Health indicator(HI)construction is a crucial task in degradation evaluation and facilitates the prognostic and health management(PHM)of rotating machinery.Excluding interference from artificial labeling,the HI constr...Health indicator(HI)construction is a crucial task in degradation evaluation and facilitates the prognostic and health management(PHM)of rotating machinery.Excluding interference from artificial labeling,the HI construction approaches in an unsupervised manner have attracted substantial attention.Nevertheless,current unsupervised methods generally struggle with two problems:(1)ignorance of both redundancy between features and global variability of features during the feature selection process;(2)inadequate utilization of information from different sampling moments.To tackle these problems,this work develops a novel unsupervised approach for HI construction that integrates multi-criterion feature selection and the Attentive Variational Autoencoder(Attentive VAE).Explicitly,a multi-criterion feature selection(Mc FS)algorithm together with an elaborately designed metric is proposed to determine a superior feature subset,considering the relevance,the redundancy,and the global variability of features simultaneously.Then,for the adequate utilization of the information from distinct sampling moments,a deep learning model named Attentive VAE is established.The Attentive VAE is solely fed with the selected features in the health state for model training and the HI is derived through the reconstruction error to reveal the degradation degree of machinery.Two case studies based on genuine experimental datasets are involved to quantitatively evaluate the superiority of the developed approach,demonstrating its superiority over other unsupervised methods for characterizing degradation processes.The effectiveness of both the Mc FS algorithm and the Attentive VAE is verified by ablation experiments,respectively.展开更多
如何通过在线监测的直接测量参数准确估计电池荷电状态(State of Charge,SOC)与健康状态(State of Health,SOH),是蓄电池管理系统建立的核心与关键。设计的SOC估算方法为镉镍蓄电池管理系统有效地监测蓄电池组性能状态和寿命状态提供基...如何通过在线监测的直接测量参数准确估计电池荷电状态(State of Charge,SOC)与健康状态(State of Health,SOH),是蓄电池管理系统建立的核心与关键。设计的SOC估算方法为镉镍蓄电池管理系统有效地监测蓄电池组性能状态和寿命状态提供基础,有助于动车组在运行过程中的安全预警;同时,为蓄电池检修与维护策略优化提供数据支撑,助力国家可持续发展战略。从蓄电池不同寿命阶段内的充电起始电压序列中提取出可描述当前最大容量的潜在特征,通过F检验(F-Test)与主成分分析(Principal Component Analysis,PCA)进行特征筛选与特征融合,获得蓄电池健康状态指标;由充放电循环试验中采集到的不同寿命阶段内的放电终止电压建立镉镍蓄电池“记忆效应”的近似表达函数;基于此,采用基于Bagging的随机森林构建放电过程中蓄电池两端电压与SOC间的关联模型,可在蓄电池放电过程中实现基于放电电压的SOC估算。最终,试验结果显示:通过SOC估算值与实际测量值的对比,得到模型均方根误差(Mean Square Error,MSE)和平均绝对误差百分比(Mean Absolute Percentage Error,MAPE)分别为0.1486和0.8112%,证明了所提出的SOC估算模型取得了较高的估算精度与较强的鲁棒性,为在线监测镉镍蓄电池SOC提供基础。展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2021YFB3400700)the China Academy of Railway Sciences Corporation Limited within the major issues of the fund(Grant No.2021YJ212)+1 种基金the National Natural Science Foundation of China(Grant Nos.12072188,12121002)the Natural Science Foundation of Shanghai(Grant No.20ZR1425200)。
文摘Health indicator(HI)construction is a crucial task in degradation evaluation and facilitates the prognostic and health management(PHM)of rotating machinery.Excluding interference from artificial labeling,the HI construction approaches in an unsupervised manner have attracted substantial attention.Nevertheless,current unsupervised methods generally struggle with two problems:(1)ignorance of both redundancy between features and global variability of features during the feature selection process;(2)inadequate utilization of information from different sampling moments.To tackle these problems,this work develops a novel unsupervised approach for HI construction that integrates multi-criterion feature selection and the Attentive Variational Autoencoder(Attentive VAE).Explicitly,a multi-criterion feature selection(Mc FS)algorithm together with an elaborately designed metric is proposed to determine a superior feature subset,considering the relevance,the redundancy,and the global variability of features simultaneously.Then,for the adequate utilization of the information from distinct sampling moments,a deep learning model named Attentive VAE is established.The Attentive VAE is solely fed with the selected features in the health state for model training and the HI is derived through the reconstruction error to reveal the degradation degree of machinery.Two case studies based on genuine experimental datasets are involved to quantitatively evaluate the superiority of the developed approach,demonstrating its superiority over other unsupervised methods for characterizing degradation processes.The effectiveness of both the Mc FS algorithm and the Attentive VAE is verified by ablation experiments,respectively.
