3558 results about "Industrial equipment" patented technology

Disclosed in the present invention are an adaptive deep transfer fault diagnosis method and system, an apparatus and a medium. The method comprises the following steps: S1: collecting vibration acceleration signals of industrial equipment under different working conditions, and dividing same into a source domain data set and a target domain data set; S2: building a self-tuning universal domain adaptive fault diagnosis model, which comprises a shared feature extractor, a known classifier and a plurality of unknown classifiers; S3: separately calculating a classification loss of known faults of the source domain, a discriminative loss of the plurality of unknown classifiers, a target domain soft consistency regularization loss and an information entropy maximization loss; S4: introducing a dynamic weighting strategy based on model uncertainty assessment to optimize the model parameters; and S5: using the model for diagnosis. The present invention can fully mine valid information in data, can establish reliable class decision boundaries, and in addition, uses the self-tuning dynamic update strategy to adjust weightings corresponding to different loss functions, thus allowing for quick generalization of the model to different industrial diagnosis scenarios.

The invention belongs to the technical field of equipment management, and discloses an industrial equipment fault prediction and health management method based on multi-sensor fusion, and the method comprises the steps: obtaining multi-source sensing data of industrial equipment, carrying out the signal decoupling analysis, and obtaining a decoupling characteristic spectrum; performing frequency domain conversion and modulation analysis to form a multi-dimensional characteristic spectrum system; analyzing the modal correlation of the multi-dimensional feature pedigree to obtain a fault feature mapping network; a mixed time sequence prediction model is constructed, residual life prediction and degradation trend evaluation are carried out, and an equipment health trend graph is obtained; establishing a health state evaluation index system, and performing reliability evaluation to obtain an equipment health state report; and generating a maintenance decision suggestion, and realizing real-time anomaly detection and maintenance suggestion pushing through edge calculation. Through multi-sensor data fusion and advanced analysis technologies, early warning and accurate prediction of industrial equipment faults are realized, and the operation reliability and production efficiency of the industrial equipment are remarkably improved.

The invention discloses an industrial equipment fault prediction method based on multi-modal data, and belongs to the technical field of specific calculation models, and the method comprises the steps: carrying out the preprocessing according to the collected multi-modal data of the operation of industrial equipment, so as to unify the format of the multi-modal data, and obtaining the structural data; extracting features of the structured data one by one according to data categories, and obtaining a multi-modal fusion feature through a dynamic fusion mechanism; according to the multi-modal fusion features, a fault prediction classification score is obtained through a deep neural network model to perform fault prediction; and when the drift parameter of the multi-modal data is greater than a preset threshold value, performing incremental training on the deep neural network model through the multi-modal data collected in real time to update parameters of the deep neural network model. Through multi-modal data unified processing, dynamic feature fusion, deep neural network modeling and an online learning mechanism, the problems of insufficient multi-modal data fusion, prediction uncertainty quantization deficiency, poor model adaptability and the like are solved.

The invention relates to the technical field of automatic industrial control, and discloses an automatic industrial equipment plc data acquisition monitoring control method and system. The method comprises the following steps: receiving a multi-source PLC data stream, and processing the multi-source PLC data stream through a multi-modal data fusion model to generate an equipment health degree evaluation matrix; constructing a dynamic control strategy generation model, and outputting a multi-target optimization instruction set; simulating the interaction influence of the equipment group by utilizing a process chain collaborative constraint model, and optimizing real-time control parameters of an instruction set; parameters are adjusted in parallel through a distributed edge computing framework, and an equipment cooperative control signal is output to an industrial monitoring center. The system comprises a distributed data acquisition module, a multi-modal fusion calculation module, a dynamic strategy generation module, an edge cooperative control module and an industrial communication gateway. According to the invention, multi-source data can be efficiently processed, equipment health can be accurately evaluated, a control strategy is optimized, equipment collaboration is realized, and industrial production efficiency and energy utilization rate are improved.

