Abstract:
Unexpected equipment failure in machines interrupts production schedules and creates
costly downtime. Therefore, the importance of timely equipment maintenance is to
extend the machine lifespan, prevent unplanned downtime, and reduce the need to buy
equipment. Rivatex East Africa Limited (REAL) has an overcapacity of looms with
inconsistent maintenance time schedules. The main objective of the research was to
establish a suitable maintenance schedule time and parameters by assessing the state of
maintenance practices of the critical equipment in the weaving section at REAL. The
specific objectives were to map out the critical equipment in the weaving section, to
model the time between maintenance operations and the number of failures and lastly, to
synthesize the system data to establish an optimized maintenance schedule and
parameters. The maintenance time schedules of rapier, and air-jet looms at REAL were
studied. Data collections were by real-time observations, questionnaires, and interviews
administered to 20 personnel using a simple random sampling method suitable for a
small population. Semi-structured interviews had both predetermined and unplanned
questions whereas both open and closed ended questionnaire were used. Failure mode
and effect analysis, fishbone diagram, Weibull distribution, and Monte Carlo simulation
were undertaken followed by regression analysis of the data. The setup of the Monte
Carlo simulation entailed 1000 instances of the random values from the systems in the
critical equipment. The data were optimized through Monte Carlos regression modeling
and Weibull distribution analysis to get shape parameter and the scale parameter of 1.47
and 1683.46 hours. Regression analysis indicated that 95.50% of the variation in mean
time between failures was due to total time and the number of failure variables in critical
equipment systems. A preliminary survey on downtime indicated up to 60 days, the
productivity was estimated at 194.76 meters, and efficiency was 90%. In conclusion, the
findings indicated that weaving looms were the critical equipment. The model’s shape
parameter of 1.47 described a steady increase in the risk of wear-out failure during the
early life of the machines. Also, the value of the shape parameter suggested early wear-
out failure and premature failures after installation. The optimal time interval for
maintenance operations was 1683.46 hours from the scale parameter. The findings
indicated that REAL’s looms had an inconsistent and incoherent maintenance time
scheduling approach. According to the results, it is recommended that preventive
maintenance schedules be done once every 1683.46 hours. Further research is
recommended to investigate non-maintenance management strategy aspects of
scheduling maintenance activities for industrial equipment, including unplanned/reactive
maintenance, preventive maintenance, and predictive monitoring.