9862 results about "Mechanical equipment" patented technology

The invention discloses an electromechanical equipment noise reduction method based on machine learning, and relates to the field of electromechanical equipment noise reduction, and the method comprises the steps: collecting sound pressure and vibration signals during the operation of electromechanical equipment, and collecting the operation state data of the electromechanical equipment; sparse reconstruction is carried out on the noise features; carrying out sound source localization on the reconstructed noise features by using a beam forming algorithm to obtain a noise distribution diagram; constructing a noise reduction model based on deep learning to obtain noise signals after noise reduction; extracting the frequency of the noise signal in a preset frequency range through a peak search algorithm; analyzing the acquired running state data by using a time-varying linear predictive coding algorithm TVLPC to obtain a noise frequency offset caused by the abnormal running state of the electromechanical equipment; generating an optimal solution by using a particle swarm algorithm; and according to the optimal solution of noise control, noise dominant frequency components are extracted for separation, and a plurality of phase-reversal noise reduction control signals are obtained. Aiming at low noise reduction precision of electromechanical equipment in the prior art, the noise reduction control precision is improved.

The invention discloses a fault diagnosis method and system based on a multi-scale adaptive hypergraph convolutional neural network, and belongs to the technical field of fault diagnosis. The method comprises the following steps: acquiring original vibration data of mechanical equipment, and carrying out data preprocessing; constructing a fault diagnosis model based on the multi-scale adaptive hypergraph convolutional neural network; and inputting the vibration data after data preprocessing into the constructed fault diagnosis model, and outputting a fault diagnosis result. According to the method, a vibration signal is decomposed into different frequency bands, features under various scales are extracted respectively, prior knowledge is fused, local topological graphs are constructed through various weighting measures, and a plurality of local topological graphs jointly form a spatial hypergraph to serve as input of a model; a dual-channel graph convolutional network is introduced to extract features, weights of different channels are adaptively distributed through an attention mechanism, and deep fusion of the features is realized. Experiments on two reference data sets show that the method can effectively improve the fault diagnosis accuracy, and has good stability and generalization.

The invention relates to the technical field of project management, in particular to a BIM-based building project resource scheduling optimization method, which comprises the following steps: based on the current progress state of a construction task, collecting the consumption of constructors, mechanical equipment and materials, comparing the task plan progress with the real-time progress, screening out construction links with the backward task progress, and establishing a construction project resource scheduling optimization model; and judging a resource type, and establishing a task progress offset. According to the method, the construction progress is accurately monitored, real-time evaluation of resource consumption is achieved, waste and scheduling lag are avoided, the construction path is optimized by means of the BIM model, the key path monitoring precision is improved, progress management and control effectiveness is ensured, key path progress deviation is analyzed, the resource shortage risk is recognized, stable supply of construction resources is guaranteed, and on the basis of the influence of resource shortage, the construction efficiency is improved. The resource distribution sequence is optimized, the resource utilization efficiency is improved, the supply chain delivery cycle is matched, the resource scheduling is optimized, the influence of resource supply and demand imbalance on the construction progress is reduced, and the continuous and stable construction progress is ensured.

The invention provides an intelligent mechanical equipment fault diagnosis method fused with a machine learning algorithm, and the method comprises the steps: obtaining a multi-source signal, and generating a complete machine-level anomaly detection result through dynamic threshold comparison and machine learning anomaly detection model processing; the multi-source signal comprises a vibration signal, a temperature signal and a pressure signal; performing signal decomposition and multi-source parameter fusion analysis processing based on a complete machine level anomaly detection result, and determining a physical subsystem of an anomaly source; time-frequency features are extracted from the operation data of the physical subsystem, and a part-level anomaly positioning result and a microdefect classification result are generated through hierarchical machine learning model processing; and performing multi-granularity data fusion processing based on a complete machine level anomaly detection result, a physical subsystem and a microdefect classification result, and generating a hierarchical diagnosis report. By adopting the method, progressive diagnosis can be carried out on the mechanical equipment hierarchically, and the accuracy of fault diagnosis is improved.

