2133results about "Machine bearings testing" patented technology

The invention relates to the technical field of bearing fault detection, and discloses a bearing fault detection method and system based on a health state index. The method comprises the following steps: collecting multi-source sensing signals at least comprising a vibration signal, a temperature signal and an acoustic signal during bearing operation; respectively performing time domain feature extraction and frequency domain feature extraction on the multi-source sensing signals, and performing normalized fusion on the extracted time domain features and frequency domain features to generate a multi-dimensional health state index sequence; constructing a long-short-term memory network model based on an attention mechanism, inputting the multi-dimensional health state index sequence into the model for training, and outputting a bearing health state prediction sequence; and calculating a dynamic early warning threshold according to the historical health state prediction sequence, comparing the current prediction value with the dynamic early warning threshold in real time, and generating a fault early warning signal. The method can improve the accuracy of bearing health state evaluation and fault early warning, and is suitable for complex operation conditions.

The invention discloses a bearing fault diagnosis method based on fusion of an improved capsule network and zero sample learning, and relates to the technical field of state monitoring and fault diagnosis of electromechanical equipment, and the method comprises the following steps: collecting a multi-mode signal during the operation of a bearing, employing an improved wavelet threshold denoising algorithm for the multi-mode signal to eliminate environmental noise, and then employing a zero sample learning algorithm for the multi-mode signal; according to the method, the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm are adopted to extract the time-frequency domain mixed features as sample data, and the GAN is combined to expand the bearing sample data, so that the data dependence of traditional deep learning is broken through, the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm, and the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm. Small sample data learning is realized, and by training a pyramid capsule network and optimizing a cross entropy loss function and combining cross-modal joint optimization and a zero sample inference engine, the diagnosis accuracy of known faults is greatly improved, and unknown fault types can be effectively inferred.

The invention belongs to the technical field of bearing fault diagnosis, and discloses a self-aligning roller bearing fault diagnosis method and system, and the method comprises the steps: obtaining operation data and sensor data, carrying out the simulation through a digital twin model, calculating the deviation between model prediction and actual measurement, and carrying out the mode recognition according to the deviation. Digital twin model parameters are dynamically calibrated in a normal mode, fault type identification and bearing positioning are carried out in combination with the calibrated model in an abnormal mode, accurate diagnosis of new installation and bearing faults in a running-in period is realized, dynamic calibration of the digital twin model parameters is realized, normal deviation and abnormal deviation are distinguished, and fault diagnosis accuracy is improved. The accuracy of fault diagnosis in the new installation and running-in period is improved, and fault type recognition and fault bearing positioning are achieved.

The invention provides a bearing fault identification method based on a dynamic generative adversarial network and expert feedback, and relates to the field of bearing fault diagnosis, and the method comprises the steps: generating a high-fidelity fault vibration signal through employing a condition generator and a triple discriminator generative adversarial network; verifying and generating sample quality through a 1D residual verification network and adding the sample quality into a training set; segmenting the vibration signals passing the test by using layered adaptive sampling, and keeping high-frequency impact characteristics in the vibration signals; a dynamic sparse attention mechanism is adopted to reduce unnecessary attention calculation and improve calculation efficiency, and different types of faults are accurately recognized in combination with a hybrid expert system classifier; and detecting the confidence of the diagnosis result, and triggering a feedback mechanism to regenerate a sample to complete autonomous iterative optimization when the confidence is low. According to the method, a generative adversarial network, a fault diagnosis model and a feedback mechanism are fused, accurate diagnosis of bearing faults is achieved through multi-level data enhancement and screening feedback, the diagnosis precision is continuously improved in continuous iteration, and the method is suitable for solving the problem that a traditional method is poor in performance under data scarcity and noise interference. The innovative closed-loop evolutionary logic of generation-diagnosis-feedback is provided, and the robustness and accuracy of fault recognition are remarkably improved.

