Predictive Maintenance for Tunneling Equipment

The core of the tunnel boring machine is a metal structure at its front, responsible for the destruction of rock, retraction of the excavation, and the forward movement of the machine. This shield structure is composed of numerous elements such as cutters, spheres, and wheels. These components are vital for the machine's functionality but are also consumables that wear out and need timely replacement. Failure to replace these parts promptly can lead to machine breakdowns, project delays, and increased operational costs.

To address this challenge companies usually think about developing a software solution to automate the process of monitoring and controlling the timeliness of component replacement. This system could visualize the TBM with all its components and allows the entry of manual inspection results regarding the technical condition of these parts. As a software's standout feature in this case we would name its predictive algorithm, which estimates the wear of components based on the history of technical works, enabling proactive maintenance.

Predictive maintenance system leverages a sophisticated algorithm that analyzes historical data on component usage and maintenance records. By studying patterns and trends in this data, the system can forecast when a component is likely to wear out and needs replacement.

This approach provides several advantages over traditional maintenance methods, which rely on fixed schedules or reactive repairs after a failure occurs. With predictive maintenance, maintenance tasks started to be performed just in time, reducing unnecessary replacements and minimizing downtime.

The predictive maintenance strategy aims to minimize unplanned downtime by accurately forecasting potential breakdowns. Companies must avoid such occurrences, as the repercussions can severely harm their reputation and financial health. Unplanned downtime can significantly delay project implementation, resulting in catastrophic financial losses, as demonstrated by an incident in the US in 2013*.

• Increased efficiency and Reduced downtime: By predicting wear and scheduling maintenance proactively, the system ensures that the TBM operates with minimal interruptions.
• Enhanced safety: Regular and timely maintenance reduces the risk of equipment failure, ensuring a safer working environment for operators and technicians.
• Cost savings: Avoiding emergency repairs and extending the life of components results in significant cost savings.
• Improved planning and Resource allocation: Maintenance activities are now better planned and resources allocated more efficiently, leading to smoother project execution.

Some real-life case studies demonstrate the system's success. In a major tunneling project, the predictive maintenance system significantly reduced downtime and maintenance costs while improving overall efficiency. Users, in turn, benefit from a simplified way to schedule maintenance proactively due to the system's ability to accurately predict component wear in a timely manner.