Failure diagnosis system using a new nonlinear mapping augmentation approach for deep learning algorithm

This paper develops a new nonlinear transformation-based augmentation method for Convolutional Neural Network (CNN) approach with vibration signals of simple, small scale and elementary reference models for the classification or prediction of vibration signals of perplex healthy or damaged systems using a smart diagnosis system. The accuracy of deep learning algorithm being highly dependent on the quantity of qualified data, the acquiring of a large set of formatted data for the training and verification of a deep learning algorithm is essential. Unfortunately, many scientific and engineering application domains do not allow access to a Big Data accurately bearing domain knowledge and the artificial intelligent (AI) based classification methods suffer from the lack of data and often end up with poor prediction. To overcome this issue, data augmentation approaches have been utilized. In many applications, however, the obtaining of data reflecting physical phenomena is even not possible. To overcome this issue, this research suggests a new nonlinear transformation-based augmentation approach mapping from the data obtained from lab-scale healthy models to the data of complex real healthy models whose data in the damaged status are hard to be obtained. The nonlinear transformation method defined between the data of lab-scale healthy models and the data of complex real healthy model is then applied to predict the data of complex real damaged models for an accurate classification. To validate the concept of the nonlinear transformation augmentation, several vibration examples including an example showing the mode switching are considered. To extract discriminating features from the vibration-based spectrograms using a deep learning algorithm, the nonlinear transformation-based augmentation and the classification between healthy and damaged structures are presented.