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 intelligent manufacturing equipment fault diagnosis, and particularly discloses a Meta-Transform driven multi-working-condition equipment bearing fault diagnosis method and system. The method aims at bearing fatigue damage risks caused by dynamic adjustment of technological parameters of a numerical control machine tool in the aerospace manufacturing process and challenges such as feature distribution offset and fault sample scarcity caused by variable working conditions. The diagnosis system is constructed through three core modules. The method comprises the following steps: firstly, reconstructing an original bearing signal into a multi-scale time-frequency feature space by adopting continuous wavelet transform; then designing a causal Transform architecture with a strict lower triangle attention mask, and realizing feature extraction and classification according to a physical causal law of fault propagation; and finally, integrating the mechanisms into a model-independent element learning framework, and realizing cross-working-condition rapid self-adaption through a self-adaption gradient pruning strategy. The bearing fault diagnosis accuracy under the condition of few samples is improved, the interpretability and generalization ability of the model are enhanced, and the industrial application practicability of bearing fault diagnosis is improved.

The invention relates to a rolling bearing early fault early warning and diagnosis method, which comprises the steps of extracting an envelope component from a bearing vibration signal, constructing a time-delay feedback stochastic resonance optimal model by taking an improved signal-to-noise ratio INSR as an optimization objective function, obtaining an output signal, obtaining a first reconstruction signal through CEEMDAN adaptive decomposition and IMF component screening, and obtaining a second reconstruction signal through the CEEMDAN adaptive decomposition and IMF component screening. Calculating the signal-to-noise ratio ISNR of the vibration signal at the fault characteristic frequency, comparing the signal-to-noise ratio ISNR with a preset initial threshold value, judging whether an early warning is given out or not, and if the early warning is given out, processing the vibration signal by using a multi-wavelet adjacent coefficient adaptive threshold value method to obtain a denoised signal; processing the denoised signal by combining a fast spectral kurtosis method and an ensemble empirical mode decomposition method to obtain a second reconstructed signal; and processing by using improved fast spectrum correlation to obtain a corresponding enhanced envelope spectrum, and comparing the enhanced envelope spectrum with a fault characteristic frequency for identification. Compared with the prior art, accurate early warning and diagnosis can be carried out on early weak faults of the rolling bearing.

The invention provides a rotating machine fault diagnosis method and system based on an improved deep adversarial migration network. The method and system are suitable for cross-working-condition intelligent diagnosis of rotating machines such as motors, fans and bearings. According to the method, based on frequency domain feature extraction of vibration signals, high-robustness image input is generated through GAF conversion and AUGMIX enhancement; constructing a feature extractor fusing multi-scale convolution and an attention mechanism, and realizing feature migration between a source domain and a target domain in combination with an improved domain adversarial neural network (DANN); and an entropy minimization classifier is introduced to improve the classification confidence of the target domain. And feature extraction and classification performance synchronous optimization are realized through end-to-end joint training, and the classification accuracy and generalization ability are significantly improved under the conditions of sample imbalance and no label target domain. Experimental results show that the method still keeps high diagnosis precision under the conditions of unbalanced samples and cross working conditions, and is suitable for an intelligent maintenance system in an industrial scene.

The invention provides an electrical equipment remote maintenance guidance method and system. The method comprises the following steps: capturing an aperiodic torque waveform and a magnetic field gradient abnormal signal by deploying a torque fluctuation sensor and a magnetoresistive sensor; performing multi-scale frequency band division on the non-periodic torque waveform, screening out a transient high-frequency component in a start-stop stage of equipment, and separating out a magnetic field polarity reversal characteristic from a magnetic field gradient abnormal signal; performing cross-domain matching on the occurrence time of the transient high-frequency component and the spatial orientation of the magnetic field polarity reversal feature to generate a mixed feature mark; calculating a frequency band overlapping degree based on the time-frequency energy distribution marked by the mixed features and generating a coherence map; and matching a fault historical case library according to the coherence map, and outputting a maintenance strategy set aiming at the rotating shaft dynamic unbalance and electromagnetic interference superposition fault. According to the invention, through fusion of torque fluctuation and magnetic field distortion collaborative analysis, the precision of mechanical and electromagnetic composite fault diagnosis of the rotating shaft is improved.

