A review on fault detection and diagnosis techniques: basics and beyond
Safety and reliability are absolutely important for modern sophisticated systems and
technologies. Therefore, malfunction monitoring capabilities are instilled in the system for …
technologies. Therefore, malfunction monitoring capabilities are instilled in the system for …
Artificial intelligence-based technique for fault detection and diagnosis of EV motors: A review
W Lang, Y Hu, C Gong, X Zhang, H Xu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The motor drive system plays a significant role in the safety of electric vehicles as a bridge
for power transmission. Meanwhile, to enhance the efficiency and stability of the drive …
for power transmission. Meanwhile, to enhance the efficiency and stability of the drive …
Highly efficient fault diagnosis of rotating machinery under time-varying speeds using LSISMM and small infrared thermal images
The existing fault diagnosis methods of rotating machinery constructed with both shallow
learning and deep learning models are mostly based on vibration analysis under steady …
learning and deep learning models are mostly based on vibration analysis under steady …
Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images
The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on
vibration analysis under steady operation, which has low adaptability to new scenes. In this …
vibration analysis under steady operation, which has low adaptability to new scenes. In this …
An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks
H Tao, P Wang, Y Chen, V Stojanovic… - Journal of the Franklin …, 2020 - Elsevier
In recent years, the technique of machine learning or deep learning has been employed in
intelligent fault diagnosis methods to achieve much success using massive labeled data …
intelligent fault diagnosis methods to achieve much success using massive labeled data …
A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings
M Sun, H Wang, P Liu, S Huang, P Fan - Measurement, 2019 - Elsevier
Fault diagnosis is an important technology in the development of modern industrial safety.
Vibration information is commonly used to determine the state of bearings. Driven by big …
Vibration information is commonly used to determine the state of bearings. Driven by big …
An unsupervised feature learning based health indicator construction method for performance assessment of machines
L Guo, Y Yu, A Duan, H Gao, J Zhang - Mechanical Systems and Signal …, 2022 - Elsevier
In order to assess the degradation process of machines, it is necessary to construct a
suitable health indicator. Existing health indicators are mainly constructed with manually …
suitable health indicator. Existing health indicators are mainly constructed with manually …
Deep adversarial domain adaptation model for bearing fault diagnosis
Fault diagnosis of rolling bearings is an essential process for improving the reliability and
safety of the rotating machinery. It is always a major challenge to ensure fault diagnosis …
safety of the rotating machinery. It is always a major challenge to ensure fault diagnosis …
An intelligent approach for bearing fault diagnosis: combination of 1D-LBP and GRA
M Kuncan - Ieee Access, 2020 - ieeexplore.ieee.org
Bearings are vital automation machine elements that are used quite frequently for power
transmission and shaft bearing in rotating machines. The healthy operation of the bearings …
transmission and shaft bearing in rotating machines. The healthy operation of the bearings …
Fault detection for semi-Markov switching systems in the presence of positivity constraints
The fault detection issue is investigated for complex stochastic delayed systems in the
presence of positivity constraints and semi-Markov switching parameters. By choosing a …
presence of positivity constraints and semi-Markov switching parameters. By choosing a …