2019 4th International Conference on System Reliability and Safety (ICSRS)最新文献_第7页

System of Systems Hazard Analysis Using HAZOP and FTA for Advanced Quarry Production 先进采石场生产中运用HAZOP和FTA的系统危害分析体系

2019 4th International Conference on System Reliability and Safety (ICSRS) Pub Date : 2019-11-01 DOI: 10.1109/ICSRS48664.2019.8987613

Faiz ul Muram, M. Javed, S. Punnekkat

{"title":"System of Systems Hazard Analysis Using HAZOP and FTA for Advanced Quarry Production","authors":"Faiz ul Muram, M. Javed, S. Punnekkat","doi":"10.1109/ICSRS48664.2019.8987613","DOIUrl":"https://doi.org/10.1109/ICSRS48664.2019.8987613","url":null,"abstract":"The advanced production systems are composed of separate and distinct systems that operate in both isolation and conjunction, and therefore forms the System-of-Systems (SoS). However, a lot of production systems are classified as safety-critical, for example, due to the interactions between machines and involved materials. From the safety perspective, besides the behaviour of an individual system in SoS, the emergent behaviour of systems that comes from their individual actions and interactions must be considered. An unplanned event or sequence of events in safety-critical production systems may results in human injury or death, damage to machines or the environment. This paper focuses on the construction equipment domain, particularly the quarry site, which solely produce dimension stone and/or gravel products. The principal contribution of this paper is SoS hazard identification and mitigation/elimination for the electric quarry site for which the combination of guide words based collaborative method Hazard and Operability (HAZOP) and Fault Tree Analysis (FTA) are used. The published studies on HAZOP and FTA techniques have not considered the emergent behaviours of different machines. The applicability of particular techniques is demonstrated for individual and emergent behaviours of machines used in the quarry operations, such as autonomous hauler, wheel loader, excavator and crusher.","PeriodicalId":430931,"journal":{"name":"2019 4th International Conference on System Reliability and Safety (ICSRS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121993578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Application of Distributed Machine Learning Model in Fault Diagnosis of Air Preheater 分布式机器学习模型在空气预热器故障诊断中的应用

2019 4th International Conference on System Reliability and Safety (ICSRS) Pub Date : 2019-11-01 DOI: 10.1109/ICSRS48664.2019.8987707

Haokun Lei, Jian Liu, Chun Xian

引用次数: 1

Research on Accelerated Life Test Method of Electronic Products Base on Multidimensional Stress Coupling 基于多维应力耦合的电子产品加速寿命试验方法研究

2019 4th International Conference on System Reliability and Safety (ICSRS) Pub Date : 2019-11-01 DOI: 10.1109/ICSRS48664.2019.8987582

Xinggao Zhu, Shi-jin Shi, Hailong Cheng, B. Jin

引用次数: 0

Transformation of Vessel Performance System into Fault-tolerant Syste - Example of Fault Detection on Speed Log 船舶性能系统向容错系统的转化——以航速日志故障检测为例

2019 4th International Conference on System Reliability and Safety (ICSRS) Pub Date : 2019-11-01 DOI: 10.1109/ICSRS48664.2019.8987652

Z. Lajic, A. Senteris, Ioannis Filippopoulos, M. Pearson

引用次数: 3

Power Resilience Assessment from Physical and Socio-Demographic Perspectives 从物理和社会人口统计学角度评估电力恢复力

2019 4th International Conference on System Reliability and Safety (ICSRS) Pub Date : 2019-11-01 DOI: 10.1109/ICSRS48664.2019.8987673

M. B. Ulak, Anil Yazici, E. Ozguven, O. A. Vanli, R. Arghandeh

{"title":"Power Resilience Assessment from Physical and Socio-Demographic Perspectives","authors":"M. B. Ulak, Anil Yazici, E. Ozguven, O. A. Vanli, R. Arghandeh","doi":"10.1109/ICSRS48664.2019.8987673","DOIUrl":"https://doi.org/10.1109/ICSRS48664.2019.8987673","url":null,"abstract":"Urban resilience is a multifaceted concept including the recovery of the physical infrastructure and various urban activities that depend on that physical infrastructure. It is relatively straightforward to quantify infrastructure resilience by tracking the recovered facilities in time and marking the time that the infrastructure is fully functioning again. However, the physical infrastructure recovery does not necessarily indicate that the urban activities bounce back to the predisaster conditions. The restoration of urban activities depends on the areas that a particular infrastructure serves (e.g., residential, commercial) and the connections with other critical facilities (e.g., health, education). It is important to investigate the infrastructure recovery and “resilience divide” with respect to the enabled services and affected populations in order to achieve all-inclusive resilience. For this purpose, we examined the resilience of different physical elements such as power feeders (i.e., underground or overhead lines), critical facilities (e.g., fire and rescue services, hospitals) and different socio-demographic segments of the population (i.e., different age groups, ethnicities, and income levels) which constitute an urban environment. The analyses were conducted using the power outages experienced after Hurricane Hermine in Tallahassee, as a case study. The findings show that overall resilience performance can be distinct and/or not homogeneous for the resilience of different physical elements, urban services, and population groups.","PeriodicalId":430931,"journal":{"name":"2019 4th International Conference on System Reliability and Safety (ICSRS)","volume":"9 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116756689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Model Optimization Method for Accelerated Degradation Test Data 加速退化试验数据的模型优化方法

