scholarly journals ANALISA BIAYA PENGARUH IMPLEMENTASI SIPEMBANGKIT TERHADAP BIAYA OPERASIONAL PEMELIHARAAN MESIN PADA PT.PLN (PERSERO) PLTD POKA-AMBON

ARIKA ◽  
2018 ◽  
Vol 12 (2) ◽  
pp. 113-122
Author(s):  
J. M. Tupan ◽  
Billy S. Tampubulon
Keyword(s):  
Preventive Maintenance ◽  
Condition Based Maintenance ◽  
Periodic Maintenance ◽  
Report Data ◽  
Operational Excellence

Perawatan mesin diesel pada bagian maintenance  PLTDsangat penting untuk mencegah terjadinya downtime  produksi. Perawatan yang paling baik digunakan adalah perawatan pencegahan sebelum terjadinya kerusakan (preventive maintenance). Sistem perawatan mesin yang dilakukan di PLTD Poka ialah periodic maintenance dan condition-based maintenance, yaitu tindakan perawatan yang dilakukan berdasarkan lamanya waktu operasi mesin yang telah ditentukan oleh pabrikan dan berdasarkan kondisi mesin. Dengan sistem tersebut ,ditemukan kesulitan perencanaan dan administrasi pemeliharaan karena fluktuatifnya pengoperasian mesin sehingga pemeliharaan sering tidak tepat waktu. Waktu antar  kerusakan adalah selang waktu terjadinya kerusakan setelah dilakukan perbaikan. Untuk menentukan waktu antar kerusakan, langkah pertama yang dilakukan adalah dengan mengukur lamanya  waktu operasi mesin setelah dilakukan perbaikan pada setiap kerusakan mesin. Sistem perencanaan pemeliharaan selama ini masih menggunakan perhitungan secara manual sehingga ada kemungkinan salah perhitungan perencanaan pemeliharaan yang berujung padati dakter capainya SFC, SLC dan Jam operasi Mesin yang diharapkan / ditargetkan. Berdasarkan permasalahan tersebut,dibuatlah“SiPembangkit”berbasis web untuk modernisasi system record dan report data harian dan bulanan pengoperasian mesin, monitoring pemeliharaan serta peningkatan performance operasional mesin. Dengan demikian operational Excellence bisa tercapai.

Lloyd's Register—Cyber CBM (Condition based maintenance)

2017 ◽  
Vol 57 (2) ◽  
pp. 623
Author(s):  
Joseph Morelos
Keyword(s):  
Real Time ◽  
Preventive Maintenance ◽  
Condition Based Maintenance ◽  
Health Maintenance ◽  
Underlying Principle ◽  
Periodic Maintenance ◽  
Safety And Reliability

Classification survey requirements are anchored on the concept of a periodic, time-based preventive maintenance regime. The underlying principle of the periodic maintenance philosophy is that parts and components of equipment, machinery, systems and structures eventually wear out, thus the safety and reliability of parts and components – indeed the entire vessel – are directly correlated with time and operational age. While development of techniques and technologies have continuously improved surveys and inspection, the periodic maintenance philosophy has been the de-facto standard in marine and offshore industries for many decades. This paper presents Lloyd Register’s Cyber condition based maintenance (CBM) technique: a scalable evaluation of components, equipment, machinery, systems and structures that detects their emergent faults, and characterises their progression to failure. This is an important step in the evolution of in-service classification:moving away from a periodic, time-based maintenance regime into an intelligent, real-time and predictive vessel health maintenance regime.

scholarly journals PENGARUH MANAJEMEN PERAWATAN SISTEM ENGKOL TERHADAP PENGOPERASIAN MESIN ANGLO BELGIAN CORPORATION TYPE 12V

ARIKA ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 1-14
Author(s):  
B. J. Camerling ◽  
D. B. Paillin ◽  
Adha B. Dharma
Keyword(s):  
Diesel Engine ◽  
Preventive Maintenance ◽  
Condition Based Maintenance ◽  
Periodic Maintenance

