Home Submit paper My papers My reviews My TPCs Chairing Travel grants Register My profile Help Log out IAMOT 2010 Change presenter for paper #1569263581: A Neural Management Maintenance System for Manufacturing Systems No changes to presenter. Property Change Add Value Conference and track The 19th International Conference for Management of Technology - Industrial and Manufacturing Authors Name ID Flag Affiliation Email Country Roubi Zaied 422787 Benha University, High Institute of Technology [email protected]Egypt Gamal Nawara 425677 Professor of Industrial Engineering [email protected]Egypt Mohamed AbdelSalam 425679 3Professor of Design and Production Engineering [email protected]Egypt Kazem Abhary 452489 Associate Professor of Mechanical Engineering [email protected]Australia Presenter Roubi Zaied Category Title A Neural Management Maintenance System for Manufacturing Systems Abstract The management of maintenance activities extremely affects the useful life of the equipments, product quality, direct costs of ma production costs. Thus, a reliable maintenance system is critical to keep acceptable level of profit and competition. This work pre Maintenance System (NMMS) considering safety and environmental issues. It combines methods applied at present to have a b maintenance of manufacturing systems. It integrates CM, adaptive PM and CBM with suitable maintenance strategy addressed NMMS would monitor the system and suggest the most appropriate maintenance actions. The main characteristics of the system opinion in a knowledge base, storing maintenance history and tracking components, alarming predetermined maintenance activ materials, updating schedules, considering limitation of resources, and measure the effectiveness of the maintenance system. T simulated. A case study application in a florescent lamps factory is in progress. Simulation and analysis of the available historica find the root of the dominant faults and find the suitable solutions to optimize the maintenance actions. Keywords Neural Management Maintenance, Maintenance Integration, Moduler System, Adaptive PM, CBM Session The program is not yet visible (chair) DOI Status accepted Printing problems Final manuscript until February 15, 2010 00:00:00 EST Document (show) Size Changed MD5 C fo 402,432 Jan 10, 2010 18:09 204117f0da794050f1297cadd190323c EDAS at 72.232.211.26 (Sun, 07 Feb 2010 13:43:02 -0500 EST) [0.320/0.561 s] 123ea30539816a9f6a887b44cabf6dbc Request help
19
Embed
Change presenter for paper #1569263581: A Neural ... Benha/Mechanical... · My profile Help Log out IAMOT 2010 Change presenter for paper #1569263581: A Neural Management Maintenance
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Home Submit paper My papers My reviews My TPCs Chairing Travel grants RegisterMy profile Help Log out
IAMOT 2010
Change presenter for paper #1569263581: A Neural Management Maintenance System for Manufacturing Systems
No changes to presenter.
Property Change Add Value
Conferenceand track The 19th International Conference for Management of Technology - Industrial and Manufacturing
Title A Neural Management Maintenance System for Manufacturing Systems
Abstract
The management of maintenance activities extremely affects the useful life of the equipments, product quality, direct costs of maproduction costs. Thus, a reliable maintenance system is critical to keep acceptable level of profit and competition. This work preMaintenance System (NMMS) considering safety and environmental issues. It combines methods applied at present to have a bmaintenance of manufacturing systems. It integrates CM, adaptive PM and CBM with suitable maintenance strategy addressed NMMS would monitor the system and suggest the most appropriate maintenance actions. The main characteristics of the systemopinion in a knowledge base, storing maintenance history and tracking components, alarming predetermined maintenance activmaterials, updating schedules, considering limitation of resources, and measure the effectiveness of the maintenance system. Tsimulated. A case study application in a florescent lamps factory is in progress. Simulation and analysis of the available historicafind the root of the dominant faults and find the suitable solutions to optimize the maintenance actions.
Now the company is going to apply a TPM program. The maintenance management is now
applying a coding system for the machines, systems and their faults (Fig. 9).
As a partial application of the proposed system, the obtained data yielded some analysis and
simulated on the PM system module. The charts in Figs. 10,11,12 are the results of simulating
the data of 6 months of the exhaust machine (EX01-1), the first production line.
5. Evaluation of the Maintenance Policy
The output of the module which arranged the faults in descending order helps the management to
monitor the dominant faults. From figure 10 and 11, it is concluded that almost the more frequent
faults cause the largest downtime. This should attract the management attention to find the root
of these faults and find the suitable solutions. The solutions might be a modification of the
machine design and/or the maintenance policy of these subsystems. However, it is clear from
Fig. 12 that MTBF increases. It means that the current maintenance policy is effective in terms of
the availability. A performance index for evaluation of the current applied maintenance system is
proposed. This index is considered as the ratio between the availability and direct maintenance
cost for each production line.
Machine name
Line No.
Side A, B or None
Section No.
Failure No.
XXX-XX-X
Fig.9. Coding system for the machines, systems and their faults
15
0
2
4
6
8
10
12
14
16
Burner Mechanical Adjust
Change parts Adjuster Shutter Plate
Do
wn
Tim
e (
hr)
Fault Name
Fig. 9. Faults sorted according to their down time
0
5
10
15
20
25
30
Burner Mechanical Adjust
Change parts Adjuster Shutter Plate
Fre
qu
en
cy o
f o
ccu
rre
nce
Fault Name
Fig. 10. Faults sorted according to their frequencies
Fig.12. Faults trend during 6 months
0
1000
2000
3000
4000
5000
6000
7000
Jan-09 Feb-09 Mar-09 Apr-09 May-09 Jun-09
MTB
F (h
r)
burner Adjuster
Shutter Plate Mechanical Adjust
Change parts
16
Applying the performance index to the efficiency of the production lines shown that the
trend of this index is going up for all the 3 lines. This confirms again that the current
maintenance policy is effective in terms of the direct maintenance cost. It was found
that the second production line outperforms the other two lines. Fig. 13 compares the
trend of performance index for line 1 and line 2.
