765 results about "Diagnosis methods" patented technology

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 provides a multi-parameter fusion drilling tool state intelligent diagnosis method, device and equipment, and the method comprises the steps: determining a stable working period through obtaining basic operation parameters of a drilling tool, and applying specific frequency excitation vibration to the drilling tool in the stable period to form an active propagation wave; a multi-point monitoring technology is adopted to obtain a response vibration signal and extract a time sequence, and a time sequence offset is obtained through differential processing; identifying a signal propagation delay section based on the time sequence offset, extracting actual propagation time, comparing the actual propagation time with standard propagation time to generate a time delay abnormal value, and determining an internal state change position; a high-damage section is determined by combining energy dissipation analysis, and fault positioning information is generated through spatial superposition; performing frequency sensitivity analysis by utilizing the damage characteristic parameters, capturing a resonance response peak value through frequency sweep excitation, and forming a secondary diagnosis result in combination with the wear severity; and finally, determining a fault development rate, generating a partition maintenance instruction, and completing intelligent diagnosis of the drilling tool state.

The invention relates to the technical field of bearing detection, and discloses a bearing fault diagnosis method and system, and the system comprises the steps that an acquisition unit collects magnetic signals of a to-be-detected bearing in a non-contact manner in the operation process of the to-be-detected bearing, and the magnetic signals specifically comprise magnetic flux density and magnetic conductivity; the preprocessing unit is used for preprocessing the acquired magnetic signals to form signal fragments to be analyzed; the first processing unit judges whether an early fault exists or not according to the preprocessed magnetic conductivity; the second processing unit extracts a fault discrimination feature vector based on Hilbert transform and wavelet packet reconstruction, inputs the fault discrimination feature vector into a historical discrimination database or a health state baseline model, and outputs a judgment result of a fault degree; the early warning unit determines an alarm level according to the fault degree and an early fault. According to the method, continuous sensing of multi-stage fault states is realized, and the recognition capability of early micro-damage such as fatigue cracks and local stripping is effectively improved.

The invention relates to the technical field of power transmission and distribution production, and discloses a data integration and multi-modal diagnosis method based on a power transmission and distribution scene, and the method comprises the following steps: S1, enhanced integration of multi-source heterogeneous data: collecting time sequence monitoring data, text procedures and image data of power transmission and distribution equipment, constructing an equipment topological correlation graph through a graph attention neural network, and carrying out the enhanced integration of the multi-source heterogeneous data; node feature embedding is optimized through self-supervised comparative learning, an adversarial variational auto-encoder is designed for edge data, and an enhanced sample is generated in combination with physical constraints of equipment. According to the data integration and multi-modal diagnosis method based on the power transmission and distribution scene, the field adaptability and reliability of a diagnosis result are improved while the model fine tuning cost is reduced, and the knowledge migration problem of a general model in the power transmission and distribution scene is solved; the introduction of a dynamic knowledge graph and a multi-dimensional evaluation system realizes the real-time integration of new regulation knowledge and the comprehensive evaluation of model performance, and ensures the sustainable evolution ability and decision transparency of the diagnosis model.

The invention discloses a multi-source heterogeneous medical data fusion and intelligent diagnosis method, and relates to the technical field of medical data processing and intelligent diagnosis, and the method comprises the specific steps: firstly, synchronously collecting medical images and clinical text data of a patient, and carrying out the correlation and integration to form a heterogeneous diagnosis data set; performing standardized feature extraction to obtain a feature set in a unified format; then constructing a parallel model, fusing features by using a cross-modal attention alignment technology, and guiding correction by means of a knowledge graph; and finally, the cross-modal diagnosis features are input into the reference model, automatic focus positioning is realized through processing, and a visual marker graph is output. Heterogeneous data of medical images and clinical texts are synchronously integrated, and the diagnosis feature reliability is improved through standardization processing, feature fusion and the like; a focus sensing mask is generated through comparison with a normal model, a multi-scale feature fusion technology is combined to realize automatic and accurate positioning of the focus, a large amount of labeled data is not needed, the process is simplified, and the diagnosis efficiency and accuracy are improved.

