In healthcare sectors, device management and maintenance systems are designed for providing improved patient care, reducing adverse incidents and to increase the utilization time of the devices. Predictive maintenance helps in reducing equipment downtime by reducing delays in corrective action process. Reliability engineering can used to anticipate which component would fail and when it is about to fail, taking into consideration the working environment, usage, and the age of the device. Most of the maintenance strategies primarily focuses on predicting the remaining useful time of any component in isolation. A major limitation to be overcome is to develop a framework that can track the degradation of multiple interacting components. This paper presents a neural network (NN) model to predict the number of parts that might fail in future and needs to be stocked. Validation is performed by predicting failures over 7 years (2013–2019) based on a simulated learning algorithm, using failure event dataset. The efficiency of the model is evident through the low values of root mean square and absolute errors.
Volume 12 | 05-Special Issue
Pages: 1067-1071
DOI: 10.5373/JARDCS/V12SP5/20201858