Purpose This article aims to systematically review the recent research advances in data-
driven machinery fault diagnosis based on machine learning algorithms, and provide …

In the majority of machines, bearings are among the most crucial components. Bearings are
so important that they have been the subject of intensive research and ongoing …

Abstract Machine learning method provides a promising way to predict the transient
emission characteristic of diesel engine due to its many advantages such as short …

Purpose Hydraulic pump failure results in a high rate of energy loss, performance
degradation, high vibration levels, and continuous noise emission. An unexpected pump …

As the core of power machinery, diesel engines often work in harsh environments.
Considering the non-linearity variation and divergence in feature distribution among …

The human-centric and resilient European industry called Industry 5.0 requires a long
lifetime of machines to reduce electronic waste. The appropriate way to handle this problem …

On the condition of variable speeds, the blade crack signal generated by the single-mode
sensor is an inherent risk of being misled by noise. Besides, it is challenging to reach the …

Modern industrial process and household equipment more often use direct drives.
According to European policy, Industry 4.0 and new Industry 5.0 need to undertake the effort …

Due to the inherent shortcomings of traditional depth models, the Transformer model based
on the self-attention mechanism has become popular in the field of fault diagnosis. The …

This paper presents a comprehensive study on the utilization of machine learning and deep
learning techniques to predict the dynamic characteristics of design parameters, exemplified …