退休再就业还是持续就业?——基于多状态生命表的城乡老年人工作余寿分析

工作余寿是分析老年人就业供给状况的重要抓手。基于CHARLS 2011-2020五轮数据,构建1936-1966年出生队列的多状态工作生命表。分析发现:男性60岁时的余寿低于女性,工作余寿却高于女性;农村人口60岁时的余寿低于城镇人口,工作余寿却高于城镇人口。城镇和农村不在业人口工作余寿都非常低,说明退出就业市场后再次返回的情况相对较少,老年人就业的主要模式是“持续就业”而非“退休再就业”,持续就业主体是农村老年人。农业户籍人口60岁时的余寿和工作余寿均比非农业户籍人口更长;在所有退休金或养老保险类型之中,参加新型农村养老保险者60岁时的余寿和工作余寿最长。

基于期望余寿理论的老年定义新思考与中国人口态势重新测算

本文基于近年来国际学术界出现的期望余寿理论与方法对重新认识老年和老龄化进行全新思考。按照期望余寿理论方法,本研究对我国人口老龄化的发展现状和未来人口态势包括老年起点、老年人口比重、劳动年龄人口比重和老年抚养比等重要人口指标作重新测算,比较传统老年定义下和全新老年定义下我国人口发展态势的不同情境。按照期望余寿15年作为老年定义的全新标准,中国整个经济社会的老龄化语境将不会如传统老年定义下那么沉重。老年起点将随着人口预期寿命的持续提高而动态变化,将从2015-2020年的61.5岁提升至21世纪中叶的近70岁;老年人口的比重仍会呈现出上升趋势,但是提升幅度和速度将不会如传统老年定义下剧烈和迅速;劳动年龄人口比重的变动总体趋于缓和,将呈现先下降并到21世纪后半叶维持稳定水平的态势;老年抚养比的提升趋势也不如传统老年定义下之迅速,在2060年出现峰值后将有降低态势。基于期望余寿理论的老年定义新思考将有助于转变全社会对于老年与老龄化的观念。

超大城市内不同区域老年人健康预期寿命的比较研究——以上海市为例

随着预期寿命的逐渐提高,单纯的生存时长已不能反映老年人生命质量,健康的生存时长才是他们的真实意愿。文章基于2013年覆盖上海各区域的上海市户籍人口分年龄死亡率、上海市老年人口生活状况与养老意愿跟踪调查数据,采用Sullivan法估算上海市及三大区域老年人的余寿与健康余寿。发现在女性老年人余寿普遍高于男性的趋势下,女性更多处于不能自理的生存状况,但低龄期女性老年人的健康预期寿命仍高于男性,到高龄期男性反超女性;全市三大地区中,老年人健康预期寿命最长的为近郊区,远郊区则是老年人健康预期寿命最短的区域,这可能与区域内老年人口年龄构成、医疗卫生资源配置、生活习性等相关。

Prediction Method Study on the Remaining Useful Life of Plant New Varieties Rights Based on Weibull Survival Function and Gaussian Model——Taking Hybrid Rice Variety for Example

In view of the difficulty in determining remaining useful life of plant new variety right in economic analysis, Weibull Survival Analysis Method and Gaussian Model to were used to study how to accurately estimate the remaining useful life of plant new variety right. The results showed that the average life of the granted rice varieties was 10.013 years. With the increase of the age of plant variety rights, the probability of the same residual life Ttreaching x was smaller and smaller, the reliability lower and lower, while the probability of the variety rights becoming invalid became greater. The remaining useful life of a specific granted rice variety was closely related to the demonstration promotion age when the granted rice variety reached its maximum area and the appearance of alternative varieties, and when the demonstration promotion age of the granted rice variety reaching the one with the maximum area, the promotion area would be decreased once a new alternative variety appeared, correspondingly with the shortening of the remaining useful life of the variety. Therefore, Weibull Survival Analysis Method and Gaussian Model could describe the remaining useful life＇s time trend, as well as determine the remaining useful life of a concrete plant variety right, clarify the entire life time of varieties rights, and make the economic analysis of plant new varieties rights more accurate and reasonable.

