6617results about "Structural/machines measurement" patented technology

The invention provides a shield tunneling machine fault detection method and system based on edge computing, and relates to the technical field of industrial Internet, and the method comprises the steps: deploying edge computing nodes on a shield tunneling machine, and carrying out the time-space alignment and feature level fusion of data collected by different sensors, and obtaining unified feature data; inputting the fused feature data into an online learning model pre-deployed in an edge computing node to obtain a fault detection result; the edge computing node uploads the fault detection result and feature data related to the fault to the cloud server, generates a fault analysis report and feeds back the fault analysis report to the edge node and the user terminal; and the edge node periodically pulls the updated online learning model and knowledge base from the cloud to realize cloud-edge collaborative model updating and knowledge evolution.

The invention relates to the technical field of grouting simulation tests, in particular to a sandy gravel stratum catastrophe grouting prevention and control virtual-real blending collaborative test method and system, and the method comprises the steps: carrying out a grouting test on a sandy gravel stratum simulation model, and obtaining grouting parameters and monitoring parameters in the grouting process; based on the resistivity and the radar data, further obtaining a slurry diffusion path and a diffusion range of the area with the higher surrounding rock grade; based on the resistivity and the optical fiber sensing data, the slurry diffusion path and the diffusion range of the area with the low surrounding rock grade are further obtained; inputting the initial grouting parameters into the trained neural network model to obtain simulation data; carrying out preprocessing and alignment operation on the previous data, and carrying out fusion analysis; and according to the fusion analysis result, grouting equipment parameters and a grouting scheme are pre-controlled and adjusted. And real-time regulation and control of a grouting scheme and equipment are realized.

The invention provides a water pump residual life prediction method based on a large model, and the method comprises the following steps: S1, collecting the multi-source heterogeneous data of the operation of a water pump in real time through a vibration sensor, a temperature sensor, a pressure sensor and a monitoring unit, the temperature sensor monitors temperature gradient changes of the bearing and the sealing cavity, the pressure sensor records inlet and outlet pressure fluctuation characteristics, and the monitoring unit extracts three-phase current harmonic components of the motor; s2, carrying out lightweight preprocessing on the multi-source heterogeneous original sensing data at an edge computing node, wherein the lightweight preprocessing comprises vibration signal noise reduction processing based on wavelet transform, temperature and pressure data calibration normalization of load segments according to working conditions, and transient abnormal data flow filtering through a sliding time window; and S3, inputting the preprocessed data stream into a cloud large model platform, and analyzing the long-period dependency relationship of the vibration signals through a Transform encoder in a time sequence feature extraction module.

The invention relates to a multi-sensor fusion intelligent actuator fault self-diagnosis method and system, and belongs to the technical field of actuator fault diagnosis. The method comprises the following steps: realizing multi-sensor clock synchronization through a unified clock source, generating a synchronous time sequence for asynchronous data such as Hall, pressure and temperature by adopting an interpolation method, and intensively acquiring signals such as rotating speed, torque and vibration; based on a current sensor and temperature data, primary fault identification is carried out through a current overload protection model, and an adaptive processing mechanism is triggered; demodulating the noise-reduced vibration signal, extracting high-frequency energy and detecting the deviation degree between the fault frequency of the bearing and a base line; bearing energy consumption abnormity is detected, and a fusion weight is dynamically set by combining the deviation degree, the energy consumption coefficient and an overload result; and carrying out weighted fusion on the multi-modal features, inputting an intelligent diagnosis model to carry out fault mode identification, and finally outputting a fault type / position and triggering a processing strategy. Accurate and rapid self-diagnosis of the actuator fault is realized.

