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IAMOT 2010
Change presenter for paper #1569263581: A Neural Management Maintenance System for Manufacturing Systems
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Conferenceand 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 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.
Keywords Neural Management Maintenance, Maintenance Integration, Moduler System, Adaptive PM, CBM
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1
International Association for Management of Technology
IAMOT 2010 Proceedings
A NEURAL MANAGEMENT MAINTENANCE SYSTEM FOR MANUFACTURING
SYSTEMS
ROUBI A ZAIED
High Institute of Technology, Benha University, Egypt,
PhD Student, Zagazig University, Faculty of Engineering, Industrial Engineering Dept., [email protected]
GAMAL NAWARA
Professor of Industrial Engineering- Faculty of Engineering, Zagazig University, Egypt, [email protected]
MOHAMMAD ABDEL-SALAM
Professor of Design and Production Engineering, Faculty of Engineering, Ain Shams University, Egypt, [email protected]
Abstract
The management of maintenance activities extremely affects the useful life of the equipments,
product quality, direct costs of maintenance and consequently production costs. Thus, a reliable
maintenance system is critical to keep acceptable level of profit and competition. This work
presents a Neural Management Maintenance System (NMMS) considering safety and
environmental issues. It combines methods applied at present to have a benefit of the vast
literature in maintenance of manufacturing systems. It integrates Corrective Maintenance (CM),
adaptive Preventive Maintenance (PM) and Condition Based Maintenance (CBM) with suitable
maintenance strategy addressed for each component/subsystem. The NMMS would monitor the
system and suggest the most appropriate maintenance actions. The main characteristics of the
system includes; integration of expert opinion in a knowledge base, storing maintenance history
and tracking components, alarming predetermined maintenance activities, alerting for spare
parts and materials, updating schedules, considering limitation of resources, and measure the
effectiveness of the maintenance system. The scheme has been designed and simulated. A case
study application in a florescent lamps factory is in progress. Simulation and analysis of the
available historical data should help the management to find the root of the dominant faults and
find the suitable solutions to optimize the maintenance actions.
Keywords: Neural Management Maintenance, Maintenance Integration, Modular System,
Adaptive PM, CBM
2
1. Introduction
The management of maintenance activities extremely affects the useful life of the equipments,
direct costs of maintenance and consequently production costs. Thus, a reliable maintenance
system is critical for any manufacturing system to keep acceptable level of profit and
competition. The main goal of maintenance is to obtain the maximum production output with the
best levels of product quality, and doing this at minimum cost and least risk of breakdowns.
Other important necessitates of modern maintenance include safety and environmental
considerations. Neural management of maintenance would efficiently integrate activities and
intelligently join different sectors in the manufacturing system.
The maintenance cost varies from 15% to 70% of total production cost. The maintenance costs
are usually high due to the high cost of restoring equipment, secondary damage and safety/health
hazards inflicted by the failures. (Shyjith et al., 2008). As affirmed by O’Donoghue and
Prendergast (2004), when properly implement an integrated maintenance management, it can
reduce emergencies by 75%, cut purchasing by 25%, increase warehouse accuracy by 95% and
improve preventative maintenance by 200%.
Jonsson (2000) reviewed the literature on maintenance management, integrated key dimensions
of maintenance within a taxonomy of maintenance configurations. He partitioned maintenance
integration in manufacturing organization into hard integration and soft integration variables.
The hard issues deal with integration supported by technology and computers. Soft integration,
on the other hand, deals with human and work organizational integration issues. Moreover, he
indicated that maintenance prevention and integration are important for the manufacturing
strategy of a company, but the mix of prevention and integration could differ between contexts.
Khan and Haddar (2003) mentioned that the major challenge for a maintenance engineer is to
implement a maintenance strategy which maximizes availability and efficiency of the equipment;
controls the rate of equipment deterioration; ensures a safe and environmentally friendly
operation; and minimizes the total cost of the operation. This can only be achieved by adopting a
structured approach to the study of equipment failure and the design of an optimum strategy for
inspection and maintenance.
Lots of works in the literature handled maintenance in manufacturing systems. These works have
different trends regarding development of modeling, policies and optimization approaches. Garg
and Deshmukh (2006) systematically categorized a published literature and then analyzed and
reviewed it methodically. They identified various emerging trends in the field of maintenance
management to help researchers specifying gaps in the literature and direct research efforts
suitably.
Mechefske and Wang (2003) outlined a fuzzy linguistic approach to achieve the inclusion of
maintenance strategies. It was concluded that some difficulties in applying CBM regarding that
not all failures can be detected by monitoring, the economics of the situation may limit the
3
number of components that can be monitored, and there will also be a number of components
and/or machines for which condition monitoring is not particularly appropriate. Finally, they
recommended that a proper maintenance program must define different maintenance strategies
for different machines.
