4068 results about "Mechanical equipment" patented technology

The invention discloses a railway engineering mechanical equipment energy consumption digital monitoring method and system, and relates to the field of railway construction equipment energy consumption management, and the method comprises the steps: obtaining the energy consumption data of each piece of to-be-selected railway engineering mechanical equipment in the operation process; preprocessing the energy consumption data in an edge processing unit to obtain a standardized feature vector; on the basis of the feature vectors, through a working condition recognition model, recognizing the current working condition of each piece of to-be-selected railway engineering mechanical equipment, and generating a working state labeling sequence; before operation task scheduling, unit energy consumption of each piece of to-be-selected railway engineering mechanical equipment is predicted based on the three-dimensional coupling atlas and the target task type, and task allocation optimization suggestions are output; and determining target railway engineering mechanical equipment based on the task allocation optimization suggestion, and outputting control parameters of the target railway engineering mechanical equipment. The key problems of energy consumption monitoring lag, working condition identification difficulty, unreasonable multi-equipment task allocation and the like in the prior art are effectively solved.

The invention belongs to the technical field of mechanical equipment wear state monitoring. The method comprises the following steps of: extracting wear rate characteristics, wear degree characteristics, cutting index characteristics, fatigue index characteristics and oxidation index characteristics in an abrasive particle sample image, and respectively clustering to obtain five groups of discrete stage labels; carrying out normalization processing on the five groups of discrete stage labels to obtain a normalized data matrix; according to the data matrix, weights corresponding to all the features are calculated in multiple modes, and the average value of the weights calculated in the multiple modes serves as the final weight of all the features; according to the final weight of each feature and the normalized data matrix, calculating a comprehensive score sequence of all the abrasive particle sample images; performing clustering according to the comprehensive score sequence to obtain a plurality of optimal clustering centers, and determining a current wear stage according to the optimal clustering centers; and more comprehensive and more accurate division of the wear stages is realized.

The invention discloses an electrostatic induction and optical imaging principle-fused online monitoring device and method for wear particles of a water-lubricated bearing, and belongs to the field of state monitoring of mechanical equipment. The device comprises a square insulation detection cavity, a detection electrode array, an electromagnetic shielding shell, a high-speed optical imaging module and a signal processing unit, the square insulation detection cavity is installed in a bearing lubrication water loop. The detection electrode array is fixed to the outer wall of the cavity and used for capturing electrostatic signals generated when the abrasion particles pass through. The electromagnetic shielding shell wraps the detection cavity and is grounded through a wire to eliminate environmental electromagnetic interference. The high-speed optical imaging module integrates a high-magnification microscope lens and a CMOS sensor, is coaxially mounted with the detection cavity, and obtains the morphology characteristics of the wear particles in real time; and the signal processing unit performs multi-modal data fusion analysis on the electrostatic signal and the optical image. Through cooperative detection of electrostatic induction and optical imaging, the monitoring precision and reliability of the wear particles of the water-lubricated bearing are remarkably improved.

The utility model discloses an auxiliary tension bracket for slitting and cutting a silicon steel coiled material, which belongs to the technical field of slitting machine tension brackets, and comprises a slitting machine, a cutter arranged on the slitting machine, support frames arranged at one end of the slitting machine and oppositely distributed, and a support seat arranged between the two support frames, limiting rollers and positioning blocks used for supporting are distributed at the upper end of the supporting base at equal intervals, supporting plates are fixedly installed at the upper ends of the positioning blocks, and an adjusting mechanism is arranged between the two supporting frames. A metal plate is conveyed to the supporting plate through the slitting machine, the servo motor is started to drive the two-way lead screw to rotate, the oppositely-distributed movable plates are made to be close to each other till the limiting rollers are close to the side edge of the metal plate, and at the moment, the slitting machine continues to convey the metal plate. And the tension bracket is fast to adjust, manual approaching is not needed, and potential safety hazards caused by approaching of workers to mechanical equipment are avoided.

The invention relates to the technical field of signal processing and state monitoring, in particular to an intelligent lubricating state monitoring method based on oil-impact pulse, which comprises the following steps of: constructing a'fluid-solid-heat-electricity 'multi-field coupled particle double-electric-layer electrostatic model, and forming a particle electrostatic characteristic analysis and monitoring method; oil physical and chemical property parameters and mechanical equipment transmission system gear impact pulse characteristics in different lubrication states are collected in real time, an oil-impact pulse-lubrication state correlation model is constructed, and a gear transmission system lubrication state monitoring method is formed in combination with a signal processing method and a deep learning algorithm. According to the invention, the real-time performance and comprehensiveness of lubrication state evaluation are enhanced, and the work and maintenance efficiency of equipment is also improved.