The invention discloses an attention mechanism and LSTM fused industrial bearing vibration time sequence signal fault prediction method and system. The method comprises the following steps: collecting a bearing vibration signal and carrying out filtering, noise reduction and normalization preprocessing; constructing a deep learning model combining the bidirectional BiLSTM and a coordinate attention mechanism to extract bidirectional time sequence features and enhance key fault features; carrying out model training by adopting a multi-target composite loss function and an Adam optimizer, and introducing an early stop mechanism to prevent overfitting; performing fault type identification and degree evaluation on the real-time vibration signal by using the trained model, and performing quantitative analysis by fusing multi-scale spectrum kurtosis features and nonlinear kinetic parameters; and finally, outputting a fault diagnosis report, and triggering multi-stage early warning based on an adaptive threshold. The method can realize high-precision and high-reliability bearing fault prediction and health state evaluation, and is suitable for intelligent operation and maintenance of industrial equipment.

A conditional temporal diffusion model-based method and apparatus for generating a time series of an industrial device, including: acquiring parameter indicator data for the time series of the industrial device; using a noise at a target time instant in a target Gaussian noise distribution as an initial variable of the time series; inputting the parameter indicator data and the initial variable into a noise prediction model constructed based on a conditional temporal diffusion model, to obtain a predictive noise output by the noise prediction model; denoising the predictive noise according to the initial variable, to obtain a target variable of the time series located at a previous time instant of the target time instant; and inputting the target variable and the parameter indicator data into the noise prediction model for an iteration, to generate the time series of the industrial device.

The invention discloses an industrial data real-time acquisition monitoring system fusing edge computing and 5G, and belongs to the technical field of industrial Internet of Things. The system is composed of a multi-source heterogeneous data acquisition module, an edge computing node cluster, a 5G communication network and a cloud analysis platform, an edge-cloud collaborative architecture is innovatively adopted, a multi-protocol adapter is integrated to realize unified access of heterogeneous data of industrial equipment, a low-delay transmission channel is constructed by using a 5G network slicing technology, and the heterogeneous data of the industrial equipment is transmitted to the cloud analysis platform. And transmitting the preprocessed data to the edge computing node in parallel. And the edge layer realizes dynamic resource scheduling by adopting a containerization technology, and realizes millisecond-level response and local decision feedback. And meanwhile, through an edge-cloud data synchronization mechanism, a distributed time sequence database is constructed, and visual monitoring and deep analysis of multi-dimensional data are supported. The system improves the real-time processing capability of industrial field data and the reliability of the system, and has the technical advantages of low time delay, high concurrency and optimized resource utilization rate.

The invention discloses an industrial equipment fault early warning analysis method and system based on a graph neural network, and relates to the technical field of intelligent fault diagnosis and equipment state monitoring, and the method comprises the steps: constructing a time slice data tensor, and forming a graph neural network input structure; on the basis of the graph neural network input structure, feature learning and correlation analysis of the graph neural network are executed, and node-level embedding representation is obtained; and based on the node-level embedding representation, calculating fault relevance response distribution of the nodes, evaluating propagation risks and positioning equipment fault sources. According to the method, the problem that a traditional method cannot effectively express a spatial structure or a cooperative relationship between measuring points in an early modeling stage is solved, the spatial perception capability of overall modeling is improved, so that a data foundation is laid for subsequent feature extraction and anomaly analysis, and the reconstruction accuracy is improved by comparing an embedded reconstruction value with an original observation value and calculating a node-level reconstruction error. Fault influence range evaluation and traceability positioning are realized, and quantitative support is provided for early warning response and maintenance decision.

The invention relates to the technical field of inspection or monitoring, in particular to an industrial equipment anomaly detection method and system based on machine vision, and the method comprises the following steps: obtaining a multi-channel pixel matrix of an industrial equipment surface image, calculating a pixel gradient and a texture direction change rate, and building a high-dimensional feature space. According to the method, the high-dimensional feature space is constructed by calculating the pixel gradient and the texture direction change rate, so that abnormal feature points are clearly distributed, the misjudgment probability is reduced, the shrinkage factor is dynamically adjusted based on the projection offset, the detection standard is optimized along with data change, and the adaptability is improved; the local gradient change rate and the texture direction change amplitude are combined to carry out offset correction, the precision is enhanced, image block division is subdivided according to structural features, the complex structure recognition capability is improved, local abnormal feature and overall feature relevance calculation is combined with weight correction, the global accuracy is enhanced, sensitive weights are introduced in abnormal point screening, and the judgment precision is improved; and the anomaly detection credibility is enhanced.

