Speeding Maintenance Performance through Time Study

2014 ◽  
Vol 607 ◽  
pp. 860-863
Author(s):  
Nolia Harudin ◽  
Sha’ri Mohd Yusof
Keyword(s):  
Preventive Maintenance ◽  
Equipment Management ◽  
Time Study ◽  
Manufacturing Environment ◽  
Machine Availability ◽  
Actual Observation ◽  
Plant Maintenance ◽  
Plant Operations ◽  
Maintenance Activities ◽  
Maintenance Performance

As more than $300 billion spent on plant maintenance and operations, U.S. industry spends as much as 80 percent of this amount to correct chronic failures of machines, systems, and people. With machines and systems becoming increasingly complex, this problem can only worsen, and there is a clear and pressing need to establish comprehensive equipment management programs that incorporate the diverse considerations that are essential to minimizing risk and lead to effective maintenance. In a production or manufacturing environment, good maintenance engineering is necessary for smooth and safe daily plant operations. This research which was conducted at one of the worldwide well known Semi Conductor Company located at Kedah, Malaysia were drive subject to improve the effectiveness of preventive maintenance activities through lean approaches. Tools such time study, spaghetti diagram and FMEA were the main key tools and concept drive throughout this research. Machine Availability is the indicator used to evaluate the improvement expected for all the proposal took in placed. With the team effort and several proposal were addressed, Machine Availability able to be improved about 0.4% which lead to improvement of weekly preventive maintenance from 4 hours on actual observation to only 1 hours as new target. It also indirectly lead to the improvement of monthly preventive maintenance which may only require 4 ½ hours instead of 5 ½ hours of previous target. The result is currently practiced and team still looking for further opportunity to improve.

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.

scholarly journals Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Tze Chiang Tin ◽  
Kang Leng Chiew ◽  
Siew Chee Phang ◽  
San Nah Sze ◽  
Pei San Tan
Keyword(s):  
Preventive Maintenance ◽  
Short Term Memory ◽  
Operational Reliability ◽  
Statistical Forecasting ◽  
Work In Progress ◽  
Dependent Behavior ◽  
Proposed Model ◽  
Forecasting Method ◽  
Semiconductor Fabrication ◽  
Maintenance Activities

Preventive maintenance activities require a tool to be offline for long hour in order to perform the prescribed maintenance activities. Although preventive maintenance is crucial to ensure operational reliability and efficiency of the tool, long hour of preventive maintenance activities increases the cycle time of the semiconductor fabrication foundry (Fab). Therefore, this activity is usually performed when the incoming Work-in-Progress to the equipment is forecasted to be low. The current statistical forecasting approach has low accuracy because it lacks the ability to capture the time-dependent behavior of the Work-in-Progress. In this paper, we present a forecasting model that utilizes machine learning method to forecast the incoming Work-In-Progress. Specifically, our proposed model uses LSTM to forecast multistep ahead incoming Work-in-Progress prediction to an equipment group. The proposed model's prediction results were compared with the results of the current statistical forecasting method of the Fab. The experimental results demonstrated that the proposed model performed better than the statistical forecasting method in both hit rate and Pearson’s correlation coefficient, r.

The Research of Medical Equipment Management Based on JCI Standard

2013 ◽  
Vol 278-280 ◽  
pp. 2226-2231 ◽  
Author(s):  
Juan Huang ◽  
Peng Gao ◽  
En Yu Guo
Keyword(s):  
Medical Treatment ◽  
Continuous Quality Improvement ◽  
Preventive Maintenance ◽  
Lower Risk ◽  
Medical Equipment ◽  
Equipment Management ◽  
Treatment Service ◽  
Patient Centered ◽  
Continuous Quality ◽  
The World

Abstract: JCI standard, regarded as the world medical treatment service criteria, represents the highest level of hospital service and hospital administration. Patient- centered, JCI establishes appropriate policies, system and process to encourage continuous quality improvement. The hospital medical equipment management department need improve the level of medical equipment management and we have carried out preventive maintenance (PM) of medical equipment according to JCI acquirement. Through JCI certification process, the medical equipment safe use has got further protection with more medical safe of patients, lower risk of medical institution and clinical medical treatment.

scholarly journals Sistemas de gestión en mantenimiento: propuesta de un TPM en una empresa productora de marcos y molduras de la región

2020 ◽  
pp. 11-17
Author(s):  
René Daniel Fornés-Rivera ◽  
Marco Antonio Conant-Pablos ◽  
Adolfo Cano-Carrasco ◽  
Roberto Carlos Gutiérrez-Beltrán
Keyword(s):  
Preventive Maintenance ◽  
Maintenance Scheduling ◽  
Total Productive Maintenance ◽  
Maintenance Costs ◽  
Monthly Average ◽  
Maintenance Program ◽  
Maintenance Activities ◽  
A Company ◽  
And Training

This research is carried out in a company that produces frames and moldings and addresses the need to develop a Total Productive Maintenance (TPM) program, as a result of the unavailability of machinery and equipment; and training that affects maintenance operations, which are not performed correctly causing costs. Currently there is a 76% availability of machinery; 78% in equipment; 42% in training and monthly average costs in machinery of $ 15,260 pesos; and in equipment of $ 1,860 pesos, in terms of maintenance costs there are no globales records. The objective was to carry out a proposal for maintenance activities, through the TPM methodology; to have an updated maintenance program. The procedure was: Describe the area under study; describe the situation of the area under study; establish TPM policies and goals; identify failures in machinery and equipment; and develop the maintenance program. It was contributed with the contribution of a maintenance program composed of: calendar, records of equipment in stock, corrective and preventive maintenance, maintenance scheduling, records of maintenance costs and catalog. Thus fulfilling the objective of this investigation.

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