3044 results about "Mechanical equipment" patented technology

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 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 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 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 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 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 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 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.

The utility model relates to the technical field of sludge treatment equipment, and particularly discloses a sludge composting aeration device. The forced ventilation device comprises a forced ventilation device, a roller, a power device and a striker plate, the roller is driven by a power device to rotate, one end of the roller is connected with a forced ventilation device through a rotary connector, a plurality of winding areas are arranged on the roller, each winding area is correspondingly provided with an air conveying hose, one end of each air conveying hose is fixedly connected with the side wall of the roller, and exhaust holes are evenly formed in the pipe wall of each air conveying hose. After fermentation is completed, the air conveying hose can be pulled away from a material stack through the roller, then mechanical equipment can be conveniently used for carrying out operations such as loading or pile turning on materials, the production efficiency is high, and damage to parts of the aeration device during operation of the mechanical equipment is avoided. The sludge composting aeration device can wind and unwind the gas conveying hoses, and the arrangement positions and intervals of the gas conveying hoses are flexible and adjustable, so that the sludge composting aeration device has good flexibility and applicability.

The invention discloses a rotating machine fault diagnosis method based on improved VMD and an energy operator, and belongs to the field of rotating machine fault diagnosis. In order to solve the defects that in the prior art, VMD decomposition parameters depend on empirical setting, and optimal IMF screening basis is insufficient, the technical scheme provided by the invention comprises the following steps: performing global optimization on penalty factors and modal decomposition number in variational modal decomposition by using a grey wolf optimization algorithm, and determining an optimal parameter combination; decomposing the collected vibration signal to obtain an intrinsic mode component; respectively calculating kurtosis and an envelope harmonic noise ratio for the intrinsic mode component, and selecting the component with the maximum fusion index as the optimal component; performing Hilbert transform on the optimal component, and calculating an energy signal; and performing fast Fourier transform on the energy signal to obtain a spectrogram and identify a fault characteristic frequency. The method is suitable for automatic identification and state monitoring of early faults of rotary mechanical equipment in industrial production.

The invention provides a coal mine crawler-type full-hydraulic underground drill rig, and belongs to the technical field of drilling mechanical equipment, the coal mine crawler-type full-hydraulic underground drill rig comprises a crawler chassis, the upper end of the crawler chassis is provided with a driving mechanism, an angle adjusting mechanism and a height adjusting mechanism, the height adjusting mechanism is provided with a mounting table, and the upper end of the mounting table is provided with a drilling mechanism; the driving mechanism comprises a lifting plate, a rotating shaft is rotatably mounted in the lower end of the lifting plate, and a rotating gear is arranged on the circumferential surface of the rotating shaft; by means of the arranged driving mechanism, a single servo motor serves as core power, power is distributed to an angle executing mechanism, a height executing mechanism and a drilling executing mechanism according to needs through a liftable rotating gear and a precise toothed plate transmission system, a redundant structure formed by stacking a plurality of drives is abandoned, the whole drilling machine driving system is highly integrated in layout, and the drilling efficiency is improved. The size is greatly reduced, the device perfectly adapts to a narrow tunnel space, and meanwhile the manufacturing cost is reduced.

The invention relates to the technical field of agricultural mechanical equipment, and provides a bimodal microwave soil disinfection device and a control method.The device comprises a chassis system, a soil conveying assembly and a disinfection assembly; the soil conveying assembly is pivotally arranged at the front end of the chassis system through a hinge mechanism, and a soil shoveling structure is arranged at the far end of the soil conveying assembly; the disinfection assembly comprises a soil conveying mechanism, a first microwave generation structure and a second microwave generation structure, the inlet end of the soil conveying mechanism is connected with the soil conveying assembly, and the outlet end of the soil conveying mechanism extends to the rear end of the chassis system; the first microwave generating structure is arranged above the soil conveying mechanism; the second microwave generating structure is arranged below the soil conveying mechanism; the two microwave generating structures are configured to be opened and closed simultaneously. Displacement disinfection of soil is achieved through the first microwave generation structure, in-situ disinfection of deep soil is achieved through the second microwave generation structure, and the disinfection uniformity and the disinfection depth are improved.