The invention relates to the technical field of bearing detection, and particularly discloses a deep groove ball bearing clearance assembly error detection method and system.The method comprises the steps that dynamic parameters of a deep groove ball bearing in each assembly stage in the assembly process under preset assembly parameters are obtained, and the dynamic parameters comprise vibration spectrum data, temperature distribution data and shaft diameter displacement; obtaining a dynamic clearance error value according to the plurality of continuous dynamic parameters; and obtaining stress gradient distribution data at a plurality of time nodes in each assembly stage according to the dynamic clearance error value. The deep groove ball bearing assembly quality evaluation and analysis method has remarkable beneficial effects in the aspects of deep groove ball bearing assembly quality evaluation and analysis. Through a series of closely associated steps, the actual state of the bearing is comprehensively and accurately reflected by comprehensively considering multiple parameters to accurately calculate local stress and judge the stress area from accurate judgment of a thermal-mechanical coupling stress area, obtaining of an external dynamic deformation comprehensive index and calculation of a total assembly error value and a clearance comprehensive evaluation index.

The invention is suitable for the technical field of high-speed train bearing fault analysis, and provides a bearing fault analysis method based on multiple attention and a Mamba network, and the method comprises the steps: obtaining an original fault signal of a high-speed train bearing; a neural architecture search network is utilized to optimize model parameters of the bearing fault analysis model; processing the original fault signal by using the bearing fault analysis model after model parameter optimization to obtain a fault analysis result of the high-speed train bearing; the bearing fault analysis model comprises a two-dimensional convolution kernel, a time pyramid attention module, an expansion mode searchable Mama network, a two-dimensional CNN network, an addition module, a sequence fusion attention module, a preprocessing module, an HAB attention network and an output module. According to the invention, the accuracy of bearing fault analysis can be improved.

The invention relates to the field of aero-engine bearing cavity structure system testing, and particularly provides an aero-engine bearing cavity inner lubricating oil flow state measuring system which comprises a test piece main body simulating an aero-engine bearing cavity, an oil gas supply and recovery system and a control system. The test piece main body comprises a rotor test piece and a stator test piece which are connected together, and the oil gas supply and recovery system comprises an oil supply system, a gas supply system, an oil return pool and an oil gas separation pump. The control system comprises a controller, and an oil supply throttle valve, an air supply throttle valve, an exhaust throttle valve, a test data acquisition assembly and a test piece main body driving device which are in signal connection with the controller. On the basis of the method, the lubricating oil-air flowing state in the bearing cavity can be monitored and evaluated in real time, and test support can be provided for follow-up bearing cavity optimization design.

The invention belongs to the technical field of ship bearings, and particularly relates to a ship bearing radial loading test bench and method for simulating ice load and water immersion working conditions, and the method comprises the steps: installing a to-be-tested intermediate bearing on a test platform, enabling the intermediate bearing to sleeve a test shaft, starting a cooling water circulation system to control the flow, cooling water enters a cooling water coil pipe to establish oil pool cooling circulation; a driving motor is started; a test shaft stably operates through equipment such as a reduction gearbox and the like; a dynamic pressure lubrication state is established, a radial dynamic load or a stable constant load is applied through a hydraulic loading system to simulate an icebreaking load working condition, and a simulated water body is injected into a water tank; the test conditions of the intermediate bearing in a ship immersion environment are constructed through water pressure adjustment, the temperatures of lubricating oil, a bearing bush and cooling water are monitored in real time, the loading force is adjusted through feedback data, and the performance of the intermediate bearing is judged after the test is finished. The problems that ice excitation and soaking environment simulation are insufficient and a loading structure is eccentric in the prior art are solved, and high-precision cooling and thermal stability evaluation are achieved.