Loures et al. (2006) presented hierarchic and modular control monitoring-maintenance
architecture. The integration is based on taking into account maintenance aspects and policies
from the conception (modeling) stage. The proposed architecture was implemented in a robot-
driven flexible cell.
Campos (2009) reviewed the available literature on the application of information and
communication technologies (ICT), more specifically, web and agent technologies in condition
monitoring and the maintenance of mechanical and electrical systems. The literature findings
were analyzed and classified in a framework which highlights the baseline technology, the
objective of the technology and the industry of application.
PM is typically a time based procedure. The more accurate the maintenance action timing, the
higher the utilization of the component life but, some machines maybe are maintained with a
significant amount of useful life remaining. Thus, PM will not always be the most economic
solution. CBM exploits the maximum life of a component as it is scheduled in accordance with
the degree of deterioration. However, limitations and deficiency in data coverage and quality
reduce the effectiveness and accuracy of CBM strategy.
Maintenance integration is necessary to increase availability and reliability of manufacturing
systems and to eliminate unnecessary maintenance costs. The integration is achieved through
optimal maintenance strategy mix to have the benefits and avoid the shortage of individual
strategies. Thus, the proper maintenance program must define different maintenance strategies
for different components and machines.
The objective of this proposal is to design and implement an optimized maintenance strategy in
manufacturing systems get-together the systems' necessities at minimum cost. The technique
utilizes the artificial intelligence (AI) concepts. The system is based on the concept of Neural
Management Maintenance System (NMMS). The proposed NMMS integrates corrective
maintenance (CM), adaptive PM, and CBM with suitable maintenance strategy specified for each
component/subsystem of the manufacturing system. Opportunistic maintenance is considered as
well. Combining these maintenance schemes can overcome the shortcomings of each individual
scheme. This sort of integration is classified as hard integration according to Jonsson (2000).
The concept of NMMS was first introduced by Polimac and Polimac (2001, 2002). It is based on
Artificial Neural Networks (ANNs) which embody computational networks based on biological
metaphor to simulate the brain action.
4
The material of this paper is organized as follows: Flowchart of the NMMS is presented in
section 2, its design in section 3 and section 4 demonstrates a case study. Section 5 is assigned
for the evaluation of the maintenance policy and finally, the conclusions of this work are
presented in section 6.
2. Flowchart of the NMMS
The NMMS is designed to run online and the plan update is triggered by failures signals or other
events. However, if the scheme is applied to a manufacturing system that is not monitored
online, update is triggered by manual data input. The system runs in a cyclic manner and the
frequency is adapted according to the production rate and limitations of the computer system
capacity. Each cycle is executed in five steps or phases; Initial-input phase, Running-input phase,
Evaluation phase, Outputs and decisions phase, and Feedback-input phase. The NMMS
flowchart is detailed in Fig. 1 and the description of all phases is next.
2.1 Initial-Input Phase
In this phase, two types of data are required; constant and renewable data. The first sort of data is
the basic constant reference data including the goals of the maintenance policy, manufacturing
facilities specifications, coding system, fixed restrictions on maintenance scheduling, and all
other initial data that being required by the NMMS. The renewable data include the table of
production plan, initial PM plan, maintenance staff availability, inventory of spare parts and
materials, and variable restrictions on maintenance scheduling. All these data can be entered
initially off line in manual manner. Establishing the goal indices need the involvement of
experts.
2.2 Running-Input Phase
The other part of inputs needed for evaluation and decision making is the dynamic state
variables. These are the operating variables covering signals for machines' running, stopping, and
failure signals, Alarms, and Cost elements. All these signals must be delivered with their exact
times of occurrence for the purpose of the proposed calculations and decision making.