The invention provides an engineering mechanical equipment remote monitoring system and method based on the Internet of Things, and relates to the technical field of equipment operation monitoring, and the method comprises the steps: carrying out the cross-scale feature response of a dynamic impact signal of engineering mechanical equipment through an intelligent calculation node, and obtaining a frequency domain migration feature and an energy attenuation feature; carrying out gradient operation on the operation thermal field data of the engineering mechanical equipment to obtain heat flow evolution characteristics; utilizing a coupling analysis model dynamics interaction map; performing space-time resonance convolution prediction on node states and edge relations of the dynamic interaction atlas based on the displacement track to obtain a state prediction curve of the engineering mechanical equipment; and according to the state prediction curve and a preset scheduling instruction, an adaptive feedback network is constructed at an edge computing node of the Internet of Things, and then remote monitoring scheduling is performed on the engineering mechanical equipment based on the adaptive feedback network, so that operation state prediction and scheduling closed-loop control of the engineering mechanical equipment can be realized; therefore, the remote monitoring stability of the engineering mechanical equipment is improved.

The invention provides a drilling and blasting method tunnel section contour and blast hole position automatic design method, and relates to the technical field of tunnel engineering, and the method comprises the following steps: S1, determining the type of a tunnel section contour; s2, performing parameterized representation and automatically drawing a contour line based on the tunnel section contour type; s3, based on the drawn tunnel section contour, conducting parameterized arrangement of cut holes, peripheral holes, bottom plate holes and auxiliary holes, and generating coordinates of various blast holes; automatically designing blast hole positions through different types of tunnel section contours; and the arrangement of the blast holes is converted into the generation of coordinate points, so that the requirements of intelligent mechanical equipment for tunnel excavation in the drilling and blasting method on the coordinates of the blast holes are met, and finally, the intelligent and refined blast hole arrangement of a tunnel excavation site in the drilling and blasting method is realized. The problems that in the contour drawing and blast hole arrangement link, manual lofting is often low in efficiency and large in error are solved, and it is avoided that the excavation section deviates from the design, the over-excavation or under-excavation phenomenon occurs, and the construction quality is affected.

A fault diagnosis method for a rolling bearing of a steel belt elevator traction machine belongs to the field of mechanical equipment detection and fault diagnosis, firstly, a KAN structure is applied to a convolutional neural network to replace a traditional linear convolution kernel, so that KAN convolution is used as a network main body, parameter efficiency is ensured, and overall flexibility and adaptability of the network are improved; secondly, a GCA-ACA double-branch fusion attention module is provided, and fault features are learned by capturing feature differences and non-local operation, so that the feature representation capacity in the steel belt elevator traction machine under different working conditions is enhanced; and the FCDE method is used for dynamic enhancement of global feature key information so as to enhance the robustness of the network. According to the invention, bearing faults of the steel belt elevator traction machine under different working conditions can be accurately diagnosed.

The invention discloses a dual-mode control switching method for unattended reconstruction of a rotary drilling rig, and particularly relates to the technical field of mechanical equipment mode switching, and the method specifically comprises the steps of S1, switching request triggering and instruction freezing, S2, full-state snapshot generation and synchronization, S3, three-level progressive arbitration decision, and S4, dual-factor verification and switching execution. And S5, network fluctuation fault tolerance. According to the invention, through overall planning of the vehicle-mounted controller, after a switching request is received, an instruction is frozen, full-state snapshots including sensor data, operation links, equipment health and environment data are synchronized, and switching is completed through three-level arbitration including safety state judgment, authority network verification and target end confirmation and then double-factor recheck. Safe seamless switching between a remote mode and a local mode can be achieved, the situations of instruction conflicts and information faults are avoided, the operation efficiency is improved, fault-tolerant processing is carried out on scenes such as network fluctuation and emergency takeover, and the reliability and safety of unattended operation are further improved.

The invention discloses a bearing performance degradation type identification method based on a vibration signal tail attenuation rate, belongs to the technical field of mechanical equipment state monitoring and fault diagnosis, and aims to improve the prediction precision of the remaining service life of a rolling bearing and improve the applicability of an intelligent prediction model by effectively identifying the bearing degradation type. The system comprises a data acquisition and preprocessing module, a tail attenuation rate calculation module, a degradation point detection module and a degradation type identification module. Firstly, feature extraction and segmentation processing are carried out on an original vibration signal of the rolling bearing; then, histogram distribution of vibration signal amplitudes is constructed, a right tail area sample is extracted, and a tail attenuation rate index representing signal extreme fluctuation attenuation behaviors is calculated through logarithmic transformation and linear fitting processing; and finally, constructing a dual judgment criterion of ratio threshold value judgment and adjacent point difference value comparison, and realizing automatic identification and classification of three typical degradation modes of bell shape, horn shape and sudden deformation.