The invention discloses an equipment fault diagnosis method and system based on a large electric power model, and the method comprises the steps: collecting and fusing voltage and current waveform data and alarm log information in a short time window before and after a fault moment, achieving the context integration of a multi-dimensional feature vector through multi-level feature coding and semantic embedding, and achieving the fault diagnosis of a large electric power model. And relevant knowledge fragment retrieval is carried out in combination with a power field knowledge base. On the basis, relevant knowledge fragments, voltage-current waveform fusion feature coding vectors and fault alarm key information semantic embedding coding feature vectors are jointly embedded into a preset Prompt template and then are input into a diagnosis engine based on large model driving, so that intelligent analysis and report generation of fault types, reasons and key features are realized. Through the mode, the limitation of dependence on single data or rules traditionally is broken through, the accuracy, automation and interpretability of fault diagnosis under complex working conditions are remarkably improved, and a solid support is provided for intelligent and high-reliability equipment operation and maintenance.

The invention discloses a thyroid tumor diagnosis method and system based on ultrasonic and cytological image conjoint analysis. The method comprises the following steps: converting a thyroid B ultrasonic image of a patient to be diagnosed into an image feature vector FUS; performing structured feature extraction on the thyroid cytological image of the patient to be diagnosed to obtain a structured feature vector FCYTO; the image feature vector FUS and the structured feature vector FCYTO are converted into a fusion Token sequence; and inputting the Token into a multi-modal feature fusion and prediction network based on a Transform architecture, and carrying out feature fusion and classification prediction so as to obtain the probability that the thyroid tumor of the patient to be diagnosed is malignant. The thyroid tumor diagnosis based on multi-modal fusion is carried out on the basis of ultrasonic and cytological images, so that the diagnosis accuracy is 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 relates to the technical field of hidden danger intelligent diagnosis, and discloses a hidden danger intelligent diagnosis method based on multi-modal feature fusion. The method comprises the following steps: firstly, synchronously acquiring a visual image, a voiceprint signal and vibration data of equipment, and constructing a multi-modal original data set; eliminating the time deviation of the multi-source data through space-time alignment processing to obtain a synchronous multi-modal data set; then respectively extracting a feature sequence of each modal, and establishing an association relationship between features by adopting a cross-modal association coding technology; and finally, inputting the fused multi-modal features into a hidden danger discrimination model, and outputting a diagnosis result and a disposal suggestion. According to the method, collaborative analysis of vision, voiceprint and vibration data is innovatively realized, the limitation of traditional single-mode detection is effectively solved, the diagnosis accuracy of equipment composite faults is improved, and a new technical means is provided for intelligent operation and maintenance of industrial equipment.

The invention discloses an intelligent automobile interpretable abnormity diagnosis method and system, and relates to the technical field of intelligent traffic. The method comprises the steps of collecting multi-dimensional sensor data based on an intelligent automobile test platform, and constructing a directed causal graph and a causal adjacency matrix which are used for describing a causal relationship between the sensor data; designing a causal constrained graph attention mechanism based on the causal adjacency matrix, and constructing a causal constraint enhanced graph attention anomaly diagnosis model; and based on the directed causal graph and the graph attention anomaly diagnosis model, constructing a hierarchical anomaly diagnosis strategy integrating a feature reconstruction error, a variable causal relationship and a graph attention network weight, positioning an anomaly root cause and identifying a propagation path of the anomaly in the sensor network. According to the invention, the problems of false correlation and lack of exception explanation ability of graph attention network learning in the prior art can be overcome, and reliable exception detection and root cause diagnosis of intelligent automobile multi-sensor data are realized.

The invention discloses a shale gas well middle and later period effusion intelligent diagnosis method based on LSTM and TCN models. The method comprises the following steps: S1, collecting and preprocessing multi-source time sequence data, performing sliding window segmentation, and generating a standardized data set; s2, manually marking an effusion starting point, a critical point and a process intervention point, and generating a training sample set with a label; s3, performing multi-layer expansion causal convolution on the time sequence convolutional network, and extracting multi-scale local features; s4, the modeling of the long-short-term memory network is globally dependent, and output diagnosis and early warning are discriminated; and S5, regularly collecting newly added data and diagnosis results, dynamically updating the sample set, and retraining and optimizing the model. According to the method, intelligent, efficient and real-time diagnosis and early warning of the liquid accumulation state in the middle and later periods of the shale gas well are achieved.