The invention belongs to the technical field of signal processing, and particularly relates to a multi-source data fusion fault diagnosis method and system based on the DS evidence theory, and the method comprises the steps: firstly collecting the multi-source information of mechanical equipment, including vibration, image and sound data, respectively calculating the corresponding characteristic indexes, carrying out the dimension reduction processing of the indexes through employing a PCA algorithm, and carrying out the fault diagnosis of the multi-source information. And carrying out fault diagnosis by adopting a Bayesian fault diagnosis model, and carrying out fault fusion diagnosis by adopting a DS evidence theory after a diagnosis result of each sensor is obtained. The method can comprehensively consider the characteristics of various data, extract more effective fault indexes, realize more comprehensive fault identification, fuse the diagnosis results of a plurality of sensors, ensure the final diagnosis accuracy, and is suitable for fault diagnosis of various complex mechanical systems.

The invention relates to the technical field of construction management, in particular to a BIM-based building construction management system, which comprises a path compression module, a priority scheduling module, an equipment allocation module, a material arrangement module and a progress coordination module. According to the method, the BIM model construction path node set is obtained, the node compression condition is judged based on the labor resource availability index of the construction scheduling schedule, high coordination of the construction path and resource configuration is achieved, the problems of resource mismatching and construction efficiency reduction are avoided, and the construction efficiency is improved based on the density comparison between the space area to which the component belongs and the construction equipment in the area. Reasonable allocation of construction equipment is achieved, full utilization of mechanical equipment is guaranteed, structural hierarchical indexes, space coordinates and construction logic dependency relationships are associated through component identification numbers, a construction feasible dependency graph and material entering batch sorting are formed, and a material entering time window is accurately determined; coordination conflicts between material approach and storage area arrangement are mitigated.

The invention belongs to the technical field of fault monitoring, and relates to a multi-source information fusion equipment fault monitoring device and a state evaluation method. According to the method, the possible abrasion problem of equipment is deduced from the time-varying characteristics of multiple information sources of equipment vibration signals, abrasive particle characteristics and lubricating oil physical and chemical characteristics, the time-varying rule in an equipment tribology system is researched, the equipment abrasion health state evolution process and the dynamic evolution mechanism are disclosed, and the equipment abrasion health state can be determined under the real working condition. A method of organically combining multi-source monitoring information feature fusion and an equipment friction wear mechanism is adopted, and a mechanical equipment comprehensive health state evaluation platform is developed, so that effective identification of performance degradation behaviors of multiple types of mechanical equipment is realized, and a maintenance decision is reasonably made by deducing the change condition of reliability. Reliability and safety of full-life-cycle service are guaranteed, and evaluation and early warning of equipment abrasion related faults are achieved.

The invention belongs to the technical field of crawler agricultural mechanical equipment, and discloses a posture-adjustable crawler chassis and a control method.The posture-adjustable crawler chassis comprises a chassis frame, a driving transmission assembly, a posture adjusting assembly, a crawler walking wheel assembly and a posture detecting assembly; the output end of the track gauge auxiliary assembly is in universal transmission connection with the track walking wheel assemblies, the posture adjusting assembly is arranged below the chassis frame, one side of the posture adjusting assembly is connected with the track walking wheel assemblies, the posture detecting assembly is arranged on the chassis frame and the track walking wheel assemblies, and the two ends of the track gauge auxiliary assembly are installed on the track walking wheel assemblies on the two sides respectively. When the crawler encounters the situation that the road surface gradient is large or the road surface fluctuation is large, the posture position of the current chassis frame is detected through the posture detection assembly, and the ground clearance or the gravity center position of the chassis frame is adjusted by calculating and controlling the posture adjusting assembly. Meanwhile, the bottoms of the crawler walking wheel assemblies are always attached to the ground by driving the transmission assemblies, and the adhesive force of the crawler to the ground is improved.

