115 results about "Kernel principal component analysis" patented technology

The invention provides a lightning protection system effectiveness evaluation method and system based on multi-dimensional research and judgment, and relates to the technical field of lightning protection, and the method comprises the steps: collecting system index data through a bidirectional feature extraction network, employing a separable convolution layer with an attention mechanism and multi-scale Fourier transform to extract features, and carrying out the analysis of the features; an index transfer function is constructed in combination with a causal reasoning network, evaluation grade division is performed by adopting kernel principal component analysis and density clustering, and system parameters are optimized through a hierarchical reinforcement learning framework, so that accurate evaluation and dynamic optimization of the state of the lightning protection system can be realized, and the reliability and the protection effect of the system are improved.

A method, system, and readable storage medium for fault line selection in distribution networks is provided. The method includes: obtaining the zero-sequence current of each feeder and the zero-sequence voltage of the busbar within a preset time window after a fault occurs; using these to process the feeder's short-time window zero-sequence instantaneous power curve cluster in the distribution network through KPCA (Kernel Principal Component Analysis) for dimensionality reduction, determining the principal component scores; and performing BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering based on these scores to identify whether a feeder is faulted. This clustering process allows for precise and rapid identification of the faulted feeder, even when the current is small, improving detection accuracy. This solves the problem of quickly identifying the faulted feeder in a small current grounding distribution network during single-phase grounding faults.

The invention relates to the technical field of ship working condition recognition, in particular to a multi-working-condition recognition method, system and device for an all-electric ship and a medium, and the method comprises the steps: obtaining the historical operation data of the all-electric ship, carrying out the feature extraction and noise reduction, and obtaining a noise reduction data set of each sample; preliminarily clustering the noise-reduced propulsion load and the first-order derivative of the propulsion load into a plurality of basic working conditions, and adding a unique basic working condition label for the noise-reduced data set; judging whether the absolute value of the first-order derivative of the propulsion load of each sample after noise reduction is higher than a preset threshold value or not, summarizing noise reduction data groups corresponding to all samples with positive results into a high-fluctuation data set, and summarizing the propulsion load, the first-order derivative of the propulsion load and the basic working condition labels corresponding to other samples into a general data set; and using a kernel principal component analysis algorithm to extract main features of data in the high-fluctuation data set and the general data set, inputting the main features into the pre-trained working condition subdivision model, and outputting a specific working condition category. The working condition identification precision and real-time performance of the all-electric ship can be improved.

The invention provides a photovoltaic output prediction method and a distributed optical storage transformer area active / reactive support capability optimization method, and belongs to the technical field of power system automation. Comprising the following steps: constructing a prediction model based on meteorological information, extracting multi-scale features of meteorological data, reducing dimensions through kernel principal component analysis, predicting photovoltaic power through a long-short-term memory network, quantifying the uncertainty of the photovoltaic power, and forming a prediction interval. Based on a photovoltaic power prediction result, a robust nonlinear programming model is established, the sum of absolute values of active / reactive power transmitted to a main power grid by a light storage area is maximized as a target, and linear and nonlinear constraints such as energy storage operation, power balance, equipment capacity and power factors are considered at the same time. The robustness under the photovoltaic output uncertainty is ensured through a budget uncertainty set; the model is solved by adopting a sequential quadratic programming algorithm. According to the method, the photovoltaic power prediction precision is effectively improved, and organic combination of high-precision prediction and robust nonlinear optimization is realized.

The invention discloses an elevator fault prediction method and system based on the big data technology, and the method comprises the steps: collecting and preprocessing elevator multi-source data, and generating a standardized data set; performing kernel function mapping and kernel principal component analysis dimensionality reduction to obtain a dimensionality-reduced feature sequence; the sequence is sent into a gating Transform in a segmented mode, and a time sequence modeling result is output; identifying a fault type based on a time sequence modeling result and predicting a future fault probability; the risk threshold is compared, the fault risk is judged, and early warning information is generated; and sending a judgment result and early warning information to an operation and maintenance end to assist in maintenance decision making. According to the method, efficient prediction and intelligent early warning of elevator faults are achieved by fusing kernel principal component analysis and gating Transform, and the operation and maintenance response efficiency and the equipment operation safety are improved.