5
Fig. 1. The NMMS flowchart
Initial-Reading phase
Running-Reading
phase
Outputs &
Decisions
phase
Table of production plan, Initial PM plan
Maintenance staff availability
Inventory of spare parts s and materials
Restrictions on maintenance scheduling
CM decisions, associated machines
Alert for inventory update
Future plan of PM, CBM, opportunistic Maintenance and their associated machines,
Future Inspections plan
Entire Health
Alarms
Dominant Faults
PM , CBM plan
Cumulative cost
End
Goal indices: Cost, Availability, Safety
No. of groups of identical machines (G),
No. of machines in each group (GM),
No. of assemblies in each machines (GMA),
No. of subassemblies in each subassembly (GMAS)
List of machine types and price list of all parts
List of fault types and their codes, remedies, costs
Criticality indices of parts, machines and groups
Start
Evaluation
phase
Update PM plan
Update Maintenance staff availability plan
Update Inventory of spare parts s and materials
For all groups, Machines and Systems:
Health, Fault status, Alarms
Stopping signals: Stand by, CM, PM
Cumulative cost
Feedback-
Input phase For all groups, Machines and Systems:
Actions achieved, used spare parts, materials and other incurred cost
For all groups, Machines and Systems:
Production rate, Faults trend, Health, Alarms, Inspection time, Possibility of
opportunistic Maintenance., Estimated PM and CBM cost, Cumulative cost
Total Production rate, General health, Total Cost
6
2.3 Evaluation Phase
The AI is employed mainly in this phase to assess different systems and machines to find out the
optimized plan. Systems are assessed in terms of production rate, faults trend, alarms, possibility
of opportunistic maintenance, next replacement, PM or CBM times and costs, MTBF, MTTR,
and Total Cost.
2.4 Outputs and decisions phase
The NMMS is ready to release its intelligent decisions and outputs after the assessment of the
whole system. These outputs are usually triggered by operation events such as failures. The
consequence of this phase updates the maintenance plan, the staff availability plan, and alert for
required inventory of spare parts and materials. Furthermore it monitors the alarms and dominant
faults to help the maintenance manager to eliminate or reduce the source of these faults.
2.5 Feedback-input phase
Calculation of the actual cost, consumed spare parts and materials, and recording the actual down
time are critical and mainly based on this phase inputs. In general, the data will be entered
manually rather automatically. It is expected to be the most inconvenient task for the
maintenance staff to feedback the NMMS by the actual achieved activities. Although this phase
is time consuming and boring, it has the greatest importance in the evaluation of the NMMS
performance
2.6 Data and information managing
For ease and convenience, data will be stored in EXCEL files while the package of AI will use a
specific MATLAB toolbox to import/export data from/to EXCEL files. The inputs file list is as
follows:
(i) The constant reference data files:
(a) Goal indices of Cost, Availability, and Safety
(b) List of groups of machines or production lines
(c) List. of assemblies in each machines
(d) List of subassemblies in each subassembly
(e) List of machine types and price list of all parts
(f) List of designed useful life of machines and components
(g) List of recommended PM actions and timing for machines and components
7
(h) List of fault types and their codes, remedies, costs
(i) List of checklists for unknown faults
(j) Criticality indices of parts and machines
(k) List of regular PM activities and required tools
(ii) The renewable data files:
(a) Restrictions on maintenance scheduling
(b) List of installation dates of all machines and components
(c) Table of production plan
(d) Table of initial replacement and PM plan
(e) Table of maintenance staff availability
(f) Table of Inventory of spare parts and materials
(iii) Feedback data files:
(a) Failures data file
(b) Actions achieved
(c) Outstanding jobs
(d) Used spare parts and materials
(e) Other incurred cost
(f) Actual performance file
3. Design of the Proposed NMMS
The system is designed to realizes the integration of maintenance in the manufacturing system
and contribute in achieving high performance. It eases the bi-directional flow of data and
information into the decision-making and planning process at all levels. The system design
simulates the brain action; consists of sorted layers of networks. Fig. 2 shows the overall
structural design of the monitoring and maintenance system. Basically each module in the
scheme will be represented by a neural network. The networks have interconnections besides
external inputs for interfacing with maintenance administrator. The proposed NMMS is modular
which consists of sorted layers of modules.
An intelligent NMMS have to:
(i) Integrate expert opinion in a knowledge base,
(ii) Store maintenance history and track components,
(iii) Alarm predetermined maintenance activities,
(iv) Alert for spare parts and materials, update schedules with occurrence of events,
consider limitation of resources, and
(v) Measure the effectiveness of maintenance system.
8
Our proposed system can be characterized as follows:
(i) It integrates CM, adaptive PM and CBM with suitable maintenance strategy addressed
for each component/subsystem.
(ii) CM is to be carried out directly when failure occurs without waiting.
(iii) PM timing will be automatically determined for subsystems and/or systems when
condition monitoring is not particularly appropriate.
(iv) CBM is based on the status of the systems and the threshold decided by the
management.
(v) The opportunistic maintenance will be considered when it is cost effective.
A system module is assigned for one component/subsystem and the number of modules depends
on the size of each machine. Since each machine consists of several functional parts, it is
necessary to perform an analysis for each functional part and then, based on the results, select the
most favorable schedule of maintenance activities. The design of each subsystem module differs
according to the assigned schedule for this subsystem. It can be CBM-system module (Fig. 3),
PM-system module (Fig. 7) or CM-system module (Fig 8).
