Towards Data Driven Dynamical System Discovery for Condition Monitoring a Reciprocating Compressor Example

A viable data driven approach for determining dynamical systems describing engineering processes would be a valuable tool in condition monitoring. The application of the SINDy algorithm for dynamical system discovery is investigated in the context of a reciprocating compressor. A feasibility study was carried out in which an attempt was made to recover a model of the compressor from synthetic data obtained from that model. A simplified model of the compressor with two degrees of freedom was developed from an existing model. Following the SINDy approach a parsimonious model was constructed from a large library of functions using sparse regression. This model has the same structure as and similar coefficients to the original model thus demonstrating the potential of this approach.