Safety and Reliability – Safe Societies in a Changing World最新文献_第5页

Time-dependent unavailability model integrating on demand-caused and standby-related failures addressing positive and negative effects of testing and maintenance 时间依赖的不可用性模型集成了需求引起的和备用相关的故障，处理测试和维护的积极和消极影响

Safety and Reliability – Safe Societies in a Changing World Pub Date : 2018-06-15 DOI: 10.1201/9781351174664-73

P. Martorell, S. Martorell, I. Martón, S. Carlos, A. Sánchez

引用次数: 2

On the level of safety knowledge in the general public 在公众的安全知识水平上

Safety and Reliability – Safe Societies in a Changing World Pub Date : 2018-06-15 DOI: 10.1201/9781351174664-19

G. Baldissone, M. Demichela, Lorenzo Comberti, Eleonora Pilone, J. Geng, L. Maida

引用次数: 0

Human reliability analysis in NPP: A plant-specific sensitivity analysis considering dynamic operator actions versus accident management actions 核电厂中人的可靠性分析:考虑动态操作人员行动与事故管理行动的电厂特定敏感性分析

Safety and Reliability – Safe Societies in a Changing World Pub Date : 2018-06-15 DOI: 10.1201/9781351174664-46

D. Kancev, S. Heussen, J. Kluegel, P. Drinovac, T. Kozlik

引用次数: 1

Machine learning modeling for massive industrial data: Railroad peak kips prediction 大规模工业数据的机器学习建模:铁路峰值kips预测

Safety and Reliability – Safe Societies in a Changing World Pub Date : 2018-06-15 DOI: 10.1201/9781351174664-144

C. Contreras, M. López-Campos, P. Escalona, R. Stegmaier, T. Grubessich

引用次数: 0

Checklist for judgement of technical facility safety level and results obtained by its application in practice 技术设施安全等级判定表及在实际应用中取得的效果

Safety and Reliability – Safe Societies in a Changing World Pub Date : 2018-06-15 DOI: 10.1201/9781351174664-149

D. Procházková, J. Procházka

引用次数: 0

A heterogeneous ensemble approach for the prediction of the remaining useful life of packaging industry machinery 包装工业机械剩余使用寿命预测的异质集成方法

Safety and Reliability – Safe Societies in a Changing World Pub Date : 2018-06-15 DOI: 10.1201/9781351174664-11

F. Cannarile, P. Baraldi, M. Compare, D. Borghi, Luca Capelli, E. Zio

{"title":"A heterogeneous ensemble approach for the prediction of the remaining useful life of packaging industry machinery","authors":"F. Cannarile, P. Baraldi, M. Compare, D. Borghi, Luca Capelli, E. Zio","doi":"10.1201/9781351174664-11","DOIUrl":"https://doi.org/10.1201/9781351174664-11","url":null,"abstract":"We present a method based on heterogeneous ensemble learning for the prediction of the Remaining Useful Life (RUL) of cutting tools (knives) used in the packaging industry. Ensemble diversity is achieved by training multiple prognostic models using different learning algorithms. The combination of the outcomes of the models in the ensemble is based on a weighted averaging strategy, which assigns weights proportional to the individual model performances on patterns of a validation set. The proposed heterogeneous ensemble has been applied to real condition monitoring knife data. It has provided more accurate RUL predictions compared to those of each individual base model. radation beyond which the equipment fails performing its function (failure threshold). Examples of modeling techniques used in degradationbased approaches are Auto-Regressive models (Gorjian et al., 2009), Relevance Vector Machines (Di Maio et al., 2012) and Semi-Markov Models (Cannarile et al., 2017a) (Cannarile et al., 2018). Direct RUL predictions approaches, instead, typically resort to machine learning techniques that directly map the relation between the observable parameters and the equipment RUL, without the need of predicting the equipment degradation state evolution towards a failure threshold (Schwabacher et al., 2007). Techniques used in direct RUL prediction approaches are, for example, Artifical Neural Networks (Wang & Vachtsenavos, 2001), Extreme Learning Machines (ELM) (Yang et al., 2017), Gaussian Processes (GP) (Baraldi et al., 2015b), etc. When few run-to-failure degradation trajectories are available, direct RUL approaches may overfit, i.e., these algorithms customize themselves too much to learn the relationship between the observable parameters and the corresponding RUL in the training set. Therefore, these methods tend to lose their generalization power, which leads to poor performance on new data. To overcome this, ensemble approaches, based on the aggregation of multiple model outcomes, have been introduced (Baraldi et al., 2013a). The basic idea is that the diverse models in the ensemble complement each other by leveraging their strengths and overcoming their drawbacks. Thus, the combination of the outcomes of the individual models in the ensemble improves the accuracy of the predictions compared to the performance of a single model (Brown et al., 2005) (Baraldi et al., 2013a). Different methods, such as ANN (Baraldi et al., 2013b), Support Vector Machine (SVM) (Liu et al., 2006) and kernel learning (Liu et al., 2015), have been used with success to build the individual models. For example, an ensemble of feedforward Artificial Neural Networks (ANN) has been embedded into a Particle Filter (PF) for the prediction of crack length evolution (Baraldi et al., 2013b) and an ensemble of datadriven regression models has been exploited for the RUL prediction of lithium-ion batteries (Xing et al., 2013). In (Rigamonti et al., 2017) a local ensemble of Echo State N","PeriodicalId":278087,"journal":{"name":"Safety and Reliability – Safe Societies in a Changing World","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125961035","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

Anti-icing expected heat loss as a risk indicator for arctic offshore logistics operations 防冰预期热损失作为北极海上物流作业的风险指标

Safety and Reliability – Safe Societies in a Changing World Pub Date : 2018-06-15 DOI: 10.1201/9781351174664-177

M. Naseri, E. Samuelsen

引用次数: 1

Operation and climate-weather change impact on maritime ferry safety 营运及气候-天气变化对海上渡轮安全的影响

Safety and Reliability – Safe Societies in a Changing World Pub Date : 2018-06-15 DOI: 10.1201/9781351174664-107

K. Kolowrocki, E. Kuligowska

引用次数: 2

The weighting method’s impact on the weighting process in decision making problems 赋权方法对决策问题中赋权过程的影响

Safety and Reliability – Safe Societies in a Changing World Pub Date : 2018-06-15 DOI: 10.1201/9781351174664-47

A. Tzioutziou, Y. Xenidis

引用次数: 1

Risk assessment and the influence of new information 风险评估和新信息的影响

Safety and Reliability – Safe Societies in a Changing World Pub Date : 2018-06-15 DOI: 10.1201/9781351174664-196

T. Stålhane, S. Johnsen

引用次数: 0