95 results about "Probabilistic neural network" patented technology

The invention relates to the technical field of artificial intelligence and data processing, in particular to a mining explosion-proof high-voltage power distribution device-oriented all-insulation state intelligent monitoring method, which specifically comprises the following steps of: acquiring training data to construct a data set; marking the data in the data set; preprocessing the data in the data set; constructing an intelligent monitoring model for the full-insulation state of the mining explosion-proof high-voltage power distribution device based on a deep probabilistic neural network, inputting the preprocessed data set into the model for training, and repeating the iterative training process until a preset iteration stopping condition is met to obtain a trained model; and inputting the preprocessed real-time data into the trained model, generating probability distributions of normal operation, slight degradation and serious degradation and corresponding uncertainty quantized values by an output layer, and obtaining a final diagnosis result according to a preset discrimination condition. According to the invention, the accuracy and anti-interference capability of insulation state diagnosis are significantly improved.

The invention relates to the technical field of intelligent evaluation and early warning methods, in particular to a nuclear radiation dose space distribution intelligent evaluation and early warning method and system, and the method comprises the following steps: obtaining multi-source monitoring data which comprises gamma radiation dose, alpha radiation dose and beta radiation dose measuring instrument data; performing fusion and enhancement processing on the multi-source monitoring data to obtain an enhanced radiation monitoring signal; based on the enhanced radiation monitoring signal, high-precision prediction of nuclear radiation dose spatial distribution is carried out through a deep probabilistic neural network model; according to the high-precision prediction result, in combination with a nuclear accident severity level standard, a radiation alert evaluation result and early warning information are generated, short-term radiation fluctuation can be captured, a long-term change trend can be identified, and the understanding ability of the system to a complex radiation field is greatly improved.

The invention relates to a wind power cabin equipment quality evaluation and maintenance decision-making method based on reinforcement learning, and belongs to the field of wind turbine generator operation and maintenance. According to the method, the state degradation model of the wind power cabin is established by using the Markov decision process, and the state transition probability is estimated in combination with the dynamic integrated probabilistic neural network, so that the prediction accuracy is improved. Weibull distribution parameter estimation is carried out on the fault data of the subcomponents of the wind turbine cabin through the grey model, life reliability analysis of the components is completed, then a reliability function of the wind turbine cabin is obtained, and equipment quality evaluation is achieved. According to the method, a maintenance method combining post maintenance and opportunity maintenance is adopted, an objective function of a maintenance decision model is established according to a component reliability function and a maintenance cost model, and an optimal maintenance strategy is solved by using an AD3QN algorithm, so that the minimum maintenance cost is realized, and the operation efficiency and economic benefits of the wind power plant are improved.

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 discloses an intelligent identification method for a rock tension-shear rupture mechanism based on acoustic emission RA-AF parameters, and belongs to the technical field of intelligent identification. The method comprises the following steps: carrying out indirect tension test and variable-angle shear test on rock, acquiring RA-AF data of the rock under tension and shear loads by adopting an acoustic emission technology, and respectively constructing RA-AF standard databases corresponding to tension and shear fracture mechanisms based on a DBSCAN clustering algorithm; wherein 4 / 5 is used as training set data, and 1 / 5 is used as test set data. A probabilistic neural network is used for learning RA-AF data feature information of a pulling and shearing fracture mechanism, an intelligent classification model of the pulling and shearing fracture mechanism is established, the accuracy rate of the model is detected, and the model which reaches the detection standard through adjustment is applied to quantitative recognition of the fracture mechanism of the same rock under different static loads. According to the method, the data processing workload of workers can be reduced, and the classification efficiency is improved.

The invention relates to the technical field of fault diagnosis, and discloses a transformer adaptive fault diagnosis method, system and equipment based on a sensor, and the method comprises the steps: collecting an operation signal of a transformer through a sensing device, carrying out the feature extraction of the operation signal, and obtaining a plurality of signal feature data; each signal feature data is input into each corresponding signal fault diagnosis model to obtain a plurality of fault diagnosis sub-results, and each signal fault diagnosis model is constructed by adopting a probabilistic neural network model; according to a preset weight value, weighted summation is carried out on the fault diagnosis sub-results, a fault diagnosis result of the transformer is obtained, and the weight value is obtained through calculation based on the diagnosis accuracy of each signal fault diagnosis model. According to the invention, comprehensive data processing and diagnostic analysis are carried out on various sensor signals, so that efficient and accurate diagnosis of the transformer fault can be realized, and thus a powerful guarantee is provided for stable operation of a power system.

