Algorithms for experience transfer between experts and novices for the operation of complex systems on the example of Utility Tunnel Boring Machines
Optimal operation of complex systems requires substantial experience by skilled operators which may take long to acquire. Such operational experience may be difficult to formalize and consequently to transfer to novices. The importance and difficulty of such transfer of experience is particularly paramount for systems operated under high uncertainty and high requirements on operational performance, such as for example, for tunnel boring machines.
In tunnel construction projects, delays entail high costs. Thus, tunnel boring machine (TBM) operators aim for high advance rates without compromising safety. This is a difficult mission in uncertain subterranean environments and often, experienced operators are better able to find optimal control parameters. The recently published paper by the chair of intelligent maintenance systems presents an intelligent decision support system that provides recommendations on the control parameters. To do so, it learns from past projects how the controls in various geological conditions impacted the boring efficiency. Then, given the control parameters set by an operator and a rough knowledge of the geological conditions, it can recommend incremental control changes toward better efficiency, leading the operator step-by-step toward a better set of control parameters.
The proposed framework comprises three steps. In the first step, the needed data sources, past TBM operational data and geological condition data, are pre-processed and cleansed. The researchers also propose an optimality score to evaluate TBM operation performance, taking into account the advance rate and the working pressure safety. The optimality scores provide an information to the operators on how optimal their current set of control parameters is with respect to the current geological conditions. In the second step, a deep learning model learns from historic data a mapping between geological conditions, control parameters, and the optimality score. It then recommends incremental control parameter adjustments in order to improve the optimality score. In the third step, the researchers propose a credibility score that evaluates the confidence in the model recommendation and helps the decision makers to develop trust in the algorithm. This process analyses the performance of the model on historic data in the neighborhood of the current conditions. Absence of similar past conditions or poor performance will lead to a low credibility score while proven performance on similar past data will rise the recommendations’ credibility. A dashboard can finally present the recommendations and their credibility measures to the operators.
The proposed approach is evaluated on a real micro-tunneling project. The case study comprised a project of six micro-tunnels (with a small diameter of about half a meter). The six micro-tunnels had a length of 688 meters each. The researchers collaborated with Herrenknecht who provided the data and the domain expertise for this project and also with the rock mechanics, engineering geology, and underground construction group at MIT.
The proposed framework demonstrates great promise for future applications.
An article of our work was also published on our department website, click here to read
The Chair of Intelligent Maintenance Systems focuses on developing intelligent algorithms to improve performance, reliability and availability of complex industrial assets and making the maintenance more cost efficient. For more information visit our webpage