Least-squares-based methods are very popular in the jet engine community for health monitoring purpose. Their isolation capability can be improved by using a prior knowledge on the health parameters that better matches the expected pattern of the solution i.e., a sparse one as accidental faults impact at most one or two component(s) simultaneously. On the other hand, complimentary information about the feasible values of the health parameters can be derived in the form of constraints. The present contribution investigates the effect of the addition of such constraints on the performance of the sparse estimation tool. Due to its quadratic programming formulation, the constraints are integrated in a straightforward manner. Results obtained on a variety of fault conditions simulated with a commercial turbofan model show that the inclusion of constraints further enhance the isolation capability of the sparse estimator. In particular, the constraints help resolve a confusion issue between high pressure compressor and variable stator vanes faults.

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