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 …

This paper presents an up-to-date review of data-driven condition monitoring of industrial
equipment with the focus on three commonly used equipment: motors, pumps, and bearings …

The extraction of early fault features from time-series data is very crucial for convolutional
neural networks (CNNs) in bearing fault diagnosis. To address this problem, a CNN …

Fault diagnosis in rotating machines plays a vital role in various industries. Bearing is the
essential element of rotating machines, and early fault detection can reduce the …

Effective fault diagnosis maximizes economic benefits by ensuring the stability of machinery
systems. Detecting the faults of the key components in machinery, such as rolling bearings …

Timely failure detection for bearings is of great importance to prevent economic losses in the
industry. In this article we propose a method based on Convolutional Neural Networks …

This paper presents a new fault feature extraction method based on the combination of local
mean decomposition (LMD) and multi-scale symbolic dynamic information entropy (MSDE) …

Under variable working conditions, a problem arises, which is that it is difficult to obtain
enough labeled data; to address this problem, an adaptive transfer autoencoder (ATAE) is …

Purpose This paper proposes an intelligent fault diagnosis method, which aims to obtain the
outstanding fault diagnosis results of the gearbox. Design/methodology/approach An …

As a critical component of rotating machinery, rolling bearings are essential for the safe and
efficient operation of machinery. Sudden faults of rolling bearings can lead to unscheduled …