The invention discloses a mechanical equipment residual service life prediction and uncertainty quantitative analysis method and device. The method comprises the following steps: acquiring multi-dimensional time sequence sensor data generated by mechanical equipment to be predicted in an operation process; inputting the multi-dimensional time sequence sensor data into a pre-trained physical constraint Bayesian neural network model; wherein the physical constraint Bayesian neural network model comprises a segmented bidirectional long short-term memory network feature extraction module, a hierarchical gating recursive regression network degradation dynamic modeling module and a Bayesian reasoning and physical constraint fusion regression module which are connected in sequence; the segmented bidirectional long-short-term memory network feature extraction module is used for processing input multi-dimensional time sequence sensor data and outputting hidden feature vectors representing local degradation dynamics of equipment; the hierarchical gating recursive regression network degradation dynamic modeling module is used for processing hidden feature vectors and capturing global degradation dynamic features through a complex numerical value hidden state updating mechanism; the Bayesian reasoning and physical constraint fusion regression module is used for carrying out Weibull distribution parameter regression based on the global degradation dynamic characteristics, introducing a deep implicit physical residual constraint and outputting a probability distribution parameter of the residual service life; and based on the probability distribution parameters, generating a residual service life prediction result and uncertainty quantitative information of the mechanical equipment.

The invention belongs to the technical field of mechanical equipment wear state monitoring. The invention provides a gearbox health state stage identification method and system based on oil characteristics, and the method comprises the steps: carrying out the preprocessing of an obtained oil abrasive particle image, and extracting the oil characteristics according to the preprocessed oil abrasive particle image; according to the oil characteristics and a pre-trained long-short-term memory neural network model, obtaining a gearbox health state classification result; training of a long-short-term memory neural network model: fusing the oil characteristics of each oil abrasive particle image sample to obtain a one-dimensional health factor, and obtaining a health index sequence according to the one-dimensional health factor of each oil abrasive particle image sample; and carrying out breakpoint detection on the health index sequence to obtain three breakpoints, and adding health state labels to all the oil abrasive particle image samples according to the obtained breakpoints. According to the method, accurate division of the wear stages and capture of the evolution trend can be automatically completed, and finally decision judgment exceeding artificial experience is formed.

The invention provides a battery replacement control method and a battery replacement system for electric mechanical equipment, and the method comprises the steps: controlling a battery replacement mechanical arm to lift a battery pack to be replaced from a battery cabin to a preset position I after the connection of the battery replacement mechanical arm and the battery pack is completed; the battery replacement mechanical arm is controlled to move the battery pack needing to be replaced from the first preset position to a battery recycling area of the battery replacement platform, and meanwhile instruction actions for controlling the battery replacement mechanical arm are recorded; the battery transposition platform is controlled to exchange positions of the new battery pack to be replaced and the battery pack to be replaced, so that the new battery pack to be replaced is moved to the position of a battery recycling area; the battery replacing mechanical arm is controlled to reversely execute the recorded instruction action, and the new battery pack to be replaced is placed at the first preset position; the battery replacing mechanical arm is controlled to move the new battery pack to be replaced from the first preset position into the battery bin; according to the invention, automatic battery replacement of the electric engineering machinery can be realized, the battery replacement efficiency and the battery replacement accuracy are improved, safety accidents are avoided, and the environmental adaptability of the battery replacement equipment is improved.

The invention discloses a fan transmission chain vibration intelligent diagnosis method based on a power sub-box, and relates to the technical field of mechanical equipment state monitoring and fault diagnosis, and the method comprises the steps: S1, obtaining a multi-channel vibration signal, collecting an original data flow from the front and rear end positions of a main shaft at the input end and the output end of a gearbox through a sensor, the method comprises the following steps: carrying out power binning on original data according to a power change range during operation of a fan, respectively storing the data in different power intervals, establishing an independent time synchronization reference, and calibrating sampling clock skew of each channel by adopting a clock synchronization algorithm to obtain a preliminary synchronization signal sequence under a unified time reference; according to the intelligent diagnosis method for the vibration of the fan transmission chain based on the power sub-boxes, the fault diagnosis accuracy, the real-time performance and the interpretability of the fan gearbox under the variable power operation condition are remarkably improved, and reliable technical support is provided for health management and intelligent operation and maintenance of wind power equipment.