The invention relates to an electric hand drill wear state prediction and health management system, which belongs to the technical field of intelligent fault diagnosis and predictive maintenance of industrial equipment, and comprises a data acquisition and preprocessing unit used for acquiring and processing a multi-modal physical signal to generate a standardized data frame; the multi-domain feature transformation unit is used for receiving the standardized data frame and transforming the standardized data frame into a health feature vector and a load feature vector; the dynamic health baseline construction unit is used for reconstructing and generating a dynamic health baseline through a depth generation model according to the time sequence of the health feature vector and the load feature vector; and the residual error sequence generation and statistical monitoring unit is used for calculating the distance between the health feature vector and the dynamic health baseline, generating a residual error sequence, and performing statistical processing on the residual error sequence to obtain a statistical magnitude. According to the invention, the interference of working condition change on health state assessment is eliminated, and pure and reliable data input is provided for subsequent accurate monitoring.

The invention discloses an industrial equipment fault prediction system based on federated learning and a privacy protection method, and relates to the field of industrial equipment fault prediction. The data acquisition preprocessing module extracts fault features through compressed sensing downsampling, screens and uploads the fault features; the federal learning training module adopts a layered architecture and a dynamic algorithm to schedule a learning rate; the fault prediction and diagnosis module constructs a space-time diagram neural network and fuses a physical model to improve generalization; the privacy protection security communication module performs homomorphic encryption storage and zero-knowledge proof verification update; the knowledge graph construction reasoning module constructs a dynamic graph, locates a fault root cause through causal reasoning, and supports cross-device knowledge migration. By adopting the quantum and federated learning technology, the industrial equipment fault diagnosis accuracy is high, the attack resistance is high, the encryption efficiency is greatly improved, the model training time is shortened, cross-equipment knowledge migration is realized, the operation and maintenance cost is reduced, and the intelligent operation and maintenance development of the industrial equipment is promoted.

The invention relates to an industrial equipment maintenance intelligent question-answering system based on multi-agent collaboration. Wherein the input unit is used for receiving text, voice, image or equipment scanning and other multi-mode user input information and analyzing the information into structured problem information; the scheduling unit performs semantic understanding and problem classification on the structured problem information based on the fine-tuned cross-language pre-training language model and an incremental training mechanism; the processing unit calls a corresponding domain agent according to the classification result, and generates an intelligent question and answer processing result including predictive maintenance suggestions, structured reply content and semantic annotation information; and the fusion unit fuses the local knowledge base, the graph database and the networking retrieval information, performs multi-hop semantic reasoning on the intelligent question and answer processing result, and generates multi-modal reply information including text description, image screenshots, prediction curves and recommendation links. The system can support multi-language and multi-mode intelligent question answering and predictive maintenance in a complex industrial maintenance scene.

The invention discloses an industrial equipment fault detection method fusing a complex relation and space-time dependence, and belongs to the technical field of industrial anomaly detection, and the method comprises the steps: constructing a plurality of adjacent matrixes, carrying out the weighted fusion to form an enhanced adjacent matrix, and comprehensively and accurately describing the complex multi-dimensional relation between industrial equipment; designing a spatial-temporal feature extraction module, extracting spatial features in parallel by using a graph convolutional neural network and a random graph attention network, extracting time features through time convolution and a multi-head attention mechanism, and dynamically fusing the spatial-temporal features by means of a gating mechanism to generate graph-level features; a state judgment layer composed of a plurality of node-level binary classifiers and a voting mechanism are adopted to comprehensively judge classification results of all nodes, so that the stability and reliability of judgment of the overall state of the industrial control system are enhanced, and the risk of misjudgment is reduced; the problems of equipment relation modeling and multi-dimensional information fusion are effectively solved, features are extracted and fused more accurately, and the accuracy and adaptability of anomaly detection are improved.

