Long Term Application of a Vehicle-Based Health Monitoring System to Short and Medium Span Bridges and Damage Detection Sensitivity

Largest portion of the bridge stock in almost any country and bridge owning organisation consists on ordinary bridges that has short or medium spans and are now deteriorating due to aging, etc. Therefore, it is becoming an important social concern to develop and put to practical use simple and efficient health monitoring systems for existing short and medium span (10 - 30 m) bridges. In this paper, one practical solution to the problem for condition assessment of short and medium span bridges was discussed. A vehicle-based measurement with a public bus as part of a public transit system (called “Bus monitoring system”) has been developed to be capable of detecting damage that may affect the structural safety of a bridge from long term vibration measurement data collected while the vehicle (bus) crossed the target bridges. This paper systematically describes how the system has been developed. The bus monitoring system aims to detect the transition from the damage acceleration period, in which the structural safety of an aged bridge declines sharply, to the deterioration period by continually monitoring the bridge of interest. To evaluate the practicality of the newly developed bus monitoring system, it has been field-tested over a period of about four years by using an in-service fixed-route bus operating on a bus route in the city of Ube, Yamaguchi Prefecture, Japan. The verification results thus obtained are also described in this paper. This study also evaluates the sensitivity of “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection. Sensitivity of “characteristic deflection” is verified by introducing artificial damage into a bridge that has ended its service life and is awaiting removal. As the results, it will be able to make a rational long-term health monitoring system for existing short and mediumspan bridges, and then the system helps bridge administrators to establish the rational maintenance strategies.

Impact of the Community-Based Active Monitoring Program on the Long Term Care Services Use and In-Patient Admissions of the Over-74 Population

Introduction: Social isolation increases in the over-74 population and it is a risk factor for death and Long Term Care (LTC) use. In order to prevent the negative consequences of social isolation on this population community interventions focused on strengthening the social network should be intensified. The aim of this paper is to describe the impact on health care use of a Community-based pro-Active Monitoring Program (CAMP) providing phone monitoring to all the clients and home visits according to the individual’s needs. Methodology: In order to provide an evaluation of the program outcomes, the rates of clients’ hospitalization and admissions to Long Term Care facilities during 2011 have been assessed. The observed rates have been compared with expected ones calculated on available information for similar population. A cost-analysis has been also carried out to analyze the program sustainability. Results: The studied sample is made up by 1408 over-74 citizens followed up during 2011 in Rome (Italy) by CAMP. The cumulative observation time was 1362 p/y;61 individuals died during 2011 (death rate 4.3%). The hospital admission rate observed among CAMP’s clients was 254‰ (357/1408;CL95% ± 91‰), lower than the 282‰ reported for the over-74 population of Rome. This translates into 39 averted hospitalization. The LTC admission rate is also reduced among CAMP’s clients (9/1,408, 6.6‰ CL95% ± 0.8‰ vs. 9.7‰ reported for a comparable sample);it translates into 4 averted LTC admissions. The averted cost ranged between 47,153 € and 220,117 € according to the range of services used by the clients, which translates into a percentage of estimated cost reduction on yearly basis ranged between 3% and 12.5% of the whole cost of services used by the studied population. Discussion: The paper suggests the capacity of CAMP to reduce both the over-74 hospitalization rate and use of LTC. Cost analysis also indicates a cost reduction as a consequence of the CAMP implementation. Further studies including a control group and a detailed cost-benefit analysis are needed to check the program sustainability on larger population.

Adaptive Change Detection for Long-Term Machinery Monitoring Using Incremental Sliding-Window

Detection of structural changes from an opera- tional process is a major goal in machine condition moni- toring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combin- ing with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hil- bert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space （RK_HS） for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor （used to control the coarse-to-fine level of detection） is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of- the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.

