Dynamics and performance modeling of multi-stage manufacturing systems using nonlinear stochastic differential equations

Mittal, U.; Yang, Hui; Bukkapatnam, Satish T. S.; Barajas, Leandro G.

doi:10.1109/coase.2008.4626530

Cited by 16 publications

(20 citation statements)

References 11 publications

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“…The proposed local recurrence modeling approach was also used for predicting the outputs of a real-world automotive power train manufacturing system [32,33], which consists of 18 stations of which 17 are allocated in tandem. One pair of stations is located in a parallel arrangement in the assembly line.…”

Section: Resultsmentioning

confidence: 99%

Local recurrence based performance prediction and prognostics in the nonlinear and nonstationary systems

Yang

Bukkapatnam

Barajas

2011

Pattern Recognition

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Section: Resultsmentioning

confidence: 99%

Local recurrence based performance prediction and prognostics in the nonlinear and nonstationary systems

Yang

Bukkapatnam

Barajas

2011

Pattern Recognition

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“…We also investigate the local stability and the basin of attraction of these periodic orbits (Theorems 3-5). The theoretical results are validated using real-world data sets on TBF and TTR acquired from an automotive manufacturing assembly line [60]. The result suggested that the model can capture certain dynamical aspects of empirical data.…”

Section: Introductionmentioning

confidence: 89%

Modeling and analysis of the coupled dynamics of machine degradation and repair processes using piecewise affine stochastic differential equations

Tran

Bukkapatnam

2015

International Journal of Non-Linear Mechanics

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This paper introduces an approach to model the coupled dynamics of recurring degradation and restoration processes that take place in real-world systems, such as manufacturing machines, in the form of nonlinear (piecewise affine) differential equations. Unlike previous methods, interactions between degradation and repair dynamics that influence downtime distributions in such manufacturing systems can be explicitly considered and dependencies beyond correlations between the time between failures (TBF) and the time to repair (TTR) can be captured. The periodic solutions of the model capture the progressive evolution of long timescale failure and repair patterns. The distribution of short timescale failure-repair cycles can be captured by providing a class of random perturbations to certain model parameters. We provide sufficient conditions for the existence and stability of the resulting nonlinear stochastic differential equation (n-SDE) model solutions that mimic the breakdown and repair patterns observed in many real-world manufacturing systems, namely, fairly regular (periodic) large breakdown and repair cycles, interspersed with highly right skewed distributions of short cycles. We also define the basin of attraction for the periodic orbit. The n-SDE model was parametrized using real-world data sets acquired from an automotive manufacturing assembly line segment, and the model solutions were compared with actual observations of TBF and TTR patterns, as well as the performance of the process. Our approach reduces the computation time by about 25% when compared to a discrete-event simulation model, which uses conventional TBF and TTR distributions, implemented on a commercial platform. Experimental investigations also suggest that the model can capture the correlations and nonlinear coupled dynamics that exist in real-world operations among TBF and TTR, which are typically ignored in traditional approaches.

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“…Dr. Yang and his research team developed a sensor-driven NSDS modeling approach [13][14][15][16] to simulate operational dynamics of a multistage assembly line. The movement of entities is treated as a fluid flow, buffer stocks are water tanks, the conveyor belt is water pipe and manufacturing stations are the valves which control the rates of flow.…”

Section: System Dynamics Modeling and Simulationmentioning

confidence: 99%

Theoretical foundations

Hu¹

2018

Oxford Scholarship Online

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Adopting the constructivist approach, especially building on Borer’s (2005a, b, 2013a) XS Model, two theoretical elements in the theory of the syntax of events are put forward. The first element concerns the specific constraints on the interaction between conceptual meaning and syntactic derivation. The content of the predicate will be integrated into the interpretation derived from the syntax via a set of Integration Conditions, according to which, the interpretation derived from syntax licenses the legitimacy of the predicate content. The second theoretical assumption is the addition of the DivP to the event phrase (EP) structure. A verbal feature is in nature an [iDiv] feature, which is equivalent to the interpretable feature provided by the classifier in the nominal domain. The stative/dynamic interpretation of an event is tied to the value of the [iDiv] feature, which further explains the grammatical distinction between two types of homogeneous predicates.

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Dynamics and performance modeling of multi-stage manufacturing systems using nonlinear stochastic differential equations

Cited by 16 publications

References 11 publications

Local recurrence based performance prediction and prognostics in the nonlinear and nonstationary systems

Local recurrence based performance prediction and prognostics in the nonlinear and nonstationary systems

Modeling and analysis of the coupled dynamics of machine degradation and repair processes using piecewise affine stochastic differential equations

Theoretical foundations

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