The invention relates to the field of mechanical equipment fault diagnosis and prediction, and discloses a mechanical equipment fault early warning system based on machine learning, which comprises a data acquisition unit for performing data acquisition on the operation state of mechanical equipment through a sensor to obtain an equipment operation state original data set; data preprocessing is carried out on the equipment operation state original data set, the data preprocessing comprises missing value filling, abnormal value elimination and noise filtering, and a standardized equipment operation data set is obtained; according to the method, adaptive weight distribution is carried out on the features based on typical operation mode labels of the equipment, dynamic weighting of the features can be realized, so that the fault prediction precision in different modes is improved, and a fused enhanced equipment feature representation set can be used for improving the fault prediction efficiency. Therefore, the model can better capture key features in equipment operation, and the robustness and accuracy of fault prediction are improved.

The invention relates to an automatic sock packaging assembly line, and belongs to the field of mechanical equipment. The device comprises a hook loading mechanism, the hook loading mechanism comprises a hook bin and a hook manipulator, the hook manipulator is matched with the hook bin, the device is structurally characterized by further comprising a feeding mechanism and a bagging mechanism, the hook loading mechanism is matched with the feeding mechanism, and the feeding mechanism is matched with the bagging mechanism; the feeding mechanism comprises a sock taking device, a folding device, a plate folding device, a feeding suction cup, a feeding conveying belt and a button punching machine, the sock taking device, the feeding conveying belt, the folding device, the feeding suction cup and the plate folding device are sequentially arranged in the conveying direction, and the button punching machine is matched with the feeding conveying belt; the folding device comprises a first folding air cylinder, a second folding air cylinder, a third folding air cylinder, a folding connecting rod and a folding connecting plate, the second folding air cylinder is installed on the first folding air cylinder, the third folding air cylinder is installed on the second folding air cylinder, the folding connecting rod is connected with the third folding air cylinder, and the folding connecting plate is connected with the folding connecting rod.

The invention discloses a livestock mechanical fault monitoring system based on edge calculation, and relates to the related technical field of mechanical fault monitoring, and the system comprises a mechanical transmission network building module which is used for building a multi-layer mechanical transmission network; the computing power configuration module is used for carrying out computing power configuration according to the criticality, the transmission dependency degree and the fault propagation influence of the node equipment and building a cloud-side platform structure; the monitoring execution link obtaining module is used for connecting a mechanical equipment sensing network to obtain a monitoring execution link; and the fault abnormal information identification module is used for dynamically distributing edge tasks and identifying fault abnormal information according to the change characteristics of the acquired data. The technical problem of poor mechanical fault monitoring and identification precision caused by insufficient real-time performance, weak fault propagation analysis capability and unreasonable computing power resource allocation of animal husbandry mechanical fault monitoring in the prior art is solved, and the technical effects of efficiently and accurately identifying faults, dynamically optimizing edge computing power and reducing the load of a cloud platform are achieved.

The invention discloses a mechanical equipment residual life prediction method based on multiple expert models, and belongs to the technical field of mechanical equipment. Comprising the steps of receiving an original mechanical vibration signal, performing preprocessing, constructing a residual life prediction model of the mechanical equipment, training the residual life prediction model of the mechanical equipment based on a preset data set, inputting a mechanical vibration signal to be detected into the trained residual life prediction model of the mechanical equipment, and performing fault category prediction of the mechanical equipment. According to the method, the accuracy, generalization and practicability of residual life prediction of mechanical equipment are remarkably improved, and through multi-scale time-frequency domain feature fusion and a double attention mechanism, the identification capability and noise immunity of the model to a complex fault mode are enhanced; the domain adaptive routing network realizes cross-device efficient knowledge migration, greatly improves the prediction performance on unseen devices, has high precision and high real-time performance, and provides reliable technical support for industrial predictive maintenance.