Computational approaches for pattern/correlation analysis based on historical data

Preventive maintenance scheduling is needed by high value manufacturing industry, and the attention is stressed on exploration of techniques that can facilitate accurate prediction of unscheduled downtimes. The objective of this project was to develop an algorithm to analyze event logs that impro...

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Main Author: Baimuratova, Aigerim
Other Authors: School of Computer Engineering
Format: Final Year Project
Language:English
Published: 2011
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Online Access:http://hdl.handle.net/10356/42732
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-427322023-03-03T20:26:32Z Computational approaches for pattern/correlation analysis based on historical data Baimuratova, Aigerim School of Computer Engineering A*STAR SIMTech Luo Ming Sudha Natarajan DRNTU::Engineering::Manufacturing Preventive maintenance scheduling is needed by high value manufacturing industry, and the attention is stressed on exploration of techniques that can facilitate accurate prediction of unscheduled downtimes. The objective of this project was to develop an algorithm to analyze event logs that improves preventive maintenance scheduling. We proposed a simplified model for failure prediction-Two Tier approach. In his approach eqiupment labelled to be in one of the two states.The state of the machine is idetified thorugh some threshold. The algorithm has four following steps: Input of restriction parameters,choice of the metric of measurement, threshold identification, time to failure estimation, censor time estimation, performance testing. In this project algorithm is implemented using C# programming language and GUI is implemented using Windows Form utilities. Database is stored using Oracle DBMS. There were two experiments with two different sets of data. The first experiment gave results both during training and testing. We did not avoid a large number of unscheduled downtimes, but we decreased the number of redundant maintenances, giving factories an opportunity to cut down the cost. During the second experiment we achieved the results only during training, but the preventive profile did not give results during testing. We tried to preprocess the data differently, but could not achieve results we hoped for. The strength of this algorithm is that it can work different types of data and it is reconfigurable according to factory specifications. Data preprocessing this algorithm is versatile and can be applied to different equipments. This algorithm is a simplified approach, is not able to track sophisticated relationship between faults and failures. Bachelor of Engineering (Computer Science) 2011-01-10T04:15:56Z 2011-01-10T04:15:56Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/42732 en Nanyang Technological University 64 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Manufacturing
spellingShingle DRNTU::Engineering::Manufacturing
Baimuratova, Aigerim
Computational approaches for pattern/correlation analysis based on historical data
description Preventive maintenance scheduling is needed by high value manufacturing industry, and the attention is stressed on exploration of techniques that can facilitate accurate prediction of unscheduled downtimes. The objective of this project was to develop an algorithm to analyze event logs that improves preventive maintenance scheduling. We proposed a simplified model for failure prediction-Two Tier approach. In his approach eqiupment labelled to be in one of the two states.The state of the machine is idetified thorugh some threshold. The algorithm has four following steps: Input of restriction parameters,choice of the metric of measurement, threshold identification, time to failure estimation, censor time estimation, performance testing. In this project algorithm is implemented using C# programming language and GUI is implemented using Windows Form utilities. Database is stored using Oracle DBMS. There were two experiments with two different sets of data. The first experiment gave results both during training and testing. We did not avoid a large number of unscheduled downtimes, but we decreased the number of redundant maintenances, giving factories an opportunity to cut down the cost. During the second experiment we achieved the results only during training, but the preventive profile did not give results during testing. We tried to preprocess the data differently, but could not achieve results we hoped for. The strength of this algorithm is that it can work different types of data and it is reconfigurable according to factory specifications. Data preprocessing this algorithm is versatile and can be applied to different equipments. This algorithm is a simplified approach, is not able to track sophisticated relationship between faults and failures.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Baimuratova, Aigerim
format Final Year Project
author Baimuratova, Aigerim
author_sort Baimuratova, Aigerim
title Computational approaches for pattern/correlation analysis based on historical data
title_short Computational approaches for pattern/correlation analysis based on historical data
title_full Computational approaches for pattern/correlation analysis based on historical data
title_fullStr Computational approaches for pattern/correlation analysis based on historical data
title_full_unstemmed Computational approaches for pattern/correlation analysis based on historical data
title_sort computational approaches for pattern/correlation analysis based on historical data
publishDate 2011
url http://hdl.handle.net/10356/42732
_version_ 1759856157279649792