1154 results about "Diagnostic methods" patented technology

The invention relates to the technical field of equipment fault intelligent diagnosis, in particular to an equipment fault intelligent diagnosis method and system based on a Modbus protocol. The method comprises the following steps: acquiring real-time operation data from target industrial equipment through a Modbus protocol, dynamically adjusting an initial sampling frequency based on an equipment operation state, and performing multiple verification and compensation correction on the acquired data to obtain a stable data stream; performing multi-scale decomposition and feature enhancement processing on the stable data stream, extracting a time-frequency domain mixed feature set, and constructing a feature evolution trajectory; inputting the feature evolution trajectory into a double-branch diagnosis model integrating equipment state prediction and fault classification, and outputting an equipment health degree score and fault type probability distribution; and constructing a dynamic fault threshold curved surface, carrying out multi-dimensional fusion decision by combining the equipment health degree score and the fault type probability distribution, and generating a graded fault early warning and maintenance strategy. According to the invention, the accuracy, timeliness and adaptability of industrial equipment fault diagnosis can be greatly improved.

The invention discloses a switch equipment performance aging intelligent diagnosis method and device based on multi-parameter fusion, relates to the technical field of power equipment abnormal defect diagnosis, and solves the problem of low precision of aging judgment of switch equipment depending on a single temperature and humidity threshold value or a single gas parameter in the prior art. The method comprises the following steps: acquiring internal and external humiture and internal gas characteristic parameters of equipment; constructing multi-channel standardized monitoring data through time synchronization, filtering and de-noising and exception elimination processing; calculating an absolute humidity difference inside and outside the cabinet, constructing a humidity expectation model, and obtaining a standardized residual error; performing offset detection and trend smoothing processing on the standardized residual error to obtain a cumulant and a smoothing trend quantity, and fusing the cumulant and the smoothing trend quantity to calculate an offset index; outputting a moisture seepage risk score, and triggering early warning when the moisture seepage risk score and the characteristic gas concentration exceed a threshold value. According to the invention, real-time high-precision evaluation of the aging state of the equipment is realized, early hidden dangers are effectively identified, and the safety and reliability of a power system are improved.

The invention relates to the technical field of intelligent diagnosis, and discloses a micro-grid fault diagnosis method and system based on data driving and unsupervised learning. The method comprises the following steps: collecting current, voltage, temperature and power data of a micro-grid and constructing a time sequence matrix; inputting a time sequence prediction network and a time sequence reconstruction network, and performing parallel processing to obtain a prediction error and a reconstruction error; carrying out weighted fusion on the two errors and constructing a two-dimensional error space to judge normal fluctuation and fault abnormity; and extracting a state variable to generate a dynamic threshold to judge a fault. The false alarm rate and the missing report rate of fault diagnosis are reduced.

The invention relates to the technical field of fault diagnosis, and discloses an inertial sensor fault data test diagnosis method and system, and the method comprises the steps: carrying out the noise suppression operation of original output data in a sliding window, and obtaining a purified data flow; calculating an expected value of the purified data stream based on a preset calibration parameter, and performing deviation comparison on the expected value and the purified data stream to obtain a residual sequence; performing time-frequency domain analysis on the residual sequence to obtain a multi-dimensional characteristic parameter; based on the operation state of the inertial sensor, performing nonlinear state space reconstruction on the multi-dimensional characteristic parameters to obtain a comprehensive fault index; performing comparative analysis on the comprehensive fault index and a preset dynamic diagnosis threshold value, and when the comprehensive fault index continuously deviates from the dynamic diagnosis threshold value, determining that the inertial sensor has a potential fault; outputting a fault early warning report according to the potential fault; according to the invention, the efficiency of fault data test diagnosis of the inertial sensor can be improved.

The invention discloses a measurement laboratory anomaly detection and diagnosis method, system and device based on deep learning and a medium, and relates to the technical field of anomaly detection and diagnos.The method comprises the steps that multi-source real-time data are collected and preprocessed; performing alignment processing based on sampling inconsistency among the data sources, and constructing unified data representation; generating a corresponding prediction result by using the prediction model; calculating a comprehensive abnormal score based on the aligned data and the prediction result; comparing the comprehensive abnormal score with a threshold value, and judging whether a comprehensive abnormal state exists or not; if the judgment result is abnormal, performing abnormal cause decoupling processing and causal inference to obtain a candidate root cause set; and inputting the candidate root cause set into a deep learning causal inference model to obtain an anomaly diagnosis result. A physical perception residual scoring mechanism is introduced, a comprehensive anomaly score is combined on the basis of anomaly detection, a weighted calculation method is adopted, and the contribution degree of each data source to an abnormal state can be accurately evaluated.

