Recently, condition monitoring (CM) and fault detection and diagnosis (FDD) techniques for
rotating machinery (RM) have witnessed substantial advancements in recent decades …

Induction motors are susceptible to many types of faults that can become catastrophic and
cause production stoppages. To predict emerging faults and avoid an unexpected failure, an …

Abstract Convolutional Neural Network (CNN) has been widely used in mechanical fault
diagnosis system, and has achieved satisfactory results. However, some limitations of the …

This paper presents an efficient approach to estimate the failures of various components in
an induction motor using motor current signature analysis. Conventional sensor-based fault …

This study examines the impact of the mother wavelet, sensor selection, and machine
learning (ML) models for smart fault diagnosis of rotating machines via discrete wavelet …

In this study, unsupervised and supervised pattern recognition were implemented in
combination to achieve real-time health monitoring. Unsupervised recognition (k-means++) …

Induction Motors (IM) aim to enhance interface technologies for more safety, reliability,
productivity, and greener operations. In addition, malfunction monitoring functionalities are …

Robotic systems are a fundamental part of modern industrial development. In this regard,
they are required for long periods, in repetitive processes that must comply with strict …

The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed
by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural …

Condition monitoring of industrial robots has the potential to decrease downtimes in highly
automated production systems. In this context, we propose a new method to evaluate health …