The invention relates to the technical field of strength detection, and discloses radiator strength detection equipment and a detection method. The method comprises the following steps: carrying out adaptive wavelet denoising and structural association mapping on collected multi-scale structural feature data and multi-environment coupling material feature data of a radiator to be detected, and carrying out multi-dimensional feature extraction, space-time alignment and interactive mapping on an obtained fusion data set to obtain a multi-physical field coupling data matrix; carrying out physical field control equation set separation iteration solution and heat dissipation cyclic load and environmental action calculation on the coupling data matrix to obtain a damage evolution state; and performing multi-dimensional damage sensitive area identification based on the damage evolution state, generating a risk distribution map, performing failure comparison and life calculation based on historical failure data of the same type of radiators, and generating an intensity comprehensive detection result. Through multi-physical field coupling analysis and multi-dimensional damage evaluation, accurate detection of the strength state of the radiator is realized, and the accuracy of structural reliability evaluation is improved.

The invention relates to the technical field of fault detection, in particular to a heating and ventilation system fault positioning system and method based on big data, and the system comprises a multi-source sensing module, a disturbance feature module, a path modeling module, a frequency spectrum matching module and a fault positioning module. In the method, a real-time disturbance sequence is constructed through time window segmentation and parameter offset calculation, separation of an active response chain and an abnormal propagation path is realized through a directional joint state vector and a topological relation table, and time asynchronism of multi-device signal transmission is eliminated by adopting a dynamic time warping algorithm. In combination with a real-time parameter bidirectional verification mechanism of a frequency domain main frequency band energy mark, a valve opening degree and a pump rotating speed, the problem of path confusion in a multi-node parameter coupling scene of a traditional method is solved, the tracing efficiency of concurrent faults in a complex pipe network system is improved, the adaptability limitation of a single-dimensional threshold mechanism to equipment performance degradation is overcome, and the method is suitable for a complex pipe network system. And the error positioning probability caused by signal delay superposition is reduced.

The invention provides an intelligent fuel gas accessory part safety monitoring method, an Internet of Things system and a medium, and relates to the field of fuel gas pipeline accessory facility monitoring. The method is executed by an intelligent gas accessory safety monitoring Internet of Things system, and comprises the following steps: determining a first abnormal value; determining a target acquisition parameter based on the first abnormal value; determining an acquisition instruction based on the target acquisition parameter; determining a second abnormal value; and determining a maintenance instruction and / or a parameter adjustment instruction based on the second abnormal value. The Internet of Things system comprises a government safety supervision management platform, a government safety supervision sensing network platform, a government safety supervision object platform, a gas company management platform, a gas company sensing network platform, a gas equipment object platform and a gas pipeline maintenance object platform. The method can be operated after a computer instruction stored in a computer readable storage medium is read. According to the method, real-time monitoring and intelligent management of the appurtenances can be realized, so that the detection result is more comprehensive and accurate.

The invention discloses a multi-modal monitoring and anomaly detection method, system and device for a belt conveying system and a medium, and belongs to the technical field of anomaly detection.The method comprises the steps that a plurality of multi-modal sensors are arranged on the whole path along the path of the belt conveying system, and original signals in the running process of the belt conveying system are collected; the original signals are preprocessed; performing environment compensation calculation to generate engineering data; mapping the engineering data to the same space coordinate system of the belt conveying system, and completing feature extraction to obtain feature vectors; multi-modal fusion analysis is carried out on the feature vectors, abnormal state identification and fault level classification are realized, and an early warning signal and a risk level are output; and executing a matched response control strategy according to the early warning signal, the risk level and the corresponding space coordinate system position. According to the method, the abnormal state can be dynamically recognized under the complex working condition, differential response control is achieved, and the recognition capability and processing precision of early-stage, multi-source and dynamic anomalies are improved.

The present invention relates to a method for checking charging stations and their functional scope by vehicle-side or charging station-side detection of relevant parameters and faults occurring during a charging process of an electric vehicle at a charging station.

