Asset Management - Revision history

imported>WikiAdmin at 10:36, 5 September 2014

2014-09-05T10:36:20Z

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	==Files==		==Files==
	<center>		<center>
	<gallery mode="traditional" widths=600px>		<gallery mode="traditional" widths=600px heights=900px>
	File:Resilience poster.png\| [[~~FMedia~~:Resilience Modelling PG Poster.pdf\|Resilience Poster]]\|link={{filepath:Resilience Modelling PG Poster.pdf}}		File:Resilience poster.png\| [[Media:Resilience Modelling PG Poster.pdf\|Resilience Poster]]\|link={{filepath:Resilience Modelling PG Poster.pdf}}
	</gallery>		</gallery>
	</center>		</center>

imported>WikiAdmin: /* Files */

2014-09-05T10:34:47Z

Files

← Older revision		Revision as of 20:34, 5 September 2014
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	==Files==		==Files==
	<center>		<center>
	<gallery mode="traditional">		<gallery mode="traditional" widths=600px>
	File:Resilience poster.png\| [[FMedia:Resilience Modelling PG Poster.pdf\|Resilience Poster]]\|link={{filepath:Resilience Modelling PG Poster.pdf}}		File:Resilience poster.png\| [[FMedia:Resilience Modelling PG Poster.pdf\|Resilience Poster]]\|link={{filepath:Resilience Modelling PG Poster.pdf}}
	</gallery>		</gallery>

imported>WikiAdmin: /* Files */

2014-09-05T10:32:39Z

Files

← Older revision		Revision as of 20:32, 5 September 2014
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	==Files==		==Files==
			<center>
			<gallery mode="traditional">
			File:Resilience poster.png\| [[FMedia:Resilience Modelling PG Poster.pdf\|Resilience Poster]]\|link={{filepath:Resilience Modelling PG Poster.pdf}}
			</gallery>
			</center>

	[[Category:Training Material for Operators]]		[[Category:Training Material for Operators]]
	[[Category:Resources]]		[[Category:Resources]]

imported>K.H.Tng at 13:36, 4 September 2014

2014-09-04T13:36:06Z

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	*Applying this RAM technique to the water industry required taking into account water quality compliancy and thus would then be able to quantify the water recycling system’s resilience.		*Applying this RAM technique to the water industry required taking into account water quality compliancy and thus would then be able to quantify the water recycling system’s resilience.
	*With user-variated real-time data, OPTAGON is able to accurately predict equipment failure and system resilience.		*With user-variated real-time data, OPTAGON is able to accurately predict equipment failure and system resilience.


	===Data Collection and Analysis===		===Data Collection and Analysis===

imported>K.H.Tng at 13:35, 4 September 2014

2014-09-04T13:35:29Z

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	Resilience, however, has traditionally been difficult to evaluate and quantify, thus has largely been ignored leading to the tendency towards a “belt and braces” approach where multiple redundancies are put in place. This consequently results in an over-engineered design that requires a higher capital and operational expenditure over the asset’s lifetime. To date, no standard resilience modelling method has been developed for the water treatment industry therefore, highlighting the need for resilience modelling and asset management in this industry.		Resilience, however, has traditionally been difficult to evaluate and quantify, thus has largely been ignored leading to the tendency towards a “belt and braces” approach where multiple redundancies are put in place. This consequently results in an over-engineered design that requires a higher capital and operational expenditure over the asset’s lifetime. To date, no standard resilience modelling method has been developed for the water treatment industry therefore, highlighting the need for resilience modelling and asset management in this industry.

	~~==Common Failures in Drinking Water Systems==~~
	*GIDEON database catalogued >2000 confirmed pathogenic outbreaks from 2003 to 2013.
	*30% of the outbreaks were associated with protozoan parasites.
	*The most common type of failure was an incident in the catchment area in conjunction with an inadequate process design.
	*Second most common type of failure occurred in the distribution system followed by an inadequate management framework and operational error.
	*Poor asset management and failures further emphasize the importance of resilience modelling in the water industry.


	~~[[File:Gideon map.png\|thumb\|600px\|left]]~~

	~~<html xmlns="http://www.w3.org/1999/xhtml">~~
	~~<head>~~
	~~<meta http-equiv="content-type" content="text/html; charset=utf-8"/>~~
	~~<script type="text/javascript" src="//www.google.com/jsapi"></script>~~
	~~<script type="text/javascript">~~
	~~google.load('visualization', '1', {packages: ['corechart']});~~
	~~</script>~~
	~~<script type="text/javascript">~~
	~~function drawVisualization() {~~
	~~// Create and populate the data table.~~
	~~var data1 = google.visualization.arrayToDataTable([~~
	~~['Pathogen', 'Outbreak (%)'],~~
	~~['Bacterial', 23],~~
	~~['Protozoan', 30],~~
	~~['Viral', 26],~~
	~~['Unknown', 9],~~
	~~['Mixed Aetiology', 12]~~
	~~]);~~
	~~var data2 = google.visualization.arrayToDataTable([~~
	~~['Causes', 'Failure (%)'],~~
	~~['Management', 9],~~
	~~['Breakage', 17],~~
	~~['Design', 60],~~
	~~['Monitoring/Maintenance', 6],~~
	~~['Operational/Human Error', 8]~~
	~~]);~~

