凸组合核函数的支持向量机高光谱图像分类

支持向量机的高光谱图像分类中,单核函数存在局限性。为了提高分类器的分类精度和支持向量机模型的泛化能力,利用高斯径向基核和多层感知核进行凸组合构造复合核函数支持向量机,证明了该函数满足作为核函数的判决Mercer条件,并进一步将凸组合核函数支持向量机应用到高光谱图像分类中,完成了建模和实验验证。实验结果表明,凸组合核函数具有较好的鲁棒性,且该类支持向量机的分类精度和KAPPA系数较单核SVM均得到了有效的提高,是一种解决多分类问题行之有效的分类器。     

基于几何分析的支持向量机推广能力推测模型

利用支持向量机分类器中支持向量分布的几何意义,构造了一种新的与样本分布相关的推广能力预测模型,该模型充分利用了支持向量分布的先验信息,它与统计学习理论中推广能力准则具有一致的几何意义.首先利用支持向量分布的几何意义出发从海量样本中选择有效边界向量代替原有训练样本,然后在有效边界向量中分别计算最小包含半径和最大分类间隔.它不需要求解二次规划就可以得到与训练样本相关的推广能力计算模型,计算量较低.本文最后的最优核函数、核参数选择仿真实验结果表明本文提出的基于几何分析的支持向量机推广能力推测模型的合理性与高效性, 该模型对于解决支持向量机中最优核函数、核参数选择具有重要意义.     

基于数据驱动的功率MOSFET剩余寿命预测

为了预测电子系统的重要器件——功率MOSFET剩余使用寿命,基于NASA实验数据,提出了一种MOSFET剩余寿命预测方法。首先利用相关向量机对劣化数据进行回归拟合,得到相关向量,并使用相关向量与回归拟合结果结合获取代表性向量。然后,基于退化模型,利用最小二乘法实现参数辨识确定劣化模型的参数。最后,将劣化模型外推到失效阈值,得到功率器件可能失效的时间,从而获得MOSFET的剩余使用寿命。实验结果表明,该方法简单有效。     

基于支持向量机的城市空气质量时间序列预测模型探究

空气污染问题在当下是一个十分严重的问题。开展空气质量监测、预测工作对于污染控制,降低危害具有重要意义。支持向量机模型是进行回归预测性能良好的工具,并可用于时间序列预测。文章采用径向基函数作为核函数,用交叉验证的方法优化参数构造支持向量机时间序列预测模型,选取某地市2013年1月至8月的空气质量指数作为空气质量参数进行实证分析,表明模型预测效果很好,具有一定实用价值。     

基于SVR数据中心空调系统瞬态热参数预测

数据中心空调系统是维持数据中心的关键设备,直接影响到数据中心的安全运行,目前对空调系统的研究大多集中在节能降耗以及气流优化等领域。在空调故障影响等瞬态变化领域的研究仍然较少。因此有必要探究空调系统故障对机房气流组织的影响,建立针对空调失效极端工况下的快速温度预测模型,为能效控制系统及运行系统提供参考。本文根据空调系统故障实验分别建立了空调冷冻水泵失效及风机失效情况下的关键位置的温度变化时间序列预测模型,模型基于线性核函数支持向量回归机。研究表明相较于非线性核函数支持向量机,线性核函数支持向量机更适合进行冷冻水泵失效时的热参数预测。     

基于TCN-AGRU的滚动轴承全寿命周期剩余使用寿命预测分析

阐述实际工程需要的滚动轴承非故障阶段剩余使用寿命（RUL）预测问题，提出基于时间卷积神经网络（TCN）与融合注意力机制的门控循环单元（AGRU）的滚动轴承剩余寿命预测模型。通过实验验证模型在滚动轴承全寿命周期的RUL预测性能。结果表明，TCN-AGRU模型在滚动轴承全寿命周期剩余寿命预测上有较好的预测性能，为滚动轴承全寿命周期剩余使用寿命预测模型的研究，提供新思路。     