Bayesian framework for satellite rechargeable lithium battery synthesizing bivariate degradation and lifetime data

Reliability and remaining useful life(RUL)estimation for a satellite rechargeable lithium battery(RLB)are significant for prognostic and health management(PHM).A novel Bayesian framework is proposed to do reliability analysis by synthesizing multisource data,including bivariate degradation data and lifetime data.Bivariate degradation means that there are two degraded performance characteristics leading to the failure of the system.First,linear Wiener process and Frank Copula function are used to model the dependent degradation processes of the RLB's temperature and discharge voltage.Next,the Bayesian method,in combination with Markov Chain Monte Carlo(MCMC)simulations,is provided to integrate limited bivariate degradation data with other congeneric RLBs'lifetime data.Then reliability evaluation and RUL prediction are carried out for PHM.A simulation study demonstrates that due to the data fusion,parameter estimations and predicted RUL obtained from our model are more precise than models only using degradation data or ignoring the dependency of different degradation processes.Finally,a practical case study of a satellite RLB verifies the usability of the model.

Residual life estimation based on bivariate Wiener degradation process with measurement errors

An adaptive method of residual life estimation for deteriorated products with two performance characteristics (PCs) was proposed, which was sharply different from existing work that only utilized one-dimensional degradation data. Once new degradation information was available, the residual life of the product being monitored could be estimated in an adaptive manner. Here, it was assumed that the degradation of each PC over time was governed by a Wiener degradation process and the dependency between them was characterized by the Frank copula function. A bivariate Wiener process model with measurement errors was used to model the degradation measurements. A two-stage method and the Markov chain Monte Carlo (MCMC) method were combined to estimate the unknown parameters in sequence. Results from a numerical example about fatigue cracks show that the proposed method is valid as the relative error is small.

Life prediction of Ni-Cd battery based on linearWiener process

Predicting the life of Ni-Cd battery for electric multiple units(EMU)can not only improve the safety and reliability of battery,but also reduce the operating costs of EMU.For this reason,a life prediction method based on linear Wiener process is proposed,which is suitable for both monotonic and non-monotonic degraded systems with accurate results.Firstly,a unary linear Wiener degradation model is established,and the parameters of the model are estimated by using the expectation-maximization algorithm(EM).With the established model,the remaining useful life(RUL)of Ni Cd battery and its distribution are obtained.Then based on the unary Wiener process degradation model,the correlation between capacity and energy is analyzed through Copula function to build a binary linear Wiener degradation model,where its parameters are estimated using Markov Chain Monte Carlo(MCMC)method.Finally,according to the binary Wiener process model,the battery RUL and its distribution are acquired.The experimental results show that the binary linear Wiener degradation model based on capacity and energy possesses higher accuracy than the unary linear wiener process degradation model.

Online residual useful life prediction of large-size slewing bearings A data fusion method

To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to achieve online RUL prediction of slewing bearings,which consisted of a reliability based RUL prediction model and a data driven failure rate(FR) estimation model.Firstly,an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR.Secondly,principal component analysis(PCA) was introduced to process multi-dimensional life-cycle vibration signals,and continuous squared prediction error(CSPE) and its time-domain features were employed as equipment performance degradation features.Afterwards,an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map(SFAM) neural network.Consequently,real-time FR of equipment can be obtained through FR estimation model,and then accurate RUL can be calculated through the RUL prediction model.Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings,and that by combining actual load condition and real-time monitored data,the calculation time is reduced by 87.3%and the accuracy is increased by 0.11%,which provides a potential for online RUL prediction of slewing bearings and other various machineries.