The invention discloses a mechanical equipment fault intelligent diagnosis method and system based on a bidirectional time sequence convolutional network and an attention mechanism, and the method comprises the steps: collecting a vibration signal of mechanical equipment, and carrying out the preprocessing of the vibration signal through employing a variational mode decomposition algorithm optimized by an improved Hemma optimization algorithm; training a mechanical equipment fault diagnosis model by adopting the sample data set with the label to obtain a trained mechanical equipment fault diagnosis model; the model comprises a bidirectional time sequence convolution module fusing multi-head self-attention, a feature fusion module, a channel attention module, a global average pooling module, a full connection module and a Softmax function which are connected in sequence. And inputting the preprocessed vibration signals into the trained mechanical equipment fault diagnosis model to obtain mechanical equipment fault category probability distribution. According to the invention, noise reduction, feature enhancement and equipment fault mode accurate identification of the vibration signal under a high-noise background can be realized.

The invention provides a performance testing device and method for relay protection, belongs to the technical field of circuit testing equipment, and solves the technical problems that the working intensity of operation and maintenance personnel is high due to the fact that an existing road is rugged and equipment is heavy, the temperature is high in summer, and mechanical equipment and the maintenance personnel are overheated due to the too high temperature. The performance testing device for relay protection comprises a moving box and an equipment cavity formed in the moving box, a mounting frame is rotationally connected in the equipment cavity, a relay protection tester is arranged on the mounting frame, a limiting plate is slidably connected to the mounting frame, and a regulation and control assembly for controlling the limiting plate to move is arranged on the mounting frame. And the limiting plate and the inner wall of the mounting frame clamp and fix the relay protection tester, and the bottom of the moving box is slidably connected with a driving box. The device has the advantages that the mobility of the device is achieved, the damping effect is achieved during moving, vibration to the device is reduced, and wind power can be generated to cool the device when the device is transferred and advances.

The utility model provides a packaging carton opening structure, which belongs to the field of packaging mechanical equipment and comprises a carton storage warehouse, one side of the carton storage warehouse is provided with a carton opening assembly used for taking out a carton from the carton storage warehouse and opening the carton, and the carton storage warehouse comprises a carton storage table. The box storage table is provided with a box placing station, a box taking station and a conveying assembly for conveying the cartons from the box placing station to the box taking station, box placing and box taking are separated in position through the box storage warehouse, and the box opening assembly can supplement the cartons at the box placing station in the box taking process; and after the cartons at the carton taking station are consumed, the cartons at the carton placing station are conveyed to the carton taking station through the conveying assembly, so that the cartons are quickly supplemented, the conflict between carton placing and carton taking is avoided, continuous carton taking and packaging can be realized, and the production efficiency is improved.

The invention provides a bidirectional synchronous gripper and a gripping method thereof, and relates to the technical field of mechanical equipment.The bidirectional synchronous gripper comprises a base, a guide rail is arranged on the surface of the top of the base, a first connecting block is slidably connected to one side of the guide rail, a second connecting block is slidably connected to the other side of the guide rail, a mounting hole is formed in the surface of the base, and an air cylinder is fixed in the mounting hole; the tail end of a piston rod of the air cylinder is connected with a first connecting block, a first rack is arranged on the side, facing the second connecting block, of the first connecting block in an extending mode and meshed with a gear, and a second rack is meshed with the opposite side, deviating from the first rack, of the gear and fixedly connected with the second connecting block. Clamping assemblies are arranged on the top face of the first connecting block and the top face of the second connecting block correspondingly, the technical problem that long-stroke two-way synchronization precision is insufficient is solved, and the technical effects that long-stroke automatic synchronous clamping is achieved, the overall structure is simplified, and the device is suitable for industrial heavy-load grabbing are achieved.

The invention relates to the technical field of mechanical equipment fault diagnosis, and particularly discloses an attention mechanism CNN-based electric actuator fault feature extraction method and system. The method comprises the following steps: through a mode of combining simulation modeling and field acquisition, obtaining a fault feature of an electric actuator; obtaining multi-source data of the electric actuator in a normal state, a known fault state and an unknown fault state, and preprocessing and enhancing the multi-source data; and constructing a lightweight convolutional neural network model containing a cross-modal attention mechanism. According to the method, multi-source signals such as current, voltage and displacement can be effectively fused, weights of different modes and characteristics are automatically distributed, and the extraction capability of key fault characteristics is enhanced; accurate distinguishing between known faults and unknown faults is realized through multi-scale feature complexity measurement and a dynamic discrimination mechanism; in combination with domain self-adaption and an incremental learning mechanism, the model can migrate between simulation and real working conditions, and has self-expansion capability for a novel fault mode.