According to the plateau area load prediction method and system based on the signal decomposition and the neural network, an original signal is decomposed into a plurality of intrinsic mode components with physical significance by adopting completely adaptive noise set empirical mode decomposition, mode aliasing is suppressed, and multi-scale features are extracted to effectively suppress load demand oscillation; the problem of non-stationarity of the load of the service area is solved; then, K-means clustering grouping is carried out on the components, similar modes are combined to reduce calculation redundancy, and the problems of heterogeneity and local feature redundancy in complex time series data are effectively solved; performing nonlinear dimension reduction processing on the grouped reconstructed signals by adopting kernel principal component analysis, eliminating noise interference and extracting key features, thereby effectively solving the multiple nonlinear problem; and finally, inputting the dimensionality-reduced features into a long-short-term memory network modeling time sequence dependency relationship, and realizing high-precision prediction through adaptive weighted integration of component prediction results.

The invention relates to the technical field of laser frequency stabilization control, and discloses a multi-mode laser frequency stabilization error feedback correction system driven by machine learning. The system comprises a multi-modal data acquisition module for acquiring various data to generate a multi-modal time sequence data set; the error feature extraction module is used for extracting an error feature tensor by using a kernel principal component analysis algorithm; the dynamic compensation modeling module is used for constructing a support vector regression model to generate a dynamic compensation strategy matrix; and the feedback control optimization module is used for designing a self-adaptive model prediction control framework to generate a closed-loop correction instruction sequence. In addition, a self-adaptive correction execution module, an error traceability analysis module and an abnormal mode recognition model are further arranged. Through multi-modal data acquisition and analysis and intelligent modeling and control, high-precision laser frequency stabilization is realized, the system can effectively adapt to a complex environment, the stability and reliability of the system are improved, and the system has a wide application prospect in the fields of laser processing, optical communication and the like.

The invention relates to the technical field of power system fault monitoring, and discloses a measurement switch fault identification and early warning system, which comprises a signal acquisition unit, a feature analysis unit, a fault judgment unit and an early warning trigger unit. The signal acquisition unit acquires a potential fluctuation sequence and an exciting current pulse sequence through a contact voltage sensor and a coil current transformer; the feature analysis unit extracts time-frequency domain and statistical domain feature groups by means of wavelet transform and moment function analysis; the fault judgment unit generates a fault judgment vector by adopting kernel principal component analysis fusion features; and the early warning triggering unit triggers four-level early warning by using dynamically updated grading threshold values (including historical reference values and online dynamic values). According to the system, through multi-modal feature fusion, dynamic threshold adjustment and a grading early warning strategy, accurate identification and grading early warning of measurement switch faults are achieved, the fault identification accuracy and early warning timeliness are improved, and the system is suitable for the field of electric power and industrial automation.

The invention discloses a switch cabinet partial discharge fault identification method based on multi-fault information fusion, and the method comprises the steps: building different partial discharge fault models in a switch cabinet, and carrying out the data collection of partial discharge signals generated by different models through a plurality of sensors; dividing the collected multi-fault multi-source signal data into a training set and a test set; carrying out noise reduction processing on the signal data by using an LMS-wavelet transform method; constructing time domain feature parameters for the denoised data, and extracting features by using a sliding window method; performing dimension reduction processing on the features by using a kernel principal component analysis (KPCA) method; and training a PNN model based on the signal features to realize fault identification. According to the method, monitoring information of various sensors is fused, the LMS-wavelet transform method and the KPCA kernel principal component analysis method are introduced at the same time to carry out noise reduction and feature dimension reduction on complex multi-source data, the operation time of fault recognition is greatly shortened, and the efficiency of switch cabinet partial discharge fault detection is improved.

The invention is suitable for the technical field of radar signal processing, and provides a tomographic SAR super-resolution imaging method based on a structured sparse network, and the method comprises the steps: firstly obtaining multi-channel SAR observation data, constructing multi-channel observation vector MMV data in a data domain, and constructing a multi-pixel signal model corresponding to the MMV data based on a neighborhood pixel elevation consistency hypothesis. The method comprises the following steps: designing a kernel principal component analysis KPCA expansion network model, carrying out dimension reduction and enhancement on MMV data, introducing a norm compressed sensing model on the basis of the MMV data after dimension reduction and enhancement, solving the compressed sensing model by using an ADMM iterative algorithm, expanding the ADMM algorithm into a deep network, reconstructing an elevation spectrum, and obtaining an SAR image super-resolution three-dimensional reconstruction result. According to the method, the super-resolution performance and the solving efficiency of three-dimensional reconstruction can be effectively improved, and high-resolution three-dimensional reconstruction of a large-scale scene can be efficiently realized.

