Vibration measurement and monitoring are essential in a wide variety of applications.
Vibration measurements are critical for diagnosing industrial machinery malfunctions …

Electrical machines are prone to faults and failures and demand incessant monitoring for
their confined and reliable operations. A failure in electrical machines may cause …

As a representative deep learning network, Convolutional Neural Network (CNN) has been
extensively used in bearing fault diagnosis and many good results have been reported. In …

The internal defect detection of magnetic tile is extremely significant before mounting.
Currently, this task is completely realized by manual operation in the magnetic tile …

Traditional deep learning (DL)-based rolling bearing fault diagnosis methods usually use
signals collected under specific working condition to train the diagnosis models. This may …

In this paper, a time segmented Fourier synchro-squeezed transform-based convolution
neural network is proposed for the bearing fault diagnosis. The proposed method acquired …

In the modern manufacturing industry, mechanical equipment plays a crucial role.
Equipment working in harsh environments for a long time is more likely to break down …

Because the hydraulic directional valve usually works in a bad working environment and is
disturbed by multi-factor noise, the traditional single sensor monitoring technology is difficult …

Dry friction clutches are prone to fault occurrences due to their continuous exposure to
thermal loading and high abrasive rate during power transmission. Fault occurrences in …

The present study proposes an ensemble-based deep neural network (DNN) model for
autonomous detection of visual faults such as glass breakage, burn marks, snail trail, and …