The invention relates to a mechanical equipment fault data identification method based on artificial intelligence, and belongs to the technical field of data processing and artificial intelligence. The problems of insensitive signal processing, insufficient feature extraction, significant noise interference, low model training efficiency and the like in fault diagnosis in the prior art are solved. According to the method, a self-adaptive normalization method based on energy density is provided, the influence of non-stationary signals is effectively inhibited, and key features are reserved; through mixed energy entropy feature extraction and inter-band energy jump penalty terms, the sensitivity to a complex fault mode is significantly enhanced; constructing a fault feature enhancement strategy based on a Gaussian potential well, and reinforcing the response of a fault feature accumulation area; a weighted loss function and a gradient directional correction mechanism are adopted, so that the robustness and accuracy of the model are improved; and in combination with an entropy weighted learning rate and a covariance scaling strategy, adaptive training is realized, and the convergence speed and adaptability are improved. According to the method, the precision and efficiency of mechanical fault diagnosis are improved, and support is provided for industrial intelligent development.

The invention relates to the technical field of mining mechanical equipment, and particularly discloses a mining mechanical equipment anomaly detection system and method. Current signals, voltage signals and vibration signals in the operation process of mining mechanical equipment are obtained; the method comprises the following steps: extracting an equipment operation current-voltage correlation feature vector and a mining mechanical equipment time frequency correlation feature vector, fusing to obtain a mining mechanical equipment anomaly classification feature vector, and obtaining a classification result through a classifier to indicate whether the mining mechanical equipment is abnormal or not, thereby realizing anomaly detection of the mining mechanical equipment.

The invention discloses a mechanical equipment multi-state selective maintenance method based on deep learning, and relates to the technical field of mechanical equipment intelligent prediction maintenance, and the method comprises the following steps: deploying a sensor at mechanical equipment to collect operation state data in real time, collecting maintenance operation logs and historical fault diagnosis record text data, and storing the collected maintenance operation logs and historical fault diagnosis record text data; generating a multi-source data stream through timestamp alignment, and preprocessing the multi-source data stream to obtain a preprocessed multi-modal data set; according to the method, the multi-modal data collected for the mechanical equipment has comprehensiveness and availability of data sources, the utilization rate of heterogeneous data is improved, the multi-modal feature data is fused by using the cross-modal association mapping model, hidden information can be mined, the invisible state can be recognized, the equipment operation condition is clearly mastered, and the method is suitable for popularization and application. And through combination of the STCN and the Weibull-deep learning hybrid model, the residual life of the mechanical equipment can be dynamically and accurately predicted.

The present invention relates to the field of real-time monitoring, and discloses a remote real-time monitoring system for on-site construction scenes, including a video monitoring module, a sensor and environment monitoring module, an intelligent safety early warning module, an equipment monitoring module and a personnel positioning module; the video monitoring module collects image data of the construction site in real time through a high-definition camera and intelligent monitoring equipment; the sensor and environment monitoring module is used to deploy temperature and humidity, wind speed, air pressure, noise, and vibration environment monitoring sensors to collect environmental data of the construction site in real time; the intelligent safety early warning module combines artificial intelligence and machine learning technology to analyze the video and sensor data of the construction site in real time and automatically identify potential safety hazards; the equipment monitoring module monitors the operating status of construction machinery and equipment through the Internet of Things technology; the personnel positioning module tracks the location and behavior of construction personnel in real time through wearable devices, positioning sensors or RFID tags. The present invention has the advantage of improving construction management efficiency.

The invention belongs to the technical field of agricultural mechanical equipment, and discloses a well cellar type zero-speed punching transplanter and a well cellar hole forming method.The well cellar type zero-speed punching transplanter comprises a rack and two zero-speed transplanting devices, the zero-speed transplanting devices are driven by a transmission device, and each zero-speed transplanting device comprises a double-crank floating type punching mechanism and a seedling throwing mechanism; the double-crank floating type punching mechanism comprises a horizontal frame, the horizontal frame extends in the advancing direction of the advancing wheels, a double-crank assembly is arranged between the horizontal frame and the rack, a floating type hole head assembly is installed on the horizontal frame, and a hole head limiting assembly is jointly arranged between the floating type hole head assembly and the horizontal frame; upright zero-speed efficient punching is achieved through the double-crank floating type punching device, the problem that a well cellar hole is oval due to relative movement between a well cellar hole head and a soil ridge is solved, the well cellar hole punching quality and efficiency of the transplanter are improved, and the effects of no impact and low noise in the punching process are achieved.