Disclosed in embodiments of the present application are a fault identification method and apparatus for a transformer, a device, and a medium. The method comprises: acquiring concentration data of dissolved gas in transformer oil; and processing the concentration data on the basis of an optimized probabilistic neural network model to obtain a fault type of a transformer, wherein the optimized probabilistic neural network model is determined on the basis of a target smooth factor parameter, the target smooth factor parameter is determined from among candidate smooth factor parameters by means of an artificial jellyfish search algorithm, and the candidate smooth factor parameters are smooth factor parameters meeting a fitness condition.

The invention discloses a transformer fault diagnosis method, system and device based on attention variation self-coding and a storage medium. The method comprises the steps that historical concentration data of all characteristic gases of a transformer, historical oil temperature data of the transformer and historical load data of the transformer are acquired; performing normalization processing on the historical data; constructing a self-attention variation self-encoding model; extracting fault features to obtain a fault feature training set, and constructing a probabilistic neural network fault classification model; performing normalization processing on the data monitored in real time, taking the processed real-time data as input data of a self-attention variation self-encoding model, dynamically predicting the concentration of the dissolved gas to obtain a prediction deviation of the concentration of the dissolved gas, and if the prediction deviation is greater than a preset threshold value, taking the prediction deviation as a fault feature; and performing fault diagnosis through a probabilistic neural network fault classification model by using the obtained fault features, and outputting a fault type. The dissolved gas prediction accuracy and the explanation capability of the model can be improved, and the fault can be accurately diagnosed.

A ship steam turbine fault diagnosis method relates to the field of ship steam turbine fault diagnosis. The invention aims to solve the problem that smoothing factors are difficult to determine when ship steam turbine fault diagnosis is carried out by utilizing a probabilistic neural network. According to the invention, the fault feature data of the steam turbine is collected, and the fault feature data is input into the probabilistic neural network fault diagnosis model to obtain a fault identification result. The steam turbine data is processed by adopting a Min-Max standardization method, so that the distribution and relative relation of the original data can be reserved, the scale consistency of different characteristics can be ensured, and the reliability of diagnostic data can be effectively ensured. The probabilistic neural network is optimized through a salat swarm intelligent optimization algorithm, an optimal smoothing factor can be obtained, and the problem of smoothing factor selection is solved. The method has the advantages of being easy to train, rapid in convergence, high in accuracy and the like, and faults can be rapidly and effectively recognized.

The invention provides a wind turbine generator fault diagnosis system and method, and the system comprises a data generation module which is used for calculating corresponding simulation data according to the fault mechanism of equipment through employing an aerodynamic system model, a transmission system model and a variable pitch system model, learning a distribution mode reflecting fault categories through a data training generation model together with historical faults, and generating virtual fault data; and the fault diagnosis module is used for performing fault diagnosis on the input operation data of the wind turbine generator by using the trained probabilistic neural network. The method has the advantages that enough training data can be generated according to training requirements, meanwhile, the distribution range of the training data is wider than that of mechanism simulation data, the training data is closer to real operation data, and the precision of a fault diagnosis model is greatly improved; compared with a single probabilistic neural network algorithm, the fault diagnosis module has the advantages that the training process of the probabilistic neural network is optimized by utilizing the genetic algorithm, and the training speed is increased.

The invention discloses a fault prediction method and device and a storage medium, and is applied to the technical field of computers, and the method comprises the steps: collecting ECS information, carrying out the statistics of error correction times completed in a memory particle, tracking an occurrence position, and positioning a potential weak address region where ECC frequently occurs in the particle; analyzing information such as fault types, fault addresses, error reporting time, error reporting memory temperatures, task types, task pressure and occupancy rates by the aid of the information reported by the BMC; through the above information, comprehensive working state information is obtained, the working state information is input into a probabilistic neural network PNN, probability distribution of different fault types occurring in a memory is predicted, the effect of assisting fault prediction and judgment is achieved, and prediction accuracy is improved. In addition, the working state information is input into the PNN, and the predicted probability that the memory bank has an uncorrectable fault can be obtained. Furthermore, the operation and maintenance personnel can set a fault excitation test scheme, and system risks brought by the CXL memory are eliminated.

