1026 results about "Diagnostic technology" patented technology

The invention belongs to the technical field of bridge diagnosis, and discloses a bridge crack intelligent diagnosis system based on multi-modal data fusion. By fusing multi-source data such as visual images, sound wave detection and vibration signals, comprehensive perception and characterization of crack features are realized; constructing a bridge crack characteristic spectrum diagram by adopting a cross-modal feature extraction and heterogeneous feature coding technology; generating a crack evolution situation map based on space-time correlation analysis and knowledge graph construction; the robustness of the system in a complex environment is improved through environmental adaptability feature enhancement and multi-scale characterization; constructing a bridge safety risk hypergraph in combination with multi-dimensional risk analysis and multi-agent collaborative diagnosis; analyzing and revealing a crack evolution mechanism by applying a causal relationship; and finally, through dynamic fusion and uncertainty quantification, a crack intelligent diagnosis comprehensive report is generated. According to the system, the limitation of traditional single-mode diagnosis is broken through, and dynamic prediction and accurate risk assessment of fracture evolution are realized.

The invention provides a wind power booster station equipment fault prediction and diagnosis method and system, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: constructing an equipment topological relation through a knowledge graph, employing a double-flow heterogeneous graph neural network to extract space-time cooperation features, generating a candidate path based on multi-hop reasoning, extracting a key evidence chain, and calculating a credibility score. And combining multi-scale fault feature reconstruction and Tsallis entropy calculation to obtain a diagnosis result. According to the invention, the fault root cause can be accurately identified, the diagnosis accuracy is improved, the false alarm rate is reduced, and decision support is provided for wind power plant equipment maintenance.

The invention relates to the technical field of power equipment fault diagnosis, in particular to a power equipment fault intelligent diagnosis method and system based on deep learning. The method comprises the following steps: automatically learning high-dimensional space-time correlation features in original time series data through a deep feature extraction network, and generating feature vectors representing potential abnormal modes of equipment; performing adaptive weight distribution on the high-dimensional space-time correlation features by using an attention enhancement mechanism, and marking a fault sensitive area to form enhanced fault features; inputting the enhanced fault features into a multi-level classifier for joint fault mode recognition and severity evaluation, and outputting a diagnosis result tensor containing a fault type and confidence; and an equipment maintenance decision signal is triggered based on the diagnosis result tensor, and the feature extraction network and classifier parameters are iteratively optimized according to feedback data, so that the intelligent level of operation and maintenance of the power equipment can be comprehensively improved.

The invention relates to the technical field of energy storage battery diagnosis, and discloses an energy storage battery fault diagnosis method and system based on a data fusion algorithm. The method comprises the following steps: acquiring historical operation data and real-time operation data of an energy storage battery in a preset operation period, and generating a historical fault data set according to the historical operation data; performing multi-source feature fusion processing on the historical fault data set to generate a fusion feature parameter set integrating voltage, current, temperature and impedance parameter joint change features; a multi-dimensional fault space is constructed based on the parameter set, coordinate axes of the multi-dimensional fault space correspond to different parameter dimensions, and spatial position coordinates represent parameter change characteristic values; calculating the fault correlation degree of the historical fault event in the multi-dimensional fault space, and determining a fault early warning index set; and extracting real-time characteristic parameters based on the real-time operation data to form a state vector, carrying out space mapping correlation calculation on the state vector and the fault early warning index set in a multi-dimensional fault space, and outputting a real-time fault correlation factor.

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.

A natural gas pipeline multi-working-condition fault diagnosis method and system based on Bayesian adversarial attack and single-source domain transfer, relating to the technical field of mechanical fault detection and diagnosis. The core of the method is using the transfer learning technology to solve the problem of insufficient generalization ability of existing deep reasoning models when processing pipeline fault diagnosis tasks under different working conditions. The method mainly comprises the following steps: constructing an attack sample generator on the basis of a Bayesian network, wherein the attack sample generator is used for generating, by adding delicately designed tiny disturbance into an input sample, an attack sample that can cause an reasoning model to make an incorrect decision, so as to mine and analyze a defect of the reasoning model; constructing a domain discriminator on the basis of the Bayesian network, wherein the domain discriminator is used for assist in generating a high-concealment attack sample by means of adversarial learning between the domain discriminator and the generator, that is, there is almost no visible difference between the high-concealment attack sample and an original sample; and constructing a classifier on the basis of the Bayesian network, and by expanding the distance between the attack sample and an original decision boundary of the reasoning model, constraining the posterior distribution of network parameters of the reasoning model to be adjusted towards a higher score of the attack sample, thereby enhancing the adaptability and robustness of the model when facing disturbance in different domains. By means of the steps, the present invention effectively solves the problem of missing reporting and false reporting risk improvement caused by poor generalization ability of traditional deep learning models under different working conditions.

