2020 IEEE International Conference on Prognostics and Health Management (ICPHM)最新文献_第3页

Application of Machine Learning Algorithms for Patient Length of Stay Prediction in Emergency Department During Hajj 机器学习算法在朝觐期间急诊科患者住院时间预测中的应用

2020 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2020-06-01 DOI: 10.1109/ICPHM49022.2020.9187055

Hassan Hijry, Richard Olawoyin

{"title":"Application of Machine Learning Algorithms for Patient Length of Stay Prediction in Emergency Department During Hajj","authors":"Hassan Hijry, Richard Olawoyin","doi":"10.1109/ICPHM49022.2020.9187055","DOIUrl":"https://doi.org/10.1109/ICPHM49022.2020.9187055","url":null,"abstract":"Hospital emergency departments (EDs) in vital locations face high patient demand during peak events such as the annual Islamic pilgrimage (the Hajj event) in Mecca, Saudi Arabia, the New Year celebration ceremony in New York, and the World Cup, etc. Variable patient arrival rates and hospital conditions, particularly the availability of beds for inpatients, impacts long waiting times and length of stay (LOS), causing pain and dissatisfaction to patients. Patient length of stay is chosen to be a measure of ED overcrowding as a compliance measure set by most hospitals. Clinicians need to get an opportunity to be proactive in ED overcrowding crises, specifically in the case of peak days. For this purpose, the research aims to build a model to forecast Hajj patient LOS, using machine learning algorithms through predictive input factors such as patient age, mode of arrival, and patient’s type of condition in the ED. Therefore, using machine learning algorithms, such as artificial neural networks, linear and logistic regressions, to forecast ED LOS allows clinicians to prepare for high levels of congestion and provide insights to determine the LOS of patients during vital times.","PeriodicalId":148899,"journal":{"name":"2020 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131324716","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: 5

Automated detection of textured-surface defects using UNet-based semantic segmentation network 基于unet语义分割网络的纹理表面缺陷自动检测

2020 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2020-06-01 DOI: 10.1109/ICPHM49022.2020.9187023

Nastaran Enshaei, Safwan Ahmad, F. Naderkhani

{"title":"Automated detection of textured-surface defects using UNet-based semantic segmentation network","authors":"Nastaran Enshaei, Safwan Ahmad, F. Naderkhani","doi":"10.1109/ICPHM49022.2020.9187023","DOIUrl":"https://doi.org/10.1109/ICPHM49022.2020.9187023","url":null,"abstract":"Over the recent years, developing a reliable auto-mated visual inspection system/approach for manufacturing and industry sectors which are moving toward smart manufacturing operations faces lots of significant challenges. Traditional visual inspection techniques which are developed based on manually extracted features, can rarely be generalized and have shown weak performance in real applications in different industries. In this paper, we propose a novel and automated visual inspection system which can outperform the statistical methods in terms of detection and the quantification of anomalies in image data for performing critical industrial tasks such as detecting micro scratches on product. In particular, an end-to-end UNet-based fully convolutional neural network for automated defect detection in industrial surfaces is designed and developed. The proposed network has the capability to accept raw images as input and the output is pixel-wise masks. In order to avoid overfitting and improve the model generalization, we use real-time data augmentation approach during our training phase. To evaluate the performance of the proposed model, we use a publicly available data set containing ten different types of textured-surfaces with their associated weakly annotated masks. The findings indicate that despite working with roughly annotated labels, our results are in agreement with previous works and show improvements regarding the detection time.","PeriodicalId":148899,"journal":{"name":"2020 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115647720","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: 14

Health Indicator Forecasting for Improving Remaining Useful Life Estimation 用于改进剩余使用寿命估计的运行状况指标预测

2020 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2020-06-01 DOI: 10.1109/ICPHM49022.2020.9187047