The invention belongs to the technical field of building envelope photo-thermal performance detection, and particularly relates to a spray type light-transmitting roof photo-thermal performance detection device and a detection method thereof. The spray type light-transmitting roof photo-thermal performance detection device comprises an external environment simulation device which comprises an external environment box and an artificial light source, and the artificial light source is installed in the external environment box; the light-transmitting roof test piece is mounted on the lower side wall of the external environment box; the spraying device comprises a nozzle, and the nozzle is located below the artificial light source and above the light-transmitting roof test piece; the heat metering device comprises a heat metering box and a plurality of detection sensors; the air supply and return system is used for circularly conveying airflow into the heat metering device; the water supply and return system is used for supplying water to the air supply and return system; the control system is used for controlling the external environment simulation device, the heat metering device, the air supply and return system, the water supply and return system and the spraying device. According to the invention, the photo-thermal performance of the light-transmitting roof under the spraying condition can be accurately evaluated.

The invention relates to the technical field of industrial equipment fault diagnosis, and provides an industrial equipment real-time fault diagnosis method and system based on a generative model, and the method comprises the following steps: collecting the original operation data of industrial equipment in real time through a plurality of sensors, carrying out the preprocessing of the original operation data, obtaining the real-time operation data, and carrying out the calculation of the real-time operation data; the preprocessing comprises abnormal value detection and standardization; constructing a generative model based on the real-time operation data, generating a normal data sample of the industrial equipment in a normal state through the generative model, and integrating the real-time operation data and the normal data sample to obtain a multi-dimensional data sample; performing feature extraction on the multi-dimensional data sample by using a deep learning model to obtain a fault diagnosis vector; performing fault mode identification on the fault diagnosis vector through a classification model to obtain a fault diagnosis result, and performing state judgment on the fault diagnosis result according to a preset threshold to obtain an industrial equipment state; and the state and the fault diagnosis result of the industrial equipment are displayed in real time through a dynamic monitoring interface. According to the method, the real-time performance and the overall efficiency of fault diagnosis are improved, and the loss caused by equipment faults can be effectively reduced.

The invention discloses a dynamic load adjusting method and system for dish-washing machine energy efficiency and water efficiency test, and the method comprises the steps: setting a test set of different dirt degrees and types of tableware proportions, carrying out the transient performance analysis of each washing mode, predicting the fluid flow and temperature distribution in a cavity of a dish-washing machine, and recognizing an abnormal region; a first objective function based on the washing effect is constructed to optimize water pump parameters to improve a water flow path, a second objective function is constructed to adjust heating element parameters, energy efficiency and water efficiency testing is conducted to determine the optimal washing period, the water pump parameters and the heating element parameters as optimal operation parameters, and a decision tree algorithm is introduced; and learning a mapping relation between the test condition and the optimal operation parameters, and adjusting the operation parameters of the dish-washing machine in real time by means of a control algorithm according to the tableware dirt degree and proportion. According to the method, the energy efficiency and water efficiency balance of the dish washing machine under different load conditions can be effectively improved, the washing effect is enhanced, energy consumption and water consumption are reduced, and meanwhile good interpretability is achieved.

The invention provides a state self-checking system for an unhooking robot, and relates to the technical field of automatic machinery, and the system comprises a real-time collection module which collects a real-time working data set of the unhooking robot; the operation evaluation module is used for comprehensively evaluating the operation of the unhooking robot based on the real-time working data set; the fault detection module is used for carrying out point cloud matching and data comparison on the unhooking robot based on the operation state evaluation information, carrying out fault detection according to a comparison result and generating a fault self-detection report; the fault tracing module is used for synchronizing the fault self-checking report to a human-computer interface for fault tracing; and the fault processing module is used for formulating fault processing suggestions and sending the suggestions to the human-computer interface to perform remote control on the unhooking robot. The technical problem that in the prior art, due to insufficient fault detection precision, tiny or complex faults cannot be found in time, and the operation stability of the robot is affected is solved, the fault detection precision and the response speed are improved, and the operation reliability and safety are enhanced.