Design and Implementation of Long-Term Single-Lead ECG Monitor

Some heart diseases need long-term monitoring to diagnose. In this paper, we present a wearable single lead ECG monitoring device with low power consumption based on MSP430 and single-lead ECG front-end AD8232, which could acquire and store patient’s ECG data for 7 days continuously. This device is available for long-term wearing with a small volume. Also, it could detect user’s motion status with an acceleration sensor and supports Bluetooth 4.0 protocol. So it could be expanded to be a dynamic heart rate monitor and/or sleep quality monitor combined with smart phone. The device has huge potential of application for health care of human daily life.

2011-2015年东洞庭湖洲滩典型植物群落样方调查数据集

固定样地调查通过长期数据的积累和精准的时空对比获取生态系统动态特征,为长期的生态系统研究提供了坚实的基础。洞庭湖湿地生态系统观测研究站按中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)统一的监测规范,对洞庭湖水文情势变化下,湿地生态系统中典型洲滩植被的物种组成和群落特征等指标进行长期定位监测。通过东洞庭湖三种典型湿地植物群落(苔草,南荻和水蓼)长期监测样地的数据进行加工处理,获得2011-2015年洞庭湖洲滩植物群落长期监测数据集。本数据集包含有植物种名、拉丁名、株(丛)数(株或丛/样方)、叶层平均高度(cm)、生殖枝平均高度(cm)、盖度(%)、物候期、优势种、植物种数、密度(株或丛/m~2)、优势种叶层高度(cm)、优势种生殖枝高度(cm)、总盖度(%)、地上绿色部分总干重(g/m~2),共14个指标,同时附有完整的背景信息。本数据集实行全过程数据质量控制,并由专家审核验证,确保数据时空上的相对一致和准确可靠。本数据集可以为探究洞庭湖水文情势下,洲滩湿地生态系统过程和演替趋势提供本底资料,为洞庭湖植被的遥感监测、生物多样性保护和湿地生态修复及适应性管理等提供数据支撑。

基于改进PSO-LSTM算法的风电机组状态监测方法研究

通过改进粒子群算法(particle swarm optimization,PSO)优化长短期记忆神经网络算法(long short-term memory,LSTM)的参数,提出了一种基于改进PSO-LSTM算法的直驱式风电机组运行状态监测方法。首先将数据采集与监控系统(supervisory control and data acquisition,SCADA)采集到的数据利用随机森林的方法进行特征筛选,得到模型的输入参数;其次采用改进PSO-LSTM网络建立有功功率的预测模型,计算出预测值与实际值的残差,根据残差的分布来确实直驱式风电机组的状态;最后利用某风电机组SCADA数据对所提预测模型进行验证分析,结果表明,PSO-LSTM预测模型相比其他三种预测模型,具有较高的预测精度,并在状态异常后最短时间内发出故障警报,保证电场的健康稳定运行。

2006-2022年中国科学院环江喀斯特生态系统观测站农田长期观测样地土壤养分数据集

中国科学院环江喀斯特生态系统观测研究站(本文中简称“环江站”)是我国西南喀斯特地区重要的农业生态系统长期野外定位观测研究站,是依照中国生态系统研究网络(Chinese Ecosystem Research Network,简称CERN)联网监测规范布置的试验样地。自2005年以来,环江站依照国家生态系统观测研究网络(National Ecosystem Research Network of China,简称CNERN)和CERN农田生态系统观测指标要求,逐一开展针对喀斯特峰丛洼地农田生态系统水分、土壤、生物、气象等环境要素的监测活动。本数据集收集、整理了环江站2006–2022年8个长期联网监测样地的土壤养分数据,包括土壤有机质、全氮、全磷、全钾、碱解氮、有效磷、速效钾、缓效钾、pH值等9项指标,均进行了严格的数据质量控制与评估,并附有完整的样地背景信息和分析方法记录。本数据集反映了桂西北喀斯特峰丛洼地农业区传统代表性作物早晚稻、玉米、大豆、桑叶、柑橘等农作地土壤常规养分含量动态变化,对指导喀斯特峰丛洼地农业生产、培育土壤地力具有参考依据。

Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs.Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task.Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last decade.Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect.This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the gearbox.This helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher precision.The results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.