The invention discloses a fault diagnosis method and system based on improved residual error and GRF feature fusion. The method comprises the following steps: integrating signals of a multi-source sensor; reducing dimensions by using kernel principal component analysis; extracting features in parallel by adopting an improved residual network, an LSTM (Long Short Term Memory) and a Transform; after normalization and projection transformation are carried out on output of each branch, feature weighted fusion is carried out by adopting a dynamic gating weight mechanism, and timing sequence modeling capability is enhanced by utilizing a gating recursion unit; and finally, stage mark embedding and dynamic zooming processing are introduced, and diagnosis results are output through a full connection layer and Softmax classification. According to the method, the global and local information of the time-frequency domain of multiple sensors is fused, and the diagnosis precision of the complex fault of the rolling bearing is remarkably improved.

The invention relates to the technical field of natural disaster risk assessment, in particular to a multi-disaster disaster-inducing factor internal correlation analysis method, which comprises four steps of data preprocessing, Bayesian network correlation model construction, space-time dynamic evolution analysis and risk early warning threshold model construction. And deep coupling analysis of disaster-inducing factors of multiple disasters such as mountain torrent-debris flow and the like is realized. The method comprises the following steps: firstly, collecting 17 disaster-inducing factor data such as terrain and rainfall, performing dimensionality reduction through kernel principal component analysis, and calculating a dynamic weight by adopting an entropy evaluation method; then constructing a Bayesian network model to quantify conditional probability association; introducing a space-time attention mechanism to optimize a multi-scale coupling weight; and finally, establishing a coupling risk early warning threshold value and outputting a relevance intensity matrix. According to the method, the problem that a traditional single-disaster analysis method cannot capture the space-time linkage effect of disaster-inducing factors is solved, the risk early warning accuracy of multiple disasters is improved, and scientific decision support is provided for regional disaster prevention and reduction.

The invention belongs to the technical field of electric data processing, and particularly relates to a data processing method for an intelligent mutual inductor to solve the technical problem that in the prior art, the accuracy of an online evaluation result of the operation state of the intelligent mutual inductor is poor, and the method comprises the following steps: S1, obtaining time sequence sampling data of current or voltage output by the intelligent mutual inductor; s2, performing noise reduction on the time sequence sampling data by adopting an unscented Kalman filtering algorithm to obtain first processed data; s3, windowing truncation is carried out on the data after the first processing, and a preliminary frequency spectrum is obtained through fast Fourier transform; s4, determining a spectral line screening threshold, and generating a corrected harmonic feature vector; and S5, performing kernel principal component analysis on the corrected harmonic feature vector, extracting the maximum feature value as nonlinear principal component energy, and taking the geometric mean of all the feature values as a kernel space generalized variance. And the high accuracy of the harmonic and inter-harmonic spectral lines screened from the frequency spectrum in the aspects of frequency and amplitude is ensured.

The invention discloses a photovoltaic power prediction method and system based on improved VMD and Stacking ensemble learning, and a storage medium, and the method comprises the following steps: S1, collecting original meteorological data, and carrying out the preprocessing of interpolation and correlation screening, and obtaining preprocessed data; s2, decomposing the preprocessed data by using the improved VMD, and performing dimensionality reduction on the decomposed data by adopting a kernel principal component analysis method to obtain meteorological characteristic data; improving VMD to dynamically update a penalty factor and a mode total number; s3, the meteorological feature data is used for training an improved Stacking integrated learning model, the improved Stacking integrated learning model is provided with an objective function used for autonomously adjusting the weights of a plurality of base learners, and an output result of the Stacking integrated learning model serves as a prediction result; the device can effectively extract the feature information in the data and fully utilize the diversity of the base learner to provide a more accurate prediction result.

The invention relates to the technical field of mine dynamic disaster prediction, in particular to a rockburst prediction method based on an improved WOA optimized FTWSVM. The method comprises the following steps: firstly, selecting rockburst intensity grade prediction index data, and reconstructing a disaster sensitive index set by adopting a kernel principal component analysis (KPCA) method; then, nucleation extension is added, a fuzzy weight is introduced to improve a double twin support vector machine to obtain an FTWSVM model, and M-Map chaotic mapping and opposition learning are applied to improve a whale search algorithm to obtain key hyper-parameters of an IWOA optimization model; and finally, training the model, establishing a prediction model based on the IWOA-FTWSVM, and verifying the performance of the prediction model through a test set. According to the rockburst intensity grade prediction method provided by the invention, parameter self-adaptability and a small sample modeling method are combined, the rockburst intensity grade prediction capability is improved, and a solution is provided for effective and timely prevention of mining rockburst disasters.