The invention relates to the technical field of road traffic management and maintenance, in particular to a method for configuring ice and snow removing and traffic keeping mechanical equipment in a regional road network, which comprises the following steps of: formulating a standard road section length standard for configuring ice and snow removing mechanical equipment; dividing the roads into different grades based on the requirements of unobstructed and unobstructed road maintenance of different road sections in the regional road network; corresponding ice and snow removing operation time limit requirements are provided according to different grades of roads and different snow conditions; according to the regional characteristics, selecting proper conventional ice and snow removing mechanical equipment types; determining the configuration number of ice and snow removing equipment based on the equipment operation parameters and the actual condition of the road; and in combination with different snow conditions and different grades of roads, an applicable ice and snow removal operation combination mode is calculated, and the number of operation combination tables is determined. According to the method, the configuration number of the equipment is determined by combining the operation parameters of the equipment with the actual condition of the road, so that the allocation of the equipment is more scientific and reasonable, the problem of resource waste is reduced, and the unobstructed and smooth capability of the road in winter is improved.

The invention relates to the field of grain storage mechanical equipment, in particular to a folding wing spiral cleaning machine which comprises a grain scraping machine and a walking chassis, the grain scraping machine is arranged on the walking chassis, the folding wing spiral cleaning machine further comprises a folding wing assembly, and the folding wing assembly is rotationally connected to the side face of the grain scraping machine and used for conveying materials to a feeding port of the grain scraping machine; the folding wing assembly and the grain scraper form a first use state and a second use state, and in the first use state, the folding wing assembly is perpendicular to the walking direction of the bin cleaning machine; in the second use state, the folding wing assembly is parallel to the walking direction of the clearance machine; in the idle state, the shell of the folding wing assembly is attached to the walking chassis and can flexibly penetrate through key passing areas such as a doorway and a channel, during material conveying, the driving motor is started to enable the driving wheel to drive the shell and a connecting structure of the shell to generate angle deflection, meanwhile, the driving motor drives the spiral blade to rotate through the driving shaft, and then grains are conveyed towards the grain scraper. The operation area of the whole machine can be greatly increased, and the original grain discharging effect can be achieved without frequent movement of the whole machine.

The invention discloses a structure cross-domain damage identification method based on a pulse graph neural network. The method is suitable for structure health monitoring in the fields of civil engineering, mechanical equipment and the like. According to the method, structure dynamic response signals are collected through a sensor array, a graph structure is constructed, the graph structure is converted into space-time pulse graph data through pulse coding, and the space-time pulse graph data are input into a pre-trained pulse graph neural network for feature extraction and recognition. According to the method, the low power consumption and event-driven characteristics of the spiking neural network are creatively combined with the spatial topology modeling capability of the graph neural network, and a domain adversarial transfer learning mechanism is introduced, so that the problem of generalization of the model in cross-domain scenes of different structures, different environments and the like is effectively solved. According to the method, accurate positioning and quantitative evaluation of damage can be realized, and the method is particularly suitable for knowledge migration and long-term online monitoring from a laboratory model to a real structure.

The present invention discloses a method for detecting abnormalities in mechanical rotating equipment under variable working conditions based on feature alignment residual GAN. The method comprises: constructing a time-frequency graph from rotating machinery data, reconstructing the time-frequency graph using an encoder GE(x) and a decoder GD(x) respectively combined with an autoencoder network of a residual module ResNet, and using a discriminator D to distinguish the authenticity of the reconstructed data and the source data to determine abnormal data. The loss function of the network contains an adversarial loss L. adv , context loss L of the generator reconstruction data error con , encoder loss L enc , and the loss L of Wasserstein distance for constructing ranking parity data classification for eigenvalue alignment w‑gan The present invention has a good effect on detecting abnormalities of rotating machinery under variable working conditions, can detect abnormalities of rotating machinery equipment in advance, ensure the long-term stable operation of industrial systems, and avoid the occurrence of serious accidents, which has important social significance.

