Kernel Principal Component Analysis Method Research Articles

Performance degradation assessment of rolling bearings based on the comprehensive characteristic index and improved SVDD

Rolling bearing is one of the most critical parts of mechanical equipment, so the performance degradation assessment of rolling bearing is vital to ensure the normal operation of the whole mechanical equipment. Aiming at the problems that a single degradation characteristic can only contain limited performance degradation information of rolling bearings, a large number of redundant characteristics exist in the high-dimension characteristic set resulting in the inability to effectively mine the characteristic information of rolling bearings, and that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings, a performance degradation assessment method of rolling bearings based on the comprehensive characteristic index and improved support vector data description (SVDD) is proposed in this paper. Firstly, to solve the parameter selection problem of variational mode decomposition (VMD), a parameter-adaptive VMD method based on salp swarm algorithm based on mixed strategy (MSSSA) is proposed. Secondly, to extract the performance degradation information of rolling bearings more comprehensively and fully, the comprehensive characteristic index is proposed. Then, a kernel locality preserving projection orthogonal kernel principal component analysis (KLPPOKPCA) method is proposed to reduce the dimensionality of the extracted multi-domain characteristic set of the rolling bearing. Finally, a support vector data description with negative samples (NSSVDD) is proposed and optimized by MSSSA to solve the problem that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings and improve the detection performance of abnormal data. The experimental results show that the proposed method can accurately divide the performance degradation process of the rolling bearing. Moreover, the comparison with other methods further highlights the superiority of the proposed method in determining the point in time of early fault of the rolling bearing.

Short-term Wind Power Forecasts based on VMD-KPCA and CFSBOA-BiLSTM

Accurate prediction of wind power is of great significance to reduce the impact of large-scale wind power connection and improve the security and stability of power grid. A short-term wind power forecasting method based on VMD-KPCA and CFSBOA-BiLSTM is proposed. Firstly, the key environmental factors restricting the volatility of wind power are fully considered, and the variational mode decomposition method is used to decompose the wind power and meteorological factors. Secondly, the kernel principal component analysis method is used to reduce the dimension and construct the sub-sequence characteristic data set. Finally, the multi-strategy improved butterfly algorithm is used to optimize the BiLSTM network hyperparameters and establish the CFSBOA-BiLSTM prediction model. The simulation analysis uses the measured data of a wind farm in northwest China. The experimental results show that the combined forecasting model can fully mine the hidden information of the data and improve the accuracy of short-term wind power forecasting. Compared with the BiLSTM model, RMSE, MAE and MAPE decreased by 60.37%, 66.6% and 67.59%, respectively.

A Novel Hybrid Deep Learning Model for Photovoltaic Power Forecasting Based on Feature Extraction and BiLSTM

In this paper, a hybrid photovoltaic power forecasting model is proposed based on bidirectional long‐short‐term memory network. Firstly, the photovoltaic power and meteorological data are decomposed by ensemble empirical mode decomposition. Secondly, key features in the meteorological subsequences are extracted by kernel principal component analysis method to eliminate the correlation and redundancy in the original meteorological sequence and reduce the input dimension of the model, then the coupling time characteristics between meteorological subsequences and photovoltaic power subsequence are further explored. Thirdly, a bidirectional long‐short‐term memory network optimized by improved particle swarm optimization based on inertia weight of anti‐sine function is proposed, and then the eventual photovoltaic power forecasting results are obtained by superimposing the predicted values of each prediction component. The experiment is carried out with the operation data of the Inner Mongolia power grid, and the results show that the proposed forecasting model exhibits higher prediction accuracy. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Application of Decision Tree Classification Algorithm in Quality Assessment of Distance Learning in Colleges

INTRODUCTION: The quality assessment technology of distance education in colleges and universities, as the critical technology for identifying the quality of distance education in colleges and universities, is conducive to the improvement of the quality of distance teaching and the progress of the existing means and methods of distance education, which makes the means of distance teaching in colleges and universities rich in science.
 OBJECTIVES: Aiming at the evaluation methods of higher education institutions, there are problems such as insufficient objectivity and comprehensiveness of the evaluation system, single process, and inadequate quantitative analysis.
 METHODS:Proposes a decision tree and intelligent optimization algorithm for the college distance teaching quality assessment method. Firstly, the kernel principal component analysis method is used to carry out dimensionality reduction analysis on the index system of college distance teaching quality assessment; then, the decision tree parameters are optimized through the marine predator algorithm to construct a college distance teaching quality assessment model; finally, the robustness and efficiency of the proposed method are verified through simulation experimental analysis.
 RESULTS: The results show that the proposed method improves the accuracy of the assessment model.
 CONCLUSION: The problem of insufficient objective and scientific evaluation and low precision of distance teaching quality assessment methods in colleges and universities is solved.

