Predictive Maintenance (PdM) Structure Using Internet of Things (IoT) for Mechanical Equipment Used into Hospitals in Rwanda

Mihigo, Irene Niyonambaza; Zennaro, Marco; Uwitonze, Alfred

doi:10.3390/fi12120224

Cited by 21 publications

(9 citation statements)

References 78 publications

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“…There are various studies available in literature that is providing intelligent method for pre-processing of data. For example, the work conducted by Bekar et al [101] is presenting the smart approach for cleaning data using machine learning approach. Gudivada et al [102] presents study that combines both Big data and ML techniques for cleaning data.…”

Section: A Data Collection Throughmentioning

confidence: 99%

WITHDRAWN: Implementation of Reliability Centered Maintenance (RCM) in the background of industry 4.0 –Issues, Challenges, and Opportunities

Mian

Yasir³

2023

Preprint

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Reliability Centered Maintenance (RCM) is a well-known maintenance method originated in the aviation sector. It has shown a huge success such that consequently this technique has been adapted in more industrial sectors. Currently, the system maintenance has become more extensive and complex task. As a result, the equipment structures and functions are continuously becoming more complicated and diverse, which ultimately makes determining their reliability much more challenging. In addition, traditional RCM techniques can no longer meet the new requirements, including real-time condition monitoring, health diagnosis, remote maintenance, and intelligent maintenance required in industry 4.0. This paper highlights the challenges, issues and difficulties in adopting RCM with industry 4.0. A conceptual model-based solution is put forward to provide solutions towards the identified challenges. The model-based solution includes effective and promising solutions by combining RCM technology with artificial intelligence, digital twins, internet of things, big data, and model-based reliability analysis technology to provide advanced real-time data based equipment management, including online health monitoring and intelligence fault diagnostics and remote maintenance. This study will provide a base for implementing RCM with Industry 4.0 to provide smart and intelligent maintenance of the equipment’s or assets.

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Section: A Data Collection Throughmentioning

confidence: 99%

WITHDRAWN: Implementation of Reliability Centered Maintenance (RCM) in the background of industry 4.0 –Issues, Challenges, and Opportunities

Mian

Yasir³

2023

Preprint

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“…Furthermore, especially with the rise of IoT, time and mission-critical applications start gaining more importance. These include the so-called Industry 4.0 [208], space applications, and predictive maintenance [209]. Reliability and resilience [210] become more and more important.…”

Section: Open Issues and Research Challengesmentioning

confidence: 99%

A beginner's guide to infrastructure‐less networking concepts

Förster,

Dede,

Könsgen

et al. 2023

IET Networks

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Infrastructure‐less networks connect communication devices end‐to‐end by managing links and routes independent of fixed networking facilities, relying on dedicated protocols running on end‐user devices. The large variety of infrastructure‐less concepts and related aspects can be confusing both for beginning Ph.D. students as well as experienced researchers who wish to get an overview of neighbouring areas to their own research foci. Frequently discussed topics such as different types of sensor‐, vehicular‐, or opportunistic networks are covered. The authors describe different networking concepts by looking at aspects such as the main properties, common applications, and ongoing research. Furthermore, the concepts by common characteristics such as node mobility, network density, or power consumption are compared. The authors also discuss network performance evaluation by describing commonly used metrics, different evaluation techniques, and software tools for simulation‐based evaluation. The references given in each section help obtain in‐depth information about the presented topics and give hints about open research questions, which can be a starting point for own investigations.

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“…In line with the PdM claim, the longtime real-time data that may provide different scenarios of the equipment’s health status were collected using the data collector developed in [ 24 ]. The data collection processes shown in Figure 2 are used in this work to collect and save data before being processed and used to build the predictive model.…”

Section: Data Gathering and Processingmentioning

confidence: 99%

On-Device IoT-Based Predictive Maintenance Analytics Model: Comparing TinyLSTM and TinyModel from Edge Impulse

Mihigo

Zennaro

Uwitonze

et al. 2022

Sensors

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A precise prediction of the health status of industrial equipment is of significant importance to determine its reliability and lifespan. This prediction provides users information that is useful in determining when to service, repair, or replace the unhealthy equipment’s components. In the last decades, many works have been conducted on data-driven prognostic models to estimate the asset’s remaining useful life. These models require updates on the novel happenings from regular diagnostics, otherwise, failure may happen before the estimated time due to different facts that may oblige rapid maintenance actions, including unexpected replacement. Adding to offline prognostic models, the continuous monitoring and prediction of remaining useful life can prevent failures, increase the useful lifespan through on-time maintenance actions, and reduce the unnecessary preventive maintenance and associated costs. This paper presents the ability of the two real-time tiny predictive analytics models: tiny long short-term memory (TinyLSTM) and sequential dense neural network (DNN). The model (TinyModel) from Edge Impulse is used to predict the remaining useful life of the equipment by considering the status of its different components. The equipment degradation insights were assessed through the real-time data gathered from operating equipment. To label our dataset, fuzzy logic based on the maintainer’s expertise is used to generate maintenance priorities, which are later used to compute the actual remaining useful life. The predictive analytic models were developed and performed well, with an evaluation loss of 0.01 and 0.11, respectively, for the LSTM and model from Edge Impulse. Both models were converted into TinyModels for on-device deployment. Unseen data were used to simulate the deployment of both TinyModels. Conferring to the evaluation and deployment results, both TinyLSTM and TinyModel from Edge Impulse are powerful in real-time predictive maintenance, but the model from Edge Impulse is much easier in terms of development, conversion to Tiny version, and deployment.

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Predictive Maintenance (PdM) Structure Using Internet of Things (IoT) for Mechanical Equipment Used into Hospitals in Rwanda

Cited by 21 publications

References 78 publications

WITHDRAWN: Implementation of Reliability Centered Maintenance (RCM) in the background of industry 4.0 –Issues, Challenges, and Opportunities

WITHDRAWN: Implementation of Reliability Centered Maintenance (RCM) in the background of industry 4.0 –Issues, Challenges, and Opportunities

A beginner's guide to infrastructure‐less networking concepts

On-Device IoT-Based Predictive Maintenance Analytics Model: Comparing TinyLSTM and TinyModel from Edge Impulse

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