[HTML][HTML] A literature review of Artificial Intelligence applications in railway systems
Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a
large number of domains, including railways. In this paper, we present a systematic literature …
large number of domains, including railways. In this paper, we present a systematic literature …
ChatGPT-like large-scale foundation models for prognostics and health management: A survey and roadmaps
PHM technology is vital in industrial production and maintenance, identifying and predicting
potential equipment failures and damages. This enables proactive maintenance measures …
potential equipment failures and damages. This enables proactive maintenance measures …
Explainable intelligent fault diagnosis for nonlinear dynamic systems: From unsupervised to supervised learning
The increased complexity and intelligence of automation systems require the development
of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected …
of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected …
Interpretable convolutional neural network with multilayer wavelet for Noise-Robust Machinery fault diagnosis
Convolutional neural networks (CNNs) are being utilized for mechanical fault diagnosis, due
to its excellent automatic discriminative feature learning ability. However, the poor …
to its excellent automatic discriminative feature learning ability. However, the poor …
Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox
This paper proposes a novel intelligent fault diagnosis method to automatically identify
different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches …
different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches …
Understanding and learning discriminant features based on multiattention 1DCNN for wheelset bearing fault diagnosis
Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling
bearings. However, these neural networks are lack of interpretability for fault diagnosis …
bearings. However, these neural networks are lack of interpretability for fault diagnosis …
Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems
Fault detection, diagnosis, identification and location are crucial to improve the sensitivity
and reliability of system protection. This maintains power systems continuous proper …
and reliability of system protection. This maintains power systems continuous proper …
A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems
T Huang, Q Zhang, X Tang, S Zhao, X Lu - Artificial Intelligence Review, 2022 - Springer
Fault diagnosis plays an important role in actual production activities. As large amounts of
data can be collected efficiently and economically, data-driven methods based on deep …
data can be collected efficiently and economically, data-driven methods based on deep …
Fault diagnosis of hydraulic systems based on deep learning model with multirate data samples
Hydraulic systems are a class of typical complex nonlinear systems, which have been widely
used in manufacturing, metallurgy, energy, and other industries. Nowadays, the intelligent …
used in manufacturing, metallurgy, energy, and other industries. Nowadays, the intelligent …
A survey on active fault-tolerant control systems
Faults and failures in the system components are two main reasons for the instability and the
degradation in control performance. In recent decades, fault-tolerant control (FTC) …
degradation in control performance. In recent decades, fault-tolerant control (FTC) …