Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis
Data61-The Commonwealth Scientific and Industrial Research Organisation (CSIRO)（联邦科学与工业研究组织（CSIRO）Data61）；Monash University（莫纳什大学）；University of Technology Sydney（悉尼科技大学）
Dilusha Weeraddana, Sudaraka MallawaArachchi, Tharindu Warnakula, Zhidong Li, Yang Wang
Australian water infrastructure is more than a hundred years old, thus has begun to show its age through water main failures. Our work concerns approximately half a million pipelines across major Australian cities that deliver water to houses and businesses, serving over five million customers. Failures on these buried assets cause damage to properties and water supply disruptions. We applied Machine Learning techniques to find a cost-effective solution to the pipe failure problem in these Australian cities, where on average 1500 of water main failures occur each year. To achieve this objective, we construct a detailed picture and understanding of the behaviour of the water pipe network by developing a Machine Learning model to assess and predict the failure likelihood of water main breaking using historical failure records, descriptors of pipes and other environmental factors. Our results indicate that our system incorporating a nonparametric survival analysis technique called "Random Survival Forest" outperforms several popular algorithms and expert heuristics in long-term prediction. In addition, we construct a statistical inference technique to quantify the uncertainty associated with the long-term predictions.
Predicting the Lifespan of Industrial Printheads with Survival Analysis
Department of Advanced Computing Sciences（高级计算科学系）；Maastricht University（马斯特里赫特大学）；Quality Processes and Validation Department（质量流程与验证部门）；Canon Production Printing（佳能生产印刷）
Interpretable Survival Analysis for Heart Failure Risk Prediction
Stanford University（斯坦福大学）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人医疗保健系统）；Kaiser Permanente Northern California Division of Research（凯撒医疗北加州研究部）；Stanford Medicine（斯坦福医学院）
Prognostics of Multisensor Systems with Unknown and Unlabeled Failure Modes via Bayesian Nonparametric Process Mixtures
Department of Industrial and Systems Engineering, University of Florida（工业与系统工程系，佛罗里达大学）；School of Forest, Fisheries, and Geomatics Sciences, University of Florida（森林、渔业与地理科学学院，佛罗里达大学）
Synthetic Survival Data Generation for Heart Failure Prognosis Using Deep Generative Models
Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University（玛希隆大学拉马蒂博迪医院医学院查克里·纳鲁博丁医学研究所）；Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University（玛希隆大学拉马蒂博迪医院医学院临床流行病学与生物统计学系）；Department of Systems Design Engineering, University of Waterloo（滑铁卢大学系统设计工程系）；Cardiovascular Unit, Department of Internal Medicine, Police General Hospital（警察总医院内科心血管科）
Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful Life
Ontario Knowledge Intelligence Centre (OKIC), Canada（加拿大安大略知识智能中心）；Canadian Institute of Interscience (CIoI), Canada（加拿大跨学科科学研究所）；DeepCox Intelligence, Canada（加拿大DeepCox智能公司）
A Structured Nonparametric Framework for Nonlinear Accelerated Failure Time Models (KAN-AFT)
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology（数学系，先进科学学院，维洛雷理工学院）；Applied Statistics Unit, Indian Statistical Institute（应用统计单位，印度统计研究所）
Predicting Long-Term Allograft Survival in Liver Transplant Recipients
University of Toronto（多伦多大学）；Vector Institute（矢量研究所）；University Health Network（大学健康网络）；Kingston Health Sciences Centre（金斯顿健康科学中心）；Max Rady College of Medicine, University of Manitoba（曼尼托巴大学马克斯·雷迪医学院）
