Quo vadis artificial intelligence?

Y Jiang, X Li, H Luo, S Yin, O Kaynak - Discover Artificial Intelligence, 2022 - Springer
The study of artificial intelligence (AI) has been a continuous endeavor of scientists and
engineers for over 65 years. The simple contention is that human-created machines can do …

Applications of machine learning to machine fault diagnosis: A review and roadmap

Y Lei, B Yang, X Jiang, F Jia, N Li, AK Nandi - Mechanical systems and …, 2020 - Elsevier
Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …

State of charge estimation for lithium-ion batteries using model-based and data-driven methods: A review

DNT How, MA Hannan, MSH Lipu, PJ Ker - Ieee Access, 2019 - ieeexplore.ieee.org
Lithium-ion battery is an appropriate choice for electric vehicle (EV) due to its promising
features of high voltage, high energy density, low self-discharge and long lifecycles. The …

[HTML][HTML] Applications of machine learning methods for engineering risk assessment–A review

J Hegde, B Rokseth - Safety science, 2020 - Elsevier
The purpose of this article is to present a structured review of publications utilizing machine
learning methods to aid in engineering risk assessment. A keyword search is performed to …

Deep learning and its applications to machine health monitoring

R Zhao, R Yan, Z Chen, K Mao, P Wang… - Mechanical Systems and …, 2019 - Elsevier
Abstract Since 2006, deep learning (DL) has become a rapidly growing research direction,
redefining state-of-the-art performances in a wide range of areas such as object recognition …

A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: Solving the small sample problem

Y Dong, Y Li, H Zheng, R Wang, M Xu - ISA transactions, 2022 - Elsevier
Intelligent fault diagnosis of rolling element bearings gains increasing attention in recent
years due to the promising development of artificial intelligent technology. Many intelligent …

Machine health monitoring using local feature-based gated recurrent unit networks

R Zhao, D Wang, R Yan, K Mao… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
In modern industries, machine health monitoring systems (MHMS) have been applied wildly
with the goal of realizing predictive maintenance including failures tracking, downtime …

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

C Ning, F You - Computers & Chemical Engineering, 2019 - Elsevier
This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …

Learning to monitor machine health with convolutional bi-directional LSTM networks

R Zhao, R Yan, J Wang, K Mao - Sensors, 2017 - mdpi.com
In modern manufacturing systems and industries, more and more research efforts have been
made in develo** effective machine health monitoring systems. Among various machine …

Deep model based domain adaptation for fault diagnosis

W Lu, B Liang, Y Cheng, D Meng… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
In recent years, machine learning techniques have been widely used to solve many
problems for fault diagnosis. However, in many real-world fault diagnosis applications, the …