The invention provides a rotary mechanical equipment fault identification method and device based on vibration data, and belongs to the field of vibration monitoring. The method comprises the following steps: acquiring a component information library of to-be-monitored rotary mechanical equipment; fault information corresponding to the component information base is determined, the fault characteristic frequency of each fault is calculated, and a fault characteristic frequency model corresponding to the component information base is established; collecting a vibration signal characteristic value of each component in the component information base in real time; extracting a vibration signal characteristic value, and matching the frequency domain characteristic value with the fault characteristic frequency based on a component information base and a fault characteristic frequency model to obtain a matching result; positioning an abnormal feature in the signal based on the time domain feature value and the geometric feature value; and identifying the fault type and degree of the mechanical equipment according to the matching result and the abnormal features. According to the mechanical equipment vibration monitoring data processing method and device, potential faults of the equipment can be found as soon as possible, and the equipment can be maintained in time.

The present invention relates to the technical field of electrical connectors, and in particular to an anti-loosening electrical connector for electromechanical equipment, comprising a coaxially plugged core barrel and a piercing barrel, wherein a plurality of cables are clamped on the core barrel, and the ends of the plurality of cables extend along the radial direction of the core barrel and are evenly distributed around the circumference of the core barrel, and the piercing barrel is provided with a plurality of piercing pieces, one end of the piercing piece is provided with a clamping groove, and the interior of the clamping groove is provided with a cutting section, a pressing section and a conductive section in sequence from bottom to top, the cutting section is used for cutting the insulating sheath of the cable, the pressing section is used for clamping the insulating sheath of the cable, and the conductive section is used for conductive connection with the wire core of the cable, and a clamping ring is rotatably provided on the piercing piece, and the clamping ring is U-shaped; in the initial state, the center line of the clamping ring coincides with the center line of the clamping groove, and the clamping ring can be clamped with the insulating sheath of the cable; the clamping ring can be rotated 90°, so that the insulating sheath is in closer contact with the wire core.

The invention belongs to the technical field of a computer system based on a specific calculation model, relates to a virtual and real dynamic interaction-based digital twin bearing fault diagnosis method, and aims to solve the problem that a bearing system in large rotary mechanical equipment is large in fault hidden danger and avoid serious consequences caused by faults. A generally used bearing is in a high-precision standard, so that system signal data under bearing fault operation is not easy to collect in a factory, and the unbalanced data sample distribution directly leads to finiteness of fault diagnosis in a real scene only based on a big data driving mode classification method, so that the system signal data cannot be easily collected in the factory. The method has good scene working condition self-adaption, can be applied to fault diagnosis of a bearing system in different industrial scenes, fully excavates limited industrial data information and rich physical priori knowledge, and ensures safe and reliable operation of an important rotating mechanical system through a digital twinning technology.

The invention relates to the technical field of agricultural mechanical equipment, and provides a laser weeding method of a weeding robot and the laser weeding robot, the weeding method comprises the following steps: obtaining weed information, and selecting a weeding mode based on the weed information; laser parameters are adjusted based on the selected weeding mode, and path planning is carried out; controlling the weeding robot to advance according to the planned path, controlling the laser beam to align with weeds to perform weeding operation, and continuously feeding back the real-time position of the laser beam; and comparing the real-time position with a predetermined target position, and maintaining the current position or adjusting the current position based on a comparison result. According to the method, the laser parameters can be adjusted based on the weeding mode, the path can be planned, and the mode can be suitable for various complex weeding environments and can adapt to the weeding environments better, so that weeds are accurately and rapidly removed.

The invention discloses a scheduling method for crushing station multi-machine cooperative control based on edge calculation, and belongs to the technical field of mining mechanical equipment automation control. All devices in the crushing station serve as edge nodes; and the edge node communicates with other edge nodes to share the result and perform multi-machine cooperative control to pre-judge the running state and fault of the equipment. According to the method, efficient operation of the crushing station multi-machine equipment under different working conditions can be kept, self-adaption and cooperative work can be achieved, continuous improvement of dynamic scheduling and control strategies can be achieved, and global optimization of equipment starting and stopping and speed optimization is ensured. Meanwhile, historical data and real-time data are utilized to the maximum extent through reinforcement learning, continuous improvement of equipment fault prediction and state maintenance is achieved, and non-planned downtime is shortened. The innovation and practicability of the multi-machine cooperative control and scheduling method of the mine crushing station remarkably improve the production efficiency and economic benefits of the system.