基于DEPSO-RVM的B787电池剩余寿命预测

锂离子电池已经被应用于B787客机,为进一步提高B787锂离子电池的可靠性,针对传统基于相关向量机的电池剩余使用寿命预测方法的不足,提出一种把相关向量机、差分进化算法和粒子群优化算法融合的的方法。通过差分进化算法和粒子群优化算法对相关向量机的参数进行优化,增强其对电池历史监测数据退化趋势的预测能力。应用卡尔曼滤波器对融合算法实施优化,将优化后的预测结果作为在线样本添加到训练集中,对提出的模型重新训练,以此来动态调整系数矩阵和相关向量以执行下一次迭代预测。基于B787锂离子电池测量数据,对所提方法的有效性和鲁棒性进行了验证。     

基于随机退化数据建模的设备剩余寿命自适应预测方法

针对现有剩余寿命预测研究中需要多个同类设备历史数据离线估计模型参数的问题,本文提出了一种基于退化数据建模的服役设备剩余寿命自适应预测方法.该方法,利用指数随机退化模型来建模设备的退化过程,基于退化监测数据运用Bayesian方法更新模型的随机参数,进而得到剩余寿命的概率分布函数及点估计.区别于现有方法,本文方法基于设备到当前时刻的监测数据,利用期望最大化算法对模型中的非随机未知参数进行在线估计,由此无需多个同类设备历史数据.最后,通过数值仿真与实例分析,验证了本文方法在剩余寿命预测时的有效性.     

基于分段核函数的支持向量机及其应用

支持向量机兼顾训练误差和推广性能,已受到机器学习领域的高度重视,而核函数的性能是支持向量机研究中的关键问题。研究了几种常见核函数对支持向量机推广性能的影响,并利用全局核函数和局部核函数的性质,提出了一种新的分段核函数的支持向量机。数据集上的仿真结果表明,该核函数对应的支持向量机泛化能力优于传统核函数对应的支持向量机,具有较好的预测性能。     

基于SK等指标和SVM的滚动轴承性能退化评估研究

文中提出一种基于谱峭度等指标和支持向量机的滚动轴承性能退化评估的新方法。针对滚动轴承全寿命过程中各个时期故障损伤程度的不同,将故障监测分为4个阶段:正常、初期、中期、末期。通过与传统指标,例如均方根值、峭度值、峰峰值指标等对比,验证了谱峭度作为初期故障特征指标的优势。选取谱峭度等指标作为特征输入,构建多分类支持向量机预测模型来预测轴承性能退化阶段。使用轴承全寿命试验数据对预测模型进行检验,证明了该方法的有效性和可行性。     

Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery

Electrical power system (EPS) is one of the most critical sub-systems of the spacecraft. Lithium-ion battery is the vital component is the EPS. Remaining useful life (RUL) prediction is an effective mean to evaluate the battery reliability. Autoregressive model (AR) and particle filter (PF) are two traditional approaches in battery prognosis. However, the parameters in a trained AR model cannot be updated which will cause the under-fitting in the long term prediction and further decrease the RUL prediction accuracy. On the other hand, the measurement function in the PF algorithm cannot be obtained in the long term prediction process. To address these two challenges, a hybrid method of IND-AR model and PF algorithm are proposed in this work. Compared with basic AR model, a nonlinear degradation factor and an iterative parameter updating method are utilized to improve the long term prediction performance. The capacity prediction results are applied as the measurement function for the PF algorithm. The nonlinear degradation factor can make the linear AR model suitable for nonlinear degradation estimation. And once the capacity is predicted, the state-space model in the PF is activated to obtain an optimized result. Optimized capacity prediction result of each cycle is utilized to re-train the regression model and update the parameters. The predictor keeps working iteratively until the capacity hit the failure threshold to calculate the RUL value. The uncertainty involved in the RUL prediction result is presented by PF algorithm as well. Experiments are conducted based on commercial lithium-ion batteries and real-applied satellite lithium-ion batteries. The results have high accuracy in capacity fade prediction and RUL prediction of the proposed method. The real applied lithium-ion battery can meet the requirement of spacecraft. All the experiments results show great potential of the proposed framework.     