According to the lightweight helicopter fault diagnosis method and device based on cloud edge cooperation and the medium, a dynamic cooperative distributed intelligent diagnosis system is constructed between the cloud end and the edge end, so that efficient lightweight reasoning of the edge end and centralized optimization training of the cloud end are realized; therefore, an aviation health management platform with self-learning, self-adaption and online updating capabilities is constructed. The whole system adopts a cloud-edge-end three-level architecture, wherein the end side is responsible for data acquisition and preprocessing; the edge side undertakes real-time diagnosis and lightweight model reasoning; and the cloud is responsible for global training, model scheduling and strategy optimization. Cyclic interaction of model parameters, task instructions and diagnosis results is achieved between the cloud and the edge through a secure communication link, and a closed-loop intelligent updating mechanism is formed. According to the method, the real-time performance, the computing power efficiency, the model generalization ability and the system adaptability of a fault diagnosis system are remarkably improved.

The invention discloses a three-dimensional positron emission tomography data image classification method based on multi-stage progressive feature extraction, and is applied to the technical field of Alzheimer's disease auxiliary diagnosis. The auxiliary diagnosis method comprises the following steps: acquiring and preprocessing PET image data of an Alzheimer's disease patient; improving the reliability of the data set by using data enhancement; performing long-range dynamic modeling on the three-dimensional voxel sequence through a stacked Lmamba block; global context semantic adaptive fusion is realized through a layer-by-layer cross-scale channel attention fusion module (CSACF), and a channel spatial perception module (CSPM) is constructed to optimize spatial feature fusion; an inverted bottleneck module is mixed with long-distance space and position information to enhance the capturing capability of the model on detail features; and finally, predicting the disease category probability through global average pooling, full connection and softmax functions. According to the method, the precision of AD early diagnosis and MCI conversion risk prediction can be greatly improved, the defects of a medical image diagnosis method of a convolutional neural network (CNN) and Transform in long-range dependence on modeling and calculation complexity are overcome, and the method has good application prospects and is suitable for AD early detection and MCI conversion risk assessment.

The invention provides a fault map learning-based early abnormality diagnosis method and system for an anion exchange membrane electrolysis system, and relates to the technical field of fault diagnosis. The method comprises the following steps of: acquiring multi-working-condition operation measurement and constructing a time sequence characteristic fragment, establishing a fault map containing parts, working conditions and measurement nodes by combining a part relationship and working condition dependency, training and calibrating a map learning model on the fault map, and extracting a self-adaptive baseline; and projecting the feature fragments to a fault map, outputting multi-level risk indexes, generating a comprehensive risk score and an anomaly candidate set, and implementing multi-scale threshold judgment and stability test in combination with a baseline to form early warning and disposal suggestions. And performing further attribution analysis on the abnormal candidate set, identifying key components, and reinjecting maintenance feedback to update the atlas and the model to form a continuously optimized knowledge base. According to the method, causal correlation modeling and interpretable diagnosis are realized, and the accuracy and timeliness of early abnormality identification of the AEM electrolysis system are effectively improved.

The invention relates to the field of traditional Chinese medicine diagnosis, and discloses a traditional Chinese medicine intelligent diagnosis system based on multi-source data fusion and an AI technology, and the system comprises a tongue picture collection and classification unit which is used for collecting tongue picture data of a patient through imaging equipment, carrying out the feature extraction of the tongue picture data, and obtaining a tongue picture feature data set; performing tongue picture pathological mode classification on the tongue picture feature data set based on a deep convolutional neural network to obtain tongue diagnosis identification result data; and the face diagnosis feature correlation analysis unit is used for inputting the tongue diagnosis identification result data into a multi-modal data fusion engine and carrying out face diagnosis pathological feature correlation analysis based on a graph attention network to obtain face diagnosis identification result data. The deep semantic understanding is realized by introducing a plurality of data acquisition modes such as three-dimensional imaging, thermal imaging, micro-expression recognition, spectral information and pulse condition harmonic analysis and combining advanced models such as a graph neural network, U-Net and Bi-LSTM.

The invention relates to the technical field of fault prediction and health management, in particular to a primary and secondary fusion complete ring main unit fault diagnosis method and system. Comprising the following steps: acquiring three-phase instantaneous voltage and current signals in real time, and converting the signals into digital transient data; performing time window preprocessing on the digital transient data, and executing wavelet packet decomposition to generate a transient feature vector; calculating and analyzing the transient feature vector through a transient zero-sequence power direction method and a support vector machine model to generate a local diagnosis result; when the local diagnosis result is that cooperative positioning needs to be started, a transient current similarity coefficient and a transient waveform intensity difference coefficient are calculated based on the digital transient data, and a comprehensive positioning result is generated; determining a fault line according to the comprehensive positioning result, and generating a remote control command; and executing a fault isolation operation based on the remote control command, and executing a PHM process. According to the method, the double local transient diagnosis and the cross-terminal cooperative positioning are deeply fused, so that the high-precision determination of the boundary of the fault section is realized.

