This paper presents a vibration analysis process using a convolutional neural network
(CNN) model and a gradient class activation map (Grad-CAM), based on a model and …

Condition monitoring is used to track the unavoidable phases of rolling element bearings in
an induction motor (IM) to ensure reliable operation in domestic and industrial machinery …

Purpose To detect bearing faults in CNC machine tools, this study proposes an intelligent
vibration-based fault diagnosis approach. Flexible manufacturing systems (FMS) make …

Bearings are critical components in rotating machinery, and it is crucial to diagnose their
faults at an early stage. Existing fault diagnosis methods are mostly limited to manual …

Convolutional neural networks (CNNs) have been applied to the field of fault diagnosis as
one of the most widely used deep learning architectures. Different input modes of CNN for …

Intelligent fault diagnosis methods based on signal analysis have been widely used for
bearing fault diagnosis. These methods use a pre-determined transformation (such as …

Periodic vibration signals captured by the accelerometers carry rich information for bearing
fault diagnosis. Existing methods mostly rely on hand-crafted time-consuming preprocessing …

With the rapid development of deep learning techniques, bearing fault diagnosis has
progressively shifted from knowledge-based methods to intelligent model-based methods …

Early and accurate detection of bearing faults is essential for the safe and reliable working of
industrial machinery units. The main problem of the traditional fault diagnosis method is …

In this paper, we propose a novel method for the classification of bearing faults using a
convolutional neural network (CNN) and vibration spectrum imaging (VSI). The normalized …