Peranan perawatan terhadap mesin diesel engine pada bagian maintenance PLTD sangat penting artinya untuk mencegah terjadinya down time produksi, dan perawatan yang paling baik digunakan adalah perawatan pencegahan sebelum terjadinya kerusakan (preventive maintenance). Sistem perawatan mesin yang dilakukan di PLTD Poka, ialah periodic maintenance dan condition-based maintenance yaitu tindakan perawatan yang dilakukan berdasarkan lamanya waktu operasi mesin yang telah ditentukan oleh pabrikan dan berdasarkan kondisi mesin. Dengan sistem tersebut, ditemukan kesulitan perencanaan dan administrasi pemeliharaan karena fluktuatifnya pengoperasian mesin sehingga pemeliharaan sering tidak tepat waktu. Waktu antar kerusakan adalah selang waktu terjadinya kerusakan setelah dilakukan perbaikan. Untuk menentukan waktu antar kerusakan, langkah pertama yang dilakukan adalah dengan mengukur lamanya waktu operasi mesin setelah dilakukan perbaikan pada setiap kerusakan mesin. Sistem perencanaan pemeliharaan selama ini masih menggunakan perhitungan pecatatan yang dilakukan secara manual sehingga ada kemungkinan salah perhitungan perencanaan pemeliharaan yang berujung pada tidak tercapainya SFC, SLC dan Jam operasi Mesin yang diharapkan/ditargetkan. Berdasarkan permasalahan tersebut, maka perlu dilakukan perawatan terhadap poros engkol sehingga bisa mencegah kerusakan yang lebih parah sehingga bisa tercapai pengoperasian mesin yang maksimal dan efisien. Penggunaan RCM diperlukan pada perawatan mesin ini sehingga hasil yang didapatkan untuk perawatan mesin lebih optimal dibandingkan dengan perawatan mesin yang dilakukan saat ini.

scholarly journals Development of a Novel Methodology for Remaining Useful Life Prediction of Industrial Slurry Pumps in the Absence of Run to Failure Data

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8420
Author(s):  
Muhammad Mohsin Khan ◽  
Peter W. Tse ◽  
Amy J.C. Trappey
Keyword(s):  
Life Prediction ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Condition Based Maintenance ◽  
Vibration Signals ◽  
The Core ◽  
Failure Data ◽  
Periodic Maintenance ◽  
Useful Life ◽  
Nonlinear Autoregressive

Smart remaining useful life (RUL) prognosis methods for condition-based maintenance (CBM) of engineering equipment are getting high popularity nowadays. Current RUL prediction models in the literature are developed with an ideal database, i.e., a combination of a huge “run to failure” and “run to prior failure” data. However, in real-world, run to failure data for rotary machines is difficult to exist since periodic maintenance is continuously practiced to the running machines in industry, to save any production downtime. In such a situation, the maintenance staff only have run to prior failure data of an in operation machine for implementing CBM. In this study, a unique strategy for the RUL prediction of two identical and in-process slurry pumps, having only real-time run to prior failure data, is proposed. The obtained vibration signals from slurry pumps were utilized for generating degradation trends while a hybrid nonlinear autoregressive (NAR)-LSTM-BiLSTM model was developed for RUL prediction. The core of the developed strategy was the usage of the NAR prediction results as the “path to be followed” for the designed LSTM-BiLSTM model. The proposed methodology was also applied on publically available NASA’s C-MAPSS dataset for validating its applicability, and in return, satisfactory results were achieved.

A Dynamic Reliability-Centered Maintenance Analysis Method for Natural Gas Compressor Station Based on Diagnostic and Prognostic Technology

2015 ◽  
Vol 138 (6) ◽  
Author(s):  
Dengji Zhou ◽  
Huisheng Zhang ◽  
Yi-Guang Li ◽  
Shilie Weng
Keyword(s):  
Natural Gas ◽  
Preventive Maintenance ◽  
Condition Based Maintenance ◽  
Equipment Management ◽  
Analysis Method ◽  
Dynamic Reliability ◽  
Maintenance Schedule ◽  
Reliability Centered Maintenance ◽  
Maintenance Decision ◽  
Maintenance Model

The availability requirement of natural gas compressors is high. Thus, current maintenance architecture, combined periodical maintenance and simple condition based maintenance, should be improved. In this paper, a new maintenance method, dynamic reliability-centered maintenance (DRCM), is proposed for equipment management. It aims at expanding the application of reliability-centered maintenance (RCM) in maintenance schedule making to preventive maintenance decision-making online and seems suitable for maintenance of natural gas compressor stations. A decision diagram and a maintenance model are developed for DRCM. Then, three application cases of DRCM for actual natural gas compressor stations are shown to validate this new method.

An Approach to Determine the Test Intervals for CBM

2009 ◽  
Author(s):  
Zhong-Zhe Chen ◽  
Hong-Zhong Huang ◽  
Yu Liu ◽  
Qiang Miao ◽  
Pei-Yu Ren
Keyword(s):  
Preventive Maintenance ◽  
Reduction Factor ◽  
Condition Based Maintenance ◽  
Sequential Test ◽  
Maintenance Strategy ◽  
Test Interval ◽  
Failure Risk ◽  
Fixed Threshold

In this paper, reduction factor, which describes the degree of service age reduction resulting from the preventive maintenance, is introduced into a condition-based maintenance strategy. The model determining the sequential test intervals is formulated under the constraint that the probability that the failure has not test successfully within each test interval should be less than a fixed threshold. The failure risk of deteriorating system can be reduced via shortening test interval. The effectiveness of the presented methods is illustrated via a numerical case.