The invention relates to the technical field of underwater vehicle fault diagnosis, in particular to a multi-mode propeller fault diagnosis method and diagnosis system. Comprising the following steps: collecting and preprocessing multi-modal data; quantizing the quality of the acoustic signal data and the visual signal data obtained in the previous step in real time; constructing a quality sensing gating network model; and training a quality sensing gating network model, and outputting a propeller fault type by using the trained quality sensing gating network model. The characteristics of the low-quality mode are dominated, enhanced and corrected through the characteristics of the high-quality mode, the interaction strength of the depth characteristics is actively controlled in real time from the source quality of signals, and high robustness and accuracy of propeller fault diagnosis can still be kept in a complex and severe environment.

The invention discloses an abnormality diagnosis method for an electric energy metering device, and the method comprises the steps: building a dynamic baseline model which reflects the real normal state change rule of a target user through the full mining of the historical multi-dimensional and fine-grained data of the target user, and carrying out the refined comparison through the data collected in real time at high frequency, thereby achieving the abnormality diagnosis of the target user. Sensitive and reliable anomaly criteria are formed by comprehensively considering absolute and relative deviations, so that possible problems of the metering device can be quickly and accurately identified in an actual operation environment. Through the mode, the adaptive capacity of the diagnosis result to individual differences and behavior change trends is remarkably improved, and efficient data security guarantee and operation and maintenance support are provided for a large-scale intelligent distribution network system.

The invention discloses a fault monitoring and diagnosing method and system in the intelligent wood formwork machining process and a storage medium, and relates to the technical field of monitoring analysis, and the method comprises the steps: collecting operation state data corresponding to wood formwork machining equipment in real time through a multi-mode sensor array, performing multi-source heterogeneous data fusion processing on the acquired operation state data corresponding to the wood template processing equipment to generate an equipment operation state feature vector corresponding to the wood template processing equipment; according to a preset fault diagnosis model, carrying out analysis processing on the equipment operation state feature vector, and according to an analysis processing result, generating fault type probability distribution and a health state score corresponding to the wood template processing equipment; and executing dynamic fault diagnosis and decision feedback according to the fault type probability distribution and the health state score, and adjusting processing parameters according to a decision feedback result. The method has the effect of improving the monitoring efficiency.

The invention relates to a historical building disease diagnosis method and device assisted by artificial intelligence, equipment and a medium. The method comprises the steps of obtaining a multi-angle image sequence of a historical building and performing preprocessing to obtain a standardized building structure image, performing edge feature extraction on the standardized image to identify dislocation feature points to generate an edge information graph, and segmenting a dislocation region through a region growing algorithm based on the edge information graph to generate an initial dislocation region distribution graph. Performing deformation degree and displacement calculation on each region in the distribution diagram to determine a dislocation severity level, comparing dislocation region changes in each period through a time sequence analysis algorithm by combining an image sequence to generate a dynamic change mode, and optimizing boundary division according to the severity level and the dynamic change mode to generate a diagnosis report; automatic identification and evolution tracking of historical building dislocation diseases are realized, the problems of high subjectivity, fuzzy boundary judgment and lack of disease evolution prediction of traditional manual investigation are solved, and the scientificity and long-term effectiveness of repair decision are improved.

The invention relates to the technical field of equipment health management and fault diagnosis, and particularly discloses a time-frequency enhanced current diagnosis method. According to the method, multi-phase current, temperature and rotating speed data are synchronously obtained, and a standardized sequence is generated through time alignment and noise suppression; self-adaptive spectrum analysis is carried out based on the working condition robust index, and a multi-scale energy diagram and frequency domain features are generated; fusion embedding is constructed through time domain feature extraction and time-frequency alignment mapping; and carrying out dynamic weight fusion and quality judgment in combination with the cross-modal consistency score, and finally generating a lightweight deployment model. According to the method, the anti-interference performance and interpretability of current diagnosis characteristics under multiple working conditions are effectively improved, accurate extraction and lightweight deployment of fault characteristics are realized, and the adaptability and reliability of a diagnosis system in a complex industrial environment are remarkably enhanced.

