Control technologies of wastewater treatment plants: the state-of-the-art, current challenges, and future directions

M Faisal, KM Muttaqi, D Sutanto, AQ Al-Shetwi… - … and Sustainable Energy …, 2023‏ - Elsevier
Existing pieces of literature on previous studies advocate the research focus by various
researchers to reach the benchmark of energy efficiency of Wastewater Treatment Plants …

[HTML][HTML] Early detection of faults in induction motors—A review

T Garcia-Calva, D Morinigo-Sotelo… - Energies, 2022‏ - mdpi.com
There is an increasing interest in improving energy efficiency and reducing operational costs
of induction motors in the industry. These costs can be significantly reduced, and the …

Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis

J Guo, Q He, D Zhen, F Gu, AD Ball - Reliability Engineering & System …, 2023‏ - Elsevier
When an abnormal situation occurs in rotating machinery, fault feature information may be
scattered on multiple sensors, and fault feature extraction through a single sensor is not …

Incipient fault detection in power distribution system: A time–frequency embedded deep-learning-based approach

Q Li, H Luo, H Cheng, Y Deng, W Sun… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
Incipient fault detection in power distribution systems is crucial to improve the reliability of
the grid. However, the nonstationary nature and the inadequacy of the training dataset due …

A comprehensive interturn fault severity diagnosis method for permanent magnet synchronous motors based on transformer neural networks

F Parvin, J Faiz, Y Qi, A Kalhor… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
This article proposes a novel deep learning (DL)-based interturn short-circuit fault (ISCF)
severity diagnosis method using the transformer neural network (TNN). The input features …

Incipient interturn short-circuit fault diagnosis of permanent magnet synchronous motors based on the data-driven digital twin model

Z Chen, D Liang, S Jia, L Yang… - IEEE journal of emerging …, 2023‏ - ieeexplore.ieee.org
As the most common fault of permanent magnet synchronous motor (PMSM), interturn short-
circuit fault (ISCF) has great harm and develops rapidly. Once it is not diagnosed in time, it …

Fault diagnosis of blast furnace iron-making process with a novel deep stationary kernel learning support vector machine approach

S Lou, C Yang, P Wu, L Kong… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
In the blast furnace iron-making process (BFIP), there still has been a significant push to
maintain a stable process and ensure maximum efficiency. Although some control systems …

Development of deep learning-based cooperative fault diagnosis method for multi-PMSM drive system in 4WID-EVs

Y Dai, L Zhang, D Xu, Q Chen… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
A deep learning-based cooperative fault diagnosis method is proposed in this study. The
proposed method aims to improve the reliability of contemporary fault diagnosis methods for …

Noise-boosted convolutional neural network for edge-based motor fault diagnosis with limited samples

L Chen, K An, D Huang, X Wang… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
Convolutional neural networks (CNNs) have been widely applied to motor fault diagnosis.
However, to obtain high recognition accuracy, massive training data are typically required …

ANC-Net: A novel multi-scale active noise cancellation network for rotating machinery fault diagnosis based on discrete wavelet transform

S Yu, S Pang, J Ning, M Wang, L Song - Expert Systems with Applications, 2025‏ - Elsevier
Rotating machinery often operates in environments filled with a variety of constantly
changing noise, and its working conditions may also vary. This variability causes changes in …