The invention discloses a current transformer real-time state diagnosis system based on Internet of Things, which relates to the technical field of electrical equipment state diagnosis and comprises a reference construction module, a disturbance excitation module, a modeling identification module, a driving unwrapping module, a self-adaptive setting module and a closed-loop control module, in the operation process of a secondary winding loop of the current transformer, a nanosecond time synchronization reference is established, a phase reference baseline is locked, current vector trajectories are continuously collected based on the time synchronization reference and the phase reference baseline, and a discrete phase residual image is constructed. According to the method, through high-precision time synchronization, phase disturbance excitation, sparse modeling identification, driving unwrapping and self-adaptive setting control, hysteresis oscillation of the secondary circuit of the current transformer is accurately identified and suppressed, the fault diagnosis accuracy and the system stability are remarkably improved, and the limitation of a traditional method in the aspects of identification sensitivity and response capability is broken through.

The invention relates to the technical field of application state diagnosis, in particular to an application state dynamic diagnosis and automatic repair method and system.The method comprises the steps that multi-source heterogeneous logs are collected and standardized in real time, key fields are extracted, context information is injected, and structured log data are generated; inputting the structured log data into a dynamic anomaly detection model, constructing a dual-path detection mechanism based on LSTM time sequence analysis and a graph neural network, identifying an abnormal mode and positioning a fault root cause; according to the output of the anomaly detection model, a repair action is triggered in a grading manner through an intelligent repair strategy engine; in the repairing process, system state changes are stored and recorded through a pre-writing type redundancy log, automatic rollback during abnormity is achieved on the basis of check point information, and data consistency and system stability are guaranteed. The problem of service interruption or data inconsistency possibly caused by traditional automatic repair is avoided, and the reliability of automatic operation and maintenance is improved.

The invention discloses a method for diagnosing an IGBT open-circuit fault and a current sensor fault of a T-type three-level inverter, belongs to the technical field of inverter fault diagnosis, and is used for solving the problems of diagnosis and positioning when a three-level T-type inverter IGBT switch tube and a sensor have faults. Comprising the following steps: classifying and marking and numbering fault types of an open-circuit fault of a T-type three-level inverter IGBT and a fault of a current sensor; fault samples are collected and divided into a training set and a test set according to the proportion of 7: 3; a TCN-LSTM-SelfAction fault diagnosis model is constructed, and model training is carried out by using the training set; the trained model is used for evaluating the accuracy of fault type diagnosis of the training set and the test set. According to the diagnosis method, the fault types and the fault positions of the IGBT open-circuit fault and the current sensor fault can be diagnosed at the same time, the TCN and the LSTM are selected to be combined, local information and global relation of fault features can be captured at the same time, and meanwhile, the performance of a model is improved by combining a self-attention mechanism in the LSTM, and the accuracy is improved.

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 relates to the technical field of electric power fault diagnosis, and discloses a transformer area line loss abnormity diagnosis method and system based on a large model, and the method comprises the steps: collecting multi-dimensional data used for supporting transformer area line loss abnormity diagnosis, carrying out the cleaning, correlation fusion and standardization processing of the multi-dimensional data, and obtaining a comprehensive data set; a multi-layer diagnosis system based on a rule model, a random forest model and a large model is constructed, and the rule model identifies the simple and conventional anomalies of the transformer area according to a preset anomaly diagnosis rule based on the basic attribute data and the power operation state data in the comprehensive data set; the random forest model locates complex anomalies and novel anomalies by mining a coupling relationship among energy access condition data, external environment influence data and line loss fluctuation in the comprehensive data set; the big model carries out fusion verification on diagnosis results of the rule model and the random forest model, and outputs a final transformer area abnormity diagnosis result; and based on the diagnosis result, a differential loss reduction strategy adaptive to the actual data characteristics of the transformer area is recommended. The working efficiency is improved.

