Remote Condition Monitoring of Elevator's Vibration and Acoustics Parameters for Optimised Maintenance Using IoT Technology

Olalere, Isaac Opeyemi; Dewa, Mendon; Nleya, Bakhe

doi:10.1109/ccece.2018.8447771

Cited by 11 publications

(7 citation statements)

References 10 publications

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“…Olalere et al [13] developed a system to reduce elevators breakdowns, by early reporting of faults and diagnosis from historical data gathered from monitoring the vibration and noise using sensors. The system is divided into three layers.…”

Section: Related Workmentioning

confidence: 99%

Elevint: A Cloud-based Internet of Elevators

Aljadani¹,

Almutairi²,

Ghaleb³

et al. 2022

IJACSA

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With the significant growth of the number of highrise buildings nowadays, the dependence on elevators has also increased. The issue that faces elevator passengers in case of breakdowns is the long waiting time for the arrival of the maintenance engineers to perform the repair, as the process of reporting is done manually. The safety concern increases when people are trapped. Most state-of-the-art approaches detect faults without providing means to facilitate the communication between elevator owners, maintenance companies, and engineers or notify them in case of breakdowns. Moreover, none of the proposed fault detection solutions rely on rules specified by experts in the field. This paper aims at addressing these issues by proposing a system that manages, monitors, detects faults and informs users of any faults instantly by sending notifications. Specifically, the paper proposes a mobile application, Elevint, that is, cloud-based and exploits the Internet of Things (IoT) technology. Elevint provides real-time monitoring of elevator operating conditions collected from sensors. The data is then transferred to the cloud, where faults are detected by applying rules that compare the current conditions with severity levels determined by experts. In the case that a fault is detected, elevator owners and maintenance companies are automatically notified. Moreover, through Elevint, maintenance companies can assign engineers to repair the fault and elevator owners can view and re-schedule the engineer's visit if needed. Testing our system on an elevator model shows 98% accuracy. In future, we intend to test it on real elevators to verify its applicability in practice.

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Section: Related Workmentioning

confidence: 99%

Elevint: A Cloud-based Internet of Elevators

Aljadani¹,

Almutairi²,

Ghaleb³

et al. 2022

IJACSA

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“…IoT capabilities enable devices to interact, communicate, evaluate, and capture real-time data, and sends decision to the actuators. All IoT devices adopts a mechanism to send or receive data such as wired or wireless, Bluetooth, cellular network and many more [11]. Communication between machines and smart devices such as sensors, controllers are done via the cloud services.…”

Section: Internet Of Thingsmentioning

confidence: 99%

Optimising Production through Intelligent Manufacturing

Olalere

Olanrewaju

2020

E3S Web Conf.

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Intelligent manufacturing system (IMS) has been the focus of most industries since Industry 4.0 revolution. IMS is being implemented through the integration of Internet of Things, (IoT), Cyber-Physical Systems (CPS), digital twin and big data analytics to optimize production through smart manufacturing. This research presents a conceptual approach of an adaptive clustering algorithm (ACA) for advanced manufacturing decision-making for smart machining manufacturing. The work considers product monitoring and assessment, machine health and operating parameters monitoring, as an important factor for intelligent decision making on a machining production line through the developed cyber twin of the machine tool for production optimisation. Cyber twin of the machine tool is developed which runs on a realtime sequence with the physical asset fussed with smart sensors and controllers enabled with cloud computing, IoT and data analytics. The ACA enables resources monitoring, production monitoring, machine condition monitoring, cloud feedback notification, product monitoring, and assessment, for intelligent decision-making from a cluster of similar machines using ANN clustering tool for self-aware, self-predict and self-reconfiguration in a smart machining production line to detect a cutting tool chipping of less than 0.25mm size. The method is proposed to optimise production by increasing productivity through intelligent decision and prediction for tool change, tool failure, maintenance, adjustment of operating parameters.

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Section: Introductionmentioning

confidence: 99%

“…Acoustic sensor is another sensor that studied in lift monitoring system [8] [9]. The study in [8] shown that combination of using vibration and acoustics data is efficient in lift fault detection. Cloudbased approach for condition monitoring (CM) and predictive maintenance (PdM) has been more studied due to the popularity of cloud computing and there are more cloud service provides such as Amazon Web Service, Google Cloud etc.…”

Section: Introductionmentioning

confidence: 99%