PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayesian Change Point Detection for Satellite Fault Prediction
Ryan Turner
In: Interdisciplinary Graduate Conference (IGC) 2010, 28-29 June 2010, Cambridge, UK.

Abstract

Change point detection makes predictions in data whose structure changes over time and belongs to a class of methods called time series methods. Machine learning is the design of algorithms whose performance improves with data. Techniques from statistics, such as change point detection, have received increased interest in recent years in machine learning. Many problems have change point structure: Stock markets exhibit change points when some significant economic event causes an increase in volatility, weather systems may exhibit change points during years of El Nino, and electronic systems show change points when devices begin to fail or configurations change. This paper focuses on detecting changes in the operation of satellite base stations. The aim is to provide predictions of device lifetimes or time until maintenance will be necessary. Typical reliability engineering uses survival analysis to find lifetime distributions and mean time between failures (MTBF). However, using change point detection we can make predictions that include information dynamically. For instance, a change in the measured weather conditions could signal an impending satellite signal loss due to an imminent storm. Modeling changing dependencies is also important; a change in the relationship between the external and internal temperature on a device should mean there is a problem with the cooling unit. In the mentioned examples, the weather change point occurred when the weather conditions changed and when the cooling unit began to malfunction, respectively. Unlike MTBF, change point detection provides probability distributions describing the likelihood of a failure, which is necessary in most cases since there is usually significant remaining uncertainty over the time until failure. The two key aspects are that change point detection can include information dynamically and model changing dependencies. We show how to use techniques from Bayesian statistics, namely change point detection, to model and understand electronic systems.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7764
Deposited By:Ryan Turner
Deposited On:17 March 2011