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IoT Based Automatic Diagnosis for Continuous Improvement

Author

Listed:
  • Rita Martinho

    (Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal)

  • Jéssica Lopes

    (Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal)

  • Diogo Jorge

    (EfficiencyRising, Lda, Erising, 1800-082 Lisboa, Portugal)

  • Luís Caldas de Oliveira

    (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal)

  • Carlos Henriques

    (Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal)

  • Paulo Peças

    (IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal)

Abstract

This work responds to the gap in integrating the Internet-of-Things in Continuous Improvement processes, especially to facilitate diagnosis and problem-solving activities regarding manufacturing workstations. An innovative approach, named Automatic Detailed Diagnosis (ADD), is proposed: a non-intrusive, easy-to-install and use, low-cost and flexible system based on industrial Internet-of-Things platforms and devices. The ADD requirements and architecture were systematized from the Continuous Improvement knowledge field, and with the help of Lean Manufacturing professionals. The developed ADD concept is composed of a network of low-power devices with a variety of sensors. Colored light and vibration sensors are used to monitor equipment status, and Bluetooth low-energy and time-of-flight sensors monitor operators’ movements and tasks. A cloud-based platform receives and stores the collected data. That information is retrieved by an application that builds a detailed report on operator–machine interaction. The ADD prototype was tested in a case study carried out in a mold-making company. The ADD was able to detect time performance with an accuracy between 89% and 96%, involving uptime, micro-stops, and setups. In addition, these states were correlated with the operators’ movements and actions.

Suggested Citation

  • Rita Martinho & Jéssica Lopes & Diogo Jorge & Luís Caldas de Oliveira & Carlos Henriques & Paulo Peças, 2022. "IoT Based Automatic Diagnosis for Continuous Improvement," Sustainability, MDPI, vol. 14(15), pages 1-28, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9687-:d:881817
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    References listed on IDEAS

    as
    1. Gyusun Hwang & Jeongcheol Lee & Jinwoo Park & Tai-Woo Chang, 2017. "Developing performance measurement system for Internet of Things and smart factory environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2590-2602, May.
    2. Mitsuhiro Fukuzawa & Ryosuke Sugie & Youngwon Park & Jin Shi, 2022. "An Exploratory Case Study on the Metrics and Performance of IoT Investment in Japanese Manufacturing Firms," Sustainability, MDPI, vol. 14(5), pages 1-21, February.
    3. Mirco Peron & Fabio Sgarbossa & Jan Ola Strandhagen, 2022. "Decision support model for implementing assistive technologies in assembly activities: a case study," International Journal of Production Research, Taylor & Francis Journals, vol. 60(4), pages 1341-1367, February.
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    Cited by:

    1. José Dinis-Carvalho & Rui M. Sousa & Inês Moniz & Helena Macedo & Rui M. Lima, 2023. "Improving the Performance of a SME in the Cutlery Sector Using Lean Thinking and Digital Transformation," Sustainability, MDPI, vol. 15(10), pages 1-20, May.

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