3878results about "Machine bearings testing" patented technology

The present invention discloses an aero engine bearing fault diagnosis method based on STFT-IncepNext, which includes the following steps: step 1: collect acceleration signals under various working conditions and fault conditions of the bearing as sample data; step 2: determine the number of sampling points of each sensor first, and perform data merging by permuting and combining data collected by sensors at different positions in the same time series, and then use short-time Fourier transform to convert the fused vibration signal into a time-frequency diagram; step 3: data preprocessing: divide into training set, test set and validation set according to fault type, and normalize the converted time-frequency diagram using average value and standard difference; step 4: build an IncepNext model, train and validate it; and step 5: use the trained IncepNext model to diagnose aero engine bearing faults and obtain a diagnosis result. The present invention can effectively identify aero engine bearing faults, can guarantee the fault identification rate, and has good accuracy.

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 discloses a rolling bearing fault diagnosis method based on an SE-ResNeXt and Informer hybrid network model, and relates to the field of industrial process control, spatial features of time series data are extracted through a plurality of convolution modules in the SE-ResNeXt, an SE module is added for adaptively adjusting the response intensity of each channel, and a fault diagnosis result is obtained. The design of a residual network is combined, so that the feature extraction process is more efficient, and fault diagnosis analysis is performed through an Informer model. In the training process of the model, cross entropy is adopted as a loss function, and optimization is carried out through an Adam optimizer, so that the prediction accuracy and stability are improved. Experimental results show that the method shows excellent fault diagnosis capability on a public data set CWRU, has good robustness and prediction precision, is significantly superior to a traditional deep learning model, and provides a new thought for state monitoring and fault prediction under complex working conditions.

The invention discloses a variable working condition rolling bearing fault diagnosis method and system, and the method comprises the steps: firstly obtaining bearing vibration signal data, temperature data and current spectrum data of a motor under different working conditions, carrying out the adaptive decomposition through employing an EEMD algorithm, and extracting a stable feature subset; then, constructing a cross-working-condition diagnosis model by utilizing transfer learning, and introducing an HMM (Hidden Markov Model) time sequence model to capture signal dynamic characteristics; during diagnosis, the working condition of the signal is firstly judged, and the corresponding model is selected for fault diagnosis. And if the diagnosis confidence is low, triggering an online updating mechanism, and performing incremental learning by using new data. Through continuous data collection and model updating, a dynamic evolution system capable of automatically adjusting the diagnosis strategy according to the working condition change is constructed, the problem that the diagnosis performance is reduced due to the working condition change in bearing fault diagnosis is effectively solved, and high-accuracy self-adaptive diagnosis is 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 discloses a mechanical rotating part fault diagnosis method and device, equipment and a medium, and relates to the technical field of fault diagnosis, and the method comprises the steps: monitoring and obtaining the current operation data of a mechanical rotating part, and carrying out the Fourier transform and continuous wavelet transform of the current operation data, obtaining a target frequency domain image and a target time frequency image under the same pixel size; inputting the target frequency domain image and the target time frequency image into a pre-training fault diagnosis model, so that a trained one-dimensional convolutional network and a trained two-dimensional convolutional network of the model carry out feature extraction and fusion on the target frequency domain image and the target time frequency image respectively, and carrying out fault classification prediction on fused image features. Outputting probability distribution results of the current operation data relative to various standard fault data sets; and taking the standard fault category corresponding to the maximum probability value in the probability distribution result as a predicted fault diagnosis result. And through similarity calculation, the fault type of the mechanical rotating part is accurately judged.

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 provides a rolling bearing fault diagnosis method driven by simulation data, and the method comprises the steps: constructing a five-degree-of-freedom dynamical model of a rolling bearing according to the failure forms of different fault types of the bearing, so as to represent the dynamic response of a bearing system in a virtual space; the method comprises the following steps: collecting vibration signals under healthy and different fault conditions in a real environment through a bearing fault simulation experiment table, and establishing a data set; the feature distribution difference between the simulation data and the measurement data is considered, and four optimization targets are constructed and are respectively used for difference feature learning and domain invariant feature learning; a Transform model is used as a feature extractor, and fault diagnosis is carried out under a cross-domain condition in combination with a mixed loss function. Compared with the prior art, the model established by the invention can perform effective diagnosis under cross-domain and sparse training data conditions, solves the problems of data feature distribution difference and sparse target domain samples in engineering practice, and has certain robustness.

