Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal
functioning states are related to specific patterns that can be extracted from vibration signals …

Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in
industrial processes. In particular, bearings are mechanical components used in most …

A novel hierarchical diagnosis network (HDN) is proposed by collecting deep belief
networks (DBNs) by layer for the hierarchical identification of mechanical system. The …

Condition monitoring and fault diagnosis of rolling element bearings (REBs) are at present
very important to ensure the steadiness of industrial and domestic machinery. According to …

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 …

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 …

Deep learning has seen increased application in the data-driven fault diagnosis of
manufacturing system components such as rolling bearing. However, deep learning …

This article aims to present a comprehensive review of the recent efforts and advances in
applying machine learning (ML) techniques in the area of diagnostics and prognostics of …

The paper presents some basic tools for vibration signals with application in predictive
maintenance of rotating machines. After an overview of the maintenance approach, the …

In the current research, the diagnosis process of fault diagnosis models is based on an
assumption that the same feature distribution exists between training data and testing data …