The invention discloses a circuit breaker service life prediction system and method based on multi-source heterogeneous data fusion and dynamic weight correction, and the method comprises the steps: collecting the current, pressure, displacement, main loop current, voltage, contact temperature, environment temperature and partial discharge of a circuit breaker, and carrying out the multi-source heterogeneous data fusion algorithm, thereby achieving the prediction of the service life of the circuit breaker. Carrying out fusion analysis on the collected data; a dynamic weight correction algorithm is executed according to an analysis result, and different types of fault weights are corrected and adjusted, so that the life prediction model is more accurate; and executing a fault identification algorithm after each action of the circuit breaker, and calculating the service life of the circuit breaker according to the weight and the health index corresponding to each fault. According to the invention, the prediction accuracy of the service life of the short-circuiter is greatly improved, and the use safety of power equipment is improved.

The invention provides an ultrafast laser heat dissipation state real-time monitoring system based on cloud computing, and relates to the technical field of laser heat dissipation, and the system comprises a laser monitoring module which is used for transmitting ultrafast laser and is internally provided with a temperature sensor so as to monitor the working temperature of a laser in real time; the data acquisition module is used for being connected with the laser monitoring module, acquiring temperature data and key parameters, including laser power and working current, of the temperature sensor in real time, and uploading the temperature data and the key parameters to the cloud server; and the cloud analysis module is used for receiving and analyzing the temperature data and the key parameter data, identifying an abnormal temperature mode, predicting a potential overheating risk and calculating heat dissipation efficiency to obtain an analysis result. According to the invention, comprehensive, real-time and intelligent management of the heat dissipation state of the laser is realized, the operation stability and safety of the laser are effectively improved, the overheating risk is reduced, the heat dissipation efficiency is optimized, and remote monitoring and data analysis services are provided for users.

The invention discloses an intelligent operation and maintenance method. The method comprises the steps of deploying a vibration sensor, a temperature sensor, a current sensor and a pressure sensor at key operation parts of building equipment; receiving an original data flow of each sensor through an edge computing node; performing space-time alignment on the processed sensor data and the equipment operation state parameters; establishing a digital twin model library on the cloud server; and performing mode comparison on the real-time sensor data stream and the fault features in the digital twin model library through a fuzzy matching algorithm, and when the vibration frequency domain features have amplitude sudden change exceeding a baseline value in a preset frequency band, the temperature change rate exceeding a preset threshold and the current harmonic distortion rate exceeding a set proportion, triggering a grading early warning signal. According to the invention, the accuracy, the real-time performance and the intelligent level of operation and maintenance of the building equipment can be obviously improved.

The invention discloses an engineering machinery fault prediction and intelligent maintenance method based on deep learning, and belongs to the technical field of intelligent operation and maintenance of engineering machinery. The method comprises the following steps: firstly, performing timestamp synchronization and feature enhancement on multi-source sensor data to generate a space-time alignment tensor; fusing the image and time sequence features through a multi-modal feature distillation network, and constructing a cross-modal unified feature vector; thirdly, calculating a fault probability and residual life distribution, and constructing a Markov decision model in combination with a resource state; and finally, dynamically optimizing the maintenance instruction by using deep reinforcement learning, and continuously updating the model through closed-loop feedback. According to the method, early-stage accurate prediction of the fault and dynamic optimization of the maintenance strategy are realized, the problems of inaccurate prediction, decision lag, resource waste and the like in a traditional method are effectively solved, and the availability rate and the maintenance economy of equipment are remarkably improved.