DCM Oriented Operation and Maintenance Expense Model for High-Speed Train

2014 ◽  
Vol 620 ◽  
pp. 644-649
Author(s):  
Bi Hong Lu ◽  
Yu Kai Li
Keyword(s):  
Preventive Maintenance ◽  
Life Cycle Cost ◽  
High Speed ◽  
Service Industry ◽  
Condition Based Maintenance ◽  
High Speed Train ◽  
Air Supply ◽  
Complicated Process ◽  
Maintenance Work ◽  
Important Significance

Manufacturing is in transition to manufacturing service industry and Life Cycle Cost (LCC) becomes an important considerate index. Operation & Maintenance Expense (OME) is the most complicated one among all the LCC expense units. Besides, it also accounts for the largest proportion. Authors proposed a LCC modeling idea, which is Expenses attached to the process, while process consumes resourses. Authors take the air-supply and brake system of high-speed train as example, conduct the system’s maintenance FMEA and risk assessment based on dependability. According to assessment results, authors match the system’s important maintenance projects, including brake control system, main air compresser etc., with their respective maintenance mode. The paper put forward the expense model of one specific maintenance work on basis of its resourse structure. Based on the main air compresser’s dependability, authors predicted its Corrective Maintenance (CM) repair times by using Monte Carlo simulation. Combined with the repair times of Preventive Maintenance (PM) and condition-based maintenance, authors obtained high-speed train’s OME model. This method has important significance for modeling the high-speed train’s LCC. Besides, it may support the expense reseach for large equipments with complicated process.

Development of Transmission Line Condition-Based Maintenance System Based on Expert System

2014 ◽  
Vol 988 ◽  
pp. 710-714
Author(s):  
Yao Jun Liu ◽  
Shi Zhong Ji ◽  
Hua Pu Chen
Keyword(s):  
Expert System ◽  
Transmission Line ◽  
Preventive Maintenance ◽  
Condition Based Maintenance ◽  
Main Approach ◽  
Maintenance System ◽  
The Moment ◽  
Line Condition

At the moment preventive maintenance is the main approach in maintaining power transmitting lines. While along with the improvement of equipment and management, the drawbacks of preventive maintenance appear more and more prominent. In the file the function and the structure of the system of status maintenance for power transmitting lines based on expert system is presented.

Smart Diagnosis Using an On-Board Middleware

2015 ◽  
Vol 789-790 ◽  
pp. 577-581
Author(s):  
Ayoub Bouroumine ◽  
Zekraoui Mustapha ◽  
Abdelilah Maach
Keyword(s):  
Data Base ◽  
Local Knowledge ◽  
Preventive Maintenance ◽  
High Tech ◽  
Global Knowledge ◽  
New Methods ◽  
Remote Maintenance ◽  
Periodic Maintenance ◽  
High Level

Recent approaches to preventive maintenance have shifted from periodic maintenance based on static parameters to a continuous and a periodic maintenance that deploys high-tech tools to track remotely the “health” of equipments. In this paper, we propose an approach to maintain a high level of reliability and to achieve the maximum efficient use of the working parts within vehicles and machines. This paper marks the use of various techniques in the remote maintenance and diagnostics by means of classical and new methods for monitoring equipments remotely. And hence identifying their failure at earlier stages and preventing their breakdown. The technique relies on an onboard device that monitors and analyses the vibrations of the targeted parts using local knowledge stored within the middleware data base and global knowledge obtained remotely from the server.

Mathematical Optimization Models for the Maintenance Policies in Production Systems

2018 ◽  
pp. 252-268
Author(s):  
Alperen Bal ◽  
Sule Itir Satoglu
Keyword(s):  
Preventive Maintenance ◽  
Production Systems ◽  
Mathematical Optimization ◽  
Condition Based Maintenance ◽  
Future Research ◽  
Reliability Level ◽  
Simulation Based ◽  
Maintenance Activities ◽  
Maintenance Policies ◽  
Critical Components

This chapter initially presents a brief information about production systems. At these systems, different types of maintenance policies are developed to cope with wear out failures. Mainly used maintenance policies can be classified as corrective, preventive, and condition-based maintenance. In the corrective maintenance, repair or replacement is applied whenever components of the machine breakdown. In the preventive maintenance approach maintenance activities are applied to the critical components on a periodic basis. On the other hand, maintenance activities are applied whenever critical reliability level is reached or exceeded. These types of maintenance policies are modeled using mathematical modeling techniques such as linear programming, goal programming, dynamic programming, and simulation. A review of current literature about the mathematical models, the simulation-based optimization studies examining these maintenance policies are categorized and explained. Besides, the solution methodologies are discussed. Finally, the opportunities for future research are presented.

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