Vibration analysis is a well-established technique for condition monitoring of rotating
machines as the vibration patterns differ depending on the fault or machine condition …

The analysis of vibration signals has been a very important technique for fault diagnosis and
health management of rotating machinery. Classic fault diagnosis methods are mainly …

Application of deep-learning techniques has been increasing, which redefines state-of-the-
art technology, especially in industrial applications such as fault diagnosis and classification …

This paper presents a convolutional neural network (CNN) based approach for fault
diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by …

Because multi-fault vibration signals in rotating machinery are often more complicated than
single faults, human-designed fault feature sets are not yet able to respond adequately to …

Detecting in prior bearing faults is an essential task of machine health monitoring because
bearings are the vital components of rotary machines. The performance of traditional …

Accurate and early detection of machine faults is an important step in the preventive
maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well …

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 …

There is an ever-increasing need to optimise bearing lifetime and maintenance cost through
detecting faults at earlier stages. This can be achieved through improving diagnosis and …

This paper proposed a novel fault recognition method for rotating machinery on the basis of
multi-sensor data fusion and bottleneck layer optimized convolutional neural network (MB …