The invention discloses a ship intelligent fault diagnosis method and system based on open label space identification. The method comprises the following steps: obtaining a multi-source sensor time sequence signal of a ship system and constructing a training sample set; carrying out feature extraction on the training sample set by utilizing a deep neural network model, and strengthening the clustering characteristics of the features by adopting a center loss function in the training process; training a classifier on the basis of feature extraction and introducing an open set loss function to form a comprehensive objective function; extracting features from a to-be-diagnosed sample, embedding the features, calculating the distance between the to-be-diagnosed sample and a known fault category feature center, and judging an unknown fault through comparison between the minimum distance and a preset threshold value; and outputting a known fault category or triggering an unknown fault alarm according to a judgment result, and dynamically expanding and updating a model knowledge base based on accumulated unknown fault samples. The method can break through the limitation of the traditional closed set hypothesis, effectively identifies the unknown fault type, and achieves the self-adaptive learning and continuous optimization of a ship fault diagnosis system.

The invention discloses a power distribution network single-phase grounding reason diagnosis method based on confidence and ambiguity evaluation. The method comprises the following steps: acquiring original diagnostic data corresponding to a waveform to be detected; determining a confidence level corresponding to the original diagnosis data according to a preset grading rule based on the similarity distance corresponding to each of the plurality of neighbor sample waveforms in the original diagnosis data; determining a plurality of candidate reasons based on the single-phase grounding reasons corresponding to the plurality of neighbor sample waveforms; based on the number of the plurality of neighbor sample waveforms, determining occurrence frequencies corresponding to the plurality of candidate reasons; determining target ambiguity based on the occurrence frequencies corresponding to the candidate reasons and the reason label semantic association matrix; and based on the confidence level and the target ambiguity, adaptively generating a diagnosis result. According to the method, the technical problem that the information completeness of the conclusion output by the existing analysis method is insufficient, so that an objective judgment basis for the conclusion reliability is lacked when the operation and maintenance personnel make decisions is solved.

The invention relates to the technical field of electric power, in particular to a line loss abnormity diagnosis method based on electric quantity fluctuation analysis. The method comprises the following steps: acquiring an electricity user and electricity data; calculating a power supply quantity change rate and a power consumption change rate according to the daily power supply quantity of the transformer area and the fluctuation degree of the daily power consumption of the transformer area, and further judging a line loss abnormity type; if the line loss abnormity type is user side abnormity, screening the electricity users, and obtaining a user side abnormity type according to the screened electricity users; if the line loss abnormity type is power supply side abnormity, calculating a power supply quantity multiple, and performing fault attribution to obtain a power supply side abnormity type; and explaining the two exception types through the SHAP so as to generate a line loss exception diagnosis report. In this way, the adaptability and robustness of the diagnosis method in a novel scene containing a distributed power supply and the like can be enhanced, the interpretability of a diagnosis result is further improved, and a clear and credible feature basis is provided for field check.

The invention belongs to the field of equipment fault diagnosis, and particularly discloses an equipment remote fault diagnosis method based on a large model. Through multi-modal data fusion, an attention mechanism reasoning model, man-machine cooperation verification and intelligent resource scheduling, the problems that maintenance excessively depends on expert experience and a mature remote scheme is lacked are solved. Specifically, according to the scheme, firstly, multi-source data are aligned and fused to form a unified feature vector, and the defect of information isolation is overcome. Afterwards, a model based on an attention mechanism can automatically focus key features, preliminary diagnosis is generated, and dependence on expert experience is reduced. And then, through a man-machine interaction verification mechanism, an expert can remotely check and correct a result, so that the diagnosis reliability is ensured. And finally, the system automatically schedules resources according to a verified result, so that rapid linkage from diagnosis to disposal is realized, remote guarantee is accurately executed, and equipment deterioration is effectively restrained.