The invention discloses a battery thermal runaway early warning method, device and equipment for an energy storage power station and a medium, the battery thermal runaway risk of the energy storage power station is predicted by using a neural network, early warning is performed before battery thermal runaway of the energy storage power station, and the safety is improved. Besides, the probabilistic neural network is trained by using a small number of training samples, and after the probabilistic neural network is converged, the back-propagation neural network is trained by using the soft label output by the probabilistic neural network, so that the problems of low learning speed and insufficient training samples in the initial training stage of the back-propagation neural network are solved, and the model training efficiency is improved. Meanwhile, the soft label can solve the problem that the back propagation neural network is prone to falling into a local minimum value in the training process.

The invention discloses a multi-domain small-scale fracture identification method based on a probabilistic neural network, and the method comprises the steps: carrying out the edge detection of the three-instant attributes of a seismic record, fully mining the fracture information of the attributes, such as amplitude, frequency, phase, and the like, and carrying out the multi-domain small-scale fracture optimization and detection through employing the probabilistic neural network technology. And finally, accurate identification of the small-scale fracture is completed. According to the innovation, the working speed and quality of small-scale fracture recognition are improved, the accuracy and credibility of small fracture prediction are enhanced, an important basis is provided for combination of geological and seismic data, and a good foundation is laid for subsequent seismic comprehensive interpretation, oil and gas reservoir dynamic and static characteristic analysis, oil and gas reservoir development scheme adjustment and the like.

The application discloses a battery thermal runaway early warning method, device and equipment of energy storage power station and medium, using neural network to predict the battery thermal runaway risk of energy storage power station, early warning before the battery thermal runaway of energy storage power station, improve the safety.In addition, first train the probabilistic neural network with a small amount of training samples, and after the convergence of the probabilistic neural network, train the back propagation neural network with the soft label output by the probabilistic neural network, solve the problem of slow learning speed and insufficient training samples in the initial training of the back propagation neural network, and improve the model training efficiency.At the same time, the soft label can solve the problem that the back propagation neural network is easy to fall into local minimum value in the training process.

The invention discloses an electro-hydraulic actuating mechanism fault diagnosis method, and relates to the field of electro-hydraulic actuating mechanism fault diagnosis. The invention aims to solve the problem that the traditional parameter optimization method is easy to fall into local optimum and high in calculation cost due to high dependence on selection of smooth factors in the existing fault diagnosis method for the electro-hydraulic actuating mechanism based on the probabilistic neural network. According to the fault diagnosis method for the electro-hydraulic actuating mechanism, the fault feature data of the electro-hydraulic actuating mechanism are collected and input into the probabilistic neural network fault diagnosis model, and a fault recognition result is obtained. The probabilistic neural network is optimized through an index-trigonometric function optimization algorithm, the optimal smoothing factor can be obtained, and the smoothing factor selection problem can be effectively solved. The method has the advantages of being easy to train, rapid in convergence, high in accuracy and the like, and faults can be rapidly and effectively recognized.

The invention provides a method for predicting the state of a wind driven generator bearing, and relates to the technical field of electrical equipment fault diagnos.The method comprises the steps that firstly, digital twin models of the wind driven generator bearing under various working conditions are established; the vibration acceleration of each measuring point of the wind driven generator is obtained through electromagnetic-structural field coupling simulation, and a feature database is constructed through obtained signal data; decomposing the signal under the fault condition in the feature database, calculating the fitness of the signal, selecting the optimal signal, and obtaining an optimal modal signal; performing time domain feature extraction on the optimal modal signal; inputting the signal feature vectors measured in different bearing states into the optimized probabilistic neural network, and finally obtaining a model capable of accurately judging the running state of the wind driven generator bearing; vibration acceleration data measured by a detection probe of the wind driven generator in practical application are input into the neural network, the current running state of the wind driven generator bearing can be distinguished, and then monitoring and prediction of the state of the wind driven generator bearing are completed.

