May - Library

67
Towards an Intelligent Holonic Maintenance System
Figure 11: The Fuzzy Decision Surface Showing The Regions of Different Strategies
The DMG could be used for practical continuous impro v e m e n t
p rocess because when machines in the top ten have been addre s s e d ,
they will then, if and only if, appropriate action has been takes, move
down the list of top ten worst machines. When they move down the
list, other machines show that they need improvement and then
re s o u rces can be directed towards the new offenders. If this practice
is continuously used then eventually all machines will be ru n n i n g
optimally.
If problems are chronic, i.e. regular, minor and usually neglected;
some of these could be due to the incompetence of the user and thus
skill level upgrading would be an appropriate solution. However, if
machines tend towards RCM then the problems are more sporadic
and when they occur could be catastrophic. Uses of maintenance
schemes such as FMEA and FTA can help determine the cause and
may help predict failures thus allowing a prevention scheme to be
devised.
F i g u re (12) shows when to apply TPM and RCM. TPM is appro p r i a t e
at the SLU range since Skill Level Upgrade of machine tool operators
is a fundamental concept of TPM. Whereas, RCM is applicable for
machines exhibiting severe failures (high downtime and low
f requency). Also CBM and FMEA will be ideal for this kind of machine
and hence a RCM policy will be most applicable. The significance of
this approach is that in one model we have RCM and TPM in a unified
model rather than two competing concepts.
Generally the easy Preventive Maintenance (PM), Fixed Ti m e
Maintenance (FTM) questions are Who, and When (eff i c i e n c y
questions). The more difficult ones are What and How (effectiveness
questions), as indicated in the figure (13).
Conclusion
The main idea is based on the fact that the 'black hole' or missing
functionality in conventional CMMSs is intelligent decision analysis
tools. A model has been proposed based on the Analytic Hierarc h y
P rocess (AHP) combined with Fuzzy Logic Control (FLC) to render a
'Decision Making Grid'. This combination provides features of both
fixed rules and flexible strategies.
This grid supports the decision making process on how assets
should be maintained; directing the business to choose to run out to
failure, upgrade operator skills, choose fixed time maintenance, or to
design out the causes (as examples of such policies). It then gives a
prioritised focus within the scope of the suggested policy in order to
dynamically adapt maintenance plans through the perf o rmance of
trade-off comparisons in a consistent approach.
The basic data requirements being simply, for example: the asset
register, a fault counter, and timer, and a hierarchical fault tree. The
role of each requirement is as follows:
- Asset Register (Machine identifier). This is to identify different
machines and plants.
- Counter of Faults (Frequency). This is the first criterion used by
the DMG. It could be obtained from any CMMS or using
Programmable Logic Controllers (PLCs).
- Timer of Faults (Down-time). This is the second criterion used by
the DMG. It could be obtained from any CMMS or using
Programmable Logic Controllers (PLCs).
- Level of Faults (Hierarchical). This is important for the AHP model.
Here the combination of structured fault codes and flexible
description needs to be considered.
These basic re q u i rements are usually easy to find in existing
CMMS. It is therefore proposed that such a model could be attached
as an intelligent module to existing CMMSs in order to transfer a black
hole concept to an intelligent black box that adds value to the
business.