scholarly journals Online Health Management for Complex Nonlinear Systems Based on Hidden Semi-Markov Model Using Sequential Monte Carlo Methods

2012 ◽  
Vol 2012 ◽  
pp. 1-22
Author(s):  
Qinming Liu ◽  
Ming Dong
Keyword(s):  
Monte Carlo ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Markov Model ◽  
Sequential Monte Carlo ◽  
Health Management ◽  
Health State ◽  
Health States ◽  
Complex Nonlinear System ◽  
Complex Nonlinear Systems

Health management for a complex nonlinear system is becoming more important for condition-based maintenance and minimizing the related risks and costs over its entire life. However, a complex nonlinear system often operates under dynamically operational and environmental conditions, and it subjects to high levels of uncertainty and unpredictability so that effective methods for online health management are still few now. This paper combines hidden semi-Markov model (HSMM) with sequential Monte Carlo (SMC) methods. HSMM is used to obtain the transition probabilities among health states and health state durations of a complex nonlinear system, while the SMC method is adopted to decrease the computational and space complexity, and describe the probability relationships between multiple health states and monitored observations of a complex nonlinear system. This paper proposes a novel method of multisteps ahead health recognition based on joint probability distribution for health management of a complex nonlinear system. Moreover, a new online health prognostic method is developed. A real case study is used to demonstrate the implementation and potential applications of the proposed methods for online health management of complex nonlinear systems.

scholarly journals Cost-effectiveness of Chloride-liberal versus Chloriderestrictive Intravenous Fluids among Patients Hospitalized in the United States

10.36469/9829 ◽  
2016 ◽  
Vol 4 (1) ◽  
pp. 90-102
Author(s):  
Louise Perrault ◽  
Dilip Makhija ◽  
Idal Beer ◽  
Suzanne Laplante ◽  
Sergio Iannazzo ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Decision Tree ◽  
Markov Model ◽  
The United States ◽  
Sensitivity Analyses ◽  
Major Surgery ◽  
Health State ◽  
Health States ◽  
Life Years ◽  
The Us

Background: Patients developing acute kidney injury (AKI) during critical illness or major surgery are at risk for renal sequelae such as costly and invasive acute renal replacement therapy (RRT) and chronic dialysis (CD). Rates of renal injury may be reduced with use of chloride-restrictive intravenous (IV) resuscitation fluids instead of chloride-liberal fluids. Objectives: To compare the cost-effectiveness of chloride-restrictive versus chloride-liberal crystalloid fluids used during fluid resuscitation or for the maintenance of hydration among patients hospitalized in the US for critical illnesses or major surgery. Methods: Clinical outcomes and costs for a simulated patient cohort (starting age 60 years) receiving either chloride-restrictive or chloride-liberal crystalloids were estimated using a decision tree for the first 90-day period after IV fluid initiation followed by a Markov model over the remainder of the cohort lifespan. Outcomes modeled in the decision tree were AKI development, recovery from AKI, progression to acute RRT, progression to CD, and death. Health states included in the Markov model were dialysis free without prior AKI, dialysis-free following AKI, CD, and death. Estimates of clinical parameters were taken from a recent meta-analysis, other published studies, and the US Renal Data System. Direct healthcare costs (in 2015 USD) were included for IV fluids, RRT, and CD. US-normalized health-state utilities were used to calculate quality-adjusted life years (QALYs). Results: In the cohort of 100 patients, AKI was predicted to develop in the first 90 days in 36 patients receiving chloride-liberal crystalloids versus 22 receiving chloride-restrictive crystalloids. Higher costs of chloride-restrictive crystalloids were offset by savings from avoided renal adverse events. Chloride-liberal crystalloids were dominant over chloride-restrictive crystalloids, gaining 93.5 life-years and 81.4 QALYs while saving $298 576 over the cohort lifespan. One-way sensitivity analyses indicated results were most sensitive to the relative risk for AKI development and relatively insensitive to fluid cost. In probabilistic sensitivity analyses with 1000 iterations, chloride-restrictive crystalloids were dominant in 94.7% of iterations, with incremental cost-effectiveness ratios below $50 000/QALY in 99.6%. Conclusions: This analysis predicts improved patient survival and fewer renal complications with chloriderestrictive IV fluids, yielding net savings versus chloride-liberal fluids. Results require confirmation in adequately powered head-to-head randomized trials.

scholarly journals Sequential Monte Carlo: Enabling Real-time and High-fidelity Prognostics

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Patrick E. Leser ◽  
Jacob D. Hochhalter ◽  
James E Warner ◽  
Geoffrey F. Bomarito ◽  
William P. Leser ◽  
...  
Keyword(s):  
Monte Carlo ◽  
State Space ◽  
Real Time ◽  
Sequential Monte Carlo ◽  
Health Management ◽  
Computation Time ◽  
Natural Approach ◽  
Damage Models ◽  
Model Parameter Uncertainty ◽  
Metallic Specimen

Uncertainty quantification and propagation form the foundation of a prognostics and health management (PHM) system. Particle filters have proven to be a valuable tool for this reason but are generally restricted to state-space damage models and lack a natural approach for quantifying model parameter uncertainty. Both of these issues tend to inhibit the real-world application of PHM. While Markov chain Monte Carlo (MCMC) sampling methods avoid some of these restrictions, they are also inherently serial, and, thus, MCMC can become intractable as model fidelity increases. Over the past two decades, sequential Monte Carlo (SMC) methods, of which the particle filter is a special case, have been adapted to sample from a single, static posterior distribution, eliminating the state-space requirement and providing an alternative to MCMC. Additionally, SMC samplers of this type can be run in parallel, resulting in drastic reductions in computation time. In this work, a potential path toward real-time, highfidelity prognostics using a combination of surrogate modeling and a parallel SMC sampler is explored. The use of SMC samplers to enable tractable parameter estimation for full-fidelity (i.e., non-surrogate-assisted) damage models is also discussed. Both of these topics are studied in the context of fatigue crack growth in a geometrically complex, metallic specimen subjected to variable amplitude loading.

Modeling and Control of Complex Nonlinear Systems Based on TS Model

2013 ◽  
Vol 380-384 ◽  
pp. 417-420
Author(s):  
Yu Chi Zhao ◽  
Jing Liu
Keyword(s):  
System Theory ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Fuzzy Model ◽  
Modeling And Control ◽  
Linear Control Theory ◽  
Complex Nonlinear Systems ◽  
And Control

The current theory of nonlinear systems is still not perfect. The modeling and control of nonlinear system problem has always been the difficulty. In a variety of methods of its study, fuzzy system theory because of having the language descriptive way similar to the human mind, can obtain and deal with the qualitative information intelligently. The theory itself also has non-linear characteristics. Therefore the use of fuzzy systems theory to establish the fuzzy model of nonlinear system can well describe the nonlinear characteristics. T-S fuzzy systems, due to the combination of the good performance of the fuzzy system to deal with nonlinear problems with the simple linear expressions, are not only suitable for modeling the nonlinear system, but also use T-S fuzzy model and the linear control theory method to design the controller. So it has been widely used in nonlinear system control problems, and has also greatly developed the T-S fuzzy system theory, appearing a lot of methods of structural and parameter identification. However, this study of T-S fuzzy rules makes us have to face the difference of different ways to select the number of rules as well as online self-adaptability of the number of rules which off-line method lacks when using T-S fuzzy model to deal with nonlinear system modeling and control problem. In view of this, this paper researches on modeling and controlling of complex nonlinear systems based on TS model from different perspectives.

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