The invention relates to the technical field of data processing, in particular to an industrial environment data acquisition device and method, and the device comprises a sensing layer, an edge calculation layer, a cloud platform layer, and an application layer. Compared with a traditional scheme in which a single sensor is adopted to work independently, software synchronization is relied on, and signal conditioning parameters are fixed, the technical scheme of the invention realizes significant breakthrough through collaborative design of a multi-mode sensor array and an intelligent signal conditioning system, microsecond-level clock synchronization is realized by adopting a hardware-level PTP protocol, and the signal conditioning precision is improved. The program control amplifier is combined with the self-adaptive anti-aliasing filter, so that the conditioning circuit can be self-adaptive to the working condition change of equipment; meanwhile, nanosecond-level real-time processing of signals is achieved through an FPGA + ARM heterogeneous processor architecture, abnormal high-speed sampling is dynamically triggered in cooperation with an ARM, compared with a traditional scheme, the real-time performance is improved by three orders of magnitude, meanwhile, invalid data transmission is reduced by 80%, and a high-timeliness and low-redundancy intelligent monitoring solution is provided for early failure early warning of industrial equipment.

The invention discloses a multi-modal dynamic fusion and incremental learning fault diagnosis method for deep vertical shaft equipment, which belongs to the technical field of industrial equipment fault diagnosis, and comprises the following four steps of: constructing a pre-training large model to perform feature extraction, and relying on a multi-layer Transformer encoder and a dual loss function, establishing a multi-modal dynamic fusion and incremental learning fault diagnosis model; mining cross-modal universal fault features from vibration, temperature and current multi-modal time sequence data; according to the method, multi-modal features are fused, multi-modal association is constructed, modal weights are dynamically adjusted through a modal gating unit and a time delay compensation attention mechanism to adapt to signal quality changes, and meanwhile time sequence deviation is corrected to achieve accurate association; incremental learning is realized by using a decoupling projection layer, and a lightweight projection module is designed for a newly added fault task to suppress disastrous forgetting; network training is optimized, pre-training loss, incremental learning loss and attention regularization loss are integrated through a multi-objective loss function, and model stability and diagnosis precision are improved. The method has the advantage that the model stability and the diagnosis precision are improved.

The invention relates to a multi-type industrial equipment PLC control method and device based on a hardware abstraction layer, and the method comprises the steps: constructing the hardware abstraction layer comprising a core layer and an adaptation layer; the core layer is provided with a standardized interface set, the adaptation layer is provided with a special adaptation module, and a control protocol is converted through a protocol analysis engine. And generating a unified instruction set based on the standardized interface set, and establishing a data interaction channel supporting priority scheduling. And identifying the newly-added equipment, matching the equipment configuration library, and generating a newly-added adapter drive if the equipment configuration library fails. And acquiring a device state by using a data interaction channel, and constructing a cooperative control logic to realize linkage. Equipment load and task queues are monitored in real time, and dynamic task scheduling and grading fault processing are carried out. And when an adapter configuration updating instruction is detected, the adaptive layer thermally loads the updated drive, and unified, intelligent, efficient and stable PLC control of multiple types of industrial equipment is realized through cooperation of the core layer and the adaptive layer.

The invention discloses an industrial area atmospheric environment monitoring management system and method, and relates to the technical field of industrial Internet of Things environment monitoring, and the method comprises the steps: triggering polarization imaging to scan a target region based on the time-space information of an abnormal event signal, obtaining the polarization characteristic data of a pollution plume, and generating a spatial distribution characteristic parameter; inputting the sound pressure abnormal indexes and the spatial distribution characteristic parameters into an industrial equipment leakage causal model, calculating a pollution source confidence coefficient, and when the pollution source confidence coefficient reaches a process safety dynamic threshold value, obtaining an industrial equipment traceability result and a leakage level; according to the traceability result of the industrial equipment and the leakage level, a hierarchical control instruction is obtained, meanwhile, the actually measured attenuation rate of the industrial pollutants is calculated, and the dynamic response effect is verified; according to the method, cross-modal fusion modeling is carried out on acoustic abnormal information and pollutant spatial distribution characteristics, so that the technical limitation that pollution source positioning is carried out by depending on a static empirical formula traditionally is broken through.

