Railway tunnel construction remains one of the most demanding environments for engineering equipment, particularly when using drill and blasting techniques. There are multiple different factors that contribute to equipment failures that can disrupt timelines and raise operational costs. In response to these challenges, a new approach has been developed to help reduce downtime and improve machinery reliability by using machine learning to monitor and predict equipment faults before they occur.
The research introduces a predictive maintenance system designed specifically for the rugged conditions of tunnel excavation. By collecting data from sensors attached to the critical machines and tracking signals such as vibration, temperature, and pressure, the system builds a real-time picture of equipment health. This data is then used to assess potential risks and forecast mechanical issues before they affect the pace or safety of the project.
Unlike traditional maintenance routines that rely on fixed schedules or respond only after failure has occurred, this data-driven system is capable of adapting to the actual condition of the equipment. It allows site managers to make timely decisions, focus attention where it’s most needed, and reduce the chances of unexpected interruptions.
What differentiates this system is how it operates as an integrated solution rather than a set of separate tools. The machine learning model at its core learns from past performance data, allowing it to interpret ongoing equipment behavior with a high degree of accuracy. Subtle patterns that may signal wear or stress are recognized early, giving teams a valuable window to intervene before minor issues turn into costly failures. This kind of insight supports not only efficiency but also greater confidence in day-to-day operations.
Supporting the research of this system is Jinshuo Zhang, whose contributions focused on both the technical and applied aspects of the project. With a foundation in mechanical engineering and embedded software, he worked on areas such as system structure and model optimization. His academic research and prior experience in intelligent control have directly informed how real-time data can be transformed into actionable feedback in complex, high-pressure construction environments.
By applying artificial intelligence to practical problems in the field, this work demonstrates how technology can play a meaningful role in modernizing infrastructure practices. The result is a step toward safer, more efficient tunneling operations and a broader shift in how heavy machinery is maintained in high-risk settings.