393 results about "Mechanical failure" patented technology

The invention relates to a simulation prediction method for the plugging life of a contact element of an electric connector, and the method comprises the steps: building a three-dimensional simulation model of a plug end and a socket end of the electric connector, and defining the material attributes and contact relationships of all parts in the three-dimensional simulation model; performing finite element grid discretization on the three-dimensional simulation model; applying a fixed constraint to the socket end, applying a displacement load for simulating the plugging process to the plug end, and performing statics solution to obtain plugging force in the plugging process of the plug end and the socket end and contact pressure data of the plug end contact piece and the socket end contact piece; and mapping a statics solution result into a fatigue analysis module, and simulating, calculating and outputting a life distribution result of the plug end contact element and the socket end contact element under repeated plugging and unplugging loads. The method has the effect of analyzing the stress-strain state of the internal contact element of the electric connector and the fatigue damage of the internal contact element under repeated plugging and unplugging so as to help monitoring personnel to discover the potential mechanical failure problem in time.

The invention provides a vibration-temperature correlation fault early warning method based on multi-sensor fusion, and relates to the technical field of rotating machine fault diagnosis, and the method achieves the efficient diagnosis of the rotating machine fault through the three-field coupling analysis of vibration, temperature and torque. Multi-dimensional health indexes are introduced, sensor data of different dimensions are normalized into a monitorable numerical value, and the health state of equipment is reflected in real time through a dynamically optimized weight coefficient. A core depth time sequence neural network model is combined with an attention mechanism and a Transform coding layer, the problem of long-term dependence is effectively solved, and the fault recognition precision is improved. According to the method, future innovation directions such as cross-device collaborative diagnosis, quantum computing acceleration and digital twinborn visualization are also exhibited, and the method has important industrial application value.

The invention discloses a wind power gear box online fault diagnosis method based on multi-source data fusion, and particularly relates to the field of mechanical fault detection, and the method comprises the steps: S1, collecting high-frequency dynamic, medium-frequency working condition and low-frequency thermal state data, and outputting a standardized data set through time alignment, quality verification and physical constraint verification; s2, performing multi-scale decomposition to retain a fault sensitive frequency band, inverting physical parameters such as gear contact stress and the like, constructing a physical cause and effect graph, and determining fault sensitive characteristics and threshold values; s3, constructing a multi-modal feature tensor, and obtaining a low-dimensional health representation vector through CP decomposition fusion, graph neural network reasoning and variational auto-encoder dimension reduction; s4, calculating a weight by using an entropy weight method, calculating a dynamic health degree in combination with a health benchmark, predicting a trend by using LSTM, and establishing a five-level health system; s5, judging a fault mode through double-layer identification, analyzing a root cause and formulating a hierarchical operation and maintenance suggestion; the method is based on multi-source fusion and data mechanism dual drive, and precise diagnosis and operation and maintenance guidance are achieved.

The invention relates to the technical field of power equipment fault diagnosis, and particularly discloses a pole-mounted circuit breaker mechanical fault diagnosis method and system, and the method comprises the steps: firstly, synchronously collecting a dynamic force-displacement signal, a high-frequency acoustic emission signal and a broadband vibration signal in the switching-on and switching-off process of a circuit breaker operating mechanism; then, the acoustic emission and vibration signals are decoupled into an impact source component and a friction source component which are statistically independent through a blind source separation algorithm; performing envelope spectrum analysis on the impact source component to extract impact characteristics, and performing energy calculation on the friction source component to obtain friction noise energy characteristics; finally, on the basis of dynamic force-displacement curve fitting analysis, in combination with impact characteristics and friction noise energy characteristics, distinguishing and delimiting of mechanical wear and lubrication degradation faults are achieved. According to the invention, the problem that the existing diagnosis technology cannot effectively identify and distinguish the coupling fault of mechanical wear and lubrication deterioration is solved, and early warning and accurate diagnosis of the mechanical fault of the pole-mounted circuit breaker are realized.