基于ARIMA和PF的锂电池剩余使用寿命预测方法

有效的电池剩余使用寿命(RUL)预测方法能够极大地提高系统的可靠性。提出一种基于自回归集成滑动平均模型(ARIMA)和粒子滤波(PF)融合预测框架,该框架由ARIMA方法和PF方法构成,ARIMA 应用于短期预测,而粒子滤波应用于长期预测。首先在线对锂离子电池进行监测,然后根据短期预测或长期预测要求执行相应的算法,得出横纵坐标分别为周期和容量的 RUL 预测图。实验结果表明,该预测框架能够快速准确地预测锂离子电池 RUL。     

Remaining useful life prediction of lithium-ion battery using an improved UPF method based on MCMC

Lithium-ion batteries are widely used as power sources in various portable electronics, hybrid electric vehicles, aeronautic and aerospace engineering, etc. To ensure an uninterruptible power supply, the remaining useful life (RUL) prediction of lithium-ion batteries has attracted extensive attention in recent years. This paper proposed an improved unscented particle filter (IUPF) method for lithium-ion battery RUL prediction based on Markov chain Monte Carlo (MCMC). The method uses the MCMC to solve the problem of sample impoverishment in UPF algorithm. Additionally, the IUPF method is proposed on the basis of UPF, so it can also suppress the particle degradation existing in the standard PF algorithm. In this work, the IUPF method is introduced firstly. Then, the capacity data of lithium-ion batteries are collected and the empirical capacity degradation model is established. The proposed method is used to estimate the RUL of lithium-ion battery. The RUL prediction results demonstrate the effectiveness and advantage.     

An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction

Lithium-ion rechargeable batteries are widely used as power sources for mobile phones, laptops and electric cars, and gradually extended to military communication, navigation, aviation, aerospace and other fields. Accurate remaining useful life (RUL) prediction of lithium-ion battery plays an important role in avoiding serious security and economic consequences caused by failure to supply required power levels. Thus, the RUL prediction for lithium-ion battery has become a critical task in engineering practices. With its superiority in handling nonlinear and non-Gaussian system behaviors, the particle filtering (PF) technique is widely used in the remaining life prediction. However, the choice of importance function and the degradation of diversity in sampling particles limit the estimation accuracy. This paper presents an improved PF algorithm, that is, the unscented particle filter (UPF) based on linear optimizing combination resampling (U-LOCR-PF) to improve the prediction accuracy. In one aspect, the unscented Kalman filter (UKF) is used to generate a proposal distribution as an importance function for particle filtering. In the other aspect, the linear optimizing combination resampling (LOCR) algorithm is used to overcome the particle diversity deficiency. It should be noted that the step coefficient K can affect the performance of LOCR algorithm, and the fuzzy inference system is applied to determine the value of step coefficient K. According to the analysis results, it can be seen that the proposed prognostic method shows higher accuracy in the RUL prediction of lithium-ion battery, compared with the existing PF-based and UPF-based prognostic methods.     

基于VMD和DAIPSO-GPR解决容量再生现象的锂离子电池寿命预测研究

锂离子电池应用时表现出的时变、动态、非线性等特征,以及容量再生现象,导致传统模型对锂离子电池剩余使用寿命(RUL)预测的准确性低,该文将变分模态分解(VMD)和高斯过程回归(GPR)以及动态自适应免疫粒子群(DAIPSO)结合,建立RUL预测模型。首先利用等压降放电时间分析法,提取健康因子,利用VMD对其进行分解处理,挖掘数据内在信息,降低数据复杂度,并针对不同分量,利用不同协方差函数建立GPR预测模型,有效捕获了数据的长期下降趋势和短期再生波动。利用DAIPSO算法优化GPR模型,实现核函数超参数的优化,建立了更准确的退化关系模型,最终实现剩余使用寿命的准确预测,以及不确定性表征。最后利用NASA电池数据进行验证,离线预测结果表明所提方法具有较高预测精度和泛化适应能力。     

A New Dynamic-Copula Based Correlated Degradation Feature for Remaining Useful Life Prediction

Feature extraction plays an important role in Remaining useful life (RUL) prediction. Feature extraction mainly depends on the performance degradation signal in the previous study, in which the dynamic correlations among different signals are ignored, and the RUL accuracy is affected. A new dynamic feature based on the correlations of the performance degradation signal is proposed. First, dynamic correlation coefficients are calculated by copula function as the multivariate correlation performance degradation features. Second, the random effect Wiener process is used for RUL prediction based on the new features, and the maximum likelihood estimation is adopted to calculate the unknown parameters of the Wiener process. Finally, the RUL estimation for solder joints under vibration load is carried out compared with the quantile and quantile-Principal component analysis (PCA) mixed feature extraction method. The research results show that the proposed method improved the prediction accuracy of RUL.     

