assistME: A Platform for Assisting Engineers in Maintaining the Factory Pipeline

In this position paper, we present our approach of utilizing mobile devices (i.e., mobile phones and tablets) for assisting engineers and experts in understanding and maintaining the factory pipelines. For this, we present a platform, called assistME, that is composed of three main components: the assistME Server, the assistME mobile infrastructure, and the co-assistME collaborative environment. In order to get full utilization of the assistME platform, we assume that an initial setup is made in the factory in such a way that it is equipped with different sensors to collect data about specific events in the factory pipeline together with the corresponding locations of these events. The assistME Server works as a central control unit in the platform and collects data from the installed sensors in the factory pipeline. In the case of any unexpected behavior or any critical situation in the factory pipeline, notification and other details are sent to the related group of engineers and experts through the assistME mobile app. Further, the co-assistME collaborative environment, equipped with a large shared screen and multiple mobile devices, helps the engineers and experts to collaborate with to understand and analyze the current situation in the factory pipeline in order to maintain it accurately.

💡 Research Summary

This position paper proposes “assistME,” a comprehensive platform designed to enhance the maintenance of complex factory pipelines in smart manufacturing environments. The core challenge addressed is the difficulty in maintaining heterogeneous systems, which requires collaboration among experts from diverse domains like system engineering and safety, each with different perspectives and terminologies.

The assistME platform is structured around three integrated components. First, the assistME Server acts as the central nervous system. It assumes a factory pipeline instrumented with a network of sensors. The server continuously collects and processes this real-time sensor data. When it detects anomalous behavior or a critical situation that deviates from expected parameters, it initiates a response.

Second, the assistME Mobile Infrastructure delivers this response directly to the responsible engineers and experts. Through a dedicated mobile application on smartphones or tablets, users receive immediate push notifications about incidents. Beyond alerts, the app allows users to request and view an interactive 3D visualization of the factory pipeline. In this visualization, critical components are highlighted in red for quick identification. Users can tap on any component, especially faulty ones, to retrieve detailed status reports and key parameter values, enabling informed initial assessment from anywhere. Communication between the mobile apps and the server is handled via standard Internet protocols (TCP/IP) over Wi-Fi.

Third, and most notably, is the co-assistME Collaborative Environment. This environment is designed for group problem-solving and decision-making. It consists of a large shared display (e.g., a wall-sized screen) and multiple mobile devices. The shared screen presents comprehensive visualizations of the system—such as its physical layout, real-time status, or failure analysis graphs—that serve as a common reference point for all collaborators. Each participant uses their own mobile device, running a co-assistME app, as a personal interaction tool. They can use it to manipulate the view on the large screen (zoom, pan, select components) and to share information and annotations with other team members. This design is highly scalable, as the number of users is not limited by physical input devices. The authors also note the platform’s potential compatibility with future wearable devices like smartwatches or data glasses. Looking ahead, they plan to extend this concept from a single-site (“locally collaborative”) environment to a “globally collaborative” one, where shared displays in multiple factory locations are synchronized in real-time, enabling distributed teams to collaborate seamlessly.

In summary, assistME presents a holistic framework that bridges data-driven monitoring, mobile-enabled situational awareness, and structured collaborative analysis. By integrating these elements into a cohesive workflow, the platform aims to reduce downtime, improve maintenance accuracy, and foster effective collaboration among experts. The paper concludes by outlining future work, including real-world evaluation studies and the development of the global collaboration feature.