The invention relates to the technical field of mechanical equipment fault diagnosis, and discloses a fault diagnosis method and system for an automatic tensioning control valve of a scraper conveyor. The method comprises the following steps: acquiring pressure and flow data of a main flow channel and an auxiliary flow channel through a sensor array, and extracting frequency domain characteristics to obtain a standardized frequency spectrum; detecting parameter coupling abnormity based on the spectrum cross correlation coefficient and generating an abnormal index; environment interference and medium viscosity data are fused, and non-linear feature distribution and correction deviation representing the mechanical state are obtained through classification and filtering analysis; matching the correction deviation with a preset fault mode, determining a fault type, and generating a fault positioning coordinate in a three-dimensional space by using an optimization algorithm; and finally, performing fluid dynamic simulation through the high-fidelity virtual model to verify the coordinate stability, and outputting accurate early warning when conditions are met. Through cooperation of multi-source information fusion and an intelligent algorithm, the accuracy of early-stage internal fault diagnosis of the complex double-flow-channel valve is improved.

The utility model discloses a chip frame removing structure. The chip frame removing structure comprises an X-axis module, an auxiliary sliding rail parallel to the X-axis module, a Y-axis module erected between the X-axis module and the auxiliary sliding rail, a Z-axis module vertically arranged on the Y-axis module, and a frame removing unit arranged on the Z-axis module. According to the chip frame removing structure provided by the invention, mechanical equipment is effectively utilized to replace manual work, so that the consumption of human resources is greatly reduced, the chip frame removing efficiency and effect are effectively improved, and the development and progress of enterprises and industries are well promoted.

The utility model discloses a coral sand particle size and shape double sorting device which comprises a plurality of sorting systems, a sorting system fixing support and a screening machine, and the sorting systems comprise a particle size sorting system, a flaky particle sorting system and a block-shaped and rod-shaped particle sorting system which are sequentially connected from top to bottom. The screening machine is compatible with an existing standard inspection screening machine and is simple in structure, complex mechanical equipment does not need to be introduced on the basis that a traditional particle size sorting function is reserved, the screen is customized according to needs during use, and the equipment transformation cost is remarkably reduced; the device disclosed by the utility model can be used for simultaneously screening various sample particles in different target particle size ranges and respectively screening the sample particles in different target particle size ranges in a sheet shape, a block shape and a rod shape.

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

The invention relates to the technical field of mechanical equipment fault detection, and discloses an equipment fault diagnosis method based on domain generalization and attention enhancement, comprising the following steps: step 1, preprocessing acoustic signals from multi-source domain equipment; step 2, constructing a dual-channel feature decoupling network composed of a machine feature encoder and a health feature encoder; 3, introducing a channel-space attention mechanism at the output end of the health feature encoder, and performing weighted enhancement on the health state features; step 4, constructing a multi-objective joint optimization function including signal reconstruction loss, feature redundancy suppression loss and causal aggregation loss; and 5, adopting a dynamic domain adaptive training strategy based on classification loss weighting and a class imbalance optimization method. According to the method, the expression ability of weak fault features and the identifiability of multi-domain data are improved, key fault features are effectively strengthened, and a redundant interference area is inhibited.

The invention discloses an air compressor rotor testing method and device, and relates to the technical field of air compressor rotor testing. Vibration signal data of a rotor at multiple rotating speeds are collected, a fast Fourier transform algorithm is adopted for analysis, frequency and phase distribution is extracted, weighted analysis is carried out on the phase distribution, and the frequency and the phase distribution are extracted; and extracting the coordinates of the initial position with uneven mass distribution to realize accurate positioning of the position with unbalanced mass of the rotor. Fitting the offset by using a least square method to obtain a mass deviation coordinate, generating an initial counterweight adjustment scheme, optimizing initial counterweight parameters by combining a gradient descent algorithm to obtain an optimized counterweight adjustment scheme, and re-iterating the counterweight parameters of the optimized counterweight adjustment scheme by using a particle swarm optimization algorithm to obtain a target counterweight scheme. And the vibration signal of the rotor meets the vibration balance standard. According to the invention, the dynamic balance test efficiency and precision of the air compressor rotor are improved, and the operation stability of rotary mechanical equipment is ensured.

The invention discloses a high-rotating-speed hydraulic generator rotor bolt looseness detection method and system, belongs to the technical field of hydropower station high-rotating-speed rotating mechanical equipment detection, and aims at solving the technical problems that hydraulic generator rotor bolt looseness detection is low in efficiency and poor in precision, real-time monitoring cannot be achieved, and safe and stable operation of a hydropower station is seriously affected. According to the invention, the image of the rotor bolt area is collected, and the image is accurately processed by using an image correction algorithm, so that image distortion is eliminated; thirdly, fusing an improved YOLOv8 target detection technology, a DeepLabV3 + semantic segmentation technology and a DeepSort multi-target tracking technology, and accurately extracting angle features of the bolt marking line; and then, a looseness identification objective function is constructed based on the angle change of the bolt, the function is solved in real time, and a detection result is output. According to the invention, high-precision, non-contact and real-time bolt looseness detection is realized, the detection efficiency and reliability are effectively improved, and a solid guarantee is provided for safe operation of a hydropower station.