The invention relates to the technical field of new energy vehicles, and discloses a new energy vehicle air conditioner compressor fault diagnosis system based on deep learning. The system comprises a data acquisition module, a model construction module, a model activation module and a result output module. The data acquisition module collects real-time operation data of the compressor to form an initial fault feature set containing a vibration spectrum feature sequence and a current fluctuation trend graph. The model construction module constructs a first diagnosis model and a second diagnosis model, the first diagnosis model reflects correlation mapping of vibration characteristics and mechanical faults, and the second diagnosis model comprises nonlinear classification rules of current characteristics and electrical faults. And the model activation module starts a corresponding target model and generates a diagnosis result according to the real-time vibration deviation and the current fluctuation abnormal data. And the result output module transmits the result to a vehicle control system to trigger a fault early warning signal or a maintenance suggestion instruction. The system can comprehensively cover mechanical and electrical fault diagnosis through dual-model collaborative operation, and is adaptive to a dynamic operation scene of the compressor.

The invention discloses an intelligent mechanical fault rapid diagnosis method. The method comprises the steps of multi-source data acquisition, data optimization processing, mechanical abnormity rapid detection, mechanical fault rapid identification, model performance optimization and mechanical fault intelligent diagnosis. The invention relates to the technical field of mechanical fault data processing, in particular to an intelligent mechanical fault rapid diagnosis method, which innovatively proposes a mechanical fault rapid diagnosis technology for abnormal trigger key sequence extraction, and improves the sensitivity and discrimination precision of mechanical fault features. A random convolution disturbance mechanism is introduced, and a multi-equipment adaptability enhanced loss function is designed, so that rapid, accurate and intelligent diagnosis of mechanical faults under complex working conditions and multi-equipment conditions is realized; an optimization algorithm is improved by adopting a disturbance guide extension strategy, a jump drive fine tuning mechanism and a multi-channel fine tuning candidate strategy, a model performance optimal parameter combination is obtained, and the stability and recognition precision of a mechanical fault recognition model are enhanced.

The invention discloses a rotating machine fault diagnosis method based on an expansion residual network, and relates to the technical field of mechanical faults, and the method comprises the steps: collecting a continuous time sequence vibration signal of a rotating machine through an acceleration sensor, obtaining a high-order harmonic energy sequence set after fast Fourier transform, and determining an abnormal trend segment; then, obtaining and updating a structural anomaly candidate set based on the anomaly trend segment and the operating environment state of the rotating machine, generating a first drift vector set according to the updated structural anomaly candidate set, and identifying a structural offset section according to the first drift vector set and historical operating data; and backtracking the structural offset section to an original time domain signal to establish a second drift vector set, and finally, inputting the first drift vector set and the second drift vector set into the expansion residual network to analyze whether the rotating machinery is evolved into a fault state.

The invention relates to the technical field of electric power operation and maintenance, in particular to a transformer fault diagnosis method, device and equipment and a storage medium, and the method comprises the steps: collecting a transformer acoustic monitoring signal based on an acoustic microphone array, carrying out blind source separation and decoupling, and constructing an acoustic signal feature set; transformer operation condition parameters are extracted, mechanical state degradation evaluation is carried out according to the acoustic signal feature set, and a mechanical state health degree index is generated; and calculating an acoustic signal receiving timestamp of the acoustic microphone array, performing sound source distribution mapping, and constructing a sound source spatial distribution diagram. According to the method, blind source separation decoupling, mechanical state degradation evaluation, sound source distribution mapping and fault situation diagnosis are performed by acquiring the acoustic signals and the operation condition parameters, so that tiny mechanical faults in the transformer can be accurately identified, intelligent maintenance decision is realized, and the method has the advantages that the tiny mechanical faults in the transformer can be detected earlier; the real-time performance and reliability of fault diagnosis are improved, and the accident risk caused by diagnosis lag is reduced.

A steer-by-wire system is provided. The steer-by-wire system includes: a first powerpack having a first motor a first controller configured to determine a first mechanical fault of the first powerpack; a second powerpack having a second motor, and a second controller configured to determine a second mechanical fault of the second powerpack; and a steering rack operably coupled to the first motor of the first powerpack and the second motor of the second powerpack, the steering rack being configured to be linearly movable in response to rotation of at least one of the first motor and the second motor.

