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.