Abstract
The success of all industries relates to attaining the satisfaction to clients with a high level of services and productivity. The success main factor depends on the extent of maintaining their equipment. To date, the Rwandan hospitals that always have a long queue of patients that are waiting for service perform a repair after failure as common maintenance practice that may involve unplanned resources, cost, time, and completely or partially interrupt the remaining hospital activities. Aiming to reduce unplanned equipment downtime and increase their reliability, this paper proposes the Predictive Maintenance (PdM) structure while using Internet of Things (IoT) in order to predict early failure before it happens for mechanical equipment that is used in Rwandan hospitals. Because prediction relies on data, the structure design consists of a simplest developed real time data collector prototype with the purpose of collecting real time data for predictive model construction and equipment health status classification. The real time data in the form of time series have been collected from selected equipment components in King Faisal Hospital and then later used to build a proposed predictive time series model to be employed in proposed structure. The Long Short Term Memory (LSTM) Neural Network model is used to learn data and perform with an accuracy of 90% and 96% to different two selected components.
Highlights
The success of industries relies on the level of production and level of services to satisfy their clients
A particular predictive model has to be developed for different components due to the fact that physical behaviors and thresholds differ from one component to another
To find the optimal parameter values that result in minimum model loss and improve the model efficiency from overfitting phenomena, at the end of each model hyperparameter turning, the created model is evaluated on both the train and test datasets, and the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are calculated using Equations (9) and (10), respectively, and the values are saved
Summary
The success of industries relies on the level of production and level of services to satisfy their clients. The main factor in improved productivity is the effective maintenance of their equipment. Among medium and small industries, the medical industry, with its mandate to save human being life, is today experiencing an increase in chronic diseases that infers a high demand of healthcare to be efficient and is authoritative in keeping a high level of their equipment reliability through severe maintenance programs. In order to satisfy the patients through effective healthcare services delivery, medical equipment plays a big impact to patients, and to the core business success [2]. There is a growing need for maintenance supervision programs in order to minimize unscheduled downtimes
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