Multiple Kernel Relevance Vector Machine Research Articles

State of Health Prediction of Lithium-Ion Batteries Based on the Discharge Voltage and Temperature

Accurate state of health (SOH) prediction of lithium-ion batteries is essential for battery health management. In this paper, a novel method of predicting the SOH of lithium-ion batteries based on the voltage and temperature in the discharging process is proposed to achieve the accurate prediction. Both the equal voltage discharge time and the temperature change during the discharge process are regarded as health indicators (HIs), and then, the Pearson and Spearman relational analysis methods are applied to evaluate the relevance between HIs and SOH. On this basis, we modify the relevance vector machine (RVM) to a multiple kernel relevance vector machine (MKRVM) by combining Gaussian with sigmoid function to improve the accuracy of SOH prediction. The particle swarm optimization (PSO) is used to find the optimal weight and kernel function parameters of MKRVM. The aging data from NASA Ames Prognostics Center of Excellence are used to verify the effectiveness and accuracy of the proposed method in numerical simulations, whose results show that the MKRVM method has higher SOH prediction accuracy of lithium-ion batteries than the relevant methods.

Temperature Compensation and Pressure Bias Estimation for Piezoresistive Pressure Sensor Based on Machine Learning Approach

The requirements for accuracy of piezoresistive pressure sensors in modern society are becoming increasingly high. Besides, a wide application range of sensor is required. However, due to the influence of material properties, many piezoresistive pressure sensors have high temperature coefficient, which limits their application temperature range. With the change of ambient temperature, the response characteristic of the sensor has strong nonlinearity. To solve the above-mentioned crucial problems, a dynamic chaos quantum-behaved particle swarm optimization optimized multiple kernel relevance vector machine (DCQPSO-MKRVM) algorithm is presented in this article. First, a basic theory of temperature effect is given and a new idea of temperature compensation is proposed. Second, the multi-kernel relevance vector machine (MKRVM) is adopted to estimate the bias values of input pressure. Through heterogeneous kernel learning method, the kernels of MKRVM maintain diversity to obtain higher estimation accuracy. Third, dynamic chaos quantum-behaved particle swarm optimization (DCQPSO) is employed to optimize the optimal sparse weights of kernel functions in MKRVM. Moreover, the dynamic parameter is applied for the boundary of chaos search between original quantum-behaved particle swarm optimization (QPSO) swarm and the chaos swarm. The experimental result indicates the complex nonlinear relationship of temperature effect, and the method proposed in this article can effectively and accurately estimate the bias of input pressure fast to achieve temperature compensation goal. The mean relative accuracy (MRA) of estimation results achieves 99.5%. It proves that the method proposed in this article is applicable and effective for industrial applications.

MKRVM Prediction of Capacitive RF-MEMS Switching Life

The elastic coefficient K, a factor affecting the life of capacitive RF-MEMS switch, is selected as the research object. First, we use differential evolution based quantum particle swarm optimization (DE-QPSO) to obtain sparse functions of multiple kernel relevance vector machine (MKRVM), and use the MKRVM algorithm to predict the lifetime of such switches. Then the experimental results show that the method can obtain the health index HI of 3.1043e+06 s, the 3.0657e+06 s. closest to the original data in 0.21 s. At the same time, the conclusion of the longest switching life is obtained when the elastic coefficient is in the range of 4-16 N/m.

A hybrid prognostic strategy with unscented particle filter and optimized multiple kernel relevance vector machine for lithium-ion battery

To make up the deficiencies of single methods in lithium-ion battery state of health (SOH) and remaining useful life (RUL) estimation, this paper presents a novel hybrid method using unscented particle filter (UPF) with optimized multiple kernel relevance vector machine (OMKRVM). Firstly, the errors between the initial estimation by UPF and the actual capacity are obtained. After that, the residuals are reconstructed by complementary ensemble empirical mode decomposition (CEEMD) to reduce interference. In addition, OMKRVM is adopted to provide multiple predictive abilities, and kernel parameters and weights of OMKRVM are yielded by the grid search. Finally, the initial estimation is corrected by the predicted residuals using OMKRVM to further improve prediction performance. The new method (UPF-OMKRVM) is compared with existing methods in predicting the degradation process of lithium-ion battery. The experimental results show that the UPF-OMKRVM has high prediction accuracy in lithium-ion battery SOH and RUL estimation.

Transformer Health Management Based on Self-Powered RFID Sensor and Multiple Kernel RVM

Health management for transformer in service is of great significance to the reliable operation of power system. This paper presents a cost-efficient transformer condition prognosis methodology. First, a power management circuit is designed to guarantee the stable performance of self-powered RFID sensor. Moreover, the sensor data storage mechanism is modified by embedding the sensor data into the ID information of RFID sensor to realize less response time and lower power consumption compared with the traditional approach. Second, a denoising method based on band restricted empirical mode decomposition and a fuzzy threshold is introduced to extract noise-free vibration signal from the measured raw signal. Third, the multiple kernel relevance vector machine (MKRVM) is imported to predict the future condition of transformer. The kernels of MKRVM keep diversity for higher prediction accuracy by using the heterogeneous kernel learning approach. Moreover, the double chain quantum genetic algorithm is employed as optimization tool to find the optimal sparse weights of basic kernel functions in MKRVM. The experimental results indicate that the modified RFID sensor can stably operate even when the load of the transformer is abruptly decreased to zero. The response time and power consumption of RFID sensor are markedly decreased by using the improved sensor data storage mechanism. Furthermore, the prediction experiment results demonstrate that the proposed approach is effective in term of health monitoring for transformer.