LaTAS-F: Locality-aware transformer architecture search with multi-source fusion for driver continuous braking intention inference

Precise inference of driver braking intention is highly correlated with traffic safety, energy consumption, and driving comfort of electrified vehicles (EVs). Until recently, gratifying results have been achieved in accurately inferring braking intention as discrete states, such as classify brake or no brake, and judge normal or emergency brake. However, accurately and quantitively predicting driver brake intensity is significant for enhancing vehicle safety, its related research is especially rarely seen. To mitigate this deficiency, a novel framework, locality-aware transformer architecture search with multi-source fusion (LaTAS-F), is put forward for continuous braking intention (CBI) inference in this paper. To consider the comprehensively complicated 'driver-vehicle' interactions, data composed by driver physical state and vehicle state are collected with multi-source sensors. A kernel principal component analysis (KPCA) method is proposed to fuse multi-source data while removing redundancies. In addition, an improved transformer with enhanced locality-aware attention mechanism (ELAM) is elaborately designed to alleviate the incapability of self-attention mechanism (SAM) in canonical transformer when capturing local context. In particular, due to the inefficiency of designing the transformer entirely by hand, a tree-structured parzen estimator (TPE) strategy is implemented to automatically design the most effective and practical model architecture. The performance of LaTAS-F framework is evaluated in various of historical and predicted horizons. Data is collected from 21 subjects, and experimental results demonstrate that our proposed framework can accurately predict CBI of 200 ms in the future, outperforming baselines with RMSE of 0.428Mpa and R2 of 0.963.

A data-based fault detection scheme for the stratospheric airship control system

This brief proposed an innovative fault detection method based on analytical data for the stratospheric airship control system. The control system considered is subject to both space disturbance and nonlinear characteristics; the faults of sensors and actuators are all taken into account. The proposed method is developed in two phases. In the first phase, the moving window kernel principal component analysis is employed to construct the fault detection model with the training data under normal operating conditions and update the fault detection model online until abnormal data are detected. Second, a fault detection model updating mechanism is designed to reduce computational complexity and cost with a clustering algorithm, which compounds the mean shift clustering with weighted Euclidean distance to reflect the data density distribution feature to make the updating to be adaptive. Finally, the proposed method is applied to detect fault for an illustrative simulation stratospheric airship control model. The fault detection results validate the effectiveness of proposed fault detection method for different sensor and actuator fault cases. Comparing to some extended moving window kernel principal component analysis method, the proposed method reduces the computational cost significantly.

Feature Extraction Algorithm of Massive Rainstorm Debris Flow Based on Ecological Environment Telemetry

In order to accurately extract the characteristics of debris flow caused by group rainstorms, effectively identify the on-site information of debris flow, and provide a scientific basis for debris flow monitoring, early warning and disaster control, this paper proposes a method for extracting the characteristics of heavy rainstorm debris flow using multiregional ecological environment remote sensing. In the ecological environment where debris flows occur frequently, remote sensing data of heavy rainstorm debris flows are preprocessed using remote sensing technology, providing an important basis for the feature extraction of debris flows. The kernel principal component analysis method and Gabor filters are innovatively used to extract the spectral and texture features of rainstorm and debris flow remote sensing images, and the convolutional neural network structure is improved based on the open source deep learning framework, integrating multilevel features to generate debris flow feature maps. The improved convolution neural network is then used to extract the secondary features of the fusion feature map, and the feature extraction of heavy rainstorm debris flow is realized. The experiment shows that this method can accurately extract the characteristics of heavy rainstorm debris flow. Fused remote sensing images of debris flow effectively ameliorate the problem of insufficient informational content in a single image and improve image clarity. When the Gabor kernel function has eight different directions, the feature extraction effect of the debris flow image in each direction of the heavy rainstorm is the best.

