Machinery fault diagnosis with imbalanced data using deep generative adversarial networks
Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating
machines, balanced training data for different machine health conditions are assumed in …
machines, balanced training data for different machine health conditions are assumed in …
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
B Rezaeianjouybari, Y Shang - Measurement, 2020 - Elsevier
Improving the reliability of engineered systems is a crucial problem in many applications in
various engineering fields, such as aerospace, nuclear energy, and water declination …
various engineering fields, such as aerospace, nuclear energy, and water declination …
Deep transfer learning with limited data for machinery fault diagnosis
Investigation of deep transfer learning on machinery fault diagnosis is helpful to overcome
the limitations of a large volume of training data, and accelerate the practical applications of …
the limitations of a large volume of training data, and accelerate the practical applications of …
Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism
In the recent years, deep learning-based intelligent fault diagnosis methods of rolling
bearings have been widely and successfully developed. However, the data-driven method …
bearings have been widely and successfully developed. However, the data-driven method …
Diagnosing rotating machines with weakly supervised data using deep transfer learning
Rotating machinery fault diagnosis problems have been well-addressed when sufficient
supervised data of the tested machine are available using the latest data-driven methods …
supervised data of the tested machine are available using the latest data-driven methods …
Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study
O AlShorman, F Alkahatni, M Masadeh… - Advances in …, 2021 - journals.sagepub.com
Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating
machinery (RM) has a vital role in the modern industrial world. However, the remaining …
machinery (RM) has a vital role in the modern industrial world. However, the remaining …
Multisource domain feature adaptation network for bearing fault diagnosis under time-varying working conditions
Intelligent fault diagnosis methods based on domain adaptation (DA) have been extensively
employed for tackling domain shift problems, and the basic diagnosis tasks under time …
employed for tackling domain shift problems, and the basic diagnosis tasks under time …
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 …
Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review
The increasing complexity of industrial processes, the continuing search for higher profits,
and increasingly demanding production constraints call for the implementation of a proactive …
and increasingly demanding production constraints call for the implementation of a proactive …
Gearbox fault diagnosis using a deep learning model with limited data sample
Massive volumes of data are needed for deep learning (DL) models to provide accurate
diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the …
diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the …