A power transformation equipment temperature anomaly identification and diagnosis method comprises the following steps: collecting state variables, performing cleaning, interpolation complementation and abnormal point elimination on multi-source data through a time synchronization mechanism, and constructing a unified data matrix; extracting statistical features and time sequence dynamic features in the time sequence based on the data matrix, and performing dimensionality reduction on redundant information in combination with a principal component analysis method to form a multi-dimensional fusion feature vector; an unsupervised learning model based on LSTM-AE is constructed, a normal working condition data learning feature reconstruction mode is utilized, and a reconstruction error is taken as a criterion to identify potential temperature anomaly; and calling a preset expert rule base and a knowledge graph, automatically analyzing dominant factors causing anomalies, and identifying typical anomaly types. According to the invention, automatic identification and classification diagnosis of the temperature abnormity of the power transformation equipment under an unsupervised condition are realized, the accuracy and response speed of fault identification are obviously improved, and the intelligence and practicability of equipment operation state monitoring are enhanced.

The invention discloses an intelligent diagnosis method and system based on a large model and knowledge retrieval enhancement, and the method comprises the steps: obtaining multi-source heterogeneous data through a distributed collection network, and building a standardized data flow; extracting multi-dimensional dynamic features to construct a state model, and identifying an abnormal mode; the key innovation lies in that the deep semantic understanding ability of a large language model is combined with a structured knowledge graph, and the exceptions are primarily screened and rechecked through a dual verification mechanism; and finally, closed-loop optimization from diagnosis to treatment is realized. The multi-source data fusion greatly expands the fault perception dimension, the dual verification mechanism of knowledge retrieval and semantic understanding effectively filters false alarms, and the false alarm rate is significantly reduced; a closed-loop feedback mechanism enables the system to have continuous learning ability, and the diagnosis precision is continuously improved along with operation time; the accuracy of fault identification and the timeliness of system response are greatly improved, and technical support is provided for conversion of operation and maintenance of electromechanical equipment from passive response to active intervention.

The invention discloses a multi-dimensional diagnosis method for river ecological degradation degree, and particularly relates to the technical field of river ecological degradation analysis. Obtaining multiple types of ecological parameters of the target river reach, and constructing a physical and chemical coupling parameter set, a biocenosis response factor set, a structure instability index set and an ecological function coupling parameter set; performing wavelet transform and principal component merging on the parameter set to form a multi-dimensional ecological feature tensor; extracting a main disturbance feature subspace by adopting high-order singular value decomposition to obtain a disturbance feature vector set; constructing a nonlinear degradation function based on an ecological stress response mechanism, outputting an ecological degradation risk value, identifying an ecological degradation weak region coordinate set, and generating a spatial degradation trend chart and an intervention priority suggestion path; the river ecological degradation degree can be comprehensively and accurately revealed, the scientificity and interpretability of diagnosis and the pertinence of treatment are improved, and the method has remarkable application value.

The invention discloses a multi-source information fusion fault diagnosis method based on an improved DS evidence theory, and relates to the technical field of industrial equipment state monitoring and intelligent fault diagnosis, and the method comprises the steps: extracting multi-scale vibration energy and impact characteristics, a thermal load change rate and oil physical and chemical characteristics through synchronously collecting vibration, temperature and lubricating oil multi-source operation information; and constructing a unified feature vector and inputting the unified feature vector into the deep belief network to realize initial probability distribution of normal, early warning and fault working conditions. The cross-modal evidence conflict is evaluated and corrected by constructing a feature coupling index, a conflict degree and a confidence entropy index, and the interference of inconsistent evidences on a diagnosis result is inhibited; and further introducing a time sequence evidence library and improving a DS recursive fusion mechanism, depicting time evolution of an equipment operation state, and constructing a health trend index to realize evolutionary fault early warning. And finally, the model is updated in combination with diagnosis result feedback, self-learning optimization and closed-loop fusion of fault diagnosis are realized, and the diagnosis accuracy and stability are improved.