基于u-shapelets聚类的刀具剩余寿命预测方法

针对不同刀具的性能衰退规律呈现出多种趋势,单一固定的全局模型难以对不同性能衰退规律的刀具进行准确剩余寿命预测的问题,提出一种基于u-shapelets聚类与长短时记忆网络(LSTM)模型相结合的刀具剩余寿命预测方法。首先,对刀具加工过程监控信号提取u-shapelets集合,并计算各u-shapelet与时间序列的距离得到距离矩阵;其次,通过基于密度聚类方法对距离矩阵进行聚类,得到聚类结果;最后,根据聚类结果基于各类别数据分别训练长短时记忆网络模型进行刀具剩余寿命的预测。以轮槽铣刀加工过程监控数据进行验证,并与Kmeans聚类、谱聚类、层次聚类、DBSCAN聚类方法进行比较,验证了所提方法的有效性。

Design of Low-Power Data Logger of Deep Sea for Long-Term Field Observation

This paper describes the implementation of a data logger for the real-time in-situ monitoring of hydrothermal systems. A compact mechanical structure ensures the security and reliability of data logger when used under deep sea. The data logger is a battery powered instrument, which can connect chemical sensors （pH electrode, H2S electrode, H2 electrode） and temperature sensors. In order to achieve major energy savings, dynamic power management is implemented in hardware design and software design. The working current of the data logger in idle mode and active mode is 15 μA and 1.44 mA respectively, which greatly extends the working time of battery. The data logger has been successftdly tested in the first Sino-American Cooperative Deep Submergence Project from August 13 to September 3, 2005.

基于小波分解-LSTM的航空发动机润滑油量模型

为了描述航空发动机润滑油量在飞机飞行中的变化,综合小波分解和长短期记忆网络(LSTM)的优点构建了小波分解-LSTM模型,模型的输入是由发动机高压转子转速、低压转子转速、飞机飞行高度、飞行姿态等参数构成的多组时间序列,输出为对应的润滑油量序列。采用实际运营中的快速存储记录器(QAR)数据,选取润滑油量波动较大的飞机飞行下降阶段进行建模。对润滑油量数据进行温度校准,去除热胀冷缩因素的影响;选择影响润滑油量变化的关键因素,包括高压转子转速、飞机姿态、飞行高度、飞行速度等11个输入因素;对这些输入因素和经温度校准后的润滑油量数据做小波分解,降低数据中噪声的影响并减小数据量,以加快后续机器学习模型的训练速度;采用LSTM神经网络训练数据模型,根据输入因素计算出润滑油量数据。结果表明:基于真实飞行数据测试结果显示,以升为单位,模型计算的润滑油量与实际润滑油量间均方误差约为0.1,说明模型能够有效描述飞机下降阶段中润滑油量的变化,可用于发动机滑油系统健康监控,为发动机滑油系统预测性维护提供新的方法支持。

基于GWO-SA-LSTM模型的调制识别算法

针对传统自动调制识别方法对现代信号识别准确率较低的问题,该文提出了GWO-SA-LSTM模型,该模型通过自注意力机制增强长短期记忆网络(LSTM)关键特征捕获能力,并利用灰狼算法(GWO)优化超参数。在真实环境的调制识别数据集上的实验表明,该文模型在10 dB信噪比下达到了97%的最高准确率,整体表现优于单纯LSTM模型。