3.1 CBM-system modules
All subsystems must be evaluated to determine whether regular monitoring is cost-effective or
not. CBM individual module is employed when it is applicable and cost effective for monitoring
a subsystem. Condition monitoring systems can identify components requiring attention and
Supervisor
interface Main monitoring and maintenance module
Machines
System's entire health, System's total cost
Fig.2. shows the overall structural design of the monitoring and maintenance system
Group module Group module Group module
Machine
module
System
module
Systems
Maintenance status
System
module
System
module
Machine
module
Machine
module
Systems
Machines
9
conditions that could lead to system failures. In some cases, replacement costs are lower than the
annual costs required to monitor systems. Unfortunately, with the present technology, not all
failures are detectable. Detectable failures develop rapidly or instantly and can be detected after
the failure has occurred. The monitoring system cannot identify non-detectable failures. The best
case for the CBM is the predictable failures, which result from gradual degradation of the
subsystem showing measurable changes with time.
As shown in Fig 3, the structural design of a typical module encloses five built-in units. Each
unit has its external inputs from the monitored system and there are interconnections among the
five units. The module outputs to its linked machine module in the higher level. The five units
are explained below
3.1.1 Fault diagnosis system (FDS) unit
The FDS unit takes its inputs from sensors and provides its outputs to faults register and decision
support units in the same module. Fault diagnosis aims to provide information for time and
location of faults that occur in the monitored system. Neural Networks are common approach
used successfully as FDS for mechanical and other engineering systems. They have the
capability to perform pattern recognition and diagnosis that are difficult to describe in terms of
analytical diagnosis algorithms since they can learn input patterns by themselves. However, other
or hybrid techniques can be used when they are robust and effective FDSs. Any FDS must
provide fault types and levels. The proposed scheme depends on deriving a characterizing model
of each fault. The states or variables that can monitor the system condition are extracted from the
mathematical model. The effective sensors to measure characterizing variable for each fault are
determined and used for fault diagnosis.
FDS unit Faults
register
unit
Cost
unit
Downtime
unit
Dynamic inputs
Sensory signals
Stop signals
Start signals
Cost of materials
Cost of Labor
Cost of Spare parts
Other costs
Fig. 3: System module for CBM
Maintenance
decision -
support unit
Constant inputs Subsystem code
Criticality indices
Faults' codes and their:
o Thresholds, o Alarm settings,
o Shutdown settings,
o Remedies,
o Costs, o Spare parts,
Shutdown request
Assumed repair-code
Next planned maintenance dates
Required spare parts' codes
Required materials' codes
Degradation rate
Dominant faults' codes Alarm codes
Cumulative maintenance cost
10
ANNs have been successfully used to diagnose common faults of a hydraulic system in our
works (El-Betar et al., 2006; Zaied and Abhary, 2009)
3.1.2 Downtime unit
The down time unit is a simple one that calculates the down time of breakdowns from the stop
and run signal times. These signal times are imported from the EXCEL files or provided online.
Fig.4 shows a typical block diagram of the downtime unit. This block diagram is based on
MATLAB-SIMULINK symbols.
Fig.4. A block diagram of the downtime unit
3.1.3 Faults register unit
It takes inputs from the FDS, calculated downtime and its output is the health of local system
(component or subsystem). A SIMULINK typical block diagram of the unit is illustrated in Fig5.
Fig.5. A block diagram of the faults register unit
11
3.1.4 Cost calculation unit
The cost unit calculates the maintenance cost based on downtime cost and other external cost
elements including spare parts, materials, labors and other overheads.
Fig.6. A block diagram of cost unit
The mathematical equations are formulated in previous SIMULINK block diagram (Fig.6).
3.1.5 Maintenance decision -support unit
Maintenance decision support unit is the core of the module. The criticality indices, thresholds
and other inputs are imported from the EXCEL files. The function of this unit is to support the
decision making in higher levels based on the production conditions. Regarding the criticality
indices, when a machine is producing a more profitable product then it is more critical than the
other as the consequences to the organization are greater in the event of a failure. The criticality
indices are assigned digits between 1 and 9.
