2015 Informs Annual Meeting

MA73

INFORMS Philadelphia – 2015

2 - Real-time Monitoring for Additive Manufacturing using Online Sparse Estimation Based Classification Kaveh Bastani, Research Assistant, Virginia Tech University, 106 Durham Hall (MC 0118) 1145 Perry Str, Blacksburg,VA, United States of America, kaveh@vt.edu, Zhenyu Kong The objective of this work is to realize real-time monitoring of additive manufacturing processes using multiple sensor signals. To achieve this objective, an approach invoking the concept of sparse estimation called online sparse estimation-based classification (OSEC) is proposed. The OSEC approach is equipped with a novel computationally fast sparse estimation algorithm to facilitate real-time monitoring applications. 3 - Diagnostic Monitoring of Multivariate Process via a LASSO-BN Formulation Yan Jin, University of Washington, 530 NE 103rd St, Seattle, WA, 98125, United States of America, yanjin@uw.edu, Guan Wang, Shuai Huang, Houtao Deng Fault detection and root-cause diagnosis are usually considered as two separate tasks in most existing process monitoring methods. While they could reinforce each other, we propose a diagnostic monitoring approach that unifies monitoring and root-cause diagnosis by integrating process monitoring, Bayesian network, and sparse learning. 4 - Multi-stage Nanocrystal Growth Identifying and Modeling via In-situ TEM Video Yanjun Qian, PhD Candidate, TAMU, 1501 Harvey Rd, Apt 806, College Station, TX, 77840, United States of America, qianyanjun09@gmail.com While in-situ transmission electron microscopy technique has caught a lot of recent attention, one of the bottlenecks appears to be the lack of automated and quantitative analytic tools. We introduce an automated tool suitable for analyzing the in-situ TEM videos. It learns and tracks the normalized particle size distribution and identifies the phase change points delineating the stages in nanocrystal growth. We furthermore produce a quantitative physical-based model. 5 - Rul Prediction Based on Noisy Condition Monitoring Signals using Constrained Kalman Filter Junbo Son, PhD Candidate, University of Wisconsin-Madison, 1513 University Avenue, Madison, WI, 53706, United States of America, json5@wisc.edu, Shiyu Zhou, Chaitanya Sankavaram, Yilu Zhang, Xinyu Du In this paper, a robust statistical prognostic method is proposed to predict the remaining useful life of individual units based on condition monitoring signals that are contaminated by severe noises. The proposed method defines a set of inequality constraints so that satisfactory prediction accuracy can be achieved regardless of the noise level. The advantageous features of the proposed method is demonstrated by both numerical studies and a case study with real world automotive battery data. MA73 73-Room 203B, CC IEEE T-ASE Invited Session: Healthcare and Service Systems Automation Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Jingshan Li, Professor, 1513 University Ave, Madison, WI, 53706, United States of America, jli252@wisc.edu 1 - N-k Power Problem in the Power Systems: A Budget Allocation Perspective Loo Hay Lee, National University of Singapore, Department of Industrial & Systems, Engineering, Singapore, iseleelh@nus.edu.sg, Chun-hung Chen, Ek Peng Chew, Giulia Pedrielli, Haobin Li, John Shortle, Yue Liu In the stochastic N-k power network problem, N choose k failures result in a probability of the entire system to fail (unsafe system). We want to determine if the system is unsafe, when failures are estimated through noisy simulations, by optimal computing budget allocation. The solution method is based on the likelihood of the system to be safe (unsafe), whose estimation is sequentially improved to be given as input to a tailored budget allocation that also considers the observed system state. 2 - Integrating OCBA and GA to Find the Approximate Pareto Patient Flow Distribution Jie Song, Peking University, Room 512,Fangzheng Building, Beijing, China, songjie@coe.pku.edu.cn, Zekun Liu, Yunzhe Qiu We develop a methodology to find the optimal macro-level patient flow distribution in terms of multi-dimension inputs and outputs for the hierarchical

healthcare system. The proposed method integrates the discrete event simulation, the multi-objective optimization, and the simulation budget allocation to comprehensively improve the overall system performances. A case study based on the real data is carried out to validate and implement the proposed method. 3 - Improving Response-Time Performance in Acute Care Delivery: A Systems Approach Xiaolei Xie, Department of Industrial Engineering, Tsinghua University, 614 Shunde Building, Tsinghua University, Beijing, China, xxie@tsinghua.edu.cn, Colleen Swartz, Paul Depriest, Jingshan Li In response to a patient with acute physiological deterioration, we study the probability that an appropriate decision is made within a desired time period, referred to as response time performance (RTP). First, a closed formula to evaluate RTP is derived by assuming exponential response time, which is followed by bottleneck analysis. Then, under general case, an approximation approach is proposed to evaluate RTP. Finally, a case study is introduced to illustrate the applicability of the method. 4 - Spatiotemporal Differentiation of Myocardial Infarctions Chen Kan, University of South Florida, 4202 E. Fowler Ave. ENB118, Tampa, FL, United States of America, chenkan@mail.usf.edu, Hui Yang This paper presents a novel warping approach to quantify the dissimilarity of disease-altered patterns in 3-lead VCGs. The hypothesis testing shows that there are significant space-time differences between healthy and diseased subjects. Further, we optimize the embedding of each VCG as a feature vector in the high- dimensional space that preserves the dissimilarity distance matrix. Experimental results demonstrated that this novel approach improves the performance of predictive models. MA74 74-Room 204A, CC Data Analytics for Quality Control and Improvement I Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Kaibo Liu, Assitant Professor, UW-Madison, 1513 University Avenue, Madison, WI, 53706, United States of America, kliu8@wisc.edu 1 - A Distribution Free Procedure for Fault Identification in High-Dimensional Processes Mehmet Turkoz, Rutgers University, 16 Rachel Terrace, Piscataway, NJ, 08854, United States of America, turkoz@scarletmail.rutgers.edu, Sangahn Kim, Young-seon Jeong, Myong K (MK) Jeong, Elsayed Elsayed, K.N. Al-khalifa, Abdel Magid Hamouda In a process with high-dimension, identifying which variables cause an out-of- control signal is a challenging issue for quality problems. Even though there are many procedures for fault identification, most of them assume the normal distribution. However, many real life problems come from multivariate non- normal distribution. We present a new fault identification method that does not assume any specific probability distribution. 2 - Broaching Process Modeling Based on Non-repeating Cyclic Signals Meg Tian, Graduate Student, Virginia Tech, 250 Durham Hall, Broaching is often used to produce complex contours by sequentially removing material via multiple cutting teeth. The broaching force signal exhibits a non- repeating cyclic pattern. A new approach is proposed to model the non-repeating cyclic signals and thus detect changes in a broaching process. 3 - Heterogeneous Recurrence T^2 Charts for Monitoring and Control of Nonlinear Dynamic Processes Yun Chen, University of South Florida, 4202 E. Fowler Ave. ENB118, Tampa, FL, United States of America, yunchen@mail.usf.edu, Hui Yang This paper presents a new approach of heterogeneous recurrence T^2 control chart for online monitoring and anomaly detection in nonlinear dynamic processes. An effective partition scheme is firstly developed to delineate local recurrence regions in the multi-dimensional continuous state space. Further, we designed a new fractal representation of state transitions among recurrence regions, and then develop new measures for on-line monitoring and predictive control of process recurrences. Blacksburg, VA, 24060, United States of America, tian0414@vt.edu, Jaime Camelio, Ran Jin, Lee Wells

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