Remaining useful life prediction of lithium-ion batteries using a fusion method based on Wasserstein GAN

Lithium-ion batteries are the main power supply equipment in many fields due to their advantages of no memory, high energy density, long cycle life and no pollution to the environment. Accurate prediction for the remaining useful life (RUL) of lithium-ion batteries can avoid serious economic and safety problems such as spontaneous combustion. At present, most of the RUL prediction studies ignore the lithium-ion battery capacity recovery phenomenon caused by the rest time between the charge and discharge cycles. In this paper, a fusion method based on Wasserstein generative adversarial network (GAN) is proposed. This method achieves a more reliable and accurate RUL prediction of lithium-ion batteries by combining the artificial neural network (ANN) model which takes the rest time between battery charging cycles into account and the empirical degradation models which provide the correct degradation trend. The weight of each model is calculated by the discriminator in the Wasserstein GAN model. Four data sets of lithium-ion battery provided by the National Aeronautics and Space Administration (NASA) Ames Research Center are used to prove the feasibility and accuracy of the proposed method.     

Battery remaining useful life prediction at different discharge rates

Lithium-ion batteries are widely used in hybrid electric vehicles, consumer electronics, etc. As of today, given a room temperature, many battery prognostic methods working at a constant discharge rate have been proposed to predict battery remaining useful life (RUL). However, different discharge rates (DDRs) affect both usable battery capacity and battery degradation rate. Consequently, it is necessary to take DDRs into consideration when a battery prognostic method is designed. In this paper, we propose a discharge-rate-dependent battery prognostic method that is able to track usable battery capacity affected by DDRs in the process of battery degradation and to predict RUL at DDRs. An experiment was designed to collect accelerated battery life testing data at DDRs, which are used to investigate how DDRs influence usable battery capacity, to design a discharge-rate-dependent state space model and to validate the effectiveness of the proposed battery prognostic method. Results show that the proposed battery prognostic method can work at DDRs and achieve high RUL prediction accuracies at DDRs.     

Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model

Prediction of lithium-ion batteries remaining useful life (RUL) plays an important role in battery management system (BMS) used in electric vehicles. A novel approach which combines empirical mode decomposition (EMD) and autoregressive integrated moving average (ARIMA) model is proposed for RUL prognostic in this paper. At first, EMD is utilized to decouple global deterioration trend and capacity regeneration from state-of-health (SOH) time series, which are then used in ARIMA model to predict the global deterioration trend and capacity regeneration, respectively. Next, all the separate prediction results are added up to obtain a comprehensive SOH prediction from which the RUL is acquired. The proposed method is validated through lithium-ion batteries aging test data. By comparison with relevance vector machine, monotonic echo sate networks and ARIMA methods, EMD-ARIMA approach gives a more satisfying and accurate prediction result.     

Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model

The prediction of Remaining useful life (RUL) and the estimation of State of health (SOH) are extremely important issues for operating performance of Lithium-ion (Li-ion) batteries in the Battery management system (BMS). A multi-scale prediction approach of RUL and SOH is presented, which combines Wavelet neural network (WNN) with Unscented particle filter (UPF) model. The capacity degradation data of Li-ion batteries are decomposed into the low-frequency degradation trend and high-frequency fluctuation components by Discrete wavelet transform (DWT). Based on the WNN-UPF model, the long-term RUL of Li-ion batteries is predicted with the low-frequency degradation trend data. The high-frequency fluctuation data and RUL prediction results are integrated effectively to estimate the short-term SOH of Li-ion batteries. The experimental results show that the proposed method achieves high accuracy and strong robustness, even if the prediction starting point is set to the early stage of Li-ion batteries' lifespan.