The invention relates to the technical field of industrial equipment health monitoring and fault diagnosis, and discloses a centrifugal pump intelligent detection method and system based on multi-source data. The centrifugal pump intelligent detection method based on the multi-source data comprises the steps that centrifugal pump multi-source heterogeneous data are collected and preprocessed; extracting multi-scale features and constructing an optimal feature subset through hierarchical feature selection; constructing a multi-level dynamic Bayesian network to establish a probabilistic reasoning framework; calculating a feature credibility weight based on the signal quality, the feature stability and the diagnosis correlation; executing multi-source evidence fusion and Bayesian state reasoning; confidence self-calibration and parameter self-adaptive adjustment are realized through historical diagnosis result feedback; the method can effectively cope with the complex and changeable environment of an industrial site, can still keep stable diagnosis performance under the condition that the sensor part loses efficacy or the data quality is uneven, improves the accuracy and reliability of fault diagnosis, and provides powerful technical support for predictive maintenance of industrial equipment.

The invention relates to the technical field of industrial equipment state monitoring and intelligent fault diagnosis, and discloses an industrial fault diagnosis method and system based on natural language fault features and large model knowledge enhanced reasoning, and the method comprises the steps: obtaining a multi-source monitoring and state analysis result from detected equipment in an equipment operation stage; converting the features into a fault feature text of a structured natural language; carrying out vectorization coding on the fault feature text, executing similarity retrieval in a pre-constructed industrial fault knowledge base, and recalling knowledge fragments; and based on the fault feature text and the recall knowledge fragment, constructing a reasoning prompt word, inputting the reasoning prompt word into a large language model for knowledge enhanced reasoning, and generating a diagnosis result. According to the method, the problems that numerical evidence and semantic knowledge are difficult to unify, the knowledge coverage and updating cost is high, and cross-working-condition migration and conclusion consistency are insufficient in an existing intelligent diagnosis technology based on a rule base or a knowledge graph are effectively solved.

The invention discloses an industrial equipment intelligent operation and maintenance management system and method based on 5G-MOM, and belongs to the technical field of industrial internet and intelligent manufacturing. The system comprises a multi-source heterogeneous data acquisition layer deployed in industrial equipment, an edge computing node cluster based on 5G, a cloud intelligent analysis platform and a man-machine collaborative operation and maintenance terminal. The method comprises the following steps of collecting equipment vibration, temperature and current multi-dimensional working condition data in real time through a 5G network; performing data cleaning and feature extraction by using edge computing nodes, and constructing an equipment operation digital twin model; a cloud deep neural network is adopted to carry out fusion analysis on the multi-dimensional time series data, and self-adaptive diagnosis and residual life prediction of a fault mode are realized; a dynamic maintenance strategy is generated based on an MOM system, and field personnel are guided to execute precise maintenance through an AR terminal. According to the invention, 5G ultra-low time delay communication and an industrial mechanism model are creatively combined, and real-time visual management and predictive maintenance decision optimization of the equipment health state are realized.

The invention discloses an industrial equipment intelligent control method and system, and the method comprises the following steps: S1, collecting and preprocessing equipment operation data, and constructing a state data set; s2, establishing a trend prediction model by adopting Gaussian process regression, and outputting a state prediction value; s3, constructing a graph structure representation model, and combining a graph convolutional network and a semi-supervised learning method to carry out joint training and identify a working condition category and an operation level; s4, based on the equipment operation data, calculating a fuzzy membership degree and a fuzzy entropy index, and generating an entropy distribution curve; s5, fusing the state prediction value, the working condition category, the operation grade and the fuzzy entropy index to generate a state evaluation result; s6, adjusting control strategy parameters according to a state evaluation result, and issuing a control instruction to an equipment control system; and S7, collecting feedback data, updating the state data set, and realizing closed-loop iterative optimization. The method has the advantages of accurate prediction, stable identification, flexible evaluation, fast control, self-learning and the like, and is suitable for complex and changeable industrial application scenes.