The invention discloses a medical diagnosis auxiliary method and system based on thinking chain visualization, and belongs to the technical field of medical auxiliary diagnosis, and the method specifically comprises the steps: obtaining the related data of a patient, generating an initial diagnosis thinking chain, detecting and recognizing a target sub-chain node needing to be updated based on the graph structure difference when new medical data is received, and updating the target sub-chain node according to the target sub-chain node. Performing local increment updating on the target sub-chain, visualizing the updated thinking chain, and dynamically updating difference detection parameters and optimizing the thinking chain according to the backtracking operation or annotation data of the reasoning node; according to the method, the calculation burden and the graphic rendering overhead are reduced, frequent jitter or breakage of the chain structure is avoided, the response efficiency and the structural stability of the system to multi-stage data input in a complex diagnosis and treatment process are remarkably improved, the transparency and the understandability of reasoning logic are enhanced, and intelligent auxiliary diagnosis better meets the actual clinical requirements.

The invention belongs to the technical field of bearing fault diagnosis, and discloses a CNN-MFKAN-based bearing fault diagnosis method and system, and the method comprises the steps: obtaining a bearing fault signal, dividing data through a sliding window, and generating a two-dimensional time-frequency image through continuous wavelet transform, and storing the two-dimensional time-frequency image; constructing a bearing fault diagnosis model in combination with CNN and MFKAN; dividing the data into a training set, a verification set and a test set; inputting the training set into a bearing fault diagnosis model for training; optimizing the model parameters and judging whether convergence occurs or not, if not, returning to the training set, and if yes, completing training and storing the optimal model parameters; and calling the optimal model parameter to carry out bearing fault judgment to obtain a fault classification result. According to the bearing fault diagnosis model, the MFKAN module is innovatively designed, the extraction capability of the model for different frequencies and different scale features is effectively enhanced, and the recognition precision and robustness of bearing faults under complex working conditions are remarkably improved.

The invention discloses a multi-agent large-model disease diagnosis knowledge reasoning system based on data dual drive, and relates to the technical field of artificial intelligence assisted medical diagnosis. The system collects patient symptom follow-up records, laboratory test results, observation diagnosis probabilities and expert diagnosis recommendation results in a multi-source manner; time sequence evolution characteristics are extracted, a time sequence diagnosis sensitivity coefficient is calculated, and early recognition of disease risks is achieved; in combination with anti-fact simulation and statistical reasoning, a causal consistency coefficient is obtained and is used for verifying causal reasonability of observation diagnosis and contrast results; based on agent group consensus analysis, calculating a game consistency coefficient for judging the credibility of a diagnosis conclusion; positioning and multi-level verification are carried out on abnormal reasoning steps and knowledge fragments, so that the reliability and safety of a result are guaranteed; continuous optimization of the diagnosis model is realized through a log analysis and knowledge backflow mechanism; according to the invention, the accuracy, interpretability and safety of disease diagnosis can be obviously improved.

The invention belongs to the technical field of aero-engine state monitoring and intelligent fault diagnosis, and discloses a multi-modal information fusion bearing fault diagnosis method based on self-supervised learning. The method comprises the following steps: firstly, through mask reconstruction self-supervision pre-training, extracting stable feature representation insensitive to mask disturbance from an unlabeled multi-modal signal, and dynamically updating each modal feature reference point by using an index moving average algorithm; in a downstream fault diagnosis task, a multi-modal joint decision model comprising a pre-training encoder, a single-modal classifier and a fusion classifier is constructed, and adaptive weighted fusion of multi-modal decision is realized through contribution degree calculation based on a cooperative game Shapley value in combination with a deviation degree of modal features and a reference point. According to the method, the dependence of the deep neural network on fault labeling data is effectively reduced, the accuracy and robustness of the diagnosis system in a multi-modal signal diagnosis scene are improved through a dynamic fusion mechanism, and the method is suitable for industrial scenes with limited sample label resources.