The invention provides a mechanical equipment structure key pressure-bearing component real-time simulation method based on digital twinning, which takes a cubic press hinge beam as an example, and combines finite element analysis, Latin hypercube sampling, a K nearest neighbor algorithm, Gaussian interpolation and an RBF (Radial Basis Function) proxy model to realize stress-strain rapid prediction and three-dimensional visualization. According to the method, an efficient prediction model is established through structure database construction, dimension reduction processing, neighbor search and interpolation calculation, a simulation result is presented in real time by utilizing Python and Unity interaction, the design efficiency and accuracy are improved, and the method is suitable for structure optimization analysis under complex working conditions.

The utility model relates to a cast iron base with a dust removal function and a grinding and polishing workstation, and belongs to the field of mechanical equipment. The dust removal base comprises a base body and is structurally characterized by further comprising a dust removal system which is matched with the base body. A threading groove is formed in the bottom of the base main body, a dust removal groove is formed in the top of the base main body, a grating net plate is arranged above the dust removal groove, a sedimentation tank and a water storage tank are further arranged at the top of the base main body, a filter plate is arranged between the sedimentation tank and the water storage tank, the dust removal groove is located above the sedimentation tank, and the grating net plate is located above the water storage tank. A liquid level sensor is arranged in the reservoir, a first flushing slope and a second flushing slope are arranged on the two sides of the dust removal groove correspondingly, and a first flushing groove and a second flushing groove are formed above the first flushing slope and the second flushing slope correspondingly; the first flushing tank and the second flushing tank are both matched with a circulating mechanism in the dust removal system.

The invention discloses a method suitable for whole-process traceability management of large-scale mechanical equipment, and belongs to the technical field of traceability management of mechanical equipment. The problem that a closed-loop data link is not realized in the whole process due to equipment identity counterfeiting and part replacement is solved. According to the technical scheme, the method comprises the following steps that S1, an NFC electronic lead seal composite identity recognition method is adopted; s2, a whole-process closed-loop tracing data link construction method; s3, establishing a multi-party collaborative authority level-to-level management model; and S4, the intelligent early warning algorithm for state monitoring is utilized to carry out safety level judgment. The method has the beneficial effects that the problems of equipment identity counterfeiting and part replacement can be solved; the full-process closed-loop data link realizes full-life-cycle transparent management; the intelligent early warning algorithm improves the equipment safety risk prevention and control capability.

The invention discloses a bearing fault diagnosis method based on wavelet time-frequency coding and a convolution visual converter. The method comprises the following steps: collecting vibration signals of a bearing under different working conditions; performing continuous wavelet transform on the one-dimensional vibration signal to generate a two-dimensional time-frequency diagram with high time-frequency resolution; carrying out coding and image enhancement on the time-frequency graph through an enhanced color mapping mode; inputting the time-frequency graph into a convolution visual converter model, and realizing local and global combined extraction of vibration features through convolution projection, a self-attention mechanism and a multi-stage feature fusion structure; the features are classified to identify a state of health or type of fault of the bearing. According to the method, the diagnosis precision and robustness in a complex working condition and high-noise environment are effectively improved, and the method is suitable for state monitoring and early fault early warning of various types of rotary mechanical equipment.

The invention relates to the technical field of mechanical equipment intelligent operation and maintenance and state monitoring, and discloses a health monitoring method for bearing state evaluation and residual life and degradation trend prediction, which comprises the following steps of: firstly, extracting time domain, frequency domain and time-frequency domain characteristics from original vibration signals of a bearing under different working conditions; according to comprehensive evaluation indexes and known bearing degradation characteristics, features having good characterization capability and trend consistency for bearing degradation performance are screened out, a novel backbone network model is constructed, deep features are mined, effective features are enhanced, meanwhile, a time dependency relationship in a long sequence is captured, and then a multi-task learning mechanism is introduced, so that the bearing degradation performance is evaluated. Through parameter sharing and joint optimization, bearing state identification, residual life prediction and performance degradation trend prediction can be synchronously completed only by training and deploying a single model. According to the method, multi-task collaborative prediction under complex working conditions is realized, and the accuracy and robustness of bearing state recognition and service life prediction are improved.