Qiyao Wang, Ahmed K. Farahat, Chetan Gupta, Haiyan Wang

{"title":"Health Indicator Forecasting for Improving Remaining Useful Life Estimation","authors":"Qiyao Wang, Ahmed K. Farahat, Chetan Gupta, Haiyan Wang","doi":"10.1109/ICPHM49022.2020.9187047","DOIUrl":"https://doi.org/10.1109/ICPHM49022.2020.9187047","url":null,"abstract":"Prognostics is concerned with predicting the future health of the equipment and any potential failures. With the advances in the Internet of Things (IoT), data-driven approaches for prognostics that leverage the power of machine learning models are gaining popularity. One of the most important categories of data-driven approaches relies on a predefined or learned health indicator to characterize the equipment condition up to the present time and make inference on how it is likely to evolve in the future. In these approaches, health indicator forecasting that constructs the health indicator curve over the lifespan using partially observed measurements (i.e., health indicator values within an initial period) plays a key role. Existing health indicator forecasting algorithms, such as the functional Empirical Bayesian approach, the regression-based formulation, a naive scenario matching based on the nearest neighbor, have certain limitations. In this paper, we propose a new ‘generative + scenario matching' algorithm for health indicator forecasting. The key idea behind the proposed approach is to first non-parametrically fit the underlying health indicator curve with a continuous Gaussian Process using a sample of run-to-failure health indicator curves. The proposed approach then generates a rich set of random curves from the learned distribution, attempting to obtain all possible variations of the target health condition evolution process over the system's lifespan. The health indicator extrapolation for a piece of functioning equipment is inferred as the generated curve that has the highest matching level within the observed period. Our experimental results show the superiority of our algorithm over the other state-of-the-art methods.","PeriodicalId":148899,"journal":{"name":"2020 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128995630","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-Based Method for Fault Detection and Isolation of Electric Drive Systems 基于模型的电力传动系统故障检测与隔离方法

2020 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2020-06-01 DOI: 10.1109/ICPHM49022.2020.9187028

Jiyu Zhang, M. Salman

引用次数: 1

Ultrasonic Guided Waves Based Identification of Elastic Properties Using 1D-Convolutional Neural Networks 基于一维卷积神经网络的超声导波弹性特性识别

2020 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2020-06-01 DOI: 10.1109/ICPHM49022.2020.9187057

M. Rautela, S. Gopalakrishnan, Karthik Gopalakrishnan, Y. Deng

引用次数: 16

Fault Diagnosis for Distributed Cooperative System Using Inductive Logic Programming 基于归纳逻辑规划的分布式协同系统故障诊断

2020 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2020-06-01 DOI: 10.1109/ICPHM49022.2020.9187032

S. Sato, Yosuke Watanabe, H. Seki, Yoshinao Ishii, Shoji Yuen

{"title":"Fault Diagnosis for Distributed Cooperative System Using Inductive Logic Programming","authors":"S. Sato, Yosuke Watanabe, H. Seki, Yoshinao Ishii, Shoji Yuen","doi":"10.1109/ICPHM49022.2020.9187032","DOIUrl":"https://doi.org/10.1109/ICPHM49022.2020.9187032","url":null,"abstract":"This paper proposes a learning and diagnosis method that can be applied immediately after a distributed system starts cooperative operation. The proposed method first learns behavioral rules for individual systems from their time series data, which are collected under independent operations. Then, anomality is detected and the system is diagnosed following the cooperative specification. The proposed method learns rules for individual systems based on ACEDIA, which is a kind of inductive logic programming; the rules are either transition rules or relationship rules that hold among variables at the same transition time. In a diagnostic phase, inconsistent rules and inconsistent specifications are obtained with ranking information against the diagnostic data, where ranking is performed through evaluation in terms of the generality on each rule and specification. We demonstrate that the proposed method correctly outputs the rules and specifications that are violated by diagnostic data. Moreover, in a case study on a simplified automotive system consisting of multiple control systems, the rules essentially related to the error were ranked higher.","PeriodicalId":148899,"journal":{"name":"2020 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132320941","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