The invention relates to the technical field of motor performance evaluation, in particular to a permanent magnet direct current pump motor performance evaluation method and system based on cloud computing. Comprising the steps of obtaining current waveform data and vibration waveform data, and obtaining a first criterion based on the current waveform data and the vibration waveform data; obtaining a second criterion according to the vibration waveform data; obtaining a third criterion according to the vibration waveform data; obtaining a fourth criterion according to the current waveform data and the vibration waveform data; judging the causal correspondence between the current step and the output change of the control loop to obtain a fifth criterion; continuously observing the change trend of high-frequency components in the vibration waveform data to obtain a sixth criterion; based on the first criterion, the second criterion, the third criterion, the fourth criterion, the fifth criterion and the sixth criterion, obtaining a classification result; and confirming measures according to a classification result. The problems of repeated false alarm, invalid maintenance and system reliability evaluation deviation caused by the fact that a permanent magnet direct current pump motor performance evaluation system cannot distinguish real mechanical faults under a low-speed cruise working condition in the prior art are solved.

The utility model relates to a pillow spring assembling and disassembling clamp which comprises an electromagnetic iron core, a coil winding, a pneumatic tool, an ejector pin, a pen-shaped air cylinder, an adjusting gasket, a supporting seat and an installing seat, the electromagnetic iron core is fixedly arranged on the supporting seat, a combination mode of the electromagnetic iron core and the pen-shaped air cylinder is adopted, and the magnetic pole face of the electromagnetic iron core adopts two sections of cambered surfaces and a plane. The two sections of cambered surfaces are used for magnetically attracting bearing springs and damping springs of different sizes respectively, the plane is used for magnetically attracting a wedge or other parts, and the height is set to be the height of the two rings of springs so that the industrial robot can work in the side frame conveniently; the cylinder mounted behind the electromagnet adopts the design of a single-pen cylinder and a round head ejector pin, so that the problem of clamping of multiple cylinders is avoided, the ejector pin round head structure effectively realizes clamping and releasing of an inner spring and an outer spring, the mechanical failure rate is reduced, and automatic disassembly and assembly operation of different types of bogie pillow springs, wedges and other parts is facilitated.

The disclosure concerns printable refractory compositions, more particularly ceramic- based pastes for 3D printing of molds for additive metal casting. In particular, the present disclosure concerns composition for forming mold regions having modified thermal conductivity and dissipation and increased release of gaseous products therefrom during heating to mitigate mechanical failure risks in an additive casting process of metal objects.

The invention belongs to the technical field of wind speed and wind direction monitoring, and particularly relates to an ocean buoy wind speed and wind direction monitoring method and system, and the method comprises the following steps: S1, setting a sampling period, and obtaining data information through a sensor; s2, preprocessing the data to obtain processed wind speed and wind direction data; s3, calculating the confidence coefficient of each sensor, and troubleshooting fault data; s4, performing weighted fusion on the data, and outputting a final wind direction and a final wind speed; the method has the advantages that in combination with a real-time confidence evaluation mechanism, when any single sensor fails due to salt spray corrosion, mechanical failure or electromagnetic interference, continuous output of wind speed and wind direction data can still be guaranteed, and the availability of the system is improved; motion noise suppression is carried out based on IMU-environment joint compensation; an adaptive decision engine and a self-diagnosis engine are constructed, the weight of a distortion sensor is reduced under extreme working conditions, and the accuracy of measurement is ensured.

The invention provides a rotating machine fault diagnosis method and system. The method comprises the following steps: collecting a vibration signal of a rotating machine to be detected; on the basis of the vibration signals, the fault type of the rotating machine to be detected is judged through a trained VLFFD model; wherein the VLFFD model is composed of a backbone network and a classification header network based on dot product similarity, the backbone network comprises a one-dimensional convolution feedforward network and a feature fusion module, and the feature fusion module is a feature fusion network based on a multivariable self-attention mechanism and a grouping query attention mechanism. According to the scheme, the adaptability and generalization ability of mechanical fault identification under different sampling signals can be enhanced, and the accuracy of rotary mechanical fault identification is guaranteed.

The invention provides a rotating machine fault diagnosis method for improving H5N1-VMD-MFWA noise reduction, and belongs to the technical field of mechanical fault diagnosis, and the method comprises the steps: obtaining vibration signals of a rotating machine under normal and fault conditions; taking the minimum envelope entropy as a fitness function, and performing parameter optimization on variational mode decomposition by adopting an improved avian influenza virus algorithm to obtain an optimal parameter [K, alpha] combination; variational mode decomposition is carried out on the vibration signal based on the optimal parameter, and K intrinsic mode function components are obtained through decomposition; and according to the correlation coefficient, the kurtosis and the envelope entropy of each component, a comprehensive evaluation index is constructed through a multi-feature weighted attention mechanism, each component is endowed with a weight, a signal is reconstructed, and the reconstructed signal is subjected to window segmentation and input into a convolutional neural network model to complete fault diagnosis. According to the method, the fault diagnosis accuracy is effectively improved, and a guarantee is provided for safe operation of mechanical equipment.

