Towards
Maintenance 5.0:
sustainability,
promotion of talent,
diversity and
empowerment
about the project
DESDEMONA project aims to develop a decision support system (DSS) to provide real-time suggestions on the appropriate operator(s) to perform maintenance tasks with high cognitive workloads.
Given the complexity of the maintenance task to be entrusted, the DSS suggests the most suitable operator considering the available workforce based on three main factors: operator profile, emotional state of the operator and available smart devices.
Duration: 2 years.
Timeline: 1 Dec 2023 – 30 Nov 2025
Timeline: 1 Dec 2023 – 30 Nov 2025
why it matters
The fifth industrial revolution, known as Industry 5.0, has a vision of an innovative, resilient, socio-centric and competitive industry. The increasing need for flexibility and adaptability of smart factories has led to greater cognitive efforts required by new industrial tasks.
Condensing maintenance as one of the main processes affecting the flexibility and adaptability of manufacturing systems, the complexity of maintenance tasks is changing rapidly. When the maintenance procedure, while supported by most intelligent devices, fails to avoid or minimize the consequences of an accident, the question that arises is, “Is the procedure wrong” or “Did the person make a mistake?”.
partners
objectives
PRIN 2022 PNRR DESDEMONA
Objective 1
Develop an analytical model to assess the cognitive workload of the most common maintenance tasks and the operator profile to which the task could be assigned
PRIN 2022 PNRR DESDEMONA
Objective 2
Explore the impact of a worker’s emotional state on the work performance during the execution of a maintenance task
PRIN 2022 PNRR DESDEMONA
Objective 3
Identifying the most suitable VR/AR/MR devices to support the operator in the maintenance task with real-time instructions
PRIN 2022 PNRR DESDEMONA
Objective 4
Develop a Decision Support System (DSS) to provide real-time suggestions on the proper operator/s to accomplish maintenance tasks with high cognitive workloads
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