Brain metastases from hepatocellular carcinoma after hepatectomy

Hepatocellular carcinoma is a highly malignant neoplasm and frequently involves extrahepatic organs but decidedly rarely occurs in brain. We describe 3 cases of brain metastases in patients suffering from post-HBV hepatocarcinoma. The ＂stroke-like＂ presentation of the cerebral localization of the disease can be explained by both the important vascularization of the tumor and the frequent hemocoagulative alterations caused by the cirrhosis. The importance of diagnostic neuroradiology is briefly addressed, with reference to the fundamental role played by MRI. Surgery of these lesions does not present any particular technical problems as long as they are located in accessible areas and the patient＇s general and neurological conditions allow it. Postoperative radiotherapy seems to improve the quality and quantity of residual life, although the number of patients described in the literature was too small to draw any definite conclusion in this regard.

Remaining useful life estimation based on Wiener degradation processes with random failure threshold

Remaining useful life(RUL) estimation based on condition monitoring data is central to condition based maintenance(CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncated normal distribution to model random failure threshold(RFT), an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter. Then, the Bayesian method was used to update the prior estimation of failure threshold. To solve the uncertainty of the censored in situ data of failure threshold, the expectation maximization(EM) algorithm is used to calculate the posteriori estimation of failure threshold. Numerical examples show that considering the randomness of the failure threshold and updating the prior information of RFT could improve the accuracy of real time RUL estimation.

Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.

Evaluation of remaining useful life for corroded pipeline with finite element simulation and reliability theory

An integrated approach was proposed to evaluate the remaining useful life(RUL)of corroded petroleum pipelines.Two types of failure modes(i.e.,leakage and burst failure)were considered,and the corresponding limit state functions(LSFs)were established with the structural reliability theory.A power-law function was applied to model the growth of corrosion defects,and the effect of external environmental factors on the growth of the pipeline s defect was considered.Moreover,the result was compared with the commonly used linear growth model.After that,a finite element simulation model was established to calculate the burst pressure of the pipeline with corrosion defects,and its accuracy was verified through hydraulic burst test and by comparison with international criteria.On that basis,the probability that the pipeline may fail was calculated with Monte Carlo simulation(MCS)and by considering the LSFs,and the pipeline s RUL was obtained accordingly.Furthermore,sensitivity analysis was conducted to determine the sensitivity parameters for the corrosion and RUL of the pipeline.The results indicate that the radial corrosion rate,wall thickness and working pressure have a great influence on the failure probability of the pipeline.Thus,corresponding measures should be adopted during the operation process of the pipeline to reduce the corrosion rate and increase the wall thickness,so as to prolong the pipeline s RUL.

Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network

In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.

A model to determining the remaining useful life of rotating equipment,based on a new approach to determining state of degradation

Condition assessment is one of the most significant techniques of the equipment’s health management.Also,in PHM methodology cycle,which is a developed form of CBM,condition assessment is the most important step of this cycle.In this paper,the remaining useful life of the equipment is calculated using the combination of sensor information,determination of degradation state and forecasting the proposed health index.The combination of sensor information has been carried out using a new approach to determining the probabilities in the Dempster-Shafer combination rules and fuzzy c-means clustering method.Using the simulation and forecasting of extracted vibration-based health index by autoregressive Markov regime switching(ARMRS)method,final health state is determined and the remaining useful life(RUL)is estimated.In order to evaluate the model,sensor data provided by FEMTO-ST Institute have been used.

Remaining Useful Life Prediction of Li-Po Batteries in UAVs Using Extended Kalman Filter

The use of batteries in UAVs （unmanned aerial vehicles） has become common due to some advantages in comparison with internal combustion engines such as weight reduction and better power control. However, in these vehicles it is critical to monitor the RUL （remaining useful life） of the batteries. This information can be used, for instance, as a decision support tool to define which missions could be assigned to the UAV until the next battery recharge. This work presents a methodology to predict the RUL of Li-Po （Lithium-Polymer） batteries. The approach uses an extended Kalman filter and an exponential model for the degradation evolution. The proposed methodology uses time series of battery terminal voltages, assuming that the discharge occurs under a constant current condition. Different discharge current levels were considered.The results showed that the proposed methodology provides good results, despite its simplicity.