According to the geomagnetic matching integrated navigation method based on the improved probabilistic neural network, the improved probabilistic neural network is utilized to indicate a trajectory for inertial navigation, geomagnetic actual measurement vectors are recognized at intervals, the method is short in training time, fixed in structure, simple in learning process and minimum in expected risk of misclassification, a Bayesian posterior probability output result can be obtained, and the method is suitable for popularization and application. The method has the advantages of strong non-linear recognition capability, good real-time performance, high matching rate and high precision, and can overcome the influence of geomagnetic measurement errors; smooth factors of the improved probabilistic neural network method change adaptively according to different pattern types, the relevance between feature vectors and pattern states is better represented, the actual effect of the input feature vectors on correct classification results is reflected, the improved probabilistic neural network can effectively improve the accuracy of geomagnetic matching positioning, and the accuracy of geomagnetic matching positioning is improved. Therefore, the matching result is more accurate and reliable, and the accuracy of the geomagnetic matching rate and the positioning precision is effectively improved especially in a geomagnetic feature weak region.

This invention discloses a photovoltaic system islanding detection method and system based on Santlet transform and Ridglet probabilistic neural network. The method includes: collecting historical voltage data of the photovoltaic inverter, preprocessing it, and then sequentially acquiring the data to be analyzed using a sliding window; performing time-frequency analysis using Santlet continuous wavelet transform to obtain the corresponding time-frequency spectrum; extracting multi-dimensional time-frequency feature vectors and inputting them into a trained Ridglet probabilistic neural network classification model to obtain the corresponding probability values; if the probability value is greater than a probability threshold, it is determined that the photovoltaic system has experienced islanding, and a trip signal is generated and sent to the grid-connected circuit breaker to disconnect it. This invention significantly improves the accuracy, speed, and reliability of islanding detection, effectively reduces the detection blind zone, and has good adaptability to complex power grid environments.

The invention relates to the technical field of fault diagnosis, in particular to a rapier loom fault diagnosis system. According to the system, fault tree analysis based on whale algorithm optimization and a probabilistic neural network are fused; the fault diagnosis method comprises the following steps: step 1, establishing a rapier loom fault tree diagnosis model; step 2, optimizing parameters of the fault tree model by using a whale algorithm; step 3, constructing a sample data set and carrying out normalization processing; and step 4, carrying out probabilistic neural network training and fault diagnosis. According to the diagnosis model based on fusion optimization of the probabilistic neural network and the fault tree algorithm, on the basis of a rapier loom fault tree diagnosis model, real-time newly-added state monitoring data of a monitoring system is introduced; and converting the data set into a sample feature vector, performing normalization processing, importing the sample feature vector into a probabilistic neural network algorithm model for training, and calculating and outputting a fault symptom probability, thereby realizing real-time, complete and rapid fault identification and positioning of the rapier loom fault diagnosis system.

The invention belongs to the field of oil and gas field exploration and development, and discloses a numerical reservoir model optimization and history fitting method and related device.The method comprises the steps that firstly, geological exploration data are utilized, a spatial clustering algorithm is adopted to automatically determine the local updating range of a structural model, and structural layers and stratums are optimized through spatial interpolation; outputting an optimized construction model and uncertainty measurement; based on the model and logging lithofacies attribute data, adaptively identifying a lithofacies updating key area and variation function parameters by applying a machine learning algorithm, and performing lithofacies simulation to output an optimized lithofacies model; combining the porosity, permeability and saturation attribute data, adaptively determining an attribute optimization range and variation function parameters by adopting a probabilistic neural network, and outputting an optimized attribute model through phase control attribute modeling; a three-dimensional oil-water two-phase black oil seepage model is constructed, a global optimization algorithm is adopted to automatically adjust parameters based on a target function, simulation data is calculated until convergence, optimal parameters are output to complete historical fitting, and efficient and reliable intelligent support is provided for complex oil reservoir development.