The invention provides an antigen epitope peptide related to connexin and application of the antigen epitope peptide. The connexin antigen epitope peptide disclosed by the invention is selected from (1) a polypeptide with an amino acid sequence as shown in SEQ ID NO: 14; and (2) a polypeptide which is derived from (1) by substituting, deleting or adding 1-2 amino acids in the amino acid sequence of SEQ ID NO: 14 and retains the binding capacity with a connexin antibody. According to the present invention, the new connexin antigenic peptide fragment sequence is identified for the first time, such that the existing autoantigen epitope map is expanded, and more importantly, the molecular basis is provided for the development of the clinical detection method with high diagnosis sensitivity and high specificity, such that the pathological mechanism of MG can be further improved, and the application prospect is broad. And new auxiliary diagnosis means and treatment targets are provided for antibody negative MG patients.

A method and a system of bearing fault diagnosis based on a dual attention mechanism to strengthen a hierarchical decision network are provided, where the method includes the following steps: collecting bearing vibration signals in different health states; based on the bearing vibration signals, constructing a hierarchical multi-class fault diagnosis model; and determining a fault position and a fault size of a bearing by using the hierarchical multi-class fault diagnosis model.

The invention discloses a multi-mode brain dysfunction auxiliary diagnosis method based on a dynamic function connection network. A two-stage collaborative learning framework from an individual brain graph to a group relation graph is constructed. Firstly, an individual multi-modal fusion brain map is constructed, node features of the individual multi-modal fusion brain map are obtained through node regularization regression analysis of an rs-fMRI time sequence, an adjacent matrix is obtained through calculation of the brain interval grey matter volume difference of a T1 image, and individual enhancement characterization is obtained through map convolutional network fusion. And then constructing a group relationship enhancement graph, taking individual representation as node features, constructing a dual-channel adjacency relationship for distinguishing homologous / heterologous connection according to age and gender, obtaining final discriminative representation through dual-channel graph attention network aggregation, and performing classification diagnosis according to the final discriminative representation. According to the method, deep fusion of multi-modal information and explicit modeling of key biological variables are realized, and an effective tool is provided for accurate and explainable auxiliary diagnosis of brain diseases.

Embodiments include a method of diagnosing gastroparesis or suspected gastroparesis, the method comprising: obtaining data representing a time series of readings from gas sensing apparatus housed within a ingestible capsule device orally ingested by a subjected; processing the readings to detect one or more gastroparesis indicator spikes in the CO2 concentration with respect to time; based on the detected one or more gastroparesis indicator spikes in the CO2 concentration with respect to time, diagnosing gastroparesis or suspected gastroparesis.

The invention provides an equipment fault diagnosis method and system, equipment and a medium, and the method comprises the steps: obtaining full-stack monitoring data of target equipment, the full-stack monitoring data being from at least two different technology stack levels of the target equipment; abnormal events in the full-stack monitoring data are detected; a propagation path of the abnormal event in a topological graph is analyzed, a starting node of the propagation path is used as a root cause node, and the topological graph is constructed based on the monitoring objects in the target equipment and the dependency relationship between the monitoring objects; and matching the abnormal features of the root cause node with known fault features in an equipment knowledge base to obtain a fault diagnosis result of the target equipment. According to the method, the starting node of the propagation path can be traced to position the root cause, and accurate matching is carried out in combination with the equipment knowledge base, so that deep faults which cannot be identified by a traditional method can be accurately positioned and diagnosed, and the problems of inaccurate root cause positioning and low efficiency caused by incomplete monitoring data and unmatched analysis models are avoided.

The invention discloses an electric power system fault diagnosis method and system based on multi-sensor fusion, and relates to the technical field of electric power fault diagnosis, electric power data of an electric power system are collected based on multi-array sensors, and a health data set of the electric power system is generated by adjusting element parameters of the electric power system; constructing an electric power digital twinborn model of the electric power system by using the element parameters and the health data, and inserting a distributed fault analysis unit into the electric power digital twinborn model to generate a parallel fault analysis model; real-time electric power data of the electric power system are collected in real time, when a fault trigger signal is detected, a driving data flow is generated, the driving data flow is introduced into the parallel fault analysis model to position a fault source, and a fault report is output; according to the method, the high-fidelity digital twinborn model and a parallel fault analysis mechanism are constructed, so that rapid, accurate and automatic positioning and diagnosis of the faults of the power system are realized.