The invention discloses a distributed energy storage system cooperative control method based on data fusion, and belongs to the field of power system energy storage control. The edge layer performs dimensionality reduction on multi-source data through kernel principal component analysis, the cloud constructs a space-time correlation model by combining a graph neural network with a long-short-term memory network, and data privacy is guaranteed by means of federal learning distributed updating. An upper layer model prediction control framework is combined with an improved whale algorithm to solve multi-target optimization of system economy, energy storage life and power grid stability; the lower-layer power type energy storage adopts self-adaptive droop control, and the energy type energy storage model predicts, controls and quantifies the degradation cost of the battery. When communication is interrupted, the analytic hierarchy process and the consistency algorithm cooperate to disperse power distribution; and when the equipment fails, convolutional neural network diagnosis is combined with alliance chain redundancy switching. Multi-source deep correlation, characteristic differentiation regulation and control and high robustness are realized, the energy storage efficiency is improved, the service life of equipment is prolonged, and the method is suitable for micro-grids, smart grids and other scenes.

The invention relates to an aero-engine fault diagnosis method and device based on tensor decomposition and a medium, and the method comprises the steps: collecting the gas path parameter time sequence data of a plurality of parts of an aero-engine, and carrying out the time alignment and working condition label labeling; for the multi-parameter data of each component, a kernel principal component analysis and automatic encoder fused feature extraction method is adopted, and parameters of different components are unified into fixed dimension features; the feature matrixes obtained after feature extraction of all the components are combined, and a time-component-feature third-order tensor is constructed; training a high-order singular value decomposition model based on the engine health monitoring data; fault diagnosis is carried out by calculating the reconstruction error of the test data and the monitoring data; and fault positioning is realized by combining core tensor difference analysis. According to the method, the problem of feature fusion of engine multi-source heterogeneous data under variable working conditions is solved, and the engine fault detection sensitivity and positioning precision are improved.

The invention belongs to the technical field of electromechanical system simulation, and particularly relates to an electromechanical simulation model evaluation method based on AI analysis, which comprises the following steps: data preprocessing, AI evaluation engine construction, dynamic index evaluation and closed-loop optimization. An electromechanical simulation model evaluation system based on AI analysis comprises the following layered architecture: a data preprocessing layer, an AI evaluation engine layer, a dynamic evaluation layer and a closed-loop optimization layer, one of kernel principal component analysis or a variational auto-encoder is introduced to carry out nonlinear feature extraction, and game Nash equilibrium is introduced to carry out model weight dynamic allocation. And sliding window anomaly detection is introduced to dynamically adjust the threshold. According to the invention, a'physical rule + AI reasoning 'dual-drive evaluation architecture of the electromechanical simulation model is initiated; the dynamic weight distribution algorithm improves the evaluation efficiency compared with a traditional method; and the three-level indexes converge step by step, so that comprehensive evaluation from local performance to global performance is realized.

The invention belongs to the technical field of fault diagnosis, and discloses a magneto-rheological semi-active suspension actuator fault diagnosis system and method, and the method comprises the steps: constructing a training set based on historical execution data; constructing a fault classification model based on a machine learning algorithm, and training the fault classification model by using the training set; and collecting real-time execution data, inputting the real-time execution data into the trained fault classification model, and outputting to obtain a fault type related to the real-time execution data. The fault classification model comprises a feature extraction layer, a feature fusion layer and a classification layer; the feature extraction layer extracts time domain fault features and dimension reduction frequency domain fault features through a convolutional neural network and a fast Fourier transform dual channel; and the feature fusion layer performs superposition fusion on the time domain fault features and the dimensionality reduction frequency domain fault features, and performs dimensionality reduction again on the combined fault features obtained through superposition fusion through kernel principal component analysis. The feature information integrity is ensured, and the diagnosis confidence is improved.

