the system further has the functions of optimizing a control strategy and compensating temperature influence according to historical data.

The invention discloses a vertical shaft construction measuring device, which relates to the technical field of laser measurement and comprises a laser plumb aligner main body, a chassis, a dial, a pointer, a laser plummet, a base and an adjusting knob. When laser measurement is carried out, a laser plummet is turned on firstly, a focused light spot directly irradiates a well bottom mark point, an initial position P1 is recorded, then a plurality of angles are rotated to record P2, P3 and P4, diagonal lines are connected to obtain a reference point, and the instrument is adjusted to enable the light spot to coincide with the reference point to complete calibration so as to measure shaft deformation. Stepped vibration induction is achieved through cooperation of a trigger block and trigger teeth, manual errors are avoided, meanwhile, friction force is increased through cooperation of a resistance block and a sliding rod, fine adjustment is assisted, in addition, through cooperation of a locking rod and a first connecting groove, the angle is locked under the gravity effect, position movement is prevented, and double-hand unlocking is achieved through cooperation of the connecting rod, a first magnetic block and a second magnetic block; safety specifications are compulsively met, and laser measurement accuracy is guaranteed through multiple measures.

The invention discloses a cable monitoring system and a cable, the cable monitoring system comprises a temperature sensing unit, a vibration sensing unit, a strain sensing unit and a processor, the temperature sensing unit is used for detecting the temperature in the cable, the vibration sensing unit is used for detecting the vibration of the cable, and the strain sensing unit is used for detecting the strain of the cable. The processor is in communication connection with the temperature sensing unit, the vibration sensing unit and the strain sensing unit, and evaluates the health state of the cable according to signals fed back by the temperature sensing unit, the vibration sensing unit and the strain sensing unit. According to the cable monitoring system, the temperature information, the vibration information and the strain information of the cable can be acquired in time by using the processor, so that the fault risk of the cable can be acquired more accurately, and the health state of the cable can be evaluated more comprehensively.

The invention discloses a refrigerator door body vibration induction detection control system, and relates to the technical field of refrigerator detection, the system comprises the following components: a vibration data acquisition subsystem, a data processing and analysis subsystem, a machine learning analysis module, a fault early warning and maintenance prompt subsystem and a data storage and management subsystem; through the high-precision piezoelectric vibration sensor and an advanced signal processing algorithm, the vibration state of the refrigerator door body can be monitored in real time, the system can accurately capture the key information of the vibration amplitude, frequency, phase and waveform of the door body, and the vibration data is deeply analyzed by using a machine learning algorithm, so that the accuracy of the vibration state of the refrigerator door body is improved. The door body fault early warning and accurate positioning function is achieved, the problems that due to the door body fault, the sealing performance of the refrigerator is reduced, and energy consumption is increased are effectively solved, the service life of the refrigerator is prolonged, and meanwhile the maintenance cost and time cost of a user are reduced.

The invention belongs to the technical field of distributed optical fiber sensing and low-altitude security and protection, and particularly relates to an unmanned aerial vehicle long-distance tracking method based on distributed optical fiber vibration sensing. The invention relates to an unmanned aerial vehicle vibration / electromagnetic fusion multi-mode identification method, a special installation formation design, a dispersion compensation and amplitude phase error compensation design, an unmanned aerial vehicle target vibration / electromagnetic fusion multi-mode identification design, a detection capability calculation method of an unmanned aerial vehicle vibration sound pressure-space attenuation law-distributed optical fiber processing equipment detection capability coupling model, a long-distance sensing optical cable and an electromagnetic monitoring node. According to the tracking method, a special optical fiber installation mode, dispersion compensation and phase error compensation of an original phase signal, a vibration / electromagnetic fusion multi-mode identification method, an unmanned aerial vehicle vibration sound pressure-space attenuation model-distributed optical fiber processing equipment detection capability corresponding method and other innovative work are adopted; a mature distributed optical fiber vibration sensing system is utilized to realize long-distance passive detection and target tracking of the low-slow-small unmanned aerial vehicle.

The invention discloses a concrete dam damage identification method based on multi-modal parameter deep learning, and the method comprises the steps: extracting the actual measurement modal parameters of a concrete dam through distributed optical fiber vibration sensing data, building a high-precision dam body dynamic model based on finite element simulation, randomly generating different material parameter combinations, obtaining the corresponding modal parameters, and carrying out the recognition of the damage of the concrete dam. And the simulation model is optimized and corrected, so that the simulation model better conforms to the actual dynamic characteristics of the dam body. In the corrected simulation model, randomly generating different damage positions, ranges and degrees, and constructing a training sample set covering multiple damage modes; and establishing a one-dimensional convolutional neural network, performing deep learning model training by using the training sample, and substituting the actually measured distributed vibration data to realize intelligent identification of the damage position, range and degree of the concrete dam. According to the method, the accuracy and adaptability of concrete dam damage identification are improved, the method can be widely applied to long-term health monitoring of hydraulic structures, and efficient and accurate technical support is provided for dam safety assessment.

The invention provides an optical cable intrusion detection method and device based on distributed optical fiber vibration sensing, belongs to the technical field of optical fiber sensing and security monitoring, and solves the problems that existing optical cable security monitoring is high in false alarm rate, poor in real-time performance and difficult to meet long-distance optical cable security requirements. The method comprises the following steps: acquiring a first vibration signal in a preset amplitude range; performing frequency filtering processing on the first vibration signal to obtain a second vibration signal; performing feature extraction processing on the second vibration signal to obtain a high-dimensional feature vector; performing multi-scale time sequence classification processing on the high-dimensional feature vector to obtain classification data; and determining the vibration state of the optical cable according to the classification data. According to the scheme, long-distance security monitoring of the optical cable is realized, and the accuracy of a detection result is improved.

