Predictive Maintenance in IoT-Monitored Systems for Fault Prevention

This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacl...

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Vydáno v:Journal of sensor and actuator networks Ročník 13; číslo 5; s. 57
Hlavní autoři: Zero, Enrico, Sallak, Mohamed, Sacile, Roberto
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.10.2024
Témata:
Additive manufacturing
Akaike information criterion (AIC)
Analysis
Case studies
Clustering
Data analysis
Decision making
Deep learning
Effectiveness
Embedded systems
Failure
Fault detection
Fault diagnosis
fault prediction
Internet of Things
internet of things (IoT)
Machine learning
Machinery
Methods
Network topologies
Neural networks
Pattern recognition
Power
Predictive maintenance
Preventive maintenance
Quality assurance
reliability analysis
Repair & maintenance
Sensors
Support vector machines
Ubiquitous computing
ISSN:2224-2708, 2224-2708
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Shrnutí:This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacles due to the lack of extensive historical data. To address this issue, we propose a novel clustering-based process that identifies potential machinery faults. The proposed approach lies in empowering decision-makers to define predictive maintenance policies based on the reliability of the proposed fault classification. Through a case study involving real sensor data from the doors of a transportation vehicle, specifically a bus, we demonstrate the practical applicability and effectiveness of our method in preemptively preventing faults and enhancing maintenance practices. By leveraging IoT sensor data and employing clustering techniques, our approach offers a promising avenue for cost-effective predictive maintenance strategies in simple machinery systems. As part of the quality assurance, a comparison between the predictive maintenance model for a simple machinery system, pattern recognition neural network, and support vector machine approaches has been conducted. For the last two methods, the performance is lower than the first one proposed.
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ISSN:2224-2708
2224-2708
DOI:10.3390/jsan13050057