The invention relates to the technical field of automobile automatic transmission, and discloses an AMT bearing operation state detection system and method under an active and passive switching working condition, and the method comprises the steps: collecting real-time data through a vibration acceleration sensor, a current sensor, a temperature sensor and a rotating speed encoder, carrying out the preprocessing of envelope demodulation, Kalman filtering and the like, and carrying out the detection of the operation state of an AMT bearing; and generating a time-frequency characteristic matrix by using variational mode decomposition and Hilbert transform. And inputting the matrix into a probabilistic neural network model adopting a sliding time window mechanism, and outputting a bearing health state probability value. A multi-parameter state evaluation model optimized by a quantum genetic algorithm is constructed, an optimal feature combination is obtained, and early warning levels and maintenance suggestions are output through a belief rule base inference device in combination with a hierarchical diagnosis control model (including an acquisition layer, an analysis layer and an execution layer). The system is further provided with a signal verification module to ensure data reliability. According to the invention, multi-source data fusion and dynamic adaptive diagnosis are realized, and the state detection precision and real-time performance of the AMT bearing under complex working conditions are improved.

The invention provides a method for automatically centering and assembling a bearing on a long-axis part, and belongs to the technical field of bearing assembly.The method comprises the steps that position information of the long-axis part and the bearing is collected in real time through a binocular camera and a laser distance measuring sensor, a central line equation is calculated through the least square method, and position deviation is determined; a centering adjustment vector is generated to control a multi-degree-of-freedom precise attitude adjustment platform to perform preliminary adjustment, a mechanical arm is matched with high-precision laser ranging to perform precise centering, the centering process is modeled into a weighted undirected graph, an optimal adjustment path is solved, optimal press fitting parameters are calculated based on a bearing press fitting physical mechanics equation, and press fitting is performed. Meanwhile, the assembling process is monitored through a force sensor and a displacement sensor, an assembling quality evaluation matrix is constructed, finally, the assembling result is detected and evaluated, and high-precision automatic assembling of the long-axis part and the bearing is achieved.

The invention belongs to the technical field of hydroelectric generating set monitoring, and relates to a water turbine bearing temperature and lubricating oil state monitoring and fault diagnosis method and system. According to the method, triple-redundancy temperature measurement values of a water turbine bearing are adopted, multiple parameters are fused, redundancy design is adopted, sensor failure diagnosis is executed, a temperature-vibration-oil pressure combined feature space is constructed according to the triple-redundancy temperature measurement values and online oil quality parameters, a fault propagation map is established, and the fault propagation map is analyzed. And finally, based on the multi-level diagnosis model and in combination with the fault propagation atlas, composite fault mode recognition is realized, a fault classification result is obtained, and the composite fault mode recognition is realized based on the multi-level diagnosis model, so that the problems of low reliability, single monitoring dimension and insufficient fault recognition capability of a sensor in the prior art are effectively solved. And the equipment reliability and the operation safety are obviously improved.

The invention discloses a truck hub maintenance-free bearing all-condition simulation testing machine, which comprises a tool table, a servo driving system, a servo hydraulic system and a temperature control simulation system, and is characterized in that the tool table is provided with a tool seat and a main shaft movably arranged on the tool seat, and a to-be-tested bearing is detachably connected between the main shaft and the tool seat; the servo driving system is in linkage fit with the main shaft to simulate the actual operation condition of the bearing, the tool table is provided with a special-shaped load plate and a mandrel, the special-shaped load plate is in linkage fit with the mandrel, and the mandrel is inserted into an inner ring shaft hole of the bearing to be tested and is detachably connected with the inner ring shaft hole of the bearing to be tested. The servo driving system is in linkage cooperation with the special-shaped load plate to simulate the load working condition of the bearing during actual operation, and the temperature control simulation system is in linkage cooperation with the bearing to be tested to simulate the actual temperature working condition of the bearing. According to the invention, the problem that a test device for carrying out a multi-working-condition simulation test on a truck hub bearing is lacked in the prior art is solved.

