This paper presents a data-driven symbolic dynamics-based method for detection of incipient faults in gas turbine engines of commercial aircraft. Detection of incipient faults in such engines could be significantly manifested by taking advantage of transient data (e.g., during takeoff). From this perspective, the fault detection and classification algorithms are built upon the recently reported work on symbolic dynamic filtering. In particular, Markov model-based analysis of steady state data is extended by taking advantage of the available transient data. The fault detection and classification procedure has been validated on the NASA C-MAPSS transient test case generator.