The application relates to a method and device for monitoring over-error of a capacitive voltage transformer, electronic equipment and a computer readable storage medium. The method comprises: performing kernel principal component analysis based on historical sample data to determine an SPE statistical quantity threshold; calculating the SPE statistical quantity of current sample data collected in real time; if the SPE statistical quantity is less than or equal to the SPE statistical quantity threshold, determining that the operation state of the transformer group is normal; and if the SPE statistical quantity is greater than the SPE statistical quantity threshold, calculating the contribution rate of each element in the current sample data to the square prediction error statistical quantity, determining a fault element, and locating an over-error transformer in the transformer group. The application effectively processes the nonlinear problem of data by using KPCA, more accurately extracts data features, realizes fault element separation, accurately locates the over-error CVT, performs online real-time monitoring on the CVT error, and improves the stability and reliability of the power system.

Described is a system for detecting and classifying new patterns of objects and images for applications where labeled data is scarce. In operation, the system trains a neural network with unlabeled images and extracts features with the neural network from both the unlabeled images and a set of labeled images to generate a feature space. Labels are propagated in the feature space using nearest neighbors, allowing for modeling of a per-class simplified distribution. An object in a new test image can then be classified using reconstruction error based on the per-class simplified distributions.

The invention provides a third industry user-oriented load adjustable characteristic evaluation and interactive regulation and control method, and aims to improve the demand side resource adjustable capability and support supply and demand coordinated regulation and control under a novel power system. The method comprises the following steps: firstly, collecting typical three-product user loads and multi-source heterogeneous data, after preprocessing and feature extraction, realizing feature dimension reduction by adopting kernel principal component analysis, and completing typical user load clustering through a K-Means clustering algorithm; and secondly, constructing a multi-dimensional user portrait evaluation system based on a clustering result, and quantitatively evaluating the adjustable potential of the typical user load in combination with a multi-dimensional time sequence load model. And finally, establishing a multi-constraint optimization scheduling model, introducing a particle swarm optimization algorithm for solving, and outputting targeted regulation and control strategies and regulation instructions of different time periods and different devices. According to the method, refined evaluation and rapid scheduling optimization of the three-production user load adjustable potential are effectively realized.

The invention discloses a customer group classification method and device based on machine learning and electronic equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the dimension reduction processing of an original data set through employing a kernel principal component analysis algorithm, obtaining a data point set after the dimension reduction, calculating the outlier factor of each data point through employing a local density factor algorithm, and obtaining an outlier classification result; removing abnormal data values by using outlier factors, calculating connection strength between nodes according to a relationship between data points, establishing a linear equation set, solving the linear equation set, obtaining an initial clustering center point, performing clustering analysis based on the initial clustering center point, classifying each data point in a data point set after dimension reduction into a cluster to which the closest centroid belongs, and performing clustering analysis based on the clustering center point. And obtaining a financial management customer group classification result until a clustering convergence condition is met. The technical problems that the real-time updating demand of large-scale customer data cannot be adapted during financial management customer classification in the related technology, the recommended product does not meet the demand, and the customer satisfaction is reduced are solved.

The invention relates to the technical field of medical information, and discloses a psychological disease treatment effect evaluation method and system adopting data analysis. The method includes: classifying standardized physiological parameters based on the model; performing kernel principal component analysis on the sleep quality parameters to obtain dimensionality reduction vectors, and performing nonlinear mapping on the dimensionality reduction vectors and the voice emotion parameters to obtain cross-modal correlation features; if the correlation coefficient exceeds the threshold value, kernel function parameters are adjusted in combination with emotion stress parameters, and features are extracted again; a dynamic weight coefficient is obtained according to the cross-modal correlation features and a weight mechanism, and a treatment effect index is calculated in a weighted mode; and identifying an index abnormal mode through abnormal detection, and generating a personalized evaluation report. Through multi-dimensional physiological data analysis and dynamic adjustment, the problem that evaluation lacks objective multi-dimensional continuous monitoring and dynamic personalized analysis is solved, and the evaluation precision and the personalized level are improved.

This invention provides a method for predicting aging based on metabolite profiles using nonlocal variance enhancement, relating to the fields of medical data analysis and metabolomics. The method acquires LC-MS and / or GC-MS metabolite profile data and the actual age of the subject to be predicted. It performs missing data checks and intra-class normalization on the data, constructs a class-constrained nonlocal variance enhancement feature extraction model, and combines multi-kernel principal component analysis and linear multi-view fusion to obtain fused metabolite profile features. The fused features are then input into a support vector regression model to output predicted age values. The aging degree is determined based on the difference between the predicted and actual age values, which improves the feature expression ability and interpretability of metabolite profile age prediction.

