Abstract:
A repairable system (tractor) is subject to deterioration or repeated failure.
At each inspection of failure status (partial, Combined and complete) a
general repair (minor replacement of parts by preventive maintenance) or
complete
overhaul
(major
replacement
of
parts
by
corrective
maintenance) is performed to restore the system to "as good as new
."state
Considerable research has been conducted on the issue of periodic
replacement times of failing systems. No doubt this is a reflection, at least
in part, of the high capital cost, of many farming systems and on the
importance of minimizing unnecessary failure costs. Despite relatively the
large body of literature on this topic, analysis of dynamic maintenance
schedule and their effect on performance of the system remains as an
open problem. In response to these challenges this study is directed to
develop a computerized recursive Markov-chain closed form analytical
solution for prediction of tractor failure, analysis, control, scheduling of
.maintenance activities and determining of tractor availability
The developed algorithm of Markov chain for failure analysis looks at a
sequence of events, defined as transition between states, and calculates
the
relative
probability
of
encountering
these
events
in
short-
run(partial),medium (combined) and long-run (complete). Hence, the
algorithm is used to evaluate reliability and availability of tractor with
.time-dependent transition rates using analytical matrix –based methods
The developed procedure is written in Microsoft Excel (Spread sheet)
operating environment. With the software the user will have the ability to
manipulate and analyze the data directly using customized menus and
.point and click mouse operation
Typically, the failures time distribution are determined and applied, using
three years tractor failure data collected from two different workshops at
Sudanese Sugar Company(Sennar and Gunied) for three medium (72-120
.hp) tractors models
To verify model accuracy, its basic functional relations (decision state
matrix) are compared with Amri and McLanghim model (2004) and with
.(WINQSB software using (t- test
For purpose of model validation real data from the field is compared with
that predicted by the model and results showed no significant difference
.(between them (P =.05
Sensitivity analysis is used to utilize the model as experimental tool to
explore the structure of various improvement scenarios using a two-step
procedure and analysis of variance. Consequently, performance of the
tractors with respect to six evaluation parameters and their ranks for the
.various workshops is quantified
