[HTML][HTML] A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery
Lithium-ion batteries are used in a wide range of applications including energy storage
systems, electric transportations, and portable electronic devices. Accurately obtaining the …
systems, electric transportations, and portable electronic devices. Accurately obtaining the …
Applications of machine learning to machine fault diagnosis: A review and roadmap
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 …
machine fault diagnosis. This is a promising way to release the contribution from human …
Deep learning algorithms for bearing fault diagnostics—A comprehensive review
In this survey paper, we systematically summarize existing literature on bearing fault
diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) …
diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) …
A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier
Timely and accurate bearing fault detection and diagnosis is important for reliable and safe
operation of industrial systems. In this study, performance of a generic real-time induction …
operation of industrial systems. In this study, performance of a generic real-time induction …
Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks
This paper presents a convolutional neural network (CNN) based approach for fault
diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by …
diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by …
An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data
Y Lei, F Jia, J Lin, S Xing… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its
ability in rapidly and efficiently processing collected signals and providing accurate …
ability in rapidly and efficiently processing collected signals and providing accurate …
Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning
J Sun, C Yan, J Wen - IEEE Transactions on Instrumentation …, 2017 - ieeexplore.ieee.org
Effective intelligent fault diagnosis has long been a research focus on the condition
monitoring of rotary machinery systems. Traditionally, time-domain vibration-based fault …
monitoring of rotary machinery systems. Traditionally, time-domain vibration-based fault …
Data-driven early fault diagnostic methodology of permanent magnet synchronous motor
Permanent magnet synchronous motor (PMSM) is one of the common core power
components in modern industrial systems. Early fault diagnosis can avoid major accidents …
components in modern industrial systems. Early fault diagnosis can avoid major accidents …
Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: A review
In the age of industry 4.0, deep learning has attracted increasing interest for various
research applications. In recent years, deep learning models have been extensively …
research applications. In recent years, deep learning models have been extensively …
LiftingNet: A novel deep learning network with layerwise feature learning from noisy mechanical data for fault classification
J Pan, Y Zi, J Chen, Z Zhou… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
The key challenge of intelligent fault diagnosis is to develop features that can distinguish
different categories. Because of the unique properties of mechanical data, predetermined …
different categories. Because of the unique properties of mechanical data, predetermined …