The invention relates to the field of reliability analysis of aircraft hydraulic systems, in particular to a two-stage residual life prediction method of an aircraft hydraulic system based on data dimension reduction, which comprises the following steps: acquiring state data of the aircraft hydraulic system and preprocessing the state data; sequentially carrying out principal component analysis and kernel principal component analysis on the preprocessed data to obtain dimension reduction data; establishing a dual-target prediction model; inputting the trend term and the periodic term into a double-target prediction model to obtain a preliminary life prediction value; inputting the dimension reduction data into a residual prediction network to obtain a life residual prediction value; the residual prediction network is composed of a one-dimensional convolutional neural network and a bidirectional long-short term memory network which are connected in sequence; adding the initial life prediction value and the life residual prediction value to obtain a final residual life prediction value; according to the method, the residual life prediction efficiency and robustness of the aircraft hydraulic system can be improved.

The invention discloses a high-voltage circuit breaker health state assessment method and system based on multi-source data fusion, and the method comprises the steps: constructing a health index model based on multi-source data fusion, and carrying out the effective dimension reduction of complex features in the multi-source data of a high-voltage circuit breaker through the kernel principal component analysis; and the dimensionality-reduced features are fused through a weighted mahalanobis distance to obtain a one-dimensional health index capable of comprehensively reflecting the operation state of the equipment. On the basis, the degradation starting point is identified based on the degradation rate, and the normal state and the potential fault state of the equipment are divided, so that the problems of dependence on a single data source, incomplete evaluation, incapability of early identification of performance degradation and the like in the prior art are solved, and the change of equipment performance can be identified earlier; and accurate evaluation and predictive maintenance of the health state of the high-voltage circuit breaker are realized.

The present invention relates to the field of automobile fault detection technology, and specifically to an automobile fault detection method, device, equipment and storage medium. The present invention first collects data at each moment of the detected automobile in real time, uses the current moment data among the data at each moment to form a current sample matrix, then applies a dynamic principal component analysis algorithm to the current sample matrix to obtain an augmented matrix of the current sample matrix, then applies a kernel principal component analysis algorithm to the augmented matrix to obtain a kernel matrix, and then constructs a principal component matrix. Finally, the eigenvectors of the principal component matrix and the kernel matrix work together to determine the detection result of the automobile. The present invention uses data for judging the detection result to come from the detected automobile itself, inputs the objective data of the automobile data into a mathematical model composed of a dynamic principal component analysis algorithm and a kernel principal component analysis algorithm, and can quantitatively detect automobile faults based on the data output by the data model, thereby improving the detection accuracy.

The present application relates to the technical field of ship operating condition identification, and specifically to a multi-operating condition identification method, system, equipment, and medium for all-electric ships, including: obtaining historical operating data of all-electric ships, performing feature extraction and noise reduction, and obtaining noise reduction data groups for each sample; preliminarily clustering the noise-reduced propulsion load and the first-order derivative of the propulsion load into multiple basic operating conditions, and adding a unique basic operating condition label to the noise reduction data group; judging whether the absolute value of the first-order derivative of the propulsion load after noise reduction of each sample is higher than a preset threshold, summarizing the noise reduction data groups corresponding to samples with all the results being yes into a high-fluctuation data set, and summarizing the propulsion load, the first-order derivative of the propulsion load, and the basic operating condition labels corresponding to other samples into a general data set; using the kernel principal component analysis algorithm to extract the main features of the data in the high-fluctuation data set and the general data set, and then inputting them into a pre-trained operating condition segmentation model to output specific operating condition categories. The present application can improve the accuracy and real-time performance of all-electric ship operating condition identification.

The invention discloses a high-reliability planetary gearbox fault diagnosis method, system, medium and equipment, and the method comprises the steps: carrying out the feature extraction and feature selection based on a random forest algorithm, and carrying out the nonlinear feature extraction based on multi-scale permutation entropy, performing multi-scale coarse graining processing on each vibration signal, calculating permutation entropy values of coarse graining sequences under different scales, forming a multi-dimensional phase space, and obtaining a feature matrix of permutation entropy; performing feature dimension reduction and feature embedding based on kernel principal component analysis; according to the fault diagnosis based on the bidirectional long-short term memory neural network, a classifier of the bidirectional long-short term memory neural network is used, a feature set is divided into a training set and a test set to serve as input layers, and then a classification result is output through two LSTM units of a hidden layer, a full connection layer and Softmax function mapping. And high-reliability planetary gearbox fault diagnosis is realized.