The invention relates to the technical field of reinforcement learning, in particular to a bunching station parameter optimization method based on extended Kalman filtering and reinforcement learning, and the method comprises the steps: randomly selecting an initial state from an environment based on an initial strategy and a preset target state, carrying out the sampling, and collecting a plurality of pieces of trajectory data composed of continuous empirical tetrads; training a probabilistic neural network by using the collected trajectory data in the first round of sampling to obtain a state transition model; carrying out extended Kalman filtering on each piece of trajectory data in combination with the state transition model, replacing the filtered state at the next moment into an experience tetrad of each piece of trajectory data, and storing the filtered state into an experience playback pool; and randomly sampling the experience tetrad from the experience playback pool by using a DDPG algorithm, learning and updating the strategy to obtain a new strategy, and repeating the steps until the strategy learning is completed. According to the method and the device, the influence caused by system errors can be relieved, the accuracy of state estimation is improved, and thus the strategy learning is more accurate.

This invention discloses a method and system for transformer partial discharge pattern recognition based on an optimized probabilistic neural network. The method first acquires the transformer partial discharge signal; then, it establishes a two-dimensional spectrum of the partial discharge phase distribution pattern based on the partial discharge signal and extracts discharge statistical features from this spectrum; finally, it inputs the discharge statistical features into the optimized probabilistic neural network for pattern recognition to obtain the partial discharge pattern. The optimized probabilistic neural network uses a pollination algorithm to optimize the smoothing factor, and the switching probability in the pollination algorithm is a nonlinear function that decreases with the number of iterations. This invention can more accurately and efficiently classify and identify different partial discharge patterns of transformers, providing a data foundation for transformer fault diagnosis and resolution.

This invention discloses a bolt loosening location method and system based on environmental vibration and multi-scale PNN. The location method includes the following steps: S1: Establishing a refined finite element simulation model of the transmission tower and dividing it into multi-scale hierarchical substructures; S2: Simulating bolt loosening conditions in the finite element simulation model and extracting operational modal analysis results; S3: Calculating the frequency change ratio and modal compliance residual matrix norm, and constructing a multi-source damage-sensitive feature vector after weighted fusion; S4: Constructing and training a probabilistic neural network with an adaptive smoothing factor adjustment mechanism; S5: Acquiring on-site environmental vibration data and identifying modal parameters; S6: Extracting and calculating the measured data of the tower using the random subspace identification method, and inputting it into the trained probabilistic neural network to obtain the location results of the main bolt loosening area. This method achieves accurate bolt loosening location through scientific substructure division and multi-source fusion strategies, providing a reference for the safe operation and maintenance of tower bolts.

This invention provides an intelligent evaluation method for rockburst hazard at working faces based on principal component analysis-probabilistic neural networks, belonging to the field of rockburst hazard evaluation technology. An evaluation index system is established and data is collected by combining rockburst hazard influencing factors, drill cuttings monitoring, critical stress index monitoring, and actual field conditions. Principal component analysis (PCA) is used to simplify the original evaluation index data, resulting in comprehensive evaluation index data containing information from the original evaluation indicators. The comprehensive evaluation index data is divided into a training set and a test set. A probabilistic neural network (PNN) is used to train the evaluation model on the data in the training set, and then the performance of the evaluation model is tested using data from the test set, and its accuracy is calculated. If the accuracy is greater than or equal to 90%, the evaluation model is considered acceptable; if the accuracy is less than 90%, it is considered unacceptable, and the PNN's smoothing factor needs to be modified and retrained until the accuracy of the evaluation result meets the set value.

The application discloses an acceleration-based personalized virtual reality black insertion method for the disabled, which comprises the following steps: step 1, constructing an acceleration-based and personalized black insertion duration dataset based on the types of the disabled users; wherein the acceleration is divided into T types according to the size, and the disabled users have N types; step 2, obtaining the acceleration of the user, and calculating the time domain features and the frequency domain features of the acceleration; step 3, constructing a probability neural network based on the time domain and frequency domain features of the acceleration, inputting the time domain features and the frequency domain features of the acceleration into the probability neural network, and classifying the acceleration; and step 4, judging the type of the disabled user, combining the acceleration classification obtained in step 3, and obtaining the personalized black insertion duration of the user through the personalized black insertion duration dataset. The probability neural network based on the time domain and frequency domain features of the acceleration can well reflect the differences between different accelerations, reduce the influence of abnormal values on the network accuracy, and improve the differences between different features.