基于卷积神经网络-长短期记忆神经网络模型利用光学体积描记术重建动脉血压波信号

目的直接动脉血压(arterial blood pressure,ABP)连续监测是侵入式的,传统袖带式的间接血压测量法无法实现连续监测。既往利用光学体积描记术(photoplethysmography,PPG)实现了连续无创血压监测,但其为收缩压和舒张压的离散值,而非ABP波的连续值,本研究期望基于卷积神经网络-长短期记忆神经网络(CNN-LSTM)利用PPG信号波重建ABP波信号,实现连续无创血压监测。方法构建CNN-LSTM混合神经网络模型,利用重症监护医学信息集(medical information mart for intensive care,MIMIC)中的PPG与ABP波同步记录信号数据,将PPG信号波经预处理降噪、归一化、滑窗分割后输入该模型,重建与之同步对应的ABP波信号。结果使用窗口长度312的CNN-LSTM神经网络时,重建ABP值与实际ABP值间误差最小,平均绝对误差(mean absolute error,MAE)和均方根误差(root mean square error,RMSE)分别为2.79 mmHg和4.24 mmHg,余弦相似度最大,重建ABP值与实际ABP值一致性和相关性情况良好,符合美国医疗器械促进协会(Association for the Advancement of Medical Instrumentation,AAMI)标准。结论CNN-LSTM混合神经网络可利用PPG信号波重建ABP波信号,实现连续无创血压监测。

GPS monitoring and analysis of ground movement and deformation induced by transition from open-pit to underground mining

To trace the potential hazards of open-pit slope in Longshou mine,global positioning system（GPS） is applied to monitoring ground movement and deformation induced by transition from open-pit to underground mining.Through long-term monitoring from 2003 to 2008,huge amounts of data were acquired.Monitoring results show that large-scale ground movement and deformation have occurred in mining area,and the movement area is ellipse-shaped.The displacement boundary of settlement trough is 2.0 km long along the exploratory line,and 1.5 km long along the strike of ore body.GPS monitoring results basically agree with the practical deformation state of open-pit slope.It is indicated that the long-term GPS monitoring is an effective way to understand the mechanism of ground movement and deformation in mine area. 更多

Thermomechanical analysis of long-term global modal and local deformation measurements of the Kishwaukee Bridge using the bootstrap

In this paper we present a comparative analysis of global frequency and local deformation data for a large concrete bridge. The asymptotic probability distributions of the central statistics are presented, and compared with empirical bootstrap estimates. Bootstrapped distributions are calculated from reference data obtained during 1999–2000 and used to develop change-point alarm criteria for the structure, using reasonable sensitivity measures developed from FEM simulations and structural analysis. The implications of the frequency data are discussed in conjunction with the strain and displacement measurements in order to discern if the load carrying capacity of the bridge has been affected. The critical need for more advanced temperature compensation models for large structures continually in thermal disequilibrium is discussed.

融合SBAS-InSAR和WaOA-LSTM的上海浦东国际机场沉降监测与预测

为了监测上海浦东国际机场(SPIA)的沉降现状并提高长短时记忆(LSTM)网络模型预测精度,本文基于短基线集合成孔径雷达干涉测量(SBAS-InSAR)技术和32景Sentinel-1A影像,获取了上海浦东国际机场2020年9月—2023年8月的时间序列沉降信息;构建了基于海象优化算法(WaOA)优化的WaOALSTM沉降预测模型,并将预测结果与合成孔径雷达干涉测量(InSAR)监测值进行对比分析。结果表明,上海浦东国际机场近三年最大沉降速率为-52.21 mm/a,最大累积沉降量达到-159.30 mm,沉降主要集中在填海区的二号、四号和五号跑道,其中五号跑道北部周围护岸区域及沿海堤坝区域最为严重;WaOA-LSTM模型预测值与监测真实值的均方根误差为2.63 mm,平均绝对误差为2.06 mm,相较于传统LSTM模型分别提升了52.87%和53.29%。研究结果为上海浦东国际机场的安全运营提供了参考。

