[vc_empty_space][vc_empty_space]
The application of RAMS to analysis life cycle cost on the operation of power generation
Nugraha H.a, Sinisuka N.I.a
a Bandung Institute of Technology, School of Electrical Engineering and Informatics, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]Since plant has a long asset life, it requires efficient maintenance planning to perform effectively throughout its life cycle to meet these operation goals. The application of Reliability, Availability, Maintainability and Safety (RAMS) and Life Cycle Cost (LCC) analysis for power generation is now developing. The focus of this paper is to demonstrate the applicability of RAMS to analysis LCC in effective maintenance planning on the operation of power generation. The paper will present approaches and models for estimating RAMS targets based on the service quality requirements of the power generation. The availability target of the power generation will be estimated by considering the maximize uptime and ability to be operated as specified requirements of the power system, whereas the safety goal of the operation will be estimated by calculating the failure probability and failure frequency of equipments. A model will be developed to achieve the availability target in both the scheduled and the condition based maintenance strategy by choosing an effective maintenance interval and detection probability respectively. This will be illustrated by a case study on the operation of coal fired steam power plants in Java Island. In order to determine the cost-effective solution, LCC should be used. The maintenance strategy with lowest LCC will be the cost effective maintenance strategy. Monte Carlo simulations will be used to develop models to achieve the objectives of this paper. © 2014 Taylor & Francis Group, London.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Condition based maintenance,Cost-effective solutions,Detection probabilities,Failure Probability,Life cycle cost analysis,Maintenance intervals,Maintenance planning,Maintenance strategies[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]