Modeling Method for Aerobic Zone of A2O Based on KPCA-PSO-SCN

Sewage treatment plants face significant problems as a result of the annual growth in urban sewage discharge. Substandard sewage discharge can also be caused by rising sewage treatment expenses and unpredictable procedures. The most widely used sewage treatment process in urban areas is the Anaerobic–Anoxic–Oxic (A2O) sewage treatment process. Therefore, modeling the sewage treatment process and predicting the effluent quality are of great significance. A process modeling method based on Kernel Principal Component Analysis–Particle Swarm Optimization–Stochastic Configuration Network (KPCA-PSO-SCN) is proposed for the A2O aerobic wastewater treatment process. Firstly, eight auxiliary variables were determined through mechanism analysis, including Chemical Oxygen Demand (COD) and ammonia nitrogen (NH4+) and nitrate nitrogen (NO3−) of influent water, pH, temperature (T), Mixed Liquor Suspended Solid (MLSS), Dissolved Oxygen (DO) and hydraulic residence time (HRT) in the aerobic zone. Dimensionality reduction was carried out using the kernel principal component analysis method based on the Gaussian function, and the eight-dimensional data were changed to five-dimensional data, which improved the running speed and efficiency of subsequent models. Then, according to the advantages of the particle swarm optimization algorithm, such as low calculation cost and fast convergence, combined with the advantages of stochastic configuration network general approximation performance, the PSO-SCN model was established to predict the three water quality indexes of effluent COD, NH4+, and NO3− for the aerobic zone. The experimental results proved the effectiveness of the model. Compared with classic water quality prediction algorithm models such as SCN, PSO-BP, RBF, PSO-RBF, etc., the superiority of the PSO-SCN algorithm model was demonstrated.

A new monitoring approach of time-varying and nonlinear processes with application to penicillin fermentation process

In the actual production process, time-varying and nonlinear problems are numerous important problems to be considered, in view of these problems, a process monitoring approach based on locally weighted probabilistic kernel principal component analysis (LWPKPCA) is proposed. First, the method selects the normal process data with a high similarity to the test samples as training data of the local model, and continuously updates the local model according to the test samples to build an accurate time-varying model. Second, by weighting the data of different importance, the role of data similar to test samples in the modeling process is strengthened. Third, the LWPKPCA model is applied to process monitoring, the monitoring indicators are established in a high-dimensional space and used to detect faults. Finally, on the basis of LWPKPCA, the penicillin fermentation process (PFP) is taken to evaluate the monitoring performance of the proposed methods. According to the comparison of the experiment results, the detection rate and accuracy rate of the LWPKPCA method is considerably better than those of probabilistic principal component analysis and probabilistic kernel principal component analysis methods. The results demonstrate that the proposed method is suitable for processing time-varying data with nonlinear characteristics, and the LWPKPCA process monitoring method is effective for improving the performance of fault detection.

Prediction of Contact Fatigue Performance Degradation Trends Based on Multi-Domain Features and Temporal Convolutional Networks.

Contact fatigue is one of the most common failure forms of typical basic components such as bearings and gears. Accurate prediction of contact fatigue performance degradation trends in components is conducive to the scientific formulation of maintenance strategies and health management of equipment, which is of great significance for industrial production. In this paper, to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional networks (TCNs) was proposed. Firstly, a multi-domain and high-dimensional feature set of vibration signals was constructed, and performance degradation indexes with good sensitivity and strong trends were initially screened using comprehensive evaluation indexes. Secondly, the kernel principal component analysis (KPCA) method was used to eliminate redundant information among multi-domain features and construct health indexes (HIs) based on a convolutional autoencoder (CAE) network. Then, the performance degradation trend prediction model based on TCN was constructed, and the degradation trend prediction for the monitored object was realized using direct multi-step prediction. On this basis, the effectiveness of the proposed method was verified using a bearing common-use data set, and it was successfully applied to performance degradation trend prediction for rolling contact fatigue specimens. The results show that using KPCA can reduce the feature set from 14 dimensions to 4 dimensions and retain 98.33% of the information in the original preferred feature set. The method of constructing the HI based on CAE is effective, and change processes versus time of the constructed HI can truly reflect the degradation process of rolling contact fatigue specimen performance; this method has obvious advantages over the two commonly used methods for constructing HIs including auto-encoding (AE) networks and gaussian mixture models (GMMs). The model based on TCN can accurately predict the performance degradation of rolling contact fatigue specimens. Compared with prediction models based on long short-term memory (LSTM) networks and gating recurrent units (GRUs), the model based on TCN has better performance and higher prediction accuracy. The RMS error and average absolute error for a prediction step of 3 are 0.0146 and 0.0105, respectively. Overall, the proposed method has universal significance and can be applied to predict the performance degradation trend of other mechanical equipment/parts.