2019 4th International Conference on System Reliability and Safety (ICSRS) Pub Date : 2019-11-01 DOI: 10.1109/ICSRS48664.2019.8987669

Xiaobing Li, Guangze Pan, Jun Ying, Xiaocui Zhu

引用次数: 0

Data Reconstruction Accuracy of Compressive Sleeping Scheme with Modified S-MAC for Body Sensor Networks 基于改进S-MAC的身体传感器网络压缩睡眠方案的数据重建精度

2019 4th International Conference on System Reliability and Safety (ICSRS) Pub Date : 2019-11-01 DOI: 10.1109/ICSRS48664.2019.8987636

Elsa Nur Fitri Astuti, I. Wahidah, F. Y. Suratman

{"title":"Data Reconstruction Accuracy of Compressive Sleeping Scheme with Modified S-MAC for Body Sensor Networks","authors":"Elsa Nur Fitri Astuti, I. Wahidah, F. Y. Suratman","doi":"10.1109/ICSRS48664.2019.8987636","DOIUrl":"https://doi.org/10.1109/ICSRS48664.2019.8987636","url":null,"abstract":"Today the main difficulty in Body Sensor Network (BSN) is the limited battery power and accuracy of the data. Some cases in health monitoring requires a long sensor life time such as health monitoring of critical patients, so that the data is needed by doctors or hospitals can be fulfilled. The Compressive Sleeping algorithm and the scheduling scheme using the Medium Access Control Sensor (S-MAC) are proposed in this research, to reduce power consumption. First, Compressive Sleeping algorithm is applied to select a several of the sensor to be activated and a several of it will go into sleeping mode, the selection is based on the sensor type and remaining battery of each sensor. The output of this algorithm is several sensors that are suitable for active. Furthermore, the active sensor will be scheduling data transmission using S-MAC, scheduling is based on the sensor priority and remaining batteries of each priority. Sensors that do not transmit data will go into temporary sleep mode, then the sensor will be reactivated if it gets a turn to transmit data to the fusion center (FC). The calculation of energy consumption is carried out on each process block. We calculated the accuracy of all reconstructed data in the FC using the Orthogonal Matching pursuit algorithm (OMP). The results of this research produce a good energy efficiency, that is, for the sensor selection ratio of 40%, the energy efficiency is 67.03 % and data accuracy is 95.5%.","PeriodicalId":430931,"journal":{"name":"2019 4th International Conference on System Reliability and Safety (ICSRS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129708351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Reliability and Maintenance Cost Forecasting for Systems with Multistate Components Using Artificial Neural Networks 基于人工神经网络的多状态部件系统可靠性与维护成本预测

2019 4th International Conference on System Reliability and Safety (ICSRS) Pub Date : 2019-11-01 DOI: 10.1109/ICSRS48664.2019.8987700

P. Do, B. Iung, C. Cavalcante

引用次数: 2

Models of Margin: From the Mathematical Formulation to an Operational Implementation 保证金模型:从数学公式到操作实现

2019 4th International Conference on System Reliability and Safety (ICSRS) Pub Date : 2019-11-01 DOI: 10.1109/ICSRS48664.2019.8987682

A. Touboul, Romain Barbedienne, Jean-Michel Edaliti

引用次数: 1

Systems Modeling Using Big Data Analysis Techniques and Evidence 使用大数据分析技术和证据的系统建模

2019 4th International Conference on System Reliability and Safety (ICSRS) Pub Date : 2019-11-01 DOI: 10.1109/ICSRS48664.2019.8987667

Sebastian T. Glavind, J. Sepulveda, J. Qin, M. Faber

{"title":"Systems Modeling Using Big Data Analysis Techniques and Evidence","authors":"Sebastian T. Glavind, J. Sepulveda, J. Qin, M. Faber","doi":"10.1109/ICSRS48664.2019.8987667","DOIUrl":"https://doi.org/10.1109/ICSRS48664.2019.8987667","url":null,"abstract":"In the present contribution, the potentials of utilizing techniques of big data analysis as a means to improve the understanding of complex probabilistic system representations are investigated. It is assumed that a probabilistic model is available for the representation of the system performances and that an adequate Monte Carlo simulation technique is available and applied for the probabilistic analysis of these. Model-based clustering analysis is then applied to establish a visual representation of the Monte Carlo simulated scenarios of events leading to different performances of the considered system. Various conditioning events on the simulated scenarios, such as specific failure events, are readily introduced by sorting. Assuming that the Monte Carlo simulated scenarios of events are utilized to establish a surrogate representation of the considered system, variance based sensitivities are derived for both the case of independent and dependent random variables. To this end, so-called ANOVA and the very recently formulated ANCOVA decomposition's are applied. The proposed scheme is illustrated on a simple example in which the probabilistic characteristics of non-linear structural performances of a moment resisting frame structure are considered. It is seen from the example that big data techniques may readily be applied to provide significant insights on which scenarios of events govern the probabilistic characteristics of the performances of the system, and with respect to how uncertainties associated with the random variables used to model the system propagate in the system and affect its responses. The latter is especially useful when aiming to reduce model complexity, but also in the context of structural health monitoring where response characteristics that contain significant information about the state of the system must be identified.","PeriodicalId":430931,"journal":{"name":"2019 4th International Conference on System Reliability and Safety (ICSRS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125430857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4