The invention provides a cross-domain equipment fault diagnosis method and system based on large and small model cooperation, and relates to the technical field of equipment fault diagnosis. According to the method, the causal field generalization structure is introduced into the small model, explicit decomposition is carried out on the stable causal law and the field specific difference, and meanwhile, the causal field generalization structure is corrected by using the large model, so that the small model can automatically identify and retain the causal relationship which is universally applicable to each device and each field; therefore, the influence of inter-domain distribution difference is effectively eliminated. Theoretical analysis shows that the generalization error of the model mainly depends on the accuracy of the stable causal item, and the structure can minimize error drift caused by distribution drift. Therefore, the robustness of health state evaluation and fault prediction can be remarkably improved in a cross-domain scene, and the fault diagnosis model can still keep the prediction capability close to the training domain level under the condition of no target domain annotation data.

The invention discloses a fault diagnosis method, device and equipment for an automatic door and a storage medium, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting door body displacement, operation speed, motor current and other core data, combining environment temperature and humidity and time period information, calculating an environment and time period influence coefficient, and carrying out the layered correction of original data, thereby obtaining second data; and the corrected data is compared with historical data, displacement, speed and current deviation rates are determined, potential abnormity is identified through comparison with a self-adaptive adjustment deviation rate threshold, and then an abnormity index is calculated to judge a fault. Meanwhile, the deviation rate change rate can be analyzed to pre-judge the fault trend, and the fault type is positioned in combination with deviation characteristics. The deviation rate threshold value is dynamically adjusted according to the accumulative operation duration, the number of times and the people flow density, and the adaptability to different scenes is improved. According to the method, real-time monitoring and accurate early warning of the automatic door are realized, and the accuracy and timeliness of fault diagnosis are improved.

The invention discloses an excitation system fault diagnosis method based on deep learning, and aims to solve the problems that an existing excitation system fault diagnosis method is poor in adaptability under complex working conditions, depends on manual feature extraction and is insufficient in novel fault recognition capability. Comprising the following steps: S1, acquiring multi-source operation data of an excitation system in real time, and constructing an original data set; s2, preprocessing and standardizing the data; s3, constructing an end-to-end diagnosis model based on the convolutional neural network and the long short-term memory network; s4, automatically extracting spatio-temporal features of the input data and generating feature vectors of health states; s5, performing fault diagnosis on the feature vector, and outputting the operation state of the excitation system; and S6, generating a fault alarm signal and a diagnosis report according to the operation state obtained by fault diagnosis. According to the method, the accuracy and the intelligent level of fault diagnosis are improved, the adaptability of the system to complex working conditions and novel faults is enhanced, and safe and reliable operation of power equipment is guaranteed.

The invention discloses a sensor fault diagnosis method, system and device based on GAT and LSTM, and a medium, and relates to the technical field of structural health monitoring, and the method comprises the steps: collecting sensor data, carrying out the marking, carrying out the preprocessing of the collected data, dividing the preprocessed data into a training set and a test set, generating a topological relation matrix and a fault positioning dictionary based on a sensor combination relation, constructing a diagnosis model, training the diagnosis model by utilizing a training set, calculating a reconstruction residual error, setting a fault judgment threshold value, inputting a test set into the diagnosis model, and judging the occurrence of a fault by comparing the reconstruction residual error with the threshold value. And matching a fault positioning dictionary according to an output result of the diagnosis model, and distinguishing fault types. By fusing data preprocessing, topological relation modeling, GAT and LSTM collaborative feature extraction and statistical threshold setting, high-precision diagnosis and precise classification of sensor faults are realized, and the method has important engineering application value.