Revealing the Invisible： A New Approach for Enhancing Industrial Safety, Reliability and Remaining Life Assessment

Today＇s industry requires more reliable information on the current status of their hard assets; prognosis for continued usability of systems and better predictability of equipment life cycle maintenance. Therefore, an innovative technique for early detection of potential failure and condition monitoring is urgently required by many engineers. This document describes a novel approach to improve industrial equipment safety, reliability and life cycle management. A new field portable instrument called the ＂IMS （indicator of mechanical stresses）＂ utilizes magneto-anisotropic （＂cross＂） transducers to measure anisotropy of magnetic properties in ferromagnetic material. Mechanical stresses including residual stresses in Ferro-magnetic parts, are ＂not visible＂ to most traditional NDT （non-destructive testing） methods; for example, radiography and ultrasonic inspection. Stress build-up can be the first indicator that something is faulty with a structure. This can be the result of a manufacturing defect; or as assets age and fatigue, stress loads can become unevenly distributed throughout the metal. We outline the evaluation of IMS as a fast screening tool to provide structural condition or deterioration feedback in novel applications for pipelines, petrochemical refinery, cranes, and municipal infrastructure.

An energy efficiency clustering routing protocol for WSNs in confined area

Wireless sensor networks (WSNs) are important application for safety monitoring in underground coal mines, which are difficult to monitor due to natural conditions. Based on the characteristic of limited energy for WSNs in confined underground area such as coal face and laneway, we presents an energy- efficient clustering routing protocol based on weight (ECRPW) to prolong the lifetime of networks. ECRPW takes into consideration the nodes' residual energy during the election process of cluster heads. The constraint of distance threshold is used to optimize cluster scheme. Furthermore, the protocol also sets up a routing tree based on cluster heads' weight. The results show that ECRPW had better perfor- mance in energy consumption, death ratio of node and network lifetime.

Dynamic Bayesian Network Based Prognosis in Machining Processes

Condition based maintenance (CBM) is becoming more and more popular in equipment main-tenance. A prerequisite to widespread deployment of CBM technology and practice in industry is effective diagnostics and prognostics. A dynamic Bayesian network (DBN) based prognosis method was investigated to predict the remaining useful life (RUL) for an equipment. First, a DBN based prognosis framework and specific steps for building a DBN based prognosis model were presented. Then, the corresponding inference algorithms for DBN based prognosis were provided. Finally, a prognosis procedure based on particle filtering algorithms was used to predict the RUL of drill-bits of a vertical drilling machine, which is commonly used in industrial process. Preliminary experimental results are promising.

Software Tool for the Implementation of the Methodology for Revitalization of High-Voltage Overhead Power Lines

In power systems, a large number of OPLs （overhead power lines） are more than 40 years old and some even exceed 50 years old. The key issue for power systems managers, public utilities companies and electrical engineers today concerns the manner in which available financial resources should be invested in these OPLs to provide the greatest impact on the power system as a whole and to address the OPLs that require urgent revitalization. This paper presents the application of the software tool RevOPL, developed using Microsoft Access utilizing the ＂methodology for revitalization of high-voltage OPLs＂. The aim is to present both the methodology and software to objectively evaluate the condition of an OPL and determine its remaining service life. The application of this software tool provides a proposal for the scheduling and scope of planned revitalization activities, which are obtained through the optimization of the technical characteristics while remaining within the available budget.

Research of the Wireless Management System for Solar Street Lamp Based on NanoLOC AVR Module

In order to improve the service life of solar street lamp, it is necessary to manage the lamp＇s battery in the form of on-line detection via wireless communanication. A wireless managonent systean for solar street lamp based on nanoLOC AVR nttlule is researched in this paper, the system can real-timely detect the solar street lamp＇s battery voltage, corrent, tonperature, internal resistance, residual capacity and so on. And the collected data is transmitted to computer＇ s management via wireless connnunication to achieve recording, storage, analysis and processing for various parameters.