For the fault diagnosis of a mechanical system, various kinds of methods have been developed so far. For a structural system having a defect, pattern recognition methods such as Hidden Markov model (HMM) and Artificial neural network (ANN) are widely used in engineering fields. A statistical model can be constructed with one of the methods using various signals that are extracted from the structural system of interest. In the present study, a HMM employing hybrid feature vector measures is proposed for the fault diagnosis of a structural system having a defect. To obtain the hybrid feature vector components, five frequency response peaks obtained with FFT and two additional components obtained with ANN are employed. For the proposed method, an active external excitation having some specific frequency components is also applied to the structure to overcome the noise effect. To verify the effectiveness of the proposed method, a numerical model of a rotating blade having a crack is employed. Acceleration signals extracted from the structural system are employed to develop the proposed model so that the location and size of the crack can be identified. Using the proposed method, the diagnostic accuracy of the identification is significantly improved even with high level of noise in the system.
- Active external moment
- Artificial neural network (ANN)
- Fault diagnosis
- Hidden Markov model (HMM)
- Structural system