The invention discloses an industrial equipment intelligent operation and maintenance method based on multi-source heterogeneous data dynamic collection and LSTM optimization, and relates to the technical field of industrial Internet of Things and industrial equipment intelligent operation and maintenance, and the method comprises the steps: collecting multi-source heterogeneous data, transmitting the multi-source heterogeneous data to an edge node, and carrying out the data preprocessing through a data preprocessing module; constructing a lightweight multi-modal LSTM model, and identifying and predicting the fault of the industrial equipment; through a self-adaptive threshold algorithm, differential weighted sampling and dynamic balance of energy consumption and precision, edge nodes realize real-time fault detection, and an operation and maintenance decision of local industrial equipment is generated; moreover, cross-factory cooperative training is carried out through a federal learning platform, and the generalization ability of the model is improved. Therefore, by the adoption of the industrial equipment intelligent operation and maintenance method based on multi-source heterogeneous data dynamic collection and LSTM optimization, the problem that response to hidden faults is lagged in a traditional method can be solved, optimal resource configuration in an industrial scene is achieved, and the operation and maintenance cost of an industrial Internet of Things terminal is reduced.

The invention discloses an actuator sealing performance test method and system, particularly relates to the technical field of industrial equipment sealing performance detection, and is used for solving the problem of positioning failure caused by signal aliasing in a multi-leakage scene in an existing method. Pressure attenuation frequency spectrum characteristics and leakage impact frequency spectrum peaks are extracted respectively by pressurizing a sealed cavity and synchronously collecting dynamic pressure and vibration signals; calculating a path loss value based on the amplitude gradient and the propagation time difference, and screening candidate leakage nodes; constructing a space grid model to solve three-dimensional coordinates of the leakage source; after the coordinates are projected to the surface of the structure model, pressure modal coherence and vibration impact energy are coupled and analyzed to generate a detection map; and finally, outputting a multi-leakage-point identification graph through spatial clustering and intensity grading. Accurate separation and spatial positioning of multiple leakage signals are achieved, and the reliability of complex sealing structure detection is remarkably improved.

The invention relates to the technical field of industrial equipment diagnosis, in particular to an AI-driven industrial equipment fault analysis method and system, and solves the problems of insufficient multi-source data fusion, weak working condition adaptability and inaccurate fault positioning in the prior art. According to the method, high-frequency time sequence signals are synchronously collected through a vibration sensor, a temperature sensor and the like, working condition parameters are marked, a dynamic confidence coefficient weight is generated based on a working condition matching historical fault library, and cross-modal features are extracted through a space-time convolutional network after multi-source data are fused. A fault mode separation module is used for decoupling the fault contribution degree of mechanical transmission, electric power and heat dissipation systems, precise diagnosis is achieved in combination with frequency domain fingerprints, time domain waveform analysis and cross decision verification, finally, the remaining life is predicted through a long-short-term memory network and a Weibull model, and the system integrates multi-source sensing, dynamic fusion and intelligent diagnosis units. The fault early warning accuracy and the equipment maintenance scientificity under complex working conditions are improved, and a solution is provided for intelligent diagnosis of industrial equipment.

The invention discloses an industrial internet of things-oriented equipment digital twin operation and maintenance management system, and relates to the technical field of equipment management. The system comprises a data acquisition and preprocessing module, a digital twin model construction module, a data transmission and storage module, a state monitoring and fault diagnosis module, an operation and maintenance decision and optimization module and a visual interaction module. The data acquisition module adaptively acquires data through a sensor and preprocesses the data; the model construction adopts multi-scale and multi-model fusion; a hybrid network architecture and an encryption technology are used for transmission and storage; performing feature fusion and transfer learning for monitoring diagnosis; reinforcement learning and multi-agent collaboration are used for decision optimization; visual interaction supports VR / AR fusion. According to the invention, multiple modules work cooperatively, accurate acquisition, efficient transmission and storage of data are guaranteed, and accurate fault diagnosis and scientific operation and maintenance decision are realized through an advanced algorithm; the operation experience is improved through visual interaction; the method also has energy consumption optimization and supply chain cooperation capabilities, and can improve the operation and maintenance efficiency of industrial equipment and enterprise benefits.