The last four units do not need soft computing because they almost use explicit mathematical
equations. Only for this intelligent unit, a neurofuzzy approach is proposed to execute mapping
between inputs and desired outputs. The desired outputs are assigned on the basis of maintenance experts and herein some rules are employed:
(i) utilizing the whole history of available information for decision making
(ii) maintenance strategies are based on the failure rate characteristics, i.e. constant or
variable, failure impact and failure rate trend
(iii) equipment does not have to be checked repeatedly if it has not been used
12
(iv) there are certain pieces of equipment that require visual inspection when they have
not been run
(v) when the machine is down, the opportunity is used to repair or replace other items,
which are found to be faulty or in need of maintenance within a short period of time
(vi) a maintenance action is considered if the deterioration level of the
system/components fall in specified trigger zones
(vii) it is suitable to apply Run-to-failure for small, non-critical, low price components
3.2 PM-subsystem modules
The PM plan is particularly applicable when wear-out is the cause of failures. An adaptive PM is
proposed and the frequency of maintenance can be adjusted to be optimized using the
information stored in the faults register unit about the condition of system. The periods of
inspections and maintenance usually take definite values because of practical considerations of
the plan, i.e., 1 day, 1 month, 3 months, 1 year and so on, allowing some tolerance margin before
or after the estimated date. From Fig. 7, the Subsystem module for PM is composed of only three
units.
3.3 CM-system modules
From Fig. 8, the Subsystem module for CM is composed of four units. The items chosen for CM
are those having insignificant failure consequences, and are best left untouched unless broken.
But for those items, whose failures may result in economic or safety hazards, either time-based
maintenance or condition-based maintenance are opted, with the prime objective of preventing
the failure before it occurs (Saranga 2004).
Cost
unit
Downtime
unit
Dynamic inputs
Last MTBF
Last MTTR
Stop signals-time
Start signals-time
Cost of materials
Cost of Labor
Cost of Spare parts
Other costs
Fig.7. System module for PM
Maintenance
decision -
support unit
Constant inputs System code
Criticaity indices
Replacement
o Thresholds, o Remedies,
o Tools,
o Spare parts
Shutdown request
Assumed repair-code
Next planned maintenance date
Required spare parts' codes
Required materials' codes
Degradation rate, System current
health
Cumulative maintenance cost
13
Muh-Guey Juang and Gary Anderson (2004) presented a model incorporates five possible
maintenance actions: minimal repair, major repair, planned replacement, unplanned replacement
and periodic scheduled maintenance. A scheduled maintenance is carried out as soon as T time
units have elapsed since the last major maintenance action, which includes a system replacement,
major repair or previous scheduled maintenance. At the Nth scheduled maintenance, the system
is replaced rather than maintained. When the system fails before age T, it either receives a major
repair (or replaced after (N-1) maintenance) or minimally repaired depending on the random
repair cost at failure. The objective was to determine the optimal plan (in terms of N and T)
which minimizes expected cost per unit of time.
3.4 Machine and group Modules
Typically, these modules are single unit modules similar to the Maintenance decision support
unit. All the units described subsections were successfully established and tested in the
MATLAB and SIMULINK environment.
4. Case Study
In practical application of the proposed NMMS, maintenance data were obtained from Toshiba-
Factory of florescent lamps. The Factory is located in Quisna (about 60km north to Cairo),
established and started production since 2004. It has three production lines; each line consists of
16 machines forming 6 groups. The factory works 24 hours daily on three shift basis. Regular
PM is carried out in the first shift only. The data sheet of the factory is summarized in table 1.
Failure
register
unit
Cost
unit
Downtime
unit
Dynamic inputs
Last MTBF
Last MTTR
Stop signals-time
Start signals-time
Cost of materials
Cost of Labor
Cost of Spare parts
Other costs
Fig.8. System module for CM
CM
Maintenance
decision -
support unit
Constant inputs System code
Criticality indices
Replacement
o Thresholds, o Remedies,
o Costs,
o Spare parts
Repair or Replacement
MTBF
MTTR
Required spare parts' codes Required materials' codes
Degradation rate
System current health
Cumulative maintenance cost
14
Table 1 Data sheet of Toshiba-Alaraby florescent lamps factory
Factory Name ELARABY for lighting technology
Location Quisna, Mubarak Industrial City 50km north to Cairo
No. of Production lines 3 lines; each line consists of 5 sequential machine groups, one buffer (100 lamps capacity)
between first and second group, and one side-feeding group
Production capacity Max line production rate is 24 lamps/min
Daily working hours 24 hours on 3 shift basis; 8am to 4pm, 4pm to 12am, 12pm to 8am
Installation starting date Nov. 2003
Production starting date Jan. 2004
PM frequency system Daily PM, Weekly PM, Fortnightly PM, 6 weeks PM, 6 month PM, Annual PM
Maintenance staff 6 Mechanical engineers, 3 electrical engineers, 16 mechanical technicians, 9 electrical
technicians
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.
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