Early fault detection of rolling bearings under online mode focuses on the evaluation of fault
occurrence without system halt and is becoming a new research hotpot. In this problem …

Currently, with the development of the Internet of Things (IoTs) and artificial intelligence, a
new IoT structure known as the artificial Intelligence of Things (AIoTs) comes into play. With …

In the last decade, the adoption of technological tools in manufacturing industry, such as the
use of the Internet of Things (IoT) and Machine Learning (ML), has led to the advent of the …

An incipient fault refers to the first change point when a system starts to deteriorate. Early
detections of incipient faults are crucial to the safety, reliability, and effective predictive …

For online early fault detection of rolling bearings in non-stop scenarios, one of the main
concerns is the model bias caused by the distribution shift between offline and online …

近年来, 深度学习技术已在滚动轴承故障检测和诊断领域取得了成功应用,
但面对不停机情况下的早期故障在线检测问题, 仍存在着早期故障特征表示不充分 …

RUL prediction of bearings plays an essential role in avoiding unwanted downtime and
improving machines' reliability. A linear reliability indicator approach for RUL prediction is …

Accurate remaining useful life (RUL) analysis of a machinery system is of great importance.
Such systems work in long-term operations in which unexpected failures often occur. Due to …

Faulty impulses from incipient damaged bearings are typically submerged in harmonics,
random shocks, and noise, making incipient fault diagnosis challenging. The prerequisite to …

In many applications, it is not easy to generate enough whole-life data for training a deep
neural network, which may reduce the performance of a health indicator (HI). To solve this …