## 1 Introduction  
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.  
Looking to the few numbers of referral hospitals in Rwanda that have not yet adopted a new technology of virtual patient's health monitoring due to the available resources [1], hospitals do always have a long queue of people looking for diagnostics and treatment. In order to satisfy the patients through effective healthcare services delivery, medical equipment plays a big impact not only to patients, but also to the core business success [2]. Accordingly, there is a growing need for maintenance supervision programs in order to minimize unscheduled downtimes.  
Unlike the equipment used in the diagnosis of diseases and treatment of patients commonly called biomedical equipment [3; 4] which are mainly maintained by expatriate companies, there are obligatory mechanical equipment that provide secondary supports and much necessary capabilities to these biomedical equipment. Like biomedical equipment, this category requires large capital investment to hospital and, for their maintenance, whether new or aged among them involve cost, time, and effort to maintain.  
To date, repair after failure is common practice in this equipment due to the fact that they fail without any notification to the maintenance team; hence, fault detection and unplanned maintenance works can completely or partially interrupt the remaining hospital activities. Moreover, their unplanned downtime may even lead to considerably worse incidents [5], including patient death.  
Coping with the technology, with the aim to increase equipment lifetime, availability, reliability, and reduce downtime, unnecessary preventive inspections, maintenance time, and untimely pressure on the maintenance team as well as associated costs, the maintenance team requires real time conditional based evidence for effective maintenance [6; 7].  
Motivated by the aforementioned matters, noticing a gap in the adoption of existing predictive maintenance architecture for these types of equipment due to different mechanical behaviors from one to another and the nonexistence of historical or real time performance data, this work:  
* proposes the Predictive Maintenance (PdM) Structure using Internet of things (IoT) for mechanical equipment used into Rwandan hospitals; and,
* describes the whole process, from real data collector development, real data gathering, up to the fault detection sights from equipment's components before they fail.  
The data that were used to build a predictive model were gathered in King Faisal Hospital to two critical components of large hospital autoclave sterilizer with a built in electric steam generator that uses steam as a sterilization agent. Long Short Time Memory (LSTM) Neural Networks was adopted to be suitable model for predicting the physical performance of two selected components with a fitting confidence of 90% and 96%, respectively, by mapping the model fitting parameters.  
The remaining part of this paper is structured, as follows: Section 2 introduces an overview of the predictive maintenance evolution, followed by Section 3, which summarizes the role of Internet of Things in predictive maintenance and highlights some previous works. Section 4 describes the used methodology and selected materials in order to develop a prototype that resulted in the proposed overall PdM structure that was powered by IoT in Section 5 and the experiment results presented in Section 6. Finally, Section 7 concludes the study.