Data driven anomaly detection via symbolic identification of complex dynamical systems

Some of the critical and practical issues regarding the problem of health monitoring of multi-component human-engineered systems have been discussed, and a syntactic method has been proposed. The method involves abstraction of a qualitative description from a general dynamical system structure, using state space embedding of the output data-stream and discretization of the resultant pseudo state and input spaces. The system identification is achieved through grammatical inference techniques, and the deviation of the plant output from the nominal estimated language gives a measure of anomaly in the system. The technique is validated on an experimental test-bed of a permanent magnet synchronous motor undergoing a gradual degradation of the encoder orientation feedback.