Signal Analysis Algorithms and Artificial Neural Network for Electromechanical Fault Detection

Abstract

Fault detection is a strategy that can be easily implemented. To ensure acceptable levels of reliability and safety, effective diagnostic methods (at the earliest stage of fault occurrence), fault monitoring, and fault handling are mandatory to avoid any production downtime or loss and to reduce additional repair costs. The detection of these faults by MCSA (Motor Current Signature Analysis) and Principal Component Analysis (PCA) has been widely explored and applied. The remarkable limitations of these approaches have prompted researchers to improve their accuracy and to enhance their complexity. In this work, we propose to study the application of ANN-GA (Artificial Neural Networks-Genetic Algorithm) combined with ESPRIT method variants for efficient faults recognizing in real-time. Computer simulations in Matlab demonstrated that the ESPRIT method variant allows satisfactory precision in discriminating bearing fault even with a noisy signal. Moreover, this algorithm is suitable for application in dataset preparation and in ANN training for the development of a classification model. According to the study finding, the Genetic Algorithm optimizes ANN architecture for identifying each fault type with very good accuracy in time or frequency domains.