The invention discloses a wind power gear box online fault diagnosis method based on multi-source data fusion, and particularly relates to the field of mechanical fault detection, and the method comprises the steps: S1, collecting high-frequency dynamic, medium-frequency working condition and low-frequency thermal state data, and outputting a standardized data set through time alignment, quality verification and physical constraint verification; s2, performing multi-scale decomposition to retain a fault sensitive frequency band, inverting physical parameters such as gear contact stress and the like, constructing a physical cause and effect graph, and determining fault sensitive characteristics and threshold values; s3, constructing a multi-modal feature tensor, and obtaining a low-dimensional health representation vector through CP decomposition fusion, graph neural network reasoning and variational auto-encoder dimension reduction; s4, calculating a weight by using an entropy weight method, calculating a dynamic health degree in combination with a health benchmark, predicting a trend by using LSTM, and establishing a five-level health system; s5, judging a fault mode through double-layer identification, analyzing a root cause and formulating a hierarchical operation and maintenance suggestion; the method is based on multi-source fusion and data mechanism dual drive, and precise diagnosis and operation and maintenance guidance are achieved.

The invention relates to the technical field of hydraulic valves, and discloses a digital hydraulic valve intelligent diagnosis system which comprises a data acquisition module, a data preprocessing and label generation module, a feature fusion module, a classification diagnosis and decision fusion module and a service life prediction and suggestion generation module. Data such as pressure, vibration, displacement, flow and pollution degree of the valve are comprehensively captured through deployment of a multi-source sensor array at key positions of the digital hydraulic valve and a self-adaptive acquisition strategy, early fault feature omission is avoided, then through preprocessing means such as soft-hard hybrid wavelet threshold denoising and multi-sensor time alignment, the data precision is effectively improved, and the accuracy of the data is improved. Then, through dynamic-static layered feature fusion and an attention weighting mechanism based on GRU, different feature advantages under steady-state and fault working conditions are fully combined, the fault feature distinction degree is greatly enhanced, and the problem that similar faults are likely to be confused is solved.

The invention discloses an automatic debugging and fault diagnosis system, which relates to the field of automatic equipment maintenance and comprises a data acquisition and preprocessing module, a chaotic feature analysis module, a fault mode identification and prediction module, a debugging and diagnosis execution module and a system management and interaction module. The chaos phenomenon in equipment operation data is deeply analyzed through the chaos feature analysis module, whether the equipment operation state is in a chaos state or not and the chaos degree are accurately judged by using chaos feature parameters such as a Lyapunov index, fractal dimension and correlation dimension, and the fault mode identification and prediction module is combined, so that the fault detection accuracy is improved. The system can recognize a potential intermittent fault mode in advance, predict the fault occurrence time and probability and send out an early warning signal, the fault diagnosis method based on the chaos theory remarkably improves the accuracy and timeliness of fault diagnosis, workers are helped to take preventive maintenance measures in time, the non-planned downtime is shortened, and the fault diagnosis efficiency is improved. The production efficiency is improved.

The invention provides a bearing fault variable working condition diagnosis method based on a multi-scale convolutional network and MAML, and relates to the technical field of equipment fault diagnosis. The method comprises the following steps: firstly, introducing fast Fourier transform to pre-process an original time domain vibration signal; secondly, a fault diagnosis model based on a multi-scale convolutional network and MAML is applied; and then, an internal and external circulation updating method based on model-independent element learning is adopted, so that the model can quickly adapt to a new task, and a relatively good prediction effect can be achieved only through a small amount of fine adjustment. According to the method, multi-scale feature extraction and meta-learning are creatively combined, the generalization ability of the model under variable working conditions is remarkably improved, the problem that a traditional fault diagnosis method depends on a single working condition and a large sample size is effectively solved, and the method is particularly suitable for small-sample and multi-working-condition bearing fault diagnosis scenes on an industrial site.

The invention relates to the technical field of aerospace bearing fault diagnosis methods, and particularly discloses an aerospace bearing fault diagnosis method based on a GADF-driven KAN-Swin Transform double-branch network, and the method comprises the following steps: 1, signal collection and GADF image generation: collecting bearing vibration signals in different health states through a channel sensor, and generating a GADF image; the method comprises the following steps of: performing window division on original time sequence data to construct a training / testing data set, converting each signal window into a two-dimensional image by adopting a Grami angular difference field method, reserving time sequence correlation characteristics of the two-dimensional image through polar coordinate transformation, normalizing the generated GADF image, storing the normalized GADF image as an RGB (Red, Green, Blue) format data set, and dividing a training set, a verification set and a testing set according to a ratio of 7: 2: 1. According to the aerospace bearing fault diagnosis method based on the GADF-driven KAN-Swin Transform double-branch network, a reliable solution is provided for intelligent health monitoring of the aerospace bearing through collaborative integration of mechanism fusion and an attention architecture.