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
Feb-08 Jun-08 Sep-08 Dec-08 Mar-09 Jul-09
Mai
nte
nan
ce p
erf
orm
ance
in
de
x Availability/Cost Ratio for L1
Fig. 13a Performance index for line 1 Fig. 13b Performance index for line 2
5.1 Cost of the NMMS application
The main elements of the maintenance management cost include direct cost and indirect cost.
Direct cost elements are spare parts and supplies cost, labor cost, and contract cost. Indirect cost
consists of overhead cost and down time cost. This approach aims to minimize total direct and
indirect costs. Cost of the FDS hardware mainly is a capital cost. The FDS cost depends on
accuracy, resolution, and response time of the required sensors. The proposed system is
considered cost effective, as it uses minimized number of sensors necessary to monitor the
system. The major cost element of this proposal is the capital cost that to be invested in the FDSs
hardware. The running cost of the maintenance software in a large scale manufacturing system is
expected to be effective. It is only the cost of running the computer system.
6. Conclusions
A comprehensive design of a Neural Management Maintenance System (NMMS) is presented
herein. The structure of the system is designed to simulate the brain action. The flowchart of the
NMMS function is presented and the design details of the modular system are explained.
Application of the NMMS in Toshiba-Factory of florescent lamps is in progress. Simulation of
the case study is run and the data analysis revealed that almost the largest downtimes are caused
by the more frequent faults. This should attract the management attention to find the root of these
faults and find the suitable solutions. However, the current maintenance policy is effective in
terms of the availability. A proposed performance index (the ratio between the availability and
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
Nov-07 Feb-08 Jun-08 Sep-08 Dec-08 Mar-09 Jul-09M
ain
ten
ance
pe
rfo
rman
ce i
nd
ex Availability/Cost Ratio L2
17
direct maintenance cost) is applied for evaluation of the current applied maintenance system on
the production lines. In terms of the performance index, the production lines shown that the trend
of this index is going up for all the 3 lines.
Acknowledgment
The authors would like first to give praise to Allaah. The authors also are grateful and to thank
the administration of Alaraby group-Factory of florescent lamps (Quisna, Egypt) for kind help,
encouragement and providing us useful data for the case study.
References
El-Betar A., M. M. Abdelhamed, A. El-Assal and Roubi A. Zaied (2006). Fault Diagnosis of a Hydraulic Power System Using an Artificial Neural Network JKAU: Eng. Sci., Vol. 17 No. 1, 117 - 137
Garg, Amik and S.G. Deshmukh (2006). Maintenance management: literature review and directions. Journal of Quality in Maintenance Engineering, vol. 12 no. 3, 2006, 205-238
O’Donoghue C.D., J.G. Prendergast (2004). Imple-mentation and benefits of introducing a computerised maintenance management system into a textile manufacturing company. Journal of Materials Processing Technology 153–154, 226–232
Mechefske, Chris K. and zheng wang (2003). Using Fuzzy Linguistics To Select Optimum Maintenance and Condition Monitoring Strategies. Mechanical Systems and Signal Processing 17(2), 305-316
Loures, E. Rocha, M. A. Busetti de Paula, E. A. Portela Santos (2006). A control-monitoring-maintenance framework based on Petri net with objects in flexible manufacturing system. Third International Conference on Production Research – Americas’ Region, 2006, (ICPR-AM06)
Loures, E. Rocha, E. A. P. dos Santos, M. A. Busetti de Paula (2006) Maintenance integration in a modular supervision framework based on Petri net with objects: Application to a robot-driven flexible cell. IEEE 2006, 0-7803-9788-6/06
Khan, Faisal I., Mahmoud M. Haddara (2003). Risk-based maintenance (RBM): a quantitative approach for maintenance/inspection scheduling and planning. Journal of Loss Prevention in the Process Industries, 16 (2003), 561–573
Gomaa, Attia, ' TPM Implementation: An Egyptian case study', The Maintenance Journal, 2005
Saranga, Haritha (2004). Opportunistic maintenance using genetic algorithms. Journal of Quality in Maintenance Engineering, vol. 10 no 1, 66-74
Jonsson, Patrik (2000). Towards an holistic understanding of disruptions in Operations Management. Journal of Operations Management, (18) 2000, 701–718
Polimac, J. and V. Polimac, (2001). Assessment of Present Maintenance Practices and Future Trends. IEEE/PES, Atlanta, October 2001.
18
Polimac, J. and V. Polimac (2002). Conceptual Development of Neural Management Maintenance. Power Systems Management and control, 17-19 April 2002, Conference Publication No. 488, IEE 2002.
Campos, Jaime (2009). Development in the application of ICT in condition monitoring and maintenance. Computers in Industry Volume 60, Issue 1, January2009, 1-20
Shyjith, K., M. Ilangkumaran and S. Kumanan (2008). Multi-criteria decision-making approach to evaluate optimum maintenance strategy in textile industry. Journal of Quality in Maintenance Engineering, vol. 14 No. 4, 375-386
Juang, Muh-Guey and Gary Anderson (2004). A Bayesian method on adaptive preventive maintenance problem optimize a non-deterministic objective function. European Journal of Operational Research, 155 (2004), 455–473
Mijailovic, Vladica (2003). Probabilistic method for planning of maintenance activities of substation components. Electric Power Systems Research 64, 53-58.
Zaied, Roubi and Kazem Abhary (2009). A Design of an Intelligent Maintenance Integrated System into Manufacturing Systems. International Conference on Industrial Technology 978-1-4244-3507-4/09-2009 IEEE. 1296-1301.