The invention relates to industrial equipment monitoring, in particular to an industrial equipment state monitoring and early warning system and method based on machine learning, and the method comprises the steps: carrying out the model training of a convolutional neural network, and obtaining a trained convolutional neural network; constructing a pruning strategy generation model based on the trained convolutional neural network, and performing multi-objective optimization solution on the pruning strategy generation model to obtain an optimized pruning strategy; pruning the trained convolutional neural network according to the optimized pruning strategy to obtain a pruned convolutional neural network; collecting a vibration signal of the industrial equipment to be monitored, converting the vibration signal into a spatial frequency image, and calculating the central component energy of the spatial frequency in the spatial frequency image; calculating an energy value according to the central component energy of the spatial frequency in the spatial frequency image; inputting the energy value into a pruning convolutional neural network for state monitoring; according to the technical scheme provided by the invention, the defect that the state of the industrial equipment is difficult to accurately monitor and early warn in real time can be effectively overcome.

The invention provides a medical equipment fault detection method and system based on machine learning, and relates to the technical field of fault detection. The method comprises the steps of monitoring motor start-stop division operation cycles, synchronously collecting and processing vibration, frequency difference and voltage signals, calculating characteristic difference to generate a dynamic sequence, extracting a non-convergence trend and phase deviation, fusing voltage and frequency fluctuation characteristics, constructing a threshold rule to judge abnormity, and outputting a light fault early warning signal. According to the invention, through precise monitoring of motor start-stop nodes, period division, collection of vibration, frequency and voltage signals, combination of time alignment and noise filtering, improvement of signal quality, cross-period calculation of characteristic difference, and extraction of non-convergence trend and phase offset, the ability of capturing tiny anomalies is enhanced; according to the method, multi-dimensional indexes such as voltage fluctuation and frequency deviation are fused, a threshold judgment mechanism in a continuous window is constructed, stable identification and early warning of equipment light faults are achieved, and the detection accuracy and response timeliness are improved.

The invention discloses a marine central air conditioner health management and fault diagnosis system, which relates to the technical field of air conditioner health management and comprises a sensor module, a data acquisition module, a health management module, a fault diagnosis module, a user interface management module, a communication module and a maintenance management module. The sensor module is used for being installed on all devices and pipelines of the central air conditioner through sensors and detecting state parameters of the central air conditioner. The data acquisition module receives analog quantity signals of various sensors. Through cooperative work of various sensors and various functional modules, various parameters of the central air conditioner can be comprehensively monitored, faults including state monitoring and fault diagnosis of key parts such as a compressor, a refrigerant water system and a cooling water system can be accurately diagnosed, the equipment state can be monitored in real time in cooperation with the health management module, and the health of the central air conditioner can be improved. And reasonable maintenance suggestions and decisions can be provided through data analysis and evaluation to prevent faults in advance, so that the reliability and the service life of the equipment are improved.

The embodiment of the invention provides a fault monitoring method and device for a liquid cooling system and a server system.The method comprises the steps that the liquid inlet temperature, the liquid outlet temperature and the flow of cooling liquid of the liquid cooling system are obtained in real time, the liquid inlet temperature is the temperature when the cooling liquid enters the liquid cooling system, and the liquid outlet temperature is the temperature when the cooling liquid flows out of the liquid cooling system; the current specific heat capacity of cooling liquid in the liquid cooling system is calculated according to the liquid inlet temperature, the liquid outlet temperature and the flow; and when the difference value between the initial specific heat capacity and the current specific heat capacity of the cooling liquid is larger than or equal to the specific heat capacity threshold value, a first alarm is given out to remind that the cooling liquid of the liquid cooling system goes bad.

The invention relates to the technical field of remote monitoring, and discloses a coal unloader remote monitoring system based on AI learning and a cloud platform, and the system comprises an impact force monitoring module, a vibration mode analysis module, an operation state evaluation module, a health diagnosis module, and an intelligent early warning module. According to the method, through calculation of the magnitude, the direction, the stress time and the stress frequency of the impact force, fine-grained recognition of coal flow impact force distribution and fusion of vibration data and impact force data are enhanced, comprehensiveness and multi-dimensional data cross analysis of fault recognition of the coal unloader are improved, the abnormal misjudgment rate is reduced, intelligent calculation based on AI learning is achieved, and the fault recognition accuracy of the coal unloader is improved. According to the invention, a health assessment result can be dynamically adjusted, misjudgment caused by single-point abnormity is avoided, a cloud data processing mode enables remote management to be converted from data collection to intelligent early warning, the prediction capability of equipment operation abnormity is enabled to be more timely, remote monitoring is combined with intelligent learning, multi-level assessment is formed, and the accuracy and decision efficiency of remote management are improved.