The invention belongs to the technical field of vibration analysis and testing of bearings, and particularly relates to a cam driven bearing wear degree monitoring method and system, and the method comprises the steps: carrying out the equal-angle resampling processing of a vibration signal through a rotating speed signal, decomposing an obtained angular domain vibration signal into a plurality of mode components through a variational mode decomposition algorithm, and carrying out the measurement of the vibration signal; according to the kurtosis value of each modal component and the correlation coefficient of each modal component and the original vibration signal, evaluating the impact saliency weight of each modal component, and performing weighted summation on the energy of each modal component to obtain comprehensive impact energy; calculating to obtain a speed decoupling wear index without the influence of the rotating speed by utilizing the comprehensive impact energy and the vibration energy calculated by the physical mapping model; and the speed decoupling wear index is compared with a preset self-adaptive alarm threshold value, and the wear state of the cam driven bearing is judged according to a comparison result. According to the invention, the problems of false alarm and missing alarm under the variable-speed working condition are solved.

The invention discloses a crane slewing bearing test tool which comprises a base and a frame plate arranged on the base, a supporting plate is horizontally arranged on the frame plate, tool bases are arranged at the two ends of the supporting plate, a mandrel is movably arranged between the two tool bases, a tool sleeve is arranged on the mandrel in a sleeving mode, and a mounting cavity is formed between the tool sleeve and the mandrel. The base is provided with a first loading piece used for applying radial loading acting force to the tool sleeve and a regulation and control piece used for being in linkage fit with the first loading piece to enable the loading direction of the first loading piece to be changed, and the frame plate is provided with a second loading piece used for applying radial loading acting force to the tool sleeve. The supporting plate is provided with a driving part used for driving the mandrel to operate so as to simulate the actual operation condition of the bearing. The problems that a traditional crane slewing bearing test tool is single in test loading direction, the actual operation working condition of the bearing is difficult to simulate, and the loading working conditions of loads in different directions during actual operation of the bearing cannot be simulated are solved.

The invention relates to a cross-bearing single sample intelligent diagnosis method based on cognitive guidance and Riemannian manifold, and belongs to the technical field of rotating machinery fault diagnosis. Aiming at the problems of insufficient global task distribution learning ability, small sample over-fitting, Euclidean modeling limitation and the like of the existing meta learning method in a cross-domain single-sample scene, a cognitive guidance Riemannian meta learning framework is provided. According to the technical scheme, the method comprises the following steps: 1) constructing cognitive prototype learning global task distribution, and guiding a model to extract high-quality general meta-knowledge from multiple tasks; 2) designing a cognitive adaptive factor to dynamically adjust source domain memory, enhancing target domain adaptation and reducing single sample deviation; and 3) introducing a Riemann metric driving strategy, mapping the data to a Grassmann manifold space, and enhancing the non-linear feature discrimination ability by using geodesic distance. According to the method, the average diagnosis accuracy in a cross-bearing single sample task reaches 93.46% and is improved by 10.04% compared with an existing optimal method, and the accuracy and generalization ability under complex working conditions are improved.

The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis model construction method based on distributed causal discovery and federated learning. According to the method, a covariance tensor containing statistical association information of an observation variable and an agent variable is obtained, a global causal graph is obtained by combining a federal causal discovery method introducing the agent variable, then a converted causal intensity matrix is embedded into a graph convolutional neural network, and finally a personalized and global diagnosis model is trained by using a FedAvg framework fused with a Dito algorithm. According to the method, distributed heterogeneous data can be effectively processed while data privacy is protected, model interpretability is improved by mining a causal relationship between variables, global generalization and client personality requirements are considered, the accuracy and generalization ability of fault diagnosis are remarkably improved, and the fault diagnosis efficiency is improved. The method is especially suitable for fault diagnosis of industrial bearings and other scenes needing dispersed sensitive data processing.