Strip Steel Surface Defects Recognition Based on SOCP Optimized Multiple Kernel RVM

Strip steel surface defect recognition is a pattern recognition problem with wide applications. Previous works on strip surface defect recognition mainly focus on feature selection and dimension reduction. There are also approaches on real-time systems that mainly exploit the autocorrection within some given picture. However, the instances cannot be used in practical applications because of a bad recognition rate and low efficiency. In this paper, we study the intelligent algorithm of strip steel surface defect recognition, where the goal is to improve the accuracy and save running time. This problem is very important in various applications, especially the process testing of steel manufacturing. We propose an approach called the second-order cone programming (SOCP) optimized multiple kernel relevance vector machine (MKRVM), which can recognize strip surface defects much better than other methods. The method includes the model parameter estimation, training, and optimization of the model based on SOCP and the classification test. We compare our approach with existing methods on strip surface defect recognition. The results demonstrate that our proposed approach can improve the recognition accuracy and reduce the time costs of the strip surface defect.

Capacity Prognostics of Lithium-Ion Batteries using EMD Denoising and Multiple Kernel RVM

Lithium-ion batteries are crucial to many types of electric equipments. Hence, lithium-ion battery capacity prognostic is significantly important, and it is yet very hard for the measured battery data that are regularly polluted by miscellaneous noises. In this paper, a battery capacity prognostic approach using the empirical mode decomposition (EMD) denoising method and multiple kernel relevance vector machine (MKRVM) approach is presented. The EMD denoising method is employed to process the measured capacity data to produce noise-free capacity data. The battery capacity prediction model using MKRVM is constructed based on the denoised capacity data. The MKRVM’s kernel keeps diversity by using multiple heterogeneous kernel learning method. Meanwhile, sparse weights of basic kernel functions are yielded by using particle swarm optimization (PSO) algorithm. The measured battery capacity data are used to demonstrate the effect of EMD denoising method, and battery capacity prediction experiments reveal that the proposed MKRVM approach can predict the battery’s future capacity precisely.

Landslide Susceptibility Mapping Based on Particle Swarm Optimization of Multiple Kernel Relevance Vector Machines: Case of a Low Hill Area in Sichuan Province, China

In this paper, we propose a multiple kernel relevance vector machine (RVM) method based on the adaptive cloud particle swarm optimization (PSO) algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-kernel structure, the kernel selection problem can be solved by adjusting the kernel weight, which determines the single kernel contribution of the final kernel mapping. The weights and parameters of the multi-kernel function were optimized using the PSO algorithm. In addition, the convergence speed of the PSO algorithm was increased using cloud theory. To ensure the stability of the prediction model, the result of a five-fold cross-validation method was used as the fitness of the PSO algorithm. To verify the results, receiver operating characteristic curves (ROC) and landslide dot density (LDD) were used. The results show that the model that used a heterogeneous kernel (a combination of two different kernel functions) had a larger area under the ROC curve (0.7616) and a lower prediction error ratio (0.28%) than did the other types of kernel models employed in this study. In addition, both the sum of two high susceptibility zone LDDs (6.71/100 km2) and the sum of two low susceptibility zone LDDs (0.82/100 km2) demonstrated that the landslide susceptibility map based on the heterogeneous kernel model was closest to the historical landslide distribution. In conclusion, the results obtained in this study can provide very useful information for disaster prevention and land-use planning in the study area.

A Multiple-Kernel Relevance Vector Machine with Nonlinear Decreasing Inertia Weight PSO for State Prediction of Bearing

The scientific and accurate prediction for state of bearing is the key to ensure its safe operation. A multiple-kernel relevance vector machine (MkRVM) including RBF kernel and polynomial kernel is proposed for state prediction of bearing in this study; the proportions of RBF kernel and polynomial kernel are determined by a controlled parameter. As the selection of the parameters of the kernel functions and the controlled parameter has a certain influence on the prediction results of MkRVM, nonlinear decreasing inertia weight PSO (NDIWPSO) is used to select its kernel parameters and controlled parameter. The RBF kernel RVM model with NDIWPSO (NDIWPSO-RBFRVM) and the polynomial kernel RVM model with NDIWPSO (NDIWPSO-PolyRVM) are used, respectively, to compare with the multiple-kernel RVM model with NDIWPSO (NDIWPSO-MkRVM). The experimental results indicate that NDIWPSO-MkRVM is more suitable for the state prediction of bearing than NDIWPSO-RBFRVM and NDIWPSO-PolyRVM.

Study of Multiple-Kernel Relevance Vector Machine Based on Kernel Alignment

The relevance vector machine (RVM) was a Bayesian framework for learning sparse regression models and classifiers, it used single kernel function to map training data from low dimension sample space to high dimension feature space. The prediction accuracy and generalization of traditional single-kernel RVM (sRVM) were not ideal both in classification and regression, so we constructed homogeneous and heterogeneous multiple kernels function (MKF) by kernel function combination in which we testified the validity of basic kernel function (BKF) and its parameters we employed by kernel alignment (KA), then we acquired optimized multiple-kernel RVM (mRVM). Experiment results on LIBSVM datasets not only indicate that both homogeneous and heterogeneous multiple-kernel RVM we constructed possess lower error rate in classification and smaller root mean square (RMS) in regression than single-kernel RVM, but also prove the effectiveness of kernel alignment.

Multiple kernel relevance vector machine for geospatial objects detection in high-resolution remote sensing images

Geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version. The proposed method learns an optimal kernel combination and the associated classifier simultaneously. Two feature types are extracted from images, forming basis kernels. Then these basis kernels are weighted combined and resulted the composite kernel exploits interesting points and appearance information of objects simultaneously. Weights and the detection model are finally learnt by a new algorithm. Experimental results show that the proposed method improve detection accuracy to above 88%, yields good interpretation for the selected subset of features and appears sparser than traditional single-kernel RVMs.