It is found that using time-frequency representations provided by the spectrogram transformation results in a reduced dependence on hyperparameter optimization and lays the foundation for the following work.
Transfer learning-based rotary machine fault diagnosis is a relatively new subject, and this innovative book synthesizes recent advances from academia and industry to provide systematic guidance.
This book will introduce the neccessary concepts of neural network and fuzzy logic, describe the advantages and challenges of using these technologies to solve motor fault detection problems, and discuss several design considerations and ...
This book presents fundamental theories and advanced methods of identifying the occurrence, locations, and degrees of faults, and also includes information on how to predict the RUL of rotating machinery.
... learning contributions on induction motors' fault diagnosis. Several types of signal processing methodologies have ... motor. Lee et al. [33] studied the convolutional deep belief network (CDBN) to classify audio signals. They ...
In this book, the following three approaches to data analysis are presented: - Test Theory, founded by Sergei V. Yablonskii (1924-1998); the first publications appeared in 1955 and 1958, - Rough Sets, founded by Zdzis©©aw I. Pawlak (1926 ...
This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019).
Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks
The aim of this book, ĄźDeep Learning for Image Processing ApplicationsĄŻ, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about ...
This book reports innovative deep learning and big data analytics technologies for smart manufacturing applications.