Quality control of textile products is an important stage in textile industries. To this end, the conventional method in fault detection is human inspection. In the present work, Wavelet transform was applied on images of simple circular knitted fabrics to diagnose five regular defects. The results showed that the method applied was accurate and fast in addition to being capable of determining ...

Aiming at the problem of early fault diagnosis rolling bearing, an detection method bearing based on a multiscale convolutional neural network and gated recurrent unit with attention mechanism (MCNN-AGRU) is proposed. This first inputs multiple time scales vibration signals into to train model through data processing then adds make predictive. Finally, reconstruction error between actual value ...

Self aligning spherical journal bearing is a type of plain bearings which has spherical surface contact. This type of bearing can accommodate a misalignment problem. The journal bearing faults degrade machine performance, decrease life time service and unexpected failure which are dangerous for safety issues. Surface vibration (SV), airborne sound (AS) and acoustic emission (AE) measurements ar...

In condition monitoring of ball bearings, traditional techniques involving vibration, acceleration may not be able to detect a growing fault due to the low impact energy generated by the relative motion of the components. This study presents an experimental evaluation for incipient fault detection of lightly loaded ball bearings by using acoustic emission method. A table top bearing test rig is...

Advances in high-performance sensing technologies enable the development of wind turbine condition monitoring systems to diagnose and predict the system-wide effects of failure events. This paper presents a vibration-based twostage fault detection framework for failure diagnosis of rotating components in wind turbines. The proposed framework integrates an analytical defect detection method with...

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represente...