Context and challenges

This client is an international high-tech company in the defence sector.

Maintaining industrial systems in operational condition at a lower cost has become a critical factor. In order to improve the industrial productivity of its machine fleet, this client implemented a condition monitoring system for its boring machine. This monitoring system includes a certain number of sensors in order to gain knowledge about the machine's state of health. The boring machine is a numerically controlled machine tool that is responsible for machining very large single parts.

In this context, this customer called on Assystem to develop a predictive maintenance solution for its boring machine, in order to carry out just-in-time maintenance, anticipate future breakdowns and thus implement the necessary actions, such as ordering spare parts in advance to prevent the machine from being unavailable for too long.

Project scope

Deployment of a PHM (Prognostics and Health Management) approach on the machine. This concept of PHM and predictive maintenance completes traditional maintenance operations by taking into account failures more proactively.

  • Implementing a monitoring system using sensors to collect information on vibrations, temperature, pressure, etc.
  • Identifying of various operating and degradation modes
  • Predicting the evolution of system degradation in order to make a prognosis
  • Integrating the solution developed on a supervision station to inform and guide the operator on the state of health of the machine and its subsystems in real time, and thus indicate when and where the next breakdown will occur and its nature

Expected client benefits

  • Higher level of safety and security through early detection of machine drifts - examples: misalignment of the tool head, failure of the cooling system
  • Increase in reliability thanks to the feedback and analysis of machine health data in real time on the HMI (Human-Machine Interface / Supervision Station)
  • Increased productivity and machine availability through spare parts control thanks to the use of advanced forecasting algorithms that predict where, when and what the next breakdown will occur
  • Financial gains induced by the reduction of very costly corrective maintenance by anticipating interventions and organising production stoppages


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