A Comparison Study of Machine Learning Enabled Filtering Methods for Battery Management 基于机器学习的电池管理滤波方法比较研究

2020 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2020-06-01 DOI: 10.1109/ICPHM49022.2020.9187029

Sara Kohtz, Pingfeng Wang

引用次数: 0

Hierarchical Classification for Unknown Faults 未知故障的层次分类

2020 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2020-06-01 DOI: 10.1109/ICPHM49022.2020.9187038

Stephen C. Adams, Tyler Cody, P. Beling, Sherwood Polter, K. Farinholt

{"title":"Hierarchical Classification for Unknown Faults","authors":"Stephen C. Adams, Tyler Cody, P. Beling, Sherwood Polter, K. Farinholt","doi":"10.1109/ICPHM49022.2020.9187038","DOIUrl":"https://doi.org/10.1109/ICPHM49022.2020.9187038","url":null,"abstract":"Data-driven prognostics and health management (PHM) models are generally trained on a set of data collected from the system under study. A standard assumption of this paradigm is that the training data contains all the normal operating conditions and fault conditions that are possible. If the training data does not contain all possible conditions, a single classifier approach will not be adequate because the PHM model could have difficulty classifying a new condition not previously seen during training. This study investigates the use of hierarchical classification in situations where the training data is incomplete in terms of the faults that are present in the testing set and characterizes the proposed problem as a transfer learning problem. The hierarchical classifier employs non-mandatory leaf node prediction where the model is not required to move to the lower levels of the hierarchy. It is hypothesized that this construction allows the classification to stop at a higher level when the fault is not present in the training data. The proposed method is demonstrated on a hydraulic actuator condition monitoring data set.","PeriodicalId":148899,"journal":{"name":"2020 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129982992","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

A Novel Evaluation Framework for Unsupervised Domain Adaption on Remaining Useful Lifetime Estimation 一种基于剩余使用寿命估计的无监督域自适应评估框架

2020 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2020-06-01 DOI: 10.1109/ICPHM49022.2020.9187058

Tilman Krokotsch, M. Knaak, C. Gühmann

引用次数: 3

Predicting Surface Roughness and Flank Wear in Turning Processes 车削过程中表面粗糙度和侧面磨损的预测

2020 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2020-06-01 DOI: 10.1109/ICPHM49022.2020.9187056

N. K. Vuong, Yang Xue, Shudong Liu, Yu Zhou, Min Wu

{"title":"Predicting Surface Roughness and Flank Wear in Turning Processes","authors":"N. K. Vuong, Yang Xue, Shudong Liu, Yu Zhou, Min Wu","doi":"10.1109/ICPHM49022.2020.9187056","DOIUrl":"https://doi.org/10.1109/ICPHM49022.2020.9187056","url":null,"abstract":"In advanced manufacturing, the tool quality is critical to ensure the product quality, and thus needs to be closely monitored. However, directly measuring the tool quality can be time-consuming and impractical for continuous manufacturing. In this work, leveraging on the Internet of Things (IoT) infrastructure and machine learning techniques, two important quality metrics for turning machinery tools, namely surface roughness and flank wear, are modeled using easily available sensory data from turning machines, including vibration data, force data, and cutting parameter information. A regression-based prediction model is proposed incorporating both time domain and frequency domain features as selected using sequential replacement feature selection algorithm. The regression model itself is selected using cross validation from multiple models including linear regression, quadratic regression, random forest, and Gradient Boosting Machine (GBM). Experiment using actual manufacturing data collected with a prototype IoT setup showed that the proposed model achieved high prediction accuracy of 0.860 and low variance as indicated by Adj-R2 of 0.722 for flank wear, and similarly for surface roughness the accuracy is 0.9525 on average and Adj-R2 is 0.7175 on average. This work demonstrated the prediction of the quality metrics of turning machinery tools, which are conventionally difficult to measure in continuous manufacturing, based on IoT sensory data and machine learning techniques.","PeriodicalId":148899,"journal":{"name":"2020 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123894011","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: 3