Integrated framework for feature extraction and weighting in coal and gas outburst classification

The prediction of coal and gas outburst is very necessary for the prevention of gas disaster, so an outburst prediction model coupled with feature extraction and feature weighting using optimized classifier is proposed. First, Pearson correlation coefficient(PCC) and symmetric uncertainty(SU) are employed to measure the effective information in outburst sample data. Second, Kernel principal component analysis(KPCA) and linear discriminant analysis(LDA) methods are used to extract the exiting discriminate information, and the extracted linear and nonlinear feature information can effectively reflect significant information of outburst influencing factors. Third, the combination of gradient boost decision tree(GBDT) and grey relation analysis(GRA) is used to weight and fuse the extracted linear and nonlinear feature components, then form a new feature set as important discriminant information. Forth, the weighted and fused features of the coal and gas outburst influencing factors are used as the input of support vector machine(SVM) classifier with optimized parameters, it can classify outburst states, and the achieved classification accuracy can obtain 95%. Finally, the proposed model and the existing outburst classification models in literatures are used to predict outburst, then the experiment results verify the effectiveness of the proposed model and conclude that the performance of the proposed predication model are significant than present outburst prediction models.

Simulation and Prediction Algorithm for the Whole Process of Debris Flow Based on Multiple Data Integration

In order to solve the problems of large errors and low accuracy in debris-flow forecasting, the simulation and prediction algorithm for the whole process of debris flow based on multiple data integrations is studied. The middleware method is used to integrate multiple GIS data sets, and the GIS spatial database after multiple data integrations is used to provide the basis of data for the whole process simulation and prediction of debris flow. The spatial cellular simulation model of debris flow is built using the cellular automatic mechanism. The improved kernel principal component analysis method is used to reduce the dimension of debris-flow prediction index data. The reduced dimension index data is input into the support vector machine, and the support vector machine is used to output the prediction results of debris flow in the space cell simulation model of debris flow. Through the simulation visualization technology, the dynamic display of the simulation prediction of the whole process of debris flow is carried out. The experimental results show that the algorithm can realize the simulation of the whole process of debris-flow changes, that the prediction results of debris flow are close to the actual results, and that the error is less than 5%, which improves the prediction accuracy of debris flow and can be used as the auxiliary basis for relevant decision-making departments.

A New Hybrid Monitoring Model for Displacement of the Concrete Dam

For the structural health diagnostic of concrete dams, the mathematical monitoring model based on the measured deformation values is of great significance. The main purpose of this paper is to reconstruct the ageing component and the temperature component in the traditional Hydraulic-Seasonal-Time (HST) hybrid model by combining the measured values. On the one hand, a better mathematical model for the ageing displacement of concrete dams is proposed combined with the Burgers model to separate the instantaneous elastic hydraulic deformation and the hysteretic hydraulic deformation, and then it subsumes the latter into the ageing deformation to describe its reversible component. According to the Burgers model, the inverted elastic modulus of the Jinping-Ⅰ concrete dam is 46.5 GPa, which is closer to the true value compared with the HST model. On the other hand, the kernel principal component analysis (KPCA) method is used to extract the principal components of the dam thermometers for replacing the period harmonic thermal factor. A multiple linear regression (MLR) model is established to fit the measured displacement of the concrete arch dam and to verify the accuracy of the proposed hybrid model. The results show that the proposed model reaches higher accuracy than the traditional HST hybrid model and is helpful to improve the interpretation of the separated displacement components of the concrete dams.