The invention discloses a mechanical fault diagnosis method based on maximum correlation kurtosis depth unrolling, and relates to the technical field of mechanical state monitoring, and the method specifically comprises the following steps: obtaining a vibration signal sliding window segmentation calculation kurtosis, and generating a window priority sequence through cross-correlation correction sorting; a high-quality window is selected to be combined with a rotating speed integral and a fast Fourier transform detection angle period to form an angle domain period boundary set, a segmentation signal calculation error is subjected to quasi-Newton optimization de-convolution to obtain a deep de-convolution fault feature signal, a Toeplitz matrix is constructed, and a multi-layer fault feature vector is weighted and extracted; according to the method, vibration signal sorting is segmented through a sliding window, so that impact positioning is enhanced, noise is reduced, definition is improved, an angle domain period is dynamically recognized, feature synchronization is achieved, error optimization unrolling is performed, impact recovery is enhanced, and feature dimensions are expanded through matrix weighted mapping. And the recognition precision and adaptability are improved through multi-scale aggregation.

The invention relates to the technical field of mechanical failure analysis, in particular to a failure detection method for a planetary gear shaft of a differential mechanism of a new energy automobile, which comprises the following steps of: processing and analyzing vibration, load and temperature signals by simulating working conditions, and extracting and analyzing typical characteristics of the signals; the aging state and the specific failure type of the planetary gear shaft can be quickly judged, the failure detection period can be effectively shortened, and the detection cost can be reduced.

The invention relates to the technical field of fault diagnosis, in particular to an automatic identification method for mechanical faults of key electrical equipment, which comprises the following steps of: deploying an acceleration sensor array on a bearing seat, measuring a temperature conduction rate, mapping vibration displacement to a temperature path node, and calculating a mechanical impedance change rate; and generating an energy flow abrupt change space identifier and an electromechanical anomaly correlation index, capturing shock wave characteristics, calculating a thermal response phase angle, evaluating direction consistency, and generating a mechanical fault type determination result. According to the method, the acceleration sensor array and the embedded thermocouple are arranged, so that the real-time monitoring capability on the fault of the key electrical equipment is remarkably enhanced, the position and the property of a fault source are more accurately identified, the vibration displacement vector is associated with the change of the temperature conduction rate, the time-space precision of fault prediction is improved, and the fault prediction accuracy is improved. The abnormal mode of energy flow is disclosed, the accuracy of fault early warning is improved, the maintenance cost is reduced, and potential key electrical equipment faults are avoided.

The utility model discloses an annular shuttle vehicle system based on a straight track switching device, the annular shuttle vehicle system is provided with a first straight track and a second straight track, and the first straight track switching device is arranged between the left end part of the first straight track and the left end part of the second straight track; a second straight track switching device is arranged between each maintenance straight track on the periphery of the first straight track and the first straight track; third straight track switching devices are arranged between the low-efficiency obstacle avoidance straight tracks on the periphery of the second straight track and the second straight track, and fourth straight track switching devices are arranged at the right end of the first straight track, the right end of the second straight track and among the low-efficiency obstacle avoidance straight tracks. The straight rail replacing device is used for replacing rails, the manufacturing difficulty of the rails is reduced, the mechanical failure rate of the vehicle body is effectively reduced, one drive can be reduced for the annular shuttle vehicle, the manufacturing cost is lower, and electrical debugging is easier.

