Identification of location and size of a defect in a structural system employing active external excitation and hybrid feature vector components in HMM

Abstract

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.

Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models

“…Krummenacher et al (2018) proposed two ML methods for detecting wheel defects using sensors installed on a railway. A similar study was performed by Choi et al (2016) in which the size and Zhang et al (2019) compared an ANN method with stochastic gradient descent (SGD), SVM, k-nearest neighbors (kNN), and DT. The ANN was developed in four categories including two with one hidden layer and two with two hidden layers of either 20 or 40 neurons (ANN-20, .…”