The present invention discloses a method for hierarchical classification of Internet of Vehicles (IoV) data based on probabilistic neural network and reinforcement learning, belonging to the field of hierarchical classification of IoV data, which includes the following steps: S1, obtaining data samples in the IoV scenario and establishing a sample data set; S2, calculating the road condition vectors of each roadside according to the established sample data set; S3, deploying agents in each roadside, and each agent makes decisions based on its own and the road condition vectors of adjacent road sides; S4, using a probabilistic neural network to sort the data in the sample data set; S5, uploading the sorted data according to the decisions of the agents to achieve hierarchical classification of IoV data. By adopting the above method for hierarchical classification of IoV data based on probabilistic neural network and reinforcement learning, the present invention can not only classify the IoV data in combination with the actual situation, but also better allocate broadband resources and ensure that emergency data is preferentially uploaded to the cloud.

The application provides an insulator contamination grade evaluation method based on an infrared image, and comprises the following steps: S1, acquiring an infrared image of a sample insulator by using an infrared imaging device, wherein the image is acquired under different temperatures, different humidities and different contamination grades; S2, preprocessing the infrared image; S3, extracting a temperature characteristic parameter from the preprocessed image; S4, constructing a probabilistic neural network, inputting the temperature characteristic parameter, the environmental temperature and the environmental humidity into the probabilistic neural network as input features, and training the probabilistic neural network; S4, acquiring an infrared image of a to-be-tested insulator in real time, inputting the temperature characteristic parameter of the to-be-tested insulator and the environmental temperature and the environmental humidity of the to-be-tested insulator into the trained probabilistic neural network after processing through steps S2 and S3, and outputting a contamination grade of the to-be-tested insulator.

The invention discloses an electric energy quality disturbance identification method based on WEE and LVQ-PNN, and belongs to the field of electric energy quality signal analysis, and the method comprises the steps: obtaining an electric energy quality disturbance signal, decomposing the electric energy quality disturbance signal, and extracting the wavelet energy entropy of the decomposed signal; according to the wavelet energy entropy, a probabilistic neural network is trained and optimized through a learning vectorization method, an optimized network is obtained, the input of the probabilistic neural network is the wavelet energy entropy, and the output of the probabilistic neural network is an electric energy quality disturbance classification recognition result; acquiring actually measured data, and identifying the actually measured data through the optimized network to obtain a power quality disturbance classification identification result; according to the technical scheme, the disturbance types of the electric energy quality signals can be effectively classified.

The invention is suitable for the technical field of decayed tooth recognition, and particularly relates to a decayed tooth recognition method based on computer vision, and the method comprises the steps: obtaining original decayed tooth image data, and carrying out the marking and preprocessing of the original decayed tooth image data; constructing a probabilistic neural network model, and training the probabilistic neural network model based on the preprocessed decayed tooth image data; and after the training is completed, processing the new image data by using the trained probabilistic neural network model, and identifying decayed teeth contained in the new image data. Through physical prior embedding and weight matrix optimization strategies, the decayed tooth classification recognition capability of the model is improved, through a weight coupling synchronization mechanism and a self-adaptive noise injection strategy, the stability of the model in different image quality and noise environments is improved, the classification result is more reliable, and the classification accuracy is improved. Physical priori knowledge enables the model to maintain high classification performance under the condition of small samples, and dependence on large-scale annotation data is reduced.

One method includes training, using data collected from sensors, a probabilistic neural network (NN) model comprising a set of model weights. The probabilistic NN model is trained to filter out data samples that induce a threshold of model uncertainty. The method includes training, at each cycle of training the probabilistic NN model and based on the set of model weights, a joint estimator to generate gradient updates to the set of model weights that are used to predict whether model updates from the sensors are anomalous. The method includes assigning, to each sensor, a confidence coefficient value that estimates a degree of confidence of the model updates.The method includes transferring the set of model weights to a subset of the sensors for which the confidence coefficient value meets a threshold.