The invention relates to an automatic detection and fault early warning system for a self-service car washer and a method thereof. The automatic detection and fault early warning system comprises a data acquisition module, a diagnosis circuit module, a water quality characteristic acquisition module, a correlation analysis module and an alarm module, according to the method, the operation state and the water quality condition of the equipment are comprehensively known by collecting various parameters in real time, potential problems are accurately recognized and an alarm is given in advance through correlation analysis and a dynamic early warning mechanism, the situations of false alarm and missing alarm are reduced, the early warning accuracy is improved, and the early warning efficiency is improved through correlation analysis and the dynamic early warning mechanism. The method adapts to the dynamic change of the equipment operation state and the water quality condition, predicts and intervenes potential problems in advance, and has the effects of improving the comprehensiveness of fault monitoring of the self-service car washer, enhancing the early warning accuracy and improving the adaptability and reliability of the system.

The invention provides a thermal behavior experiment system and method under a narrow rectangular channel reflooding cooling condition. The system comprises a heating water storage tank, a nitrogen tank, a steam tank, a shield pump, a preheating section, a first direct-current power supply, a second direct-current power supply, a narrow rectangular channel test section, a steam-water separator, a surge tank, a flow control valve, a silencer and an electromagnetic valve. The experimental working medium is deionized water, and re-submerging cooling experiments under different system pressures, working medium supercooling degrees, heating wall surface heat flux densities, working medium submerging speeds, heating wall surface initial temperatures and channel interior initial liquid levels can be carried out on an experimental section of a narrow rectangular channel structure. The device is suitable for narrow rectangular channel structures with different sizes, and can be used for carrying out re-submerging experiments on experiment sections with single-plate double-channel, double-plate three-channel and more channels. The system pressure and the inlet flow in the submerging process can be kept stable, and it can be ensured that the supercooling degree of a working medium, the heating wall surface temperature and the initial liquid level are controllable.

The invention provides a heat dissipation performance test system and method, and a storage medium, which can be applied to the technical field of equipment performance test. The heat dissipation performance test system comprises a heating element which is used for simulating the operation heating state of equipment; the liquid cooling device comprises a liquid cooling circulation pipeline and an adjusting structure installed in the liquid cooling circulation pipeline, and a heating element is installed in the liquid cooling circulation pipeline; the controller is used for changing the operation state of the adjusting structure so as to adjust the flow of the cooling liquid in the liquid cooling circulation pipeline and realize heat dissipation of the heating element; the processor is used for executing the following operations: acquiring N groups of basic data corresponding to N moments in the heat dissipation process of the heating element; based on the N groups of basic data, calculating to obtain M groups of heat dissipation performance data corresponding to the M different heat dissipation duration intervals; and based on the M groups of heat dissipation performance data, determining an equipment heat dissipation performance test result of the liquid cooling device, where M and N are integers greater than 1.

Provided is a three-dimensional loading structure of an ultra-large physical simulation facility for deep engineering disasters. The three-dimensional loading structure comprises a horizontal ultra-large reaction frame, a vertical ultra-large reaction frame, ultra-long stroke lifting and locking hydraulic cylinders, five array distributed hydraulic actuator groups and a linear distributed hydraulic actuator group, wherein the horizontal ultra-large reaction frame adopts a frame structure being formed by splicing eight segments of arch beams, circular in an outer part and square in an inner part, and wrapped with prestressed carbon fibers, and the vertical ultra-large reaction frame adopts a double-beam four-column preload frame structure; the five array distributed hydraulic actuator groups are distributed around the horizontal ultra-large reaction frame and at a top of the vertical ultra-large reaction frame, and the linear distributed hydraulic actuator group is located at a center of a bottom of the vertical ultra-large reaction frame.

