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A case study on modelling and analysing machine breakdowns

A case study on modelling and analysing machine breakdowns
A case study on modelling and analysing machine breakdowns
Most manufacturing models to date have assumed independence of all random variables in the system. In practice, autocorrelation effects are present in production lines time series. In this thesis, we extend this literature by studying autocorrelation in machine times to failure in detail. Our work focuses on the practical aspects of detecting and modelling autocorrelated uptimes, as well as including them in simulations.

We apply a practical procedure to detect autocorrelation in uptimes. The procedure has very mild assumptions and compensates for the number of machines it is applied to, ensuring that the probability of a Type I error is kept low.

We then provide two ways to model autocorrelated times to failures. The first is to use ARMA models including GARCH terms. We also provide a method based on the Markov-Modulated Poisson Process, a special case of the Markov Arrival Process.

For both methods discussed above, we provide diagnostic plots and a quantitative way to select the most appropriate model for a given series of uptimes. This allows us to automatically select an appropriate model.

Finally, to enable Ford to use our methods in simulation, we provide a way to generate simulated uptimes from each of our models.
University of Southampton
Pan, Shu
3bd5f8c2-9b37-460f-9817-eafa9f2f021e
Pan, Shu
3bd5f8c2-9b37-460f-9817-eafa9f2f021e
Avramidis, Athanasios
d6c4b6b6-c0cf-4ed1-bbe1-a539937e4001

Pan, Shu (2018) A case study on modelling and analysing machine breakdowns. University of Southampton, Doctoral Thesis, 287pp.

Record type: Thesis (Doctoral)

Abstract

Most manufacturing models to date have assumed independence of all random variables in the system. In practice, autocorrelation effects are present in production lines time series. In this thesis, we extend this literature by studying autocorrelation in machine times to failure in detail. Our work focuses on the practical aspects of detecting and modelling autocorrelated uptimes, as well as including them in simulations.

We apply a practical procedure to detect autocorrelation in uptimes. The procedure has very mild assumptions and compensates for the number of machines it is applied to, ensuring that the probability of a Type I error is kept low.

We then provide two ways to model autocorrelated times to failures. The first is to use ARMA models including GARCH terms. We also provide a method based on the Markov-Modulated Poisson Process, a special case of the Markov Arrival Process.

For both methods discussed above, we provide diagnostic plots and a quantitative way to select the most appropriate model for a given series of uptimes. This allows us to automatically select an appropriate model.

Finally, to enable Ford to use our methods in simulation, we provide a way to generate simulated uptimes from each of our models.

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A Case Study on Modelling and Analysing Machine Breakdowns - Version of Record
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Published date: March 2018

Identifiers

Local EPrints ID: 422174
URI: https://eprints.soton.ac.uk/id/eprint/422174
PURE UUID: 0b906978-2c06-49d0-9d4c-7829be66c8b7

Catalogue record

Date deposited: 18 Jul 2018 16:30
Last modified: 13 Mar 2019 18:18

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Contributors

Author: Shu Pan
Thesis advisor: Athanasios Avramidis

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