The experiment addresses critical operational and technical challenges in the current maintenance management landscape, specifically the “disconnect” between field operations and digital systems. Currently, maintenance personnel rely on Human-Machine Interfaces (HMIs) that display high volumes of notifications, which are often ignored due to information overload. There is a lack of voice-enabled interfaces, forcing workers to rely on screens when hands-free operation is needed. Furthermore, reporting is often delayed until workers return to an office, leading to significant data loss and reduced accuracy in predictive maintenance insights.

Another major pain point is the ineffective integration of maintenance documentation with AI systems. Current solutions cannot dynamically access factory-specific knowledge, such as Original Equipment Manufacturer (OEM) handbooks, standard operating procedures, and maintenance logs. Text-based interfaces are inadequate for the immediate, contextual needs of shop floor conditions. Consequently, valuable technical knowledge remains inaccessible to non-technical operators during critical maintenance tasks, limiting the adoption and effectiveness of advanced manufacturing assistance solutions.

Objective 1: 

The primary objective is to enhance maintenance operations by transforming complex technical data into actionable guidance. This involves integrating voice-enabled AI with simulated real-time sensor data and documentation using the OpenVoiceOS framework. By doing so, the experiment aims to provide manufacturers with intelligent digital assistants that streamline maintenance workflows and improve decision-making on the shop floor.

Objective 2:

The experiment seeks to significantly improve accessibility for non-technical operators. By developing advanced AI capabilities that are accessible via intuitive voice and text interfaces, the project aims to lower the barrier to entry for using sophisticated predictive maintenance tools. This ensures that operators can interact naturally with the system without needing specialized data science expertise.

Objective 3: 

A further objective is to establish a continuous learning cycle through a RAG-LLM architecture. The system is designed to incorporate operator feedback loops, allowing the AI models to refine their accuracy and relevance over time based on human input. This objective creates a scalable solution that adapts to specific manufacturing environments and improves its troubleshooting capabilities through actual usage.

The experiment targets the manufacturing sector, specifically focusing on the home appliances industry and broader maintenance management applications. The use case context involves the “G45 cavity production line” at the BEKO Europe facility in Cassinetta, Italy. This setting serves as the industrial validation environment where the system will be tested against the rigors of discrete manufacturing processes.

The operational environment for this specific experiment utilizes a simulation-based approach using historical production and maintenance data provided by BEKO Europe. The target users are maintenance managers, engineers, and shop floor operators who require immediate assistance with equipment anomalies. Relevant constraints include the need for strict adherence to data privacy regulations (GDPR and AI Act) regarding voice data, and the technical requirement to integrate with existing legacy documentation and machine-extracted data logs.