The invention provides a mechanical equipment fault diagnosis model construction method, a diagnosis method, a system and equipment, and relates to the technical field of mechanical fault diagnosis, and the fault diagnosis method comprises the steps: obtaining the vibration signal data of a rotating machine in real time; and inputting the vibration signal data into the fault diagnosis model to obtain the fault type of the rotating machine. According to the method, the specific fault type of the rotating machine can be accurately identified, so that measures can be taken in time for repair or maintenance, further deterioration of faults is effectively avoided, equipment safety is protected, production efficiency is improved, and maintenance cost is reduced.

The invention discloses a fault prediction and health management system for a belt conveyor, relates to the technical field of intelligent monitoring and management of mechanical equipment, and aims to solve the problems of fault recognition lagging and high misjudgment rate caused by insufficient consideration of real-time performance and high reliability required by industrial equipment. Motor current fluctuation, roller temperature change rate, belt tension distribution and vibration spectrum data are acquired in real time through a data acquisition unit; the fault feature extraction unit extracts abnormal features based on wavelet transform, and calculates an operation stability index through a principal component analysis method; the risk assessment unit combines the abnormal features, the abrasion loss and the stability index, and adopts fuzzy logic reasoning to judge the fault risk level; and the intelligent regulation and control unit dynamically regulates and controls the operation state or gives an alarm according to grading processing, so that accurate early warning and intelligent response of early faults of the equipment are realized.

The invention provides a domain generalization-based unsupervised abnormal sound detection method and system for mechanical equipment, and the method comprises the steps: constructing an SE-ECA fusion attention mechanism module based on a global context, constructing an MFCC branch network architecture and a Log-Mel branch network architecture, adding the modules to each branch network architecture, and carrying out the detection of the abnormal sound of the mechanical equipment. Audio feature extraction is carried out on the MFCC spectrogram features and the Log-Mel spectrogram features respectively; embedding the audio features extracted by the two branch network architectures into a channel dimension for merging to form a comprehensive feature vector, and classifying the comprehensive feature vector; an anomaly detection model is constructed and trained, the anomaly detection model uses a Gaussian mixture model to calculate a global anomaly score, then uses a local anomaly factor to detect local anomaly, and then performs weighted averaging, so that normal sound features can be automatically learned, anomaly can be detected, and the anomaly detection performance of equipment is improved.

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 welding clamping mechanisms, and particularly relates to a seamless steel pipe welding device for mechanical equipment machining, which comprises a machining platform, a sliding seat is slidably mounted on the machining platform, a supporting plate is arranged above the sliding seat, a lifting plate is arranged on the side wall of the supporting plate, and a sand plate slidably sleeves the lifting plate. A welding gun is further arranged on the machining platform. Two clamping mechanisms are arranged on the machining platform and used for clamping and fixing a steel pipe. A vertical grinding mechanism is arranged on the supporting plate. By arranging the vertical grinding mechanism and the transverse grinding mechanism, burrs on a steel pipe can be ground and removed in the horizontal direction and the vertical direction at the same time, and the seamless welding effect is improved; and by arranging the washing mechanism, water can be sucked in a reciprocating mode and sprayed to the sand plate, so that the burr removing work is smoother, and the ground burrs can be washed away.

The invention discloses a mining mechanical equipment type selection management control platform based on a network, and relates to the technical field of mining mechanical equipment, and the control platform comprises a data collection module, a data processing and analysis module, and a type selection recommendation module. Multi-source heterogeneous data from a mining area geological exploration department, an equipment sensor, a satellite remote sensing image and a meteorological station are ingeniously fused, and the data are effectively integrated together through normalization processing and weight distribution, so that a comprehensive and accurate information basis is provided for subsequent model selection recommendation; the data processing and analysis module further performs cleaning, classification, coding and feature extraction on the data, a quantitative relation model between working condition factors and equipment performance is established, and the intelligent data analysis and processing capability enables the platform to perform quantitative analysis according to actual working condition requirements of a mining area. And equipment models and parameters with high adaptability and lowest full life cycle cost are accurately recommended.