The invention discloses a high-voltage diverter and a sampling device, and belongs to the technical field of diverters, the high-voltage diverter comprises a main body mechanism, and the main body mechanism is internally provided with a disconnection mechanism used for automatic disconnection when a fault occurs and two terminal mechanisms used for wiring; through the linkage design of the built-in temperature sensing piece and the disconnecting mechanism, automatic and accurate fault protection is achieved, when the internal temperature of equipment abnormally rises due to faults and exceeds a safety threshold value, the temperature sensing piece is heated to deform and triggers the disconnecting mechanism, energy stored by the extension spring is used for rapidly driving the on-off connecting piece to act, a main circuit is reliably disconnected, and the safety of the equipment is improved. Meanwhile, the action can be linked with the top protruding block to be self-locked and protrude out of the surface of the protection shell, an intuitive and striking mechanical fault indicator is formed, accident expansion is effectively prevented, definite fault point positioning can be provided for operation and maintenance personnel, the troubleshooting time is shortened, and the safety and maintainability of the system are improved.

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.

The invention relates to the field of horizontal milling and boring machines, in particular to a counter weight anti-falling device of a numerical control horizontal milling and boring machine and the numerical control horizontal milling and boring machine. The practical application value of the counter weight anti-falling device of the numerical control horizontal boring and milling machine is embodied in multiple dimensions, in the aspect of equipment protection, major mechanical faults such as machine tool main body structure damage and guide rail deformation caused by counter weight falling can be effectively avoided, and the service life of equipment is remarkably prolonged; in the aspect of safety production, the risk of personal injury to operators due to accidental falling of the heavy hammer is thoroughly eliminated; and in the aspect of economy, the non-planned downtime is shortened through preventive maintenance, and the maintenance cost is greatly reduced. Actual test verifies that the response time of the counter weight anti-falling device is less than 50ms, the early warning accuracy rate reaches 99.9% or above, the safety protection requirement of a high-precision numerical control machine tool is completely met, and potential safety hazards existing in the industry for a long time are thoroughly solved.

The application is a silicon steel winding double deviation correction control system and method with temperature monitoring, which comprises a control system and a storage module, a calculation module, an HMI module, a deviation correction module one, a deviation correction module two and a winding machine control module connected with the control system. The application has the following advantages: it can ensure continuous and stable production as much as possible, does not produce deviation, can reasonably control the winding tension and ensure product quality. The configuration of the system is improved, the control is optimized and the industry problems can be better solved. It can also be applied in the silicon steel notch line of the factory, and stable tension and continuous operation can better ensure the quality and effect of the silicon steel surface notch, and can improve the surface treatment process quality treatment effect. When the two sets of deviation correction modules work at the same time, deviation correction still occurs, that is, the continuous abnormal deviation of the deviation correction module two cannot be adjusted, and the system can be prewarned for some mechanical failure or imbalance, and the alarm shutdown can be used as a maintenance warning signal of the winding line and as a basis for system maintenance and maintenance.

The application relates to the technical field of wind turbine fault diagnosis, and discloses a wind turbine gearbox output end bearing comprehensive diagnosis method based on multi-model cooperation. The method predicts bearing temperature trends through a dynamic power-shaft temperature model, carries out temperature early warning by using a power-temperature difference model, and cooperates with multidimensional data such as mechanical vibration characteristics (time domain / frequency domain impact energy reflecting bearing mechanical damage), bearing temperature trends and temperature difference analysis (eliminating environmental and load interference to position real fault temperature rise) to carry out confidence calculation. The method can distinguish mechanical faults such as roller peeling and inner ring cracking from operation faults such as heat dissipation failure, breaks through the traditional "single-point alarm" mode, constructs a "root cause analysis-turbine power reduction / triggered shutdown" whole-process closed loop, and significantly improves the safety, reliability and operation efficiency of the turbine operation.

The application discloses a dangerous rock mass deterioration multifunctional test device and method, and through the setting of a self-weight stress loading frame and a detachable rock deterioration experiment cabin, deep coupling of mechanical load and environmental erosion is realized, technical defects of mutual disconnection of a stress field and an environmental field in traditional tests are solved from the root, the whole process of rock samples from deterioration evolution to final mechanical failure does not need to experience secondary transportation and repeated installation, a crack initiation and evolution mechanism under the guidance of a stress field is effectively restored, and therefore, the accuracy of experimental data in characterization of real damage laws of dangerous rock mass is remarkably improved.