A multivariate monitoring method based on kernel principal component analysis and dual control chart

The kernel principal component analysis (KPCA) method has been widely applied in process monitoring. However, the KPCA method still has two challenging problems that need to be solved: the principal component selection problem and the problem of determining Gaussian kernel width without fault data. The principal component selection plays an important role in the performance of the KPCA method, which is determined by the cumulative contribution rate, cross-validation, average eigenvalue, etc. However, these selection methods ignore the principal components corresponding to small eigenvalues, which are still very important for fault detection. Therefore, this paper proposes a multivariate monitoring method based on the KPCA and Dual Control Chart (KPCA-DCC), which adopts a scheme of monitoring all principal components and solves the selection problem. The Gaussian kernel width is usually determined by training an appropriate coefficient to times the variable number, which may not be possible to train the coefficient in the absence of fault data while the industrial process works at the initial stage of its operation. Furthermore, different systems may have the same number of variables, and their Gaussian kernel widths should vary because of different variables’ features. Therefore, this paper proposes an empirical equation for estimating Gaussian kernel width according to data characteristics, which makes the Gaussian kernel width can be estimated without fault data. In addition, this paper also gives the detection theory of the DCC method from the probability perspective, which is not presented in the previous work. The Tennessee Eastman (TE) process and blast furnace ironmaking process are adopted to verify the effectiveness of the proposed methods.

Prediction of gas explosion pressures: A machine learning algorithm based on KPCA and an optimized LSSVM

A gas explosion, as a common accident in public life and industry, poses a great threat to the safety of life and property. The determination and prediction of gas explosion pressures are greatly important for safety issues and emergency rescue after an accident occurs. Compared with traditional empirical and numerical models, machine learning models are definitely a superior approach. However, the application of machine learning in gas explosion pressure prediction has not reached its full potential. In this study, a hybrid gas explosion pressure prediction model based on kernel principal component analysis (KPCA), a least square support vector machine (LSSVM), and a gray wolf optimization (GWO) algorithm is proposed. A dataset consisting of 12 influencing factors of gas explosion pressures and 317 groups of data is constructed for developing and evaluating the KPCA-GWO-LSSVM model. The results show that the correlations among the 12 influencing factors are eliminated and dimensioned down by the KPCA method, and 5 composite indicators are obtained. The proposed KPCA-GWO-LSSVM hybrid model performs well in predicting gas explosion pressures, with coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) values of 0.928, 26.234, and 12.494, respectively, for the training set; and 0.826, 25.951, and 13.964, respectively, for the test set. The proposed model outperforms the LSSVM, GWO-LSSVM, KPCA-LSSVM, beetle antennae search improved BP neural network (BAS-BPNN) models and reported empirical models. In addition, the sensitivity of influencing factors to the model is evaluated based on the constructed database, and the geometric parameters X1 and X2 of the confined structure are the most critical variables for gas explosion pressure prediction. The findings of this study can help expand the application of machine learning in gas explosion prediction and can truly benefit the treatment of gas explosion accidents.

An Improved Genetic-XGBoost Classifier for Customer Consumption Behavior Prediction

Abstract In an increasingly competitive market, predicting the customer’s consumption behavior has a vital role in customer relationship management. In this study, a new classifier for customer consumption behavior prediction is proposed. The proposed methods are as follows: (i) A feature selection method based on least absolute shrinkage and selection operator (Lasso) and Principal Component Analysis (PCA), to achieve efficient feature selection and eliminate correlations between variables. (ii) An improved genetic-eXtreme Gradient Boosting (XGBoost) for customer consumption behavior prediction, to improve the accuracy of prediction. Furthermore, the global search ability and flexibility of the genetic mechanism are used to optimize the XGBoost parameters, which avoids inaccurate parameter settings by manual experience. The adaptive crossover and mutation probabilities are designed to prevent the population from falling into the local extremum. Moreover, the grape-customer consumption behavior dataset is employed to compare the six Lasso-based models from the original, normalized and standardized data sources with the Isometric Mapping, Locally Linear Embedding, Multidimensional Scaling, PCA and Kernel Principal Component Analysis methods. The improved genetic-XGBoost is compared with several well-known parameter optimization algorithms and state-of-the-art classification approaches. Furthermore, experiments are conducted on the University of California Irvine datasets to verify the improved genetic-XGBoost algorithm. All results show that the proposed methods outperform the existing ones. The prediction results provide the decision-making basis for enterprises to formulate better marketing strategies.