The invention provides a digital mechanical equipment data management system based on cloud computing. The digital mechanical equipment data management system comprises a data acquisition module for acquiring equipment state and environment information; the feature coding module is composed of a gating circulation unit and a multi-layer perceptron and is used for extracting high-dimensional features; the feature fusion module fuses the high-dimensional features and outputs sparse joint codes; the cloud computing model construction module constructs a model and introduces a trainable enhancement layer; the state interaction module enables the code to obtain an advanced interaction state based on a model; the state synchronization module synchronizes a device state and environment information through MechLayer; the implicit state generation module obtains an implicit state by combining normalization and enhanced connection; operating an environment perception modeling module to improve perception estimation modeling; the perception learning vector generation module obtains a learning vector; the state prediction module predicts an operation state; and the track decoding module processes the input to obtain an equipment running track. The equipment management efficiency and the operation reliability can be improved.

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 provides a hilly and mountainous area tracked vehicle intelligent chassis system and algorithm based on environmental perception and road surface recognition, and belongs to the technical field of agricultural mechanical equipment. The system comprises a mechanical structure part and a control system part. According to the algorithm, multi-modal sensing and fusion decision making of the mobile terminal are achieved, the FPN + ResNet50 algorithm, the IPM image inverse perspective algorithm, the 3D sparse convolutional graph neural network, the TSDF truncation signature distance algorithm, the MDU algorithm, the gating modal sensing convolutional network and the Pi-Sigma fuzzy neural network learning algorithm are innovatively combined, the recognition precision and the decision making reliability are guaranteed, and meanwhile, the fusion decision making efficiency of the mobile terminal is improved. And the calculation amount and the calculation cost are reduced, and the recognition efficiency is improved, so that more accurate, timely and reliable recognition perception and feedback control are realized. The algorithm comprises the steps of image acquisition, image feature extraction, feature fusion, feature comparison and the like, and the method is not only suitable for field operation of tracked vehicles in complex hilly and mountainous areas, but also suitable for transfer driving of the tracked vehicles in a working environment.

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 double-row steel sheet pile cofferdam construction method, which comprises the following steps of: before construction, measuring the original riverbed elevation of a cofferdam piling area, organizing steel pipes to enter a site according to a cross-section diagram and a construction sequence of a cofferdam, timely removing and recording if stone blocks and the like at the river bottom are measured in the measurement process, and before construction, checking and maintaining all mechanical equipment, so that the construction efficiency is improved. After entering the field, the steel pipes are stacked according to specifications, varieties and classifications, before pile conveying, the models and the number of the steel pipes used during construction are determined, and the steel pipes are arranged strictly according to the designed section. Compared with a construction scheme of adopting a Larsen steel sheet pile cofferdam and a temporary construction platform, the construction cost, the rental cost, the earthwork transportation cost, the earthwork compaction cost, the earthwork filling excavation filling cost and the like of temporary facilities can be saved, building construction is carried out in a water area needing a large area, the construction period is long, and the construction cost is low. The method is more economical and reasonable when being used for constructing structures in water under the conditions of high water level and the like.

The invention discloses a mechanical equipment lightweight intelligent fault diagnosis method based on a Kolmogorov-Arnold network, and the method comprises the steps: firstly obtaining original vibration monitoring data of mechanical equipment under different fault types, and constructing an equipment vibration monitoring fault data set; a KAN-based fault diagnosis model is constructed, and a learnable nonlinear function is adopted to replace a fixed linear weight in convolution; then, the output is converted into a one-dimensional feature vector through KAN convolution operation; inputting the one-dimensional feature vector into a KANLinear layer so as to realize the mapping of the high-dimensional feature to different fault types; then calculating training loss, taking the total loss as an optimization target of a training stage, and updating model parameters by using a gradient descent method; iteratively optimizing the fault diagnosis model; and finally, testing the fault diagnosis model by using the test set. According to the method, high diagnosis precision can be maintained, meanwhile, the model parameter quantity is remarkably reduced, the calculation cost is reduced, and therefore the overall calculation efficiency is improved.