The invention provides a driving station state monitoring system for a gravity energy storage transportation track, and relates to the technical field of intelligent control, and the system comprises a calculation module which is used for responding to a primary early warning signal, collecting the real-time oil pressure data of a gear box, a bearing temperature rise rate curve and a dynamic torque feedback value of a target standby driving station, and calculating an availability index; the correction module is used for determining the position of a gearbox input end flange and a driving motor output end coupler as a first monitoring point and the position of connection between a bearing seat and an output shaft as a second monitoring point on a transmission link of the target standby driving station, and respectively calculating the axial temperature gradient difference and the radial vibration energy ratio between the two monitoring points; and generating a dynamic correction value through a preset coupling coefficient, and compensating the availability index based on the dynamic correction value to obtain a corrected index. The operation reliability of the gravity energy storage system can be improved.

A system and method for estimating remaining useful life of a bearing associated with an electric machine is provided. The electric machine is configured to transfer rotational energy to a load via a shaft supported by the bearing. The method includes obtaining operational data associated with the electric machine in real-time from one or more sources. Further, the operational data is analyzed to detect a misalignment of the shaft of the electric machine. Upon detecting the misalignment, using a virtual model of at least the electric machine to simulate a real-time behaviour of at least the electric machine is simulated to achieve a simulation result indicative of a misalignment value corresponding to the misalignment of the shaft. Further, a remaining useful life of the bearing is predicted based on the misalignment value.

The invention relates to the technical field of data processing and mode recognition, in particular to a bearing fault diagnosis method based on multi-scale feature fusion, which comprises the following steps: fusing multi-source data such as vibration, acoustic emission and rotating speed, performing angle domain resampling by using rotating speed data, generating a two-dimensional order spectrogram, and stacking to construct a three-dimensional working condition information tensor; a master-slave modulation heterogeneous neural network is adopted, high-dimensional spatial-temporal features are extracted through a main branch three-dimensional convolutional network, time sequence details are extracted from an original sequence through an auxiliary branch one-dimensional convolutional network, affine transformation parameters are generated, and dynamic modulation is achieved on the high-dimensional features; and the output state vector is mapped to a fault evolution knowledge graph, probability prediction is carried out through a graph attention network and by introducing a Monte Carlo discarding mechanism, a probability mean value is calculated as a fault classification result, and the diagnosis confidence is quantified by a probability variance. According to the invention, through multi-scale feature fusion and dynamic modulation, the problem of insufficient feature discrimination caused by scale mismatch under variable working conditions is solved.

The invention discloses a rapid batch online detection method and system for miniature bearings, and relates to the technical field of bearing relevance, and the method comprises the steps: carrying out the cooperative collection of miniature bearings, and generating a multi-dimensional detection data set; traversing the key detection area to carry out dynamic space registration, and positioning the key detection area of the miniature bearing; traversing the key detection area to carry out defect feature extraction, generating a defect probability distribution diagram, and carrying out dynamic segmentation on the defect probability distribution diagram to generate a defect type label group; and on-line sorting is conducted on the miniature bearings based on the defect type label group, a sorting instruction set is obtained, and a detection quality report is generated and fed back to a production control system. The technical problems that in the prior art, the miniature bearing detection efficiency is low, the sampling inspection precision is insufficient, and the quality of the bearing is difficult to detect and control online in real time are solved, rapid batch online detection of the miniature bearing is achieved, and the technical effect of improving the miniature bearing detection efficiency and precision is achieved.

The invention discloses a motor fault diagnosis method and system based on color image fusion symmetric point mode, and the method comprises the steps: converting a vibration signal and an electromagnetic signal of a motor into symmetric point mode images, and generating a color signal image fusing feature information; respectively abstracting the color signal images fused with the feature information into nodes and edges in a high-dimensional semantic space so as to construct graph structure data; and performing diagnosis classification on the graph structure data of the vibration signals and the electromagnetic signals by using respective capsule graph network models, and fusing diagnosis classification results of the vibration signals and the electromagnetic signals through a voting mechanism to obtain a final diagnosis classification result. According to the method, multi-channel time domain signals are converted into image expressions with dense information and consistent geometry, and unified feature modeling is carried out on the images based on a depth map structure network with topology perception capability, so that motor fault diagnosis with high diagnosis precision and strong robustness is realized.

