Failure prognosis and applications—A survey of recent literature
Fault diagnosis and prognosis are some of the most crucial functionalities in complex and
safety-critical engineering systems, and particularly fault diagnosis, has been a subject of …
safety-critical engineering systems, and particularly fault diagnosis, has been a subject of …
Deep-learning-based open set fault diagnosis by extreme value theory
Existing data-driven fault diagnosis methods assume that the label sets of the training data
and test data are consistent, which is usually not applicable for real applications since the …
and test data are consistent, which is usually not applicable for real applications since the …
Data stream classification with novel class detection: a review, comparison and challenges
Developing effective and efficient data stream classifiers is challenging for the machine
learning community because of the dynamic nature of data streams. As a result, many data …
learning community because of the dynamic nature of data streams. As a result, many data …
A small sample focused intelligent fault diagnosis scheme of machines via multimodules learning with gradient penalized generative adversarial networks
Intelligent fault diagnosis of machines has long been a research hotspot and has achieved
fruitful results. However, intelligent fault diagnosis is a difficult issue in the case of a small …
fruitful results. However, intelligent fault diagnosis is a difficult issue in the case of a small …
Multiscale deep graph convolutional networks for intelligent fault diagnosis of rotor-bearing system under fluctuating working conditions
The rotor-bearing system is widely used in various high-end electro-hydraulic equipment,
which provides specific support, rotation, and other integral functions. However, the …
which provides specific support, rotation, and other integral functions. However, the …
Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery
X Zhao, M Jia, M Lin - Measurement, 2020 - Elsevier
Generally, the measured health condition data from mechanical system often exhibits
imbalanced distribution in real-world cases. To enhance fault diagnostic accuracy of the …
imbalanced distribution in real-world cases. To enhance fault diagnostic accuracy of the …
A just-in-time-learning-aided canonical correlation analysis method for multimode process monitoring and fault detection
In this article, a just-in-time-learning (JITL)-aided canonical correlation analysis (CCA) is
proposed for the monitoring and fault detection of multimode processes. A canonical …
proposed for the monitoring and fault detection of multimode processes. A canonical …
Information fusion and semi-supervised deep learning scheme for diagnosing gear faults in induction machine systems
R Razavi-Far, E Hallaji… - IEEE Transactions …, 2018 - ieeexplore.ieee.org
There has been an increasing interest in the design of intelligent diagnostic systems for
industrial applications. The key requirement in the design of practical diagnostic systems is …
industrial applications. The key requirement in the design of practical diagnostic systems is …
Online semisupervised broad learning system for industrial fault diagnosis
X Pu, C Li - IEEE transactions on industrial informatics, 2021 - ieeexplore.ieee.org
Recently, broad learning system (BLS) has been introduced to solve industrial fault
diagnosis problems and has achieved impressive performance. As a flat network, BLS …
diagnosis problems and has achieved impressive performance. As a flat network, BLS …
Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models
S Shamsirband, N Mehri Khansari - Journal of Zhejiang University …, 2021 - Springer
A loss of integrity and the effects of damage on mechanical attributes result in macro/micro-
mechanical failure, especially in composite structures. As a progressive degradation of …
mechanical failure, especially in composite structures. As a progressive degradation of …