This invention provides an equivalent substitute, its preparation method, and its application for testing the mechanical properties of RDX-based non-aluminum-containing press-loaded explosives under temperature load. The equivalent substitute is prepared by replacing the energetic material RDX in the RDX-based non-aluminum-containing press-loaded explosive formulation with the non-energetic material ammonium sulfate. The equivalent substitute is used to replace the actual explosive for testing its mechanical properties under temperature load. The equivalent substitute, by mass percentage, comprises the following raw material components: 95.5%–96.5% ammonium sulfate, 2%–3% polyvinyl acetate, and 0.5%–1.5% stearic acid, with the total mass percentage of the above components being 100%. The mechanical properties of the molded explosive charge made with the equivalent substitute provided by this invention, such as compressive strength and tensile strength (compressive strength of 2.9-27.3 MPa and tensile strength of 1.1-5.9 MPa at -55 to 70°C), are basically consistent with those of real explosives under different temperature loads. This can truly reflect the changes in the mechanical properties of the actual explosive charge during environmental and aging tests, and can provide data support for the mechanical failure assessment of explosives.

The invention belongs to the technical field of mechanical fault diagnosis, and particularly relates to a self-adaptive fault diagnosis method based on the field of multi-classifier aided confrontation double residual error open set, which comprises the following steps: collecting vibration data of key parts of a rotating machine, inputting the vibration data into a trained rotating machine fault detection model after data preprocessing, obtaining a fault diagnosis result of the rotating machine; according to the model, a dynamic unknown threshold weighted adversarial training strategy is adopted, inter-domain distribution difference is eliminated, and the flexibility of a domain adaptive network is enhanced through a dynamic threshold, so that a feature extractor is better focused on domain invariant information in a sample, and cross-domain open set domain adaptation is realized.

The invention provides a fault emergency system and method for failure of a loading power source of a transfer machine, and relates to the technical field of fault emergency of the transfer machine, and the system comprises a lifting appliance mechanism which is used for grabbing, fixing and releasing a lifting object, and adjusting the horizontal posture of a lifting appliance in real time; the slewing mechanism is used for rotating the transshipment mechanical upper vehicle left and right and adjusting the horizontal direction of the hoisted object; the luffing mechanism is used for adjusting the elevation angle of the luffing mechanism through stretching and retracting of a luffing oil cylinder, and the vertical height and the horizontal distance of the hung object are changed. And the erecting mechanism is used for changing the vertical height and the horizontal amplitude of the luffing mechanism and the hoisted object. Leveling, rotation adjustment and variable-amplitude falling of the lifting appliance are achieved through manual operation, it is ensured that a lifted object is safely and stably placed on the ground, and equipment damage or safety accidents are effectively avoided; the stroke of the proportional control valve is adjusted according to the number of turns of screwing of the knob, the opening degree of the balance valve is controlled, the variable amplitude is slowly reduced, and unnecessary loss caused by too large opening is avoided.

The application discloses a kind of based on adaptive wavelet decomposition's rotating machinery fault analysis method and system, wherein, method includes the vibration signal of the rotating machinery to be analyzed is collected and data preprocessing is carried out, obtains data sample;Depth feature extraction processing is carried out to the data sample by feature extraction module, and depth feature is obtained;The depth feature is input to adaptive wavelet decomposition model, and target decomposition feature is output and obtained;The target decomposition feature is subjected to fault type discrimination processing, and obtains fault analysis result.The adaptive wavelet decomposition model is carried out in the embodiment of the application wavelet filter and the automatic learning of wavelet decomposition route, and then separates fault component and interference component in rotating machinery fault analysis task, effectively improves the anti-noise performance of method, and can be widely applied in intelligent monitoring technical field.

The utility model belongs to ground robot technical field especially a factory ground robot with self -cleaning track structure aims at the track of traditional factory ground robot of current easy to be attached to impurity, manual dismounting cleaning maintenance cost is high, influence operation, through scraper cleaning existence blind angle, it is difficult to clean sticky dirt, heavy load is easy to wear and tear and stagnate the problem, present and propose following scheme, including ground robot body, the both sides of ground robot body are rotatably connected with track, in the utility model, through the cleaning box and nylon brush that set up in the both sides of ground robot body, realize full -automatic cleaning, reduce maintenance cost, dust collection subassembly passes through passageway, dust extraction device and the dust collection of connecting pipe with filter screen collect dust, avoid secondary pollution, keep the environment clean and prolong the life, the cleaning assembly is through water pump, cleaning pipe and shower nozzle group flush track, cooperate brush and remove stubborn dirt, realize physical to chemical cleaning upgrade, reduce mechanical failure.