The invention relates to the technical field of intelligent diagnosis of power equipment, and provides a multi-mode fusion extra-high voltage converter transformer fault diagnosis method and system, and the method comprises the steps: collecting a multi-mode signal from a sensor of an extra-high voltage converter transformer, and classifying the signal into a plurality of modes; performing intra-modal feature reinforcement learning on the multi-modal data by adopting a SimCLR framework to obtain a discriminative representation feature vector zm of each modal; inputting the zm into a Transform branch encoder of a corresponding mode, and obtaining context feature vector enhancement representation hm of each mode; mapping the hm of different modalities to the same dimension in a unified manner, and performing weighted attention fusion to obtain feature vectors zf of all modalities after fusion; and inputting a multi-layer perceptron classifier (MLP) to obtain a fault category prediction result of the extra-high voltage converter transformer. According to the method, the multi-mode signals are processed and fused in parallel, and the fault recognition capability of the model on the extra-high voltage converter transformer in the complex operation state is improved.

The invention discloses a switch cabinet latent fault remote diagnosis system, method, device and medium, and belongs to the technical field of fault diagnosis, and the system comprises a multi-dimensional sensor sensing module, a data transmission module, a data fusion analysis module, and a data storage and fault detection module. The multi-dimensional sensor sensing module is used for acquiring multi-dimensional data of the operation state of the switch cabinet; the data transmission module is used for transmitting the multi-dimensional data to the data fusion analysis module; the data fusion analysis module is used for performing data preprocessing on the multi-dimensional data to obtain preprocessed multi-dimensional data, and determining a fault classification result according to the preprocessed multi-dimensional data; and the data storage and fault detection module is used for performing fault identification based on the real-time monitoring data and the fault classification result. According to the invention, early recognition and accurate diagnosis of the latent fault of the switch cabinet are realized, and the downtime and maintenance cost of equipment are reduced.

The invention relates to the technical field of power electronic device diagnosis, and discloses a reverse conducting IGBT intelligent power module fault automatic diagnosis method and system. The method comprises the following steps: acquiring multi-source monitoring data including a grid voltage waveform, a collector current waveform and a shell temperature change curve when the power module operates; then establishing a dynamic feature extraction model, performing time domain and frequency domain conjoint analysis on the multi-source monitoring data, and generating a feature parameter set; then constructing a fault feature space, and mapping the feature parameter set to a high-dimensional space to form a feature vector distribution diagram; carrying out regional division on the feature vector distribution map by adopting a self-adaptive clustering algorithm, and identifying an abnormal feature aggregation region; and finally, comparing the abnormal feature gathering area with a preset fault feature library through a mode matching engine, and outputting a fault type identification result. According to the method, multi-source data can be integrated to realize dynamic feature extraction and adaptive fault identification, and the real-time performance and accuracy of fault diagnosis are improved.

Disclosed are an online fault identification method for a current sensor, a diagnosis apparatus, and a fault-tolerant control system, relating to the technical field of diagnosis of faults of current sensors. The method includes defining a fault of the current sensor, and setting a corresponding label; then simulating a motor drive system, so as to obtain operation data in a normal mode and a fault mode, and making a data set; designing a neural network, and performing optimization processing on the neural network through the data set, so as to obtain an intelligent diagnosis model; and finally deploying the intelligent diagnosis model in an edge apparatus, so as to perform real-time online diagnosis on the fault of the current sensor; where the fault of the current sensor includes a saturation fault and a noise fault.