The invention belongs to the technical field of bridge engineering, particularly relates to a bridge stress state diagnosis method based on digital twinning and movable sensing, and aims to diagnose the real-time stress state of a bridge with diseases. The method comprises the steps of obtaining existing disease space data of a bridge, implanting disease parameters in a twin model, and generating an initial twin body of the bridge with the disease; a modular monitoring station formed by movable sensing devices is deployed on a bridge, data are transmitted to an edge computing terminal through a Mesh ad hoc network between the devices, and the edge computing terminal executes data preprocessing. In the monitoring period, vehicle load, temperature and humidity, displacement or vibration data are injected into the initial twinborn body of the diseased bridge in real time, an FEM-disease coupling algorithm is established to dynamically correct a structural stiffness matrix, and a digital twinborn body is formed. And establishing a stress state diagnosis index system, comparing the responses of the digital and healthy twins under the same load, calculating the variation amplitude of each diagnosis index, marking a structural health degree map, and judging whether the bridge has an abnormal stress state.

The invention belongs to the technical field of image detection, and discloses an enteroscopy auxiliary diagnosis method and system based on artificial intelligence, and the method comprises the steps: obtaining enteroscopy image data in an enteroscopy process, and synchronously extracting corresponding auxiliary collection information from the enteroscopy image data; caching the enteroscope image data and the auxiliary acquisition information frame by frame and keeping timestamps aligned; performing standardized correction and adaptive enhancement processing on the enteroscope image data after timestamp alignment; a self-adaptive gamma dynamic enhancement mechanism is introduced, and intestinal tract wrinkles are reserved to obtain an enhanced image; lesion positioning and category screening are conducted on the enhanced image through the target detection model, multi-scale feature aggregation and time sequence consistency constraint are conducted in combination with auxiliary collection information, and lesion candidate areas are generated; performing morphological, texture and boundary structure analysis on the lesion candidate region, and extracting lesion morphological features; and the efficiency and the reliability of enteroscopy diagnosis can be improved.

The invention discloses a multi-modal contrast learning fault diagnosis method for a small sample scene, and the method comprises the following steps: carrying out the enhancement of a one-dimensional signal and two-dimensional image fused fault data set through physical simulation for the small sample scene with scarce industrial fault data, and constructing a positive and negative sample pair through a plurality of data enhancement strategies; based on heterogeneous multi-modal fault data, designing a double-flow encoder architecture of a time sequence branch and an image branch, extracting depth features and mapping the depth features to a unified feature space through a projection head; performing supervised contrast learning pre-training based on intra-modal and inter-modal dual contrast loss; supervision fine tuning is carried out based on multiple loss functions such as physical guidance, so that accurate diagnosis of equipment faults is realized in a small sample scene. According to the fault diagnosis method under the unbalanced sample and limited labeling conditions, the problem that a traditional data driving model depends on large-scale labeling samples is effectively relieved through supervised comparative learning and cross-modal information alignment.

The invention relates to the technical field of equipment fault prediction and diagnosis, and discloses an equipment fault prediction and diagnosis method and system based on dynamic feature fusion. Comprising the steps of collecting a multi-modal monitoring signal, preprocessing to generate a standardized time series data stream, extracting a feature vector through a local detail sensing network and a global trend capturing network, generating a comprehensive feature vector by using an adaptive feature fusion module, and outputting an operation state category and a confidence score through a classification decision network. According to the method, the multi-modal time sequence features can be effectively fused, the feature weight is dynamically adjusted, the equipment operation state is accurately identified, and the accuracy and reliability of fault prediction and diagnosis are improved.

The invention relates to a deep learning-based early-stage amyloidosis nephropathy identification and diagnosis method, belongs to the technical field of amyloidosis nephropathy identification, and solves the problem that early-stage amyloidosis cannot be accurately identified in the prior art. The method comprises the following steps: acquiring kidney pathological section images of different individuals to construct a training sample set; constructing a link model comprising a first region segmentation model, a second region segmentation model and a third region segmentation model; the first region segmentation model is used for segmenting a cortex region in the pathological image; the second region segmentation model is used for segmenting a glomerular region; the third region segmentation model is used for identifying amyloidosis of a glomerular region; training the link model based on the training sample set to obtain a trained link model; and inputting a to-be-identified kidney pathological section image into the trained link model to identify whether amyloidosis exists or not. And efficient and accurate early-stage amyloidosis identification is realized.