基于DCC-LSTM的钻井液微量漏失智能监测方法

钻井过程中发生钻井液漏失时,现有的井漏智能监测方法,难以获取长时数据序列特征,无法实现对微量漏失的及时监测和预警,进而容易导致更为严重的漏失发生。为此,提出了一种结合扩张因果卷积网络(Dilated and Causal Convolution,DCC)特征映射能力和长短期记忆网络(Long Short-Term Memory,LSTM)时序特征提取能力的DCC-LSTM钻井液微量漏失智能监测方法,弥补长短期记忆网络对于长期记忆衰减的不足,实现了对钻井液微量漏失的准确监测和预测。研究结果表明:①DCC-LSTM井漏智能监测模型利用扩张因果卷积网络提取监测参数的长时特征,并将其映射为短序列表示,利用长短期记忆网络处理特征短序列获取监测数据的长时变化趋势,实现了微量漏失的准确监测;②扩张因果卷积网络层数确定方法可以获得最佳网络层数,得到的DCC网络结构使LSTM对长时序列趋势信息的遗忘减少24%;③与其他井漏监测方法相比,DCC-LSTM网络能够准确监测早期微量漏失,井漏预警时间最长可提前26 min,监测准确率由96.9%提升至99.4%,漏报率由6.4%降低为1.1%。结论认为,该方法能够获取监测参数的长时趋势变化特征,经矿场试验验证与其他方法相比有明显优势,为微量漏失监测和预测提供一种可行的方法,对油气钻井井漏风险的防控具有重要指导意义。

基于ISCSO-LSTM模型的刀具磨损预测

为进一步提高刀具磨损量预测模型的准确度,实现对刀具加工过程的在线监控。提出一种基于改进的沙猫算法(improved sand cat swarm optimization,ISCSO)和长短期记忆神经网络(long short-term memory,LSTM)的刀具磨损量预测模型。利用刀具的加速度振动信号为输入样本,应用长短期记忆神经网络对铣刀磨损值进行预测。针对沙猫算法收敛精度低等问题,引入混沌映射、非线性收敛因子和对立点检测机制,利用改进的沙猫算法优化长短期记忆神经网络的参数。实验结果表明ISCSO-LSTM模型的刀具磨损预测精度明显高于LSTM模型。

非均匀碱-硅酸反应下RC梁长期膨胀特征研究

碱-硅酸反应(ASR)能够产生膨胀应力,引起混凝土膨胀、开裂甚至破坏,而钢筋会对碱-硅酸反应引起的膨胀产生抑制作用,但目前仅用材料试验无法对钢筋的约束作用进行评估。围绕钢筋的约束作用,以钢筋混凝土(RC)梁为研究对象,采用加速ASR反应试验,研究了34个月内不同碱溶液浸泡区域和深度下RC梁的膨胀特性。进一步分析了钢筋对混凝土碱-硅酸反应膨胀的约束机理。结果表明:当浸泡位置和浸泡深度不同时,RC梁结构表现出不同的膨胀特征,直接浸入碱溶液的部位膨胀率较大,钢筋周围的膨胀率明显降低。钢筋能够对膨胀产生直接和间接两种约束作用。研究成果可为ASR反应损伤后钢筋混凝土的结构力学性能评估提供参考。

2015-2018年鼎湖山典型森林生态系统繁殖期和越冬期鸟类名录及群落特征数据集

鸟类多样性及其群落结构特征是生态系统中生物多样性长期监测的重要指标。鼎湖山国家级自然保护区内分布有包括季风常绿阔叶林(monsoon evergreen broad-leaved forest,MEBF)、针阔叶混交林(mixed Pinus massoniana/broad-leaved forest,MF)和马尾松针叶林(Pinus massoniana coniferous forest,PF)在内的典型森林植被类型且保存完好,其中季风常绿阔叶林具有400多年的保护历史。本数据集整理和统计了鼎湖山保护区2015–2018年季风常绿阔叶林、针阔叶混交林和马尾松针叶林3种典型森林植被类型鸟类繁殖期和越冬期群落长期监测数据,野外数据采集包括鸟类种类、个体数量、时间、距离、离地高度、生境类型、活动基质及行为等内容,数据处理包括各林型鸟类群落的多样性指数、均匀性指数、优势度指数和相似系数,以及不同调查期(繁殖期和越冬期)和全年鸟类物种的出现频次、个体数量与平均密度,共记录到鸟类9目35科89种。本数据集为该区域进一步加强鸟类群落研究提供了基础数据,也为深入探究动植物多类群种间互作及森林生态系统保护与管理提供重要数据支撑。