The invention relates to the technical field of motor fault diagnosis, in particular to a fault diagnosis method of a drilling machine variable frequency driving motor based on IMSOA-MCNN-BIGRU, and the method comprises the steps: collecting the current data of an experiment platform motor, and adding interference to simulate the data of a real drilling machine driving motor; a population initialization strategy of an SOA algorithm is improved, opposite-reverse learning is introduced in an iteration process, and an adaptive worst resampling mechanism after stagnation monitoring is added; the size of a convolution kernel, the size of a hidden layer, an initial learning rate and L2 regularization intensity of the MCNN-BIGRU model are optimized based on IMSOA, and optimized parameters are endowed to the MCNN-BIGRU model again so as to construct an IMSOA-MCNN-BIGRU classification model; and inputting the current data into which the interference is added into the IMSOA-MCNN-BIGRU classification model to obtain a diagnosis result. According to the method, the MCNN and the BIGRU are combined and complemented, the characterization capability and the fault judgment precision of complex non-stationary signals are improved, the MCNN-BIGRU model is optimized by using the improved sea gull optimization algorithm, and the performance of the model is improved.

The invention discloses a bacterial polypeptide Gfo and application thereof in preparation of rheumatoid arthritis diagnosis products, and belongs to the technical field of rheumatoid arthritis diagnosis. The amino acid sequence of the bacterial polypeptide Gfo is LNSHGFLPETE. A diagnostic product prepared from the bacterial polypeptide Gfo can effectively detect that the level of an anti-bacterial polypeptide Gfo antibody in a biological sample of a rheumatoid arthritis patient is obviously higher than that of healthy people and other rheumatoid immune diseases similar to rheumatoid arthritis. In addition, in CCP antibody and RF negative patients, the increase is still significant. Therefore, the antibacterial polypeptide Gfo antibody is an important biomarker of rheumatoid arthritis, and provides an effective new method for diagnosis of RA, especially diagnosis of serum-negative RA.

The invention belongs to the technical field of motor fault diagnosis, and particularly relates to a method for constructing a motor fault diagnosis model, which comprises the following steps: deploying a multi-modal sensor and carrying out data acquisition; preprocessing the collected data at the edge end; carrying out multi-modal data fusion and carrying out credibility estimation on the multi-modal fusion data; learning and identifying the depth representation of the fault mode according to the self-supervised time sequence comparison representation; incremental recognition and disastrous forgetting suppression are adopted; performing influence estimation and linear correction according to environmental causality; a non-linear compensation and edge-cloud cooperative triggering strategy of uncertainty gating is constructed; according to the method, the fault identification accuracy and the recall rate can be remarkably improved under the complex working condition, the false alarm rate and the missing report rate are remarkably reduced, the robustness to environment covariant and short-time interference is enhanced, and meanwhile, an end-cloud cooperative fault diagnosis system which supports online incremental updating, privacy protection, real-time alarm of a limited bandwidth field station and cloud continuous optimization is supported.

The invention discloses a heat exchange equipment intelligent fault diagnosis method and system based on digital twinning, relates to the technical field of digital twinning intelligent diagnosis, and is used for solving the problem that concurrent valve jamming and multi-equipment coupling faults are difficult to quickly and accurately position and safely dispose. Design configuration, maintenance history and real-time working condition data are written into a twin library through semantic mapping and unified time synchronization, and virtual-real synchronization is kept. Secondly, fusing the flow, the pressure difference, the temperature sequence and the color-infrared image, generating a health vector by means of a dimension reduction network, and performing real-time early warning; and after early warning occurs, a twin copy is cloned by using the combination of the abnormal equipment and the regulating valve, parallel simulation is performed on the jam disturbance of the injection valve, and a concurrent fault is locked through comprehensive similarity. Finally, a knowledge base is called to evaluate candidate disposal strategies, an optimal scheme is selected to be issued and executed, dynamic correction is monitored through residual errors, and a diagnosis-decision-execution closed loop is achieved. According to the method, the equipment reliability is improved, and the energy utilization rate is increased.

The invention relates to the technical field of fault diagnosis, in particular to a few-label self-supervised learning fault diagnosis method based on an interpretable neural network, and the method comprises the steps: collecting time domain data of a sensor of an aero-engine under different health conditions, carrying out the standardization processing, and dividing the time domain data into a pre-training set, a training set, a verification set and a test set; performing fast Fourier transform and data enhancement on the time domain data in the pre-training set to obtain frequency domain data and enhanced time domain data; constructing a pre-training framework, and performing pre-training to convergence based on the frequency domain data and the enhanced time domain data to obtain a pre-trained time encoder; constructing a fault diagnosis model based on the pre-trained time encoder, and performing iterative training to convergence by using the training set to obtain a trained fault diagnosis model; and performing fault diagnosis on the test set by using the trained fault diagnosis model, and performing visual interpretation on the diagnosis process of the fault diagnosis model by using a gradient weighting class activation mapping technology.