The invention relates to the field of water cloth wrapping and unwrapping machines, in particular to an automatic water cloth wrapping and unwrapping machine running state monitoring method and system. The method comprises the following steps: acquiring observation data at a target moment; constructing a window, constructing a coordinate system by taking the serial number of the sampling moment in the window as an abscissa and the rotating speed as an ordinate, and calculating the initial noise probability of the target moment according to the position of the sampling moment in the coordinate system; constructing the rotating speed at each sampling moment in the window into a rotating speed sequence, constructing the traction speed at each sampling moment in the window into a traction speed sequence, and calculating a correction factor according to the rotating speed sequence and the traction speed sequence for correcting the initial noise probability to obtain a final noise probability; and determining the filtering length of the observation data according to the final noise probability, and filtering the observation data by using Vondrak to complete state monitoring. According to the technical scheme, the accuracy of the monitoring result of the running state of the water cloth wrapping and unwrapping machine can be improved.

The invention relates to the technical field of sensors, and discloses a detection and management method and system for purification equipment. The method comprises the following steps: carrying out three-phase coupling dynamic modeling on a gas phase flow state, a particle phase movement track and a sensor phase interface characteristic in the purification equipment to obtain a gas mass transfer characteristic parameter; performing response characteristic analysis on a real-time detection signal of a sensor array in the purification equipment to obtain a sensor calibration compensation parameter; based on the sensor calibration compensation parameters and the gas mass transfer characteristic parameters, deposition kinetics analysis of pollutants on the surface of the sensor is carried out, and equipment operation state evaluation data are obtained; and performing spatial layout optimization on the sensor array according to the equipment operation state evaluation data and the gas flow consistency constraint condition to obtain an optimal layout scheme and generate a staged adaptive management control instruction. According to the invention, self-adaptive adjustment and intelligent switching of the sensor management strategy are realized, and the detection performance and reliability of the purification equipment sensor system are significantly improved.

The invention discloses a coal mill fault detection system based on an intelligent deep learning technology and a method thereof. The system and the method are suitable for state monitoring and fault diagnosis of coal mill equipment in the iron and steel industry. According to the system, an improved Diffusion model is adopted to generate diversified fault data, and a fault classification model is constructed based on a ResNet structure, so that continuous real-time monitoring and intelligent diagnosis of coal mill equipment are realized. The system uses vibration signals and PLC data collected by a sensor to generate diversified new data for training a downstream classification model. The system can identify various fault types such as bearing damage, coupler damage and foundation looseness, and provides an alarm prompt through a real-time early warning mechanism, so that the operation reliability and the maintenance efficiency of equipment are remarkably improved. The intelligent fault detection system has the advantages of being stable in data generation, high in fault recognition accuracy and high in application scene adaptability, the problem of scarcity of fault data is solved, and an efficient solution is provided for a predictive maintenance strategy in the field of steel manufacturing.

A large-scale underground shield docking model test platform and a test method using the same are provided. The test platform includes a test soil tank, a partition wall, a shield docking model, and a freezing system. The test soil tank is of an underground foundation pit structure, the partition wall is disposed in the test soil tank to separate the test soil tank into a filling area and a non-filling area. The shield docking model is disposed in the filling area and is a reduced-scale test model. The shield docking model is provided with the freezing system, including a refrigeration system, a coolant circulation system, and freezing pipes. The freezing pipes are arranged on the shield docking model and are connected to the refrigeration system through the coolant circulation system. The refrigeration system is placed on a laminate in the non-filling area of the test soil tank.