	~~// Create and draw the visualization.~~
	~~new google.visualization.PieChart(document.getElementById('visualization1')).~~
	~~draw(data1, {title:"Pathogen Outbreaks"});~~
	~~new google.visualization.PieChart(document.getElementById('visualization2')).~~
	~~draw(data2, {title:"Causes of Failure"});~~
	}


	~~google.setOnLoadCallback(drawVisualization);~~
	~~</script>~~
	~~<style>~~
	~~.chart-container {~~
	~~width: 100%;~~
	~~margin: 0 auto;~~
	~~text-align: center;~~
	}
	~~.pie-chart-container {~~
	~~width: 372px;~~
	~~margin: 0 auto;~~
	~~text-align: center;~~
	~~display: -moz-inline-stack;~~
	~~display: inline-block;~~
	~~zoom: 1;~~
	*display: inline;
	}
	~~</style>~~
	~~</head>~~
	~~<body style="font-family: Arial;border: 0 none;">~~
	~~<div class="chart-container">~~
	~~<div class="pie-chart-container">~~
	~~<div id="visualization1" style="width: 372px; height: 224px;"></div>~~
	~~</div>~~
	~~<div class="pie-chart-container">~~
	~~<div id="visualization2" style="width: 372px; height: 224px;"></div>~~
	~~</div>~~
	~~</div>~~
	~~</body>~~
	~~</html>~~
	~~<p>~~

	==Objectives==		==Objectives==

imported>K.H.Tng: /* Data Collection and Analysis */

2014-09-04T11:25:57Z

Data Collection and Analysis

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	<!-- Map -> Table -->		<!-- Map -> Table -->

imported>K.H.Tng at 11:25, 4 September 2014

2014-09-04T11:25:26Z

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	===Data ~~Sourcing~~ and ~~Collection~~===		===Data Collection and Analysis===
	*Equipment failure and performance data is sourced from 7 water recycling plants worldwide.		*Equipment failure and performance data is sourced from 7 water recycling plants worldwide.
	*Relevant information is collected from a wide array of data sources.		*Relevant information is collected from a wide array of data sources.
			*Cataloged equipment data is sorted and mapped according to process equipment specified in the model (Reference Plant).
			*Equipment arranged with design and operational capacities based on functional location.
			*Operation & Maintenance (O&M) Manuals provide vital information on equipment availability.
	*Operating history and logs from 64 years of cumulative operation were collected and the Mean Time Between Failure (MTBF) and the Mean Time To Repair (MTTR) values for specific process equipment and assets were calculated.		*Operating history and logs from 64 years of cumulative operation were collected and the Mean Time Between Failure (MTBF) and the Mean Time To Repair (MTTR) values for specific process equipment and assets were calculated.
			*Equipment criticality is determined based on failure and maintenance data.

	<!-- Map -> Table -->		<!-- Map -> Table -->

	~~===Data Analysis and Mapping===~~
	*Cataloged equipment data is sorted and mapped according to process equipment specified in the model (Reference Plant).
	*Equipment arranged with design and operational capacities based on functional location.
	*Operation & Maintenance (O&M) Manuals provide vital information on equipment availability.
	*MTBF and MTTR are also calculated if not previously provided.
	*Equipment criticality is determined based on failure and maintenance data.

imported>K.H.Tng: /* Data Sourcing and Collection */

2014-09-04T11:23:29Z

Data Sourcing and Collection

← Older revision		Revision as of 21:23, 4 September 2014
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	*Equipment failure and performance data is sourced from 7 water recycling plants worldwide.		*Equipment failure and performance data is sourced from 7 water recycling plants worldwide.
	*Relevant information is collected from a wide array of data sources.		*Relevant information is collected from a wide array of data sources.
			*Operating history and logs from 64 years of cumulative operation were collected and the Mean Time Between Failure (MTBF) and the Mean Time To Repair (MTTR) values for specific process equipment and assets were calculated.

	<!-- Map -> Table -->		<!-- Map -> Table -->

imported>K.H.Tng: /* Data Analysis and Mapping */

2014-09-04T11:22:40Z

Data Analysis and Mapping

← Older revision		Revision as of 21:22, 4 September 2014
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	*MTBF and MTTR are also calculated if not previously provided.		*MTBF and MTTR are also calculated if not previously provided.
	*Equipment criticality is determined based on failure and maintenance data.		*Equipment criticality is determined based on failure and maintenance data.


	<center>		<center>
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	</gallery>		</gallery>
	</center>		</center>


	===Resilience Modelling and Sensitivity Analysis===		===Resilience Modelling and Sensitivity Analysis===

imported>K.H.Tng: /* Resilience Modelling and Sensitivity Analysis */

2014-09-04T11:22:19Z

Resilience Modelling and Sensitivity Analysis

← Older revision		Revision as of 21:22, 4 September 2014
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	*Sensitivity analysis would further highlight which input variable would have the greatest impact on the system.		*Sensitivity analysis would further highlight which input variable would have the greatest impact on the system.
	*“What-if” scenarios would test the robustness of the reference plant and aid with process optimisation.		*“What-if” scenarios would test the robustness of the reference plant and aid with process optimisation.


	<center>		<center>