A Sparse Estimation Approach to Fault Isolation

Abstract

Least-squares-based methods are very popular in the jet engine community for health monitoring purposes. In most practical situations, the number of health parameters exceeds the number of measurements, making the estimation problem underdetermined. To address this issue, regularization adds a penalty term on the deviations of the health parameters. Generally, this term imposes a quadratic penalization on these deviations. A side effect of this technique is a relatively poor isolation capability. The latter feature can be improved by recognizing that abrupt faults impact at most one or two component(s) simultaneously. This translates mathematically into the search for a sparse solution. The present contribution reports the development of a fault isolation tool favoring sparse solutions. It is very efficiently implemented in the form of a quadratic program. As a validation procedure, the resulting algorithm is applied to a variety of fault conditions simulated with a generic commercial turbofan model.