Feature-based intelligent models for optimisation of percussive drilling.

As a rotary-percussion system, the vibro-impact drilling (VID) system utilises resonantly induced high frequency periodic impacts alongside existing drill-string rotation to cut through downhole rock layers. Due to the inhomogeneous nature of the rock layers, the system often experiences multi-stability which generates different categories of impact motions as drilling continues downhole. Some impact motions yield better drilling performance in terms of rate of penetration (ROP) and bit life-span when compared to others. As an optimisation strategy, the present study adopts feature-based classification algorithms including multi-layer perceptron, support vector machine and long short-term memory network as intelligent models for categorising impact motions from a one-degree-of-freedom impact oscillator representing the percussive bit-rock impacts of the VID system. This way, high-performance impacts can be easily detected and maintained while undesirable low-performance impacts are well avoided to increase ROP, improve bit life-span and save cost. In this study, scarce and limited classes of experimental impact data are merged with inexhaustibly simulated impact data to train different network models. By means of cross-validation, the trained networks were tested on separate sets of only-simulation and only-experimental data. Results show that extracting appropriate features from raw impact data is essential for optimising the performance of each network model. About 42% of the feature-based networks yield accuracies greater than 91% while about 67% yield accuracies greater than 77% on both simulation and experimental impact motion data.

LSTM-based approach for predicting periodic motions of an impacting system via transient dynamics.

Dynamically impacting systems are characterised with inherent instability and complex non-linear phenomena which makes it practically difficult to predict the steady state response of the system at transient periods. This study investigates the ability of a data driven machine learning method using Long Short-Term Memory networks to learn the complex nonlinearity associated with co-existing impact responses from limited transient data. A one-degree-of-freedom impact oscillator has been used to represent the bit-rock interaction for percussive drilling. Simulated data results show velocity measurements to contribute most to predicting steady state responses from transient dynamics with most of the network models reaching an accuracy of over 95%. Limitations to practically measurable variables in dynamic systems warranted the development of a feature based network model for impact motion classification. Experimental data from a two-degrees-of-freedom impacting system representing percussive bit penetration has been used to demonstrate the effectiveness of this method. The study thus provides a precise and less computational means of detecting and avoiding underperforming impact modes in percussive drilling.