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 relates to the technical field of industrial bearing detection equipment. The invention discloses an industrial bearing detection device and method, and the device comprises a pedestal plate, the pedestal plate is disposed on the ground, the top of the pedestal plate is provided with a bearing clamping device, the bearing clamping device comprises an outer ring adjustment assembly, an inner ring adjustment assembly, and an automatic clamping assembly, the outer ring adjustment assembly is disposed at the top of the pedestal plate, and the inner ring adjustment assembly is disposed at the bottom of the pedestal plate. The inner ring adjusting assembly is arranged over the outer ring adjusting assembly, the inner ring adjusting assembly and the outer ring adjusting assembly are symmetrically arranged, and the multiple automatic clamping assemblies are divided into two groups and symmetrically arranged on the outer ring adjusting assembly and the inner ring adjusting assembly. According to the invention, bearings of different sizes can be detected in a rotation state, detection equipment of a corresponding size does not need to be replaced, inner ring rotation and outer ring rotation of the bearing can be synchronously detected, and the detection efficiency is high.

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 relates to the technical field of bearing degradation analysis, and discloses a generator bearing degradation evaluation method and system based on a metal self-repairing technology. The method comprises the following steps: collecting vibration, balance and temperature data of a bearing; extracting balance and wear characteristics, and establishing a degradation index system; injecting a self-repairing material; monitoring a repairing effect, and obtaining improvement data; predicting the health state of the bearing through a memory network; and generating an evaluation report, optimizing a repair strategy, and predicting the residual life. According to the method, the health state of the bearing can be accurately evaluated, personalized optimization of a metal self-repairing material formula is achieved, whole-process monitoring and effect evaluation are conducted on the repairing process, finally, accurate prediction of the remaining service life of the bearing is achieved, and scientificity and efficiency of wind turbine generator bearing maintenance are improved.

The invention relates to the technical field of bearing fault detection, in particular to a cloud-edge collaborative bearing fault detection method and a related product, and the method comprises the steps: carrying out the preprocessing of collected signal data of a bearing in different states; performing feature extraction on the preprocessed signal data by using CNN, inputting the extracted features into a GRU model, and generating a preliminary fault detection result based on the output of the GRU model; according to the cloud detection method, fusion processing and feature extraction are carried out on collected signal data, a local binary residual network model is established, result conversion is carried out on an output result of the local binary residual network model through a multi-layer perceptron and a hidden layer, and a final bearing fault detection result is generated. The bearing fault detection method effectively solves the technical problems that the maintenance time is too long, the production process is influenced and the maintenance cost is increased due to the fact that the bearing fault detection method in the prior art cannot judge the bearing fault type.

The utility model discloses a bearing testing device, which comprises a substrate and a bearing main body, the substrate is provided with a power frame and two groups of load frames, the top end of the power frame is provided with a laser distance measuring sensor, one side of the power frame is provided with a motor, an output shaft of the motor is connected with a driving frame, the driving frame is connected with a rotating shaft mechanism, and the rotating shaft mechanism is connected with the bearing main body. The rotating shaft mechanism comprises a base disc, a first supporting piece and a second supporting piece are arranged on the base disc, a conical adjusting block is arranged between the first supporting piece and the second supporting piece, a screw rod is arranged at one end of the conical adjusting block, and the rotating shaft mechanism is sleeved with the bearing body; the distance between the first supporting piece and the second supporting piece can be adjusted according to the size of a bearing inner ring, the device is suitable for fixing bearings of different specifications and sizes, the driving force in actual use is simulated through the rotating shaft mechanism, the load in actual use is simulated through the hydraulic cylinder, and the variable quantity of a bearing structure is recorded through the laser distance measuring sensor.

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 belongs to the technical field of a computer system based on a specific calculation model, relates to a virtual and real dynamic interaction-based digital twin bearing fault diagnosis method, and aims to solve the problem that a bearing system in large rotary mechanical equipment is large in fault hidden danger and avoid serious consequences caused by faults. A generally used bearing is in a high-precision standard, so that system signal data under bearing fault operation is not easy to collect in a factory, and the unbalanced data sample distribution directly leads to finiteness of fault diagnosis in a real scene only based on a big data driving mode classification method, so that the system signal data cannot be easily collected in the factory. The method has good scene working condition self-adaption, can be applied to fault diagnosis of a bearing system in different industrial scenes, fully excavates limited industrial data information and rich physical priori knowledge, and ensures safe and reliable operation of an important rotating mechanical system through a digital twinning technology.

The invention relates to a rolling bearing intelligent fault diagnosis method based on digital twinning and partial domain self-adaption, and the method comprises the following steps: building a bearing high-fidelity digital twinning model based on a geometric model, a physical model, a behavior model and a rule model according to the specific model of a target bearing; based on the constructed digital twinborn model, vibration simulation responses of the bearing under different preset faults are obtained; under the driving of an attention mechanism, in combination with a residual convolutional network of an improved CBAM module, domain discrimination features are extracted from the time-frequency image converted from the vibration simulation signal; based on a weight mechanism of a two-stage domain discriminator, adaptively measuring contribution of each source sample, and giving a lower weight to an outlier sample; a confrontation transfer strategy is introduced to reduce the distribution difference between different domains, and effective migration of fault diagnosis knowledge from a virtual space to a physical space is realized. According to the method, the problems of fault sample insufficiency and data label missing are effectively solved through a digital twin technology, and the problem of low fault diagnosis precision caused by poor generalization ability of a digital twin model and inconsistency of data distribution and data labels is solved through partial domain self-adaption.

