Preventive maintenance of taper bearing using Arduino in the application of industry 4.0

Subekti, Subekti; Pranoto, Hadi; Salmon, Beni Rukasah; Yusuf, Setyo Qomarudin; Suyadiyanto, Suyadiyanto; Ariyadi, Ari Slamet; Hamid, Abdul

doi:10.21744/irjeis.v6n4.953

“…In Furthermore, in the industrial sector, AI is transforming manufacturing processes through predictive maintenance. IoT sensors and AI algorithms are utilized to predict equipment failures, thus optimizing operational efficiency [73]. Hadi [25] illustrate the use of deep reinforcement learning in developing predictive maintenance models for effective resource management in industrial IoT.…”

Section: Trendsmentioning

confidence: 99%

The Future of AI in IoT: Emerging Trends in Intelligent Data Analysis and Privacy Protection

Marengo

2023

Preprint

2

0

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This paper explores the dynamic intersection of Artificial Intelligence (AI) and the Internet of Things (IoT), focusing on emerging trends in intelligent data analysis and privacy protection. Employing a systematic literature review, we analyze recent advancements and challenges in AI-IoT integration. The paper highlights key developments in sensor data anomaly detection, AI-driven big-data analytics, and IoT-based communication techniques. It delves into the complexities of privacy protection in IoT, examining innovative solutions like federated learning and blockchain technologies. The study also addresses the challenges in AI for IoT, prioritizing the most pressing issues such as data security, ethical implementation, and compliance with regulations like GDPR. The research underscores the need for adaptable solutions that balance technological advancements with privacy and security considerations, setting a path for future exploration in AI-empowered IoT applications.

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“…In Furthermore, in the industrial sector, AI is transforming manufacturing processes through predictive maintenance. IoT sensors and AI algorithms are utilized to predict equipment failures, thus optimizing operational efficiency [73]. Hadi [25] illustrate the use of deep reinforcement learning in developing predictive maintenance models for effective resource management in industrial IoT.…”

Section: Trendsmentioning

confidence: 99%

The Future of AI in IoT: Emerging Trends in Intelligent Data Analysis and Privacy Protection

Marengo

2023

Preprint

1

0

View full text Add to dashboard Cite

This paper explores the dynamic intersection of Artificial Intelligence (AI) and the Internet of Things (IoT), focusing on emerging trends in intelligent data analysis and privacy protection. Employing a systematic literature review, we analyze recent advancements and challenges in AI-IoT integration. The paper highlights key developments in sensor data anomaly detection, AI-driven big-data analytics, and IoT-based communication techniques. It delves into the complexities of privacy protection in IoT, examining innovative solutions like federated learning and blockchain technologies. The study also addresses the challenges in AI for IoT, prioritizing the most pressing issues such as data security, ethical implementation, and compliance with regulations like GDPR. The research underscores the need for adaptable solutions that balance technological advancements with privacy and security considerations, setting a path for future exploration in AI-empowered IoT applications.

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“…These systems allow academic, research and development entities, as well as companies to take part in the development of new, fit for purpose solutions, dedicated to solving each specific set of problems, often not solvable with the commercially available systems and solutions. Systems that answer similar challenges are described in published literature, applied to aerodynamic studies or mechanical components wear monitoring [ 16 , 17 ]. In these, software developed with MATLAB, LabView or Python, among others, allows data visualization and storage.…”

Section: Introductionmentioning

confidence: 99%

Development of an Open-Source Injection Mold Monitoring System

Gomes

¹

,

Cadete

²

,

Ferreira

³

et al. 2023

Sensors

3

0

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In the highly competitive injection molding industry, the ability to effectively collect information from various sensors installed in molds and machines is of the utmost relevance, enabling the development of data-based Industry 4.0 algorithms. In this work, an alternative to commercially available monitoring systems used in the industry was developed and tested in the scope of the TOOLING 4G project. The novelty of this system is its affordability, simplicity, real-time data acquisition and display in an intuitive Graphical User Interface (GUI), while being open-source firmware and software-based. These characteristics, and their combinations have been present in previous works, but, to the authors’ knowledge, not all of them simultaneously. The system used an Arduino microcontroller-based data acquisition module that can be connected to any computer via a USB port. Software was developed, including a GUI, prepared to receive data from both the Arduino module and a second module. In the current state of development, data corresponding to a maximum of six sensors can be visualized, at a rate of 10 Hz, and recorded for later usage. These capabilities were verified under real-world conditions for monitoring an injection mold with the objective of creating the basis of a platform to deploy predictive maintenance. Mold temperature, cavity pressure, 3-axis acceleration, and extraction force data showed the system can successfully monitor the mold and allowed the clear distinction between normal and abnormal operating patterns.

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Preventive maintenance of taper bearing using Arduino in the application of industry 4.0

Cited by 3 publications

References 9 publications

The Future of AI in IoT: Emerging Trends in Intelligent Data Analysis and Privacy Protection

The Future of AI in IoT: Emerging Trends in Intelligent Data Analysis and Privacy Protection

The Future of AI in IoT: Emerging Trends in Intelligent Data Analysis and Privacy Protection

Development of an Open-Source Injection Mold Monitoring System

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