The invention discloses a multi-point distributed high-precision industrial equipment operation monitoring method and system, and relates to the technical field related to industrial equipment monitoring, and the method comprises the steps: independently collecting the operation parameter data of equipment through a plurality of sensor nodes; transmitting the operation parameter data to a central control unit in real time for fusion processing, and generating an equipment operation state feature set; performing fault prediction and health assessment on the equipment operation state feature set to obtain an equipment health score; and dynamically triggering an alarm signal according to the equipment health score, determining a target sensor node, and generating a target maintenance instruction. The technical problems that in the prior art, the equipment operation monitoring efficiency is low, the overall condition of the equipment is difficult to master comprehensively, a flexible monitoring and early warning mechanism is lacked, the fault position of the equipment cannot be accurately positioned and quickly responded and processed, and industrial equipment cannot efficiently and stably operate are solved. The technical effect of improving the accuracy and stability of equipment fault positioning is achieved.

The invention discloses a complex industrial scene-oriented multi-dimensional data fusion monitoring method and system, and belongs to the technical field of industrial automation and data analysis, and the method comprises the following steps: carrying out credibility evaluation and self-repairing processing on multi-source heterogeneous data of industrial equipment; performing cross-modal time sequence alignment on the repaired multi-source heterogeneous data, and enhancing the time sequence alignment precision; dynamically adjusting the fusion weight of the multi-source heterogeneous data based on the real-time working condition of the equipment; taking the output value after the weight fusion as an edge weight to guide the fusion of the multi-source heterogeneous data; and performing abnormal propagation path simulation analysis in combination with a digital twinborn model. According to the method, through multi-dimensional data fusion, dynamic adjustment and application of the digital twinborn model, efficient industrial fault monitoring and prediction are realized, and high reliability and safety of equipment are ensured.

The invention relates to the technical field of industrial equipment state monitoring, and provides a servo cable bending stress prediction method based on multi-sensor fusion. Aiming at the problems of single data dimension, poor generalization ability of a machine learning model and insufficient real-time performance in the prior art, the method comprises the following steps of: firstly, acquiring and preprocessing a multi-source historical data set by deploying a multi-modal sensor; and then, constructing a neural network fusion layer to fuse the preprocessed data set, and generating comprehensive time-frequency domain fusion data. Thirdly, constructing a trainable deep neural network hybrid architecture, performing deep feature extraction on the fused data, and executing pattern correlation analysis to obtain a comprehensive correlation matrix; and finally, directly inputting new data acquired in real time into the prediction neural network, and performing time sequence prediction reasoning in combination with the comprehensive incidence matrix to obtain final stress prediction data. According to the method, the accuracy of predicting the bending stress of the servo cable is remarkably improved, and the adaptability and robustness of the system are enhanced.

The invention discloses an industrial equipment life cycle management system based on digital twinning, and relates to the technical field of industrial equipment, the system realizes synchronous monitoring and dynamic comparison of real-time operation states of a physical body and a twinning body, and the twinning body evolution process is frozen in time through a signal receiving module; the twinborn and physical body parameters are compared and analyzed through the real-time monitoring and processing module, an abnormal time sequence signal can be quickly identified, a forbidding instruction can be accurately triggered, and the abnormality determining module can be combined with a parameter comparison result to determine an abnormality triggering reason. The correction module can classify and identify endogenous or exogenous anomalies according to physical body abnormal performance, accurately generate a correction instruction and dynamically update a twin body model; and the life feedback module generates abnormity reduction information based on physical body abnormity characteristics, calculates the life reduction amount in combination with a standard life consumption curve, outputs accurate operation and maintenance management information, and realizes quantitative management of the life cycle health state after abnormity.

The invention discloses a valve vibration impact test method and system based on digital twinning, and belongs to the technical field of industrial equipment test and evaluation. The method comprises the following steps: firstly, deploying a multi-source sensor network, constructing a physical entity layer of a valve vibration impact test, collecting and preprocessing various physical monitoring data, and constructing a digital twinborn body layer; secondly, real-time data assimilation with a physical test bed is realized through parameter calibration and updating of a digital twinborn model based on various physical monitoring data and the digital twinborn layer; and finally, executing a virtual vibration impact test, realizing prediction and evaluation of global response and key part information of the physical entity according to a test result, and finally realizing interaction and performance prediction. And the valve vibration impact test is realized through the valve vibration impact test system. According to the method, the fault diagnosis capability, the early damage identification capability and the valve state prediction and evaluation capability can be remarkably improved, and the defect of insufficient field monitoring information is overcome.