The invention discloses a traceable self-evolution multi-agent high-speed train axle box bearing fault diagnosis method, which comprises the following steps of: firstly, acquiring a bearing working condition parameter and a vibration signal, processing to generate an envelope spectrum and characteristic data, and packaging the envelope spectrum and the characteristic data; five types of agents are initialized, data are distributed by a coordinator agent, and each agent outputs a standardized evidence unit containing information such as a unique identifier and a reasoning track under the constraint of a cue word; the coordinator constructs an evidence graph, processes the confidence coefficient of the fault component through a weighted average or specific fusion strategy, and inquires a contradictory conclusion to obtain supplementary reasoning; generating a hierarchical structured detection report based on the multi-source information; and receiving engineer recheck information, updating the case knowledge base, and dynamically optimizing cue word and agent weight. According to the method, multi-source information is fused through multi-agent cooperation, so that diagnosis conclusion traceability and system self-evolution are realized, and the accuracy, adaptability and reliability of fault diagnosis under complex working conditions are improved.

The invention discloses a rolling bearing lightweight fault diagnosis method and system based on multi-source signal fusion. Vibration, temperature and rotating speed signals are synchronously collected, and timestamps are calibrated; respectively carrying out denoising and normalization preprocessing; dividing and aligning windows; differential feature extraction: extracting time-frequency features of the vibration signals by using a one-dimensional residual CNN, and extracting abnormal measurement of the temperature / rotating speed signals by using an LSTM in combination with an isolated forest algorithm; carrying out self-adaptive weighted fusion on the features through an attention mechanism; the lightweight diagnosis model (through knowledge distillation, pruning and quantification) deduces and outputs the fault category, the health index and the confidence coefficient. The system correspondingly comprises an acquisition module, a preprocessing module, a feature extraction module, a fusion module and a diagnosis module. The method improves the early fault sensitivity, enhances the variable working condition robustness, supports the real-time deployment of edge equipment, and is suitable for the intelligent monitoring of industrial bearings.

The invention discloses a fault identification method and device for a rotating mechanism, computer equipment and the rotating mechanism. The method comprises the following steps: acquiring rotating speed information and a time domain vibration signal of the rotating mechanism; converting the time domain vibration signal into an angular domain vibration signal based on the rotating speed information; performing feature extraction on the angular domain vibration signal based on the autocorrelation matrix of the angular domain vibration signal and the filter coefficient to update the filter coefficient and obtain a target filter coefficient; and determining fault characteristics of the rotating mechanism based on a target vibration signal obtained by processing the angular domain vibration signal based on the target filtering coefficient. According to the method provided by the invention, the fault features of the unsteady vibration signals of the rotating mechanism can be effectively extracted, so that reliable fault diagnosis of the rotating mechanism is realized.

The invention relates to the technical field of modeling, in particular to an aero-engine main bearing kinetic model correction method based on sensitive feature fusion, which comprises the following steps: constructing an aero-engine main bearing kinetic model; simulation data are generated according to the collected actual measurement data, time-frequency domain features of the actual measurement data and the simulation data are extracted, and an initial feature set is constructed; based on a binary improved horse swarm optimization algorithm, sensitive features are screened out from the initial feature set, a sensitive feature set is constructed, a component matrix coefficient of the sensitive feature set is calculated in combination with a principal component analysis method, and a fused sensitive feature expression is obtained; calculating fusion sensitive features of the measured data and the simulation data by using the fusion feature expression, and taking a difference calculation formula of the two as a target function for model correction; and performing optimization based on a horse group optimization algorithm and the target function, updating model parameters of the aeroengine main bearing dynamic model, and realizing correction of the aeroengine main bearing dynamic model.