A review of vibration-based gear wear monitoring and prediction techniques
Gearbox plays a vital role in a wide range of mechanical power transmission systems in
many industrial applications, including wind turbines, vehicles, mining and material handling …
many industrial applications, including wind turbines, vehicles, mining and material handling …
Damage detection techniques for wind turbine blades: A review
Blades play a vital role in wind turbine system performances. However, they are susceptible
to damage arising from complex and irregular loading or even cause catastrophic collapse …
to damage arising from complex and irregular loading or even cause catastrophic collapse …
Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises
Anomaly detection of machine tools plays a vital role in the machinery industry to sustain
efficient operation and avoid catastrophic failures. Compared to traditional machine learning …
efficient operation and avoid catastrophic failures. Compared to traditional machine learning …
A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor
The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating
machinery (RM) have critical importance for early diagnosis to prevent severe damage of …
machinery (RM) have critical importance for early diagnosis to prevent severe damage of …
Cross-domain fault diagnosis of rolling element bearings using deep generative neural networks
Despite the recent advances on intelligent fault diagnosis of rolling element bearings,
existing research works mostly assume training and testing data are drawn from the same …
existing research works mostly assume training and testing data are drawn from the same …
A two-stage transfer adversarial network for intelligent fault diagnosis of rotating machinery with multiple new faults
Recently, deep transfer learning based intelligent fault diagnosis has been widely
investigated, and the tasks that source and target domains share the same fault categories …
investigated, and the tasks that source and target domains share the same fault categories …
A systematic review on imbalanced learning methods in intelligent fault diagnosis
The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …
achievements and significantly benefited industry practices. However, most methods are …
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 …
Role of artificial intelligence in rotor fault diagnosis: A comprehensive review
Artificial intelligence (AI)-based rotor fault diagnosis (RFD) poses a variety of challenges to
the prognostics and health management (PHM) of the Industry 4.0 revolution. Rotor faults …
the prognostics and health management (PHM) of the Industry 4.0 revolution. Rotor faults …
A case study of conditional deep convolutional generative adversarial networks in machine fault diagnosis
J Luo, J Huang, H Li - Journal of Intelligent Manufacturing, 2021 - Springer
Due to the real working conditions, the collected mechanical fault datasets are actually
limited and always highly imbalanced, which restricts the diagnosis accuracy and stability …
limited and always highly imbalanced, which restricts the diagnosis accuracy and stability …