Research on deep learning video game simulation algorithm based on western wind music

Deep learning technology and graphics computing technology have made important progress in recent years, making video game technology develop. Western orchestral music based on video game technology has been significantly improved in both human–computer experience and game fun. Therefore, an optimized reinforcement learning (Deep Q Networks, DQN) video game simulation algorithm is proposed. The first is to optimize the network activation function, integrate the ReLU and Softlus functions to design a new piecewise activation function, and use it as a fully connected function of the network. Secondly, the Gabor filter is improved and replaces the traditional filter of the network. Finally, the improved filter and the game image are used to obtain the directional features, and the dimensionality reduction of the features is realized by the kernel principal component analysis (PCA) method, the network training parameters are obtained, and the game simulation is completed. In order to verify the improved DQN game simulation model, two games were selected to participate in the experiment. In the game test of the orchestra simulator, the proposed ReLU-Softplus is the best in the orchestra experience, with the highest score of 126, while the average score of ReLU, Sigmaid, Tanh and ReLU-Softplus are 59.6, 57.2, 52.6 and 64.7. Compared with the other three functions, ReLU-Softplus has a 13.2% improvement. It can be seen that ReLU-Softplus has a better experience of wind games. We still take the pipe game as the test object, adopt the improved Gabor and ReLU-Softplus, and use KPCA to reduce the dimension of the game data features. When the curvature coefficient is 0.3, the DQN model has the highest score in the game test, with a score of 159.6, and the best average score is 93.5, which is better than 73.6 without the improved Gabor. It can be seen that the improved DQN game simulation algorithm can significantly optimize the game scoring effect. The research content has important reference value for improving the experience of western wind music video games and improving the game scoring mechanism.

Driving Style Recognition of Leading Vehicles based on Semi-supervised Gaussian Mixture Model

Driving style recognition of leading vehicles is important to ensure driving safety and improve the efficiency of road traffic. Current supervised learning based methods achieve high recognition accuracy, but their annotations are labor-intensive and time-consuming in practical scenarios. Although unsupervised learning based methods do not require manual labeling, their recognition accuracy is still low. In order to reduce the labeling cost and improve the accuracy of driving style recognition simultaneously, we propose a driving style recognition method based on semi-supervised Gaussian mixture model. Firstly, the driving data is collected and pre-processed by OBD-II vehicle recorder, and 22-dimensional driving style feature parameters are selected and constructed. Secondly, the kernel principal component analysis method is used to reduce the dimensionality and obtain 6-dimensional discriminative principal components. Finally, the Gaussian mixture model is trained by utilizing both the labeled data and unlabeled data. Concretely, the model parameters are measured by the expectation maximization algorithm and the likelihood function. The simulation experimental results demonstrate that the proposed method can effectively improve the driving style recognition accuracy, as well as reduce the reliance on labeled data.

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Data Preprocessing and Improved ELM

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) can provide an important reference for the safe operation of LIBs. At present, there are some problems in the prediction of RUL of LIBs, such as redundancy or deficiency of characteristic parameters, local regeneration in degradation characteristics, and poor stability of single model prediction. In this paper, an RUL prediction method combining kernel principal component analysis (KPCA), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), whale optimization algorithm (WOA), and extreme learning machine (ELM) is proposed. Firstly, in the data preprocessing stage, the KPCA method is used to reduce the dimension of the extracted indirect health indicators (HI), and then the ICEEMDAN algorithm is introduced to eliminate the local regeneration phenomenon in the fusion HI sequence. Secondly, in the model construction stage, a capacity prediction model based on WOA-ELM and a fusion HI prediction model are built to predict the intrinsic mode functions (IMF) components of the fusion HI sequence. The prediction results are superimposed and then input into the capacity prediction model to realize RUL indirect prediction. Finally, the proposed method is verified by using NASA battery degradation data sets at different temperatures and experimental environments. The results show that the RUL prediction accuracy of the proposed method is greatly improved, and it has good stability and robustness.

Fault Detection via Occupation Kernel Principal Component Analysis

Reliable operation of automatic systems is heavily dependent on the ability to detect faults in the underlying dynamics. While traditional model-based methods have been widely used for fault detection, data-driven approaches have garnered increasing attention due to their ease of deployment and minimal need for expert knowledge. In this paper, we present a novel principal component analysis (PCA) method that uses occupation kernels. Occupation kernels result in feature maps that are tailored to the measured data, have inherent noise-robustness due to the use of integration, and can utilize irregularly sampled system trajectories of variable lengths for PCA. The occupation kernel PCA method is used to develop a reconstruction error approach to fault detection and its efficacy is validated using numerical simulations.