Maintenance Strategies and Failure‐Cost Model for Medical Equipment

Abstract

This paper proposes a mathematical maintenance model that analyses the effect of maintenance on the survival probability of medical equipment based on maintenance history and age of the equipment. The proposed model is simulated in Scilab using real data extracted from maintenance history of anaesthesia machine from Draeger. The analysis using survival approach reveals that conducting preventive maintenance on the selected medical equipment had a positive impact on survival of equipment. The model is then used to analyse the cost of maintenance scenarios, and an appropriate scenario is proposed for anaesthesia machine. A new failure-cost model is developed, which may be used to calculate the number of failures of equipment and the annual maintenance cost. The proposed models may be used as a planning tool for selecting maintenance strategies for various medical equipments.In many applications, equipment failures may be divided into two categories, random failures (unpredictable) and those due to deterioration (ageing). The processes in which medical equipment may fail may be represented by probabilistic mathematical models and not by deterministic models. 9 It is assumed that medical equipment follows a constant failure rate, and the chance of a failure occurring in any future at time interval is the same. A constant failure rate (or hazard rate) model is a special case where the hazard function, associated to the probability of failure in a future at time if the device is still working at the beginning of time t, is constant.According to Troyer, 7 the exponential survival and the Weibull distribution are the most widely used reliability model machines, and the exponential survival is the most basic one with the constant failure rate or the flat section of the bathtub curve. Troyer 7 further claims that most industrial machines spend most of their lives in the constant failure rate (exponential), and Tarpey 17 indicated that most electronic components do not ware out, and their failure times are accurately described by an exponential distribution.A. KHALAF ET AL.This confirms the results because, for short periods, the probability of failure is governed only by PM, whereas for long periods, the effect of PM is small compared with CM. Failure-cost model verificationThe failure model in Equation (5) is simulated in Scilab, and a comparison is obtained from the closed-form expression to that obtained by simulation. Figure 8 in the following text shows the number of failures using the simulation and analytical methods for the anaesthesia machine. It may be noticed that the values obtained fit very well. Figure 8. Number of failures PA from simulation and analytical results A. KHALAF ET AL.

Dependability-based maintenance optimization in healthcare domain

“…Using field and usage data, the proposed CBM criteria helps to predict medical equipment deterioration allowing the relaxation of unnecessary PM. Khalaf et al (2015) proposed a mathematical model to analyze the effect of PM and CM on the survival probability of medical equipment. This model is used to analyze maintenance costs and choose the appropriate scenarios for medical maintenance.…”

Maintenance as a tool for increasing the efficiency of chemicaltechnological process

“…Maintenance programs range from the very simple to the quite sophisticated ones. Perhaps, the simplest plan is to adopt a rigid maintenance schedule where predefined activities are carried out at fixed time intervals (Khalaf et al 2015).…”

Failure Mode Structured Preventive Maintenance Scheduling With Changing Failure Rates in Industry 4.0 Environment

“…However, many results were far from ideal (Cullum et al, 2018). Several factors determine the design of maintenance schedule, including the maintenance manager's decision, recommendations from manufacturers, and long-term experience (Khalaf et al, 2015). Expected life of components changes due to system aging, wear and tear (Soh et al, 2012).…”