The invention relates to a rotating machine fault diagnosis method based on a self-supervised time-frequency comparison fusion network, and belongs to the technical field of fault diagnosis. The method comprises the following steps: converting a time sequence signal into a frequency signal by using fast Fourier transform, and respectively performing data enhancement on the time frequency signal; initializing model parameters, adopting a multi-stage contrast learning strategy, and combining instance-stage, clustering-stage and fusion-stage contrast loss to obtain a pre-training target loss function; variables in the target loss function are selected and updated until convergence or the maximum iteration number is reached, and the pre-training model is stored; pre-training model parameters are loaded, a cross entropy loss function is adopted, and a fine tuning target loss function is obtained through label guidance; selecting and updating the target loss function of the fine tuning model until convergence or reaching the maximum iteration number, and storing the fine tuning model; and loading the trained fine-tuning model parameters, and inputting diagnosis data to obtain a prediction label, namely a diagnosis result. According to the invention, the precision and efficiency of fault diagnosis of the rotating machinery are obviously improved.

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 an aero-engine bearing fault diagnosis method based on adaptive parameter FMD, and relates to the technical field of bearing fault diagnosis. A signal collected by a sensor is decomposed through FMD, and a kurtosis-correlation coefficient (K-C) screening criterion is adopted to screen and reconstruct a modal component. Meanwhile, in order to solve the defect that input parameters of FMD decomposition need to be manually set, a comprehensive screening index composed of skewness, kurtosis and information entropy is used as a target function to optimize the number N of decomposition modes and the size L of a filter. And finally, performing envelope demodulation on the optimal parameter reconstruction signal, and extracting fault features. A simulation signal, a data center signal of a certain university, an intermediate bearing fault signal of a test bed and a main bearing fault signal of an aero-engine are adopted to carry out validity verification on the method. Experimental results show that the processed bearing vibration signal has obvious fault features, can accurately decompose fault modes, and has robustness to other interferences and noises.

The invention discloses a bearing noise processing method for bearing fault diagnosis, and relates to the technical field of data processing, and the method comprises the steps: obtaining a target model of a target bearing, carrying out the retrieval in a historical fault database based on the target model, and determining a historical bearing fault record set; determining a plurality of differentiated historical bearing fault record sets; obtaining a plurality of noise feature set values and a plurality of noise feature associated bandwidths; obtaining a plurality of noise monitoring feature sets; and performing cross-scale interactive fusion to obtain a target interactive fusion noise monitoring feature set, and taking the target interactive fusion noise monitoring feature set as a bearing noise processing result. According to the method, the technical problem of low bearing fault diagnosis reliability caused by insufficient bearing noise analysis depth in the prior art is solved, and the technical effects of deeply analyzing the bearing noise data, accurately obtaining the bearing noise data and improving the bearing fault diagnosis accuracy are achieved.

The invention discloses a bearing temperature early warning method and system based on the Internet of Things, and relates to the technical field of bearing temperature testing. The bearing temperature early warning method based on the Internet of Things comprises the following steps of performing data acquisition; performing anomaly analysis and deviation analysis; executing step-by-step adjustment; and carrying out early warning operation. According to the method, the temperature anomaly index and the operation environment deviation index are obtained by conducting anomaly analysis and deviation analysis on the collected bearing temperature and operation environment data respectively, the anomaly moment is marked through the temperature anomaly index and the preset anomaly threshold value, and then whether step-by-step adjustment is executed or not is judged through the operation environment deviation index and the preset deviation threshold value. And finally, early warning operation is performed according to the temperature anomaly index, so that the effect of improving the bearing temperature anomaly monitoring accuracy is achieved, and the problem of insufficient association between the operation environment change and the temperature anomaly in the bearing temperature monitoring process in the prior art is solved.

The invention relates to the technical field of bearing testing, and discloses a radial magnetic suspension bearing supporting characteristic testing device which comprises a base, loading measuring mechanisms are arranged on the two sides of the top of the base, a height adjusting mechanism is arranged on the top of the base, a tested bearing is arranged in the height adjusting mechanism, and a rotor limiting mechanism is arranged on the top of the base. The loading measuring mechanism comprises a connecting frame, iron core supports are arranged on the inner walls of the periphery of the connecting frame, tension sensors are fixedly installed between the iron core supports and the connecting frame in a threaded mode, loading coils are wound on the outer walls of the two sides of the four iron core supports, and displacement sensors are fixedly installed on the inner walls of the four iron core supports in a threaded mode. The radial magnetic suspension bearing supporting characteristic testing device can meet various testing requirements and loading scenes, can accurately measure the static supporting characteristic of the magnetic suspension bearing at any radial position, and lays an experimental foundation for characteristic analysis of a rotor dynamic system, design of a control system and the like.