The invention discloses an industrial equipment fault diagnosis method and system under supplementary mark constraint, and belongs to the technical field of industrial intelligent operation and maintenance and fault prediction health management. The method comprises the steps of collecting and preprocessing equipment vibration signals; constructing a one-dimensional residual error-based double-end deep network as a complementary mark learning model; a joint probabilistic loss function is generated by using a complementary mark based on maximum likelihood estimation, so that the model learns fault features from a negative tag; and high-precision fault classification is realized by using the trained model. According to the method, the complementary mark learning normal form in weak supervised learning is applied to industrial equipment fault diagnosis, more than 90% of diagnosis accuracy is obtained on a plurality of standard bearing data sets such as CWRU and MFPT, the data marking threshold is remarkably reduced, the problems of scarcity of fault samples and difficulty in marking in an industrial scene are effectively solved, and the fault diagnosis accuracy is improved. The method has important practical application value.

The invention belongs to the cross field of multiple subjects such as artificial intelligence, computer vision, natural language processing and medical image processing, and discloses a thyroid ultrasound-assisted diagnosis method and system based on text-driven vision pre-training and cross-modal feature fusion. Wherein the model building and training stage comprises the following steps of: preprocessing to obtain an unlabeled image-text pair data set and a labeled image-text pair data set; an image encoder and a text encoder are constructed, pre-training is carried out, and text-driven visual pre-training is realized; using a cross-modal splicing attention fusion module (CMCAF) to realize fusion of text features and image features, inputting the fused text features and image features to a full-connection layer classifier, and performing training optimization through supervision; the application stage is to predict benign and malignant thyroid nodules for the to-be-predicted image-text pairs. The medical image multi-modal fusion method has the advantages of high accuracy, low dependence on pathological labels, high model interpretability, good clinical deployment feasibility and the like, and the problem of the existing AI model in the aspect of medical image multi-modal fusion is effectively solved.

A method for diagnosing nasal cytology based on deep learning includes: reading in blocks a nasal cytology to be diagnosed in a sliding window; performing preprocessing on the window image and inputting the preprocessed window image into a trained cell detection model, a coordinate and a score of a bounding box of a detected target being obtained based on feature maps, and a bounding box set being obtained by filtering out bounding boxes with scores below a predetermined threshold; cropping an image from the window image based on coordinates of bounding boxes in the bounding box set, performing preprocessing on the image and inputting the preprocessed image into a trained cell classification model to output a cell category of the corresponding image, and performing post-processing to obtain a cell category of the nasal cytology to be diagnosed.

The invention discloses a server hardware link diagnosis method and a server hardware link diagnosis system, relates to the technical field of computer system fault diagnosis, and discloses the server hardware link diagnosis method and the server hardware link diagnosis system. Through the steps of obtaining a diagnosis rule configuration file, collecting software and hardware state data, carrying out matching analysis to generate a diagnosis path, executing diagnosis processing, carrying out correlation analysis, generating a fault report and the like, the problems of software and hardware diagnosis splitting and path stiffness in the prior art are solved, and collaborative diagnosis and dynamic path generation of software and hardware faults are realized. And the fault positioning accuracy and the system recovery timeliness are improved.

The invention relates to the technical field of medical image processing and artificial intelligence diagnosis, in particular to an oral and maxillofacial surgery image recognition and diagnosis method and system based on deep learning, and the method comprises the following steps: multi-modal image collection and cooperative preprocessing: collecting an oral and maxillofacial surgery CBCT image, a cone beam CT curved surface tomography image, an oral endoscope image and an ultrasonic image, a standardized multi-modal image data set is obtained through inter-modal registration and an adaptive enhancement algorithm; according to the method, a traditional diagnosis framework of'single-mode image + manual film reading 'is broken through, and a three-order diagnosis logic of'multi-mode image cooperative enhancement-cross-scale feature dynamic fusion-focus typing and risk hierarchical linkage' is innovatively provided; and accurate identification, typing and malignant transformation risk prediction of common oral and maxillofacial surgery diseases (such as jaw cyst, wisdom tooth impediment, temporomandibular joint disorder and maxillofacial tumor) are realized.