2 edition of **Quality monitoring in manufacturing systems: a partially observed Markov chain approach.** found in the catalog.

Quality monitoring in manufacturing systems: a partially observed Markov chain approach.

Ralph Gerard Whiting

- 223 Want to read
- 12 Currently reading

Published
**1985**
.

Written in English

The Physical Object | |
---|---|

Pagination | 202 leaves |

Number of Pages | 202 |

ID Numbers | |

Open Library | OL14759418M |

David J. Aldous. On simulating a Markov chain stationary distribution when transition probabilities are D. J. Aldous, P. Diaconis, J. Spencer, and J. M. Steele, editors, Discrete Probability and Algorithms, volume 72 of IMA Volumes in Mathematics and its Applications, pages Springer-Verlag, Abstract: We present an algorithm which, given a n-state Markov chain whose. () Distributed nonlinear filtering of partially observed Markov chains over WSNs: Truncating the ADMM. 49th Asilomar Conference on Signals, Systems and Computers, () A Proximal Gradient Algorithm for Decentralized Composite Optimization.

Markov chain approach to describe the system of inspection and the level of corrective adjustment was based on the location of the measured quality characteristics on the control chart. Mehrafrooz & Noorossana () extended the model of Linderman et al. () and presented. The application of CI to sensor systems is a hot topic, as shown by the following (non-exhaustive) list of problems, that is comprised of different applications of CI to sensor systems reported during the first half of Computational efficiency (power control) Cooperative processing (swarm intelligence, fog computing, etc.) in sensor networks.

stochastic systems. An important problem in the adaptive control of a finite state Markov chain was solved, and significant progress was made along more general directions. A controlled switching diffusion model was developed to study the hierarchical control of flexible manufacturing systems and significant results were obtained. In the area of. Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A standardized representation for BN models will aid in their communication and exchange across the.

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Model order estimation for the partially observed Markov chain (POMC), also called the hidden Markov model, is addressed.

Model order is defined as the number of independent model parameters. Simulation is used to investigate the small sample performance of a number of model order criteria, as applied to POMC models with low state dimension and binary by: 1. This paper provides sufficient conditions for the optimal value in a discrete-time, finite, partially observed Markov decision process to be monotone on the space of state probability vectors ordered by likelihood by: Analysis of an adaptive control scheme for a partially observed controlled Markov chain IEEE Transactions on Automatic Control, Vol.

38, No. 6 Discrete-Time Controlled Markov Processes with Average Cost Criterion: A SurveyCited by: This paper provides sufficient conditions for the optimal value in a discrete-time, finite, partially observed Markov decision process to be monotone on the space of state probability vectors ordered by likelihood ratios.

The paper also presents sufficient conditions for the optimal policy to be monotone in a simple machine replacement problem, and, in the general case, for the optimal policy Cited by: On two-state quality control under Markovian deterioration Metrika, Vol.

29, No. 1 Optimal stopping in a partially observable binary-valued markov chain with costly perfect informationCited by: In this article we study the optimal control of a partially observed Markov chain for which a mean squared cost functional is minimized.

Both a terminal cost and a running cost are considered. This paper examines monotonicity results for a fairly general class of partially observable Markov decision processes. When there are only two actual states in the system and when the actions taken are primarily intended to improve the system, rather than to inspect it, we give reasonable conditions which ensure that the optimal reward function and the optimal action are both monotone in the.

Realistic models assume that inventory systems are partially observed with imperfect information, on such models, the Markov Chain and MDP's formulation is no longer appropriated, for this.

A Markov chain/model is a special type of dynamic model which quantifies the probabilistic evolution of a system where present state provides all relevant information about the future behavior and.

The paper formulates the optimal control problem for a class of mathematical models in which the system to be controlled is characterized by a finite-state discrete-time Markov process. Advanced monitoring approaches are a major need in flexible and high-performance manufacturing systems.

Although advanced tools and approaches are available for machines, taking advantage of a wide range of sensors and models [3], the monitoring of human operators is a less developed area both in terms of research and industrial applications [4].

The switching process of regimes is governed by a Markov chain, and the functioning process of the system follows another Markov chain with different transition probability matrices under.

The health status evolving from normal to broken condition of a milling tool is needed as an object of assessment in condition-based maintenance. This paper proposes continuous hidden Markov models (CHMM) to assess the status of the tool online based on the normal dataset in the same case.

A wavelet-packet decomposition technology is used to feature extraction and the CHMM is trained by Baum. () A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems.

Journal of Signal Processing Systems() To the 80th birthday of R.L. Stratonovich. We validate our approach using visual inspections that monitor the state of the shelf and compare them to the HMM’s predictions.

We test the proposed approach on 14 products and 10 stores. We specify our model using a hierarchical Bayes approach and use a Monte Carlo–Markov chain methodology to estimate the model parameters. We identify three. Quality, maintenance and production control are fundamental functions for achieving desired production targets in multi-stage manufacturing systems.

These aspects have been traditionally treated almost in isolation. However, a strong relation between equipment availability, product quality and system productivity do exist. PLANNING AND SCHEDULING PROBLEMS IN MANUFACTURING SYSTEMS WITH HIGH DEGREE OF RESOURCE DEGRADATION Approved by: Dr.

Jay H. Lee, Advisor School of Chemical and Biomolecular Engineering Georgia Institute of Technology Dr. Hayriye Ayhan School of Industrial and Systems Engineering Georgia Institute of Technology Dr. Matthew J. Realff, Advisor. Credit ratings and accounting-based Altman Z-scores are two important sources of information for assessing the creditworthiness of this paper we build a model based on a double hidden Markov model, (DHMM), to extract information about the “true” credit qualities of firms from both the Z-scores evaluated from the accounting ratios of the firms and their posted credit ratings.

apply our approach to the problem of QCD of a partially observed graph signal. Index Terms—Quickest change detection, GLR CuSum, sam-pling policy, graph sampling I. INTRODUCTION Quickest change detection (QCD) is the problem of detect-ing an abrupt change in a system while keeping the detection delay to a minimum.

In a usual scenario, a. In the classical Markov chain approach, the transitions are viewed under a “rate-based” perspective; in our model, a “time-based” view was used in order to exploit the discrete event simulation framework to introduce the states generator behaviours as a “mirror” of the components (machines) in the real system, operated in the time.

Methods of statistical process control (SPC) help to monitor and improve processes in manufacturing and service industries. For such a process, certain quality characteristics are measured at discrete times t ∈ ℕ: ={ 1,2, }, thus leading to a (possibly multivariate) stochastic process (X t)ℕ of contin.

Software quality is recognized as being very significant for achieving competitiveness in the software industry, so improvements in this area are gaining increasing importance. Software quality improvements can only be achieved by managing all of the factors that influence it.

However, in a real business system, there are a great number of factors impacting software quality, while the.More generally, I am interested in Bayesian computational methods such as Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC). On a somewhat more theoretical level I am interested in proving posterior contraction rates for Bayesian procedures (joint work with Geurt Jongbloed and Lixue Pang, in particular on censoring/shape.