The invention provides a highway subgrade void recognition method, device and system and a storage medium, and belongs to the technical field of signal processing, and the method comprises the steps: obtaining corresponding original time domain vibration signals generated when a current vehicle passes through a plurality of grating measurement points in a grating array vibration sensing optical cable arranged below a highway surface layer; extracting dominant frequency features corresponding to each grating measuring point from the original time domain vibration signal; determining a signal fluctuation characteristic value of an area where the plurality of grating measuring points are located based on the dominant frequency characteristic corresponding to each grating measuring point; and determining a roadbed void identification result of an area where the plurality of grating measuring points are located based on the signal fluctuation characteristic values. The void identification is carried out according to the quantized data of the signal energy fluctuation between the grating measuring points, the identification mode is simple and rapid, and the fluctuation value of the dominant frequency characteristic is used as an identification index, so that the high-sensitivity discrimination of the abnormal state in the roadbed structure is realized, and the accuracy and efficiency of the void identification of the highway roadbed are improved.

The invention relates to a wearable flexible vibration sensing sensor for a humanoid robot and a preparation method of the wearable flexible vibration sensing sensor. The sensor structure comprises an upper packaging layer, a lower packaging layer, a moisture absorption layer, a hydrogel layer, a graphene layer, an electrode layer and a flexible circuit board. Wherein the upper packaging layer and the lower packaging layer protect the internal structure, the moisture absorption layer enhances the moisture absorption capacity in the environment, the hydrogel layer maintains moisture and forms a concentration gradient, the graphene layer enhances the sensitivity of the sensor, the electrode layer collects ion migration to generate voltage, and a signal amplification circuit and a wireless transmission module are embedded in the flexible circuit board. The method is characterized in that a vibration signal is converted into voltage change through ion migration between the hydrogel layer and the graphene layer, and accurate vibration sensing is achieved. The sensor has high sensitivity, quick response and good flexibility, is suitable for being integrated into the humanoid robot, and improves the environmental perception and interaction performance of the humanoid robot.

The embodiment of the invention discloses a tactile feedback method and device, terminal equipment and a storage medium, which are used for timely responding to a pressing operation of a user on a side key, carrying out tactile feedback and reducing the probability of misoperation. The method provided by the embodiment of the invention comprises the following steps: in response to a pressing operation of a user on a side key of the terminal equipment, obtaining pressing data; determining a current vibration sensing waveform corresponding to a current application scene of the terminal equipment and the pressing data, wherein different application scenes and different pressing data correspond to different vibration sensing waveforms; and performing tactile feedback on the pressing operation according to the current vibration sensing waveform.

The invention discloses a dam safety monitoring and crack prediction method and system based on vibration sensing data, relates to the technical field of data processing, and solves the problems of incomplete data acquisition, data processing lag and insufficient prediction capability in existing dam safety monitoring. Adaptive normalization processing based on a dynamic window is carried out on the dam vibration data; a prediction model is constructed, an LSTM network captures time dependence of dam vibration data, an attention mechanism network fuses LSTM network output and external environment variables to extract key time steps, and an output layer predicts the probability of crack generation according to the key time steps; inputting the preprocessed dam vibration data into a prediction model to obtain a crack generation probability; calculating a dynamic threshold according to the external environment factor, and comparing the crack probability to obtain a crack prediction result; by collecting dam vibration data at multiple positions, the crack risk is predicted in advance based on the prediction model.

The invention discloses a cutting fluid drainage nozzle of a high-measurement slicing machine, which belongs to the technical field of cutting processing equipment and comprises a base, a drainage panel, an adjustable composite damping support module, a piezoelectric adjusting strip, a microcirculation flow channel network, a vibration sensing system and a control system. Comprising a high-molecular polymer damping material layer and a high-strength light alloy restraint layer, a piezoelectric ceramic actuator is embedded, passive damping energy absorption and active vibration counteracting are achieved, a piezoelectric adjusting strip is fixed in a drainage groove, and the dual functions of local vibration suppression and flow control are achieved; vibration sensing monitors the vibration state in real time through a plurality of piezoelectric acceleration sensors, the control system achieves working condition self-adaption and frequency division cooperative control through a neural network algorithm, cooperative work is achieved through multiple vibration reduction mechanisms, the vibration suppression effect is improved, the line breakage rate is reduced, meanwhile, the functions of fault diagnosis and predictive maintenance are achieved, and the reliability of the system is improved. And the cutting precision and efficiency and the equipment intelligence level are improved.

The invention discloses a vibration sensing bridge detection robot and a dynamic characteristic detection method. The robot comprises a walking and fixing system, a signal acquisition system and an electromechanical control system. The bridge dynamic characteristic detection method detects the bridge and identifies the vibration frequency and modal shape of the bridge. According to the invention, the detection robot strides on the anti-collision guardrail of the bridge to carry out detection operation, traffic control is not needed, and identification of the dynamic characteristics of the bridge can be realized without installing a structure health detection system on the bridge; the bridge detection robot does not need to be fixed by manually distinguishing the widths of the guardrails during debugging before detection operation, self-adjustment and fixation of the robot can be achieved only through remote control of wireless signals, the safety risk possibly caused by high-speed passing vehicles to detection personnel in the detection process is reduced, and the detection efficiency is improved. And the detection cost and the labor cost are saved.

A personal care device drive unit comprises a main body housing a motor and enabling releasable connection to any selected one of a set of different functional units. A controller of the personal care device drive unit generates an output signal associated with the selected one of the functional units connected to the main body in dependence on a sensed motor current and a sensed vibration occurring in the main body. The use of both vibration sensing and current sensing (which detects the electrical motor load resulting from driving the selected one of the functional units connected to the main body) enables multiple different functional units to be identified more reliably.