The invention relates to the technical field of transformer fault diagnosis, in particular to a transformer fault diagnosis method and system based on generative acoustic large model enhancement, and the method comprises the steps: constructing a generative acoustic large model, generating specified virtual reference Mel spectrum feature data through an acoustic semantic prompt, and carrying out the recognition of the Mel spectrum feature data; configuring a condition self-encoding model to perform feature reconstruction based on the diagnosis condition label and the current reference Mel spectrum feature, and calculating a reconstruction feature error metric value of the reference Mel spectrum feature and the corresponding reconstruction Mel spectrum feature; comparing the calculated reconstructed feature error metric value with a fault diagnosis error metric threshold value corresponding to the diagnosis condition label; and based on the time sequence characteristics of the reference Mel spectrum features, comparing the fault classification statistical information obtained by statistics with the fault diagnosis statistical judgment conditions to determine the fault diagnosis state of the transformer to be subjected to fault diagnosis. The transformer fault diagnosis accuracy can be improved, and the fault diagnosis cost is greatly reduced.

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 belongs to the technical field of aircraft fault diagnosis, and particularly relates to an aircraft fuel control system fault path mining and root cause diagnosis method. The method comprises the following steps: providing a fault propagation-oriented fault root cause diagnostic graph model to realize more interpretable fault source positioning, and backtracking from an alarm node to a fault source based on propagation mechanism information; constructing a variable propagation graph of the aircraft fuel control system in combination with correlation coefficient analysis and a Lingam causal algorithm, and then performing correction based on a system simulation structure in a manual inspection mode so as to establish an interpretable data structure; introducing an edge propagation quantization factor and a path scoring criterion integrating nodes, edges and path lengths to measure the possibility of candidate fault propagation paths; and learning an optimal threshold set of each node in the propagation graph structure by adopting a particle swarm optimization algorithm. The method supports path-level transparent backtracking, improves the accuracy and flexibility of the model, and can excavate a plurality of fault propagation paths.

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 motor fault diagnosis method and system based on voiceprint analysis, and relates to the related field of motor fault diagnosis technology, and the method comprises the steps: collecting sound signals, vibration data and working condition parameters during the operation of a motor, carrying out the preprocessing, separating the voiceprint features of the motor through a harmonic vector analysis method, and removing the irrelevant sound source interference; obtaining a pre-training comparison learning model through a small amount of motor fault data in combination with data enhancement, fault feature analysis and similarity calculation; constructing and training a motor fault diagnosis model, taking the motor voiceprint features, the vibration data and the working condition parameters as input, embedding a pre-training comparison learning model to learn fault information in the motor voiceprint features, extracting fault features through a time delay neural network, inputting motor operation data which are collected and preprocessed in real time into the trained model, and performing motor fault diagnosis. And outputting a judgment result of the motor fault type. The problem that an existing motor fault diagnosis model excessively depends on labeled data is solved, and model generalization is improved.

The invention discloses an OLED (Organic Light Emitting Diode) production equipment fault diagnosis terminal capable of being remotely monitored, which relates to the technical field of fault diagnosis, and comprises the following steps: acquiring key process parameters of equipment to obtain equipment operation data; performing operation state analysis according to the equipment operation data to obtain an abnormal index; performing fault mode identification according to the abnormal index to obtain an equipment fault type; performing abnormal influence analysis according to the equipment fault type to obtain key influence parameters; performing remote abnormality diagnosis according to the key influence parameters to obtain a diagnosis result; and performing abnormal trend analysis according to the diagnosis result to obtain the equipment health state. Through parameter deviation degree calculation and multi-parameter correlation analysis, the collected key process parameters are subjected to real-time comprehensive analysis, the parameter deviation degree is quantified, coupling abnormity among the parameters is identified, the accuracy and comprehensiveness of fault diagnosis are improved, and the false alarm rate and the missing report rate are reduced.