Risk-based fault prediction of chemical processes using operable adaptive sparse identification of systems (OASIS)
Fault prediction has arisen as a basic monitoring strategy that predicts an abnormal event
occurring in near future based on the current symptoms observed in a process. Such a …
occurring in near future based on the current symptoms observed in a process. Such a …
Predicting temperature of permanent magnet synchronous motor based on deep neural network
H Guo, Q Ding, Y Song, H Tang, L Wang, J Zhao - Energies, 2020 - mdpi.com
The heat loss and cooling modes of a permanent magnet synchronous motor (PMSM)
directly affect the its temperature rise. The accurate evaluation and prediction of stator …
directly affect the its temperature rise. The accurate evaluation and prediction of stator …
Feature-aligned stacked autoencoder: a novel Semisupervised deep learning model for pattern classification of industrial faults
Autoencoder is a widely used deep learning method, which first extracts features from all
data through unsupervised reconstruction, and then fine-tunes the network with labeled …
data through unsupervised reconstruction, and then fine-tunes the network with labeled …
Statistical process monitoring of the Tennessee Eastman process using parallel autoassociative neural networks and a large dataset
In this article, the statistical process monitoring problem of the Tennessee Eastman process
is considered using deep learning techniques. This work is motivated by three limitations of …
is considered using deep learning techniques. This work is motivated by three limitations of …
A BCI based alerting system for attention recovery of UAV operators
J Park, J Park, D Shin, Y Choi - Sensors, 2021 - mdpi.com
As unmanned aerial vehicles have become popular, the number of accidents caused by an
operator's inattention have increased. To prevent such accidents, the operator should …
operator's inattention have increased. To prevent such accidents, the operator should …
Multiple-kernel MRVM with LBFO algorithm for fault diagnosis of broken rotor bar in induction motor
W Zhao, L Wang - IEEE Access, 2019 - ieeexplore.ieee.org
Induction motors are key equipments widely used in modern industries. Fault diagnosis of
broken rotor bar (BRB) timely and accurately is very important to ensure the reliable …
broken rotor bar (BRB) timely and accurately is very important to ensure the reliable …
Data-driven fault detection for chemical processes using autoencoder with data augmentation
Process monitoring plays an essential role in safe and profitable operations. Various data-
driven fault detection models have been suggested, but they cannot perform properly when …
driven fault detection models have been suggested, but they cannot perform properly when …
Dynamic prediction of high-temperature points in longwall gobs under a multi-field coupling framework
W Liu, Z Song, M Wang, P Wen - Process Safety and Environmental …, 2024 - Elsevier
Spontaneous coal combustion (SCC) in longwall gobs is a serious disaster and requires
prediction tools to accurately and reliably predict the temperature in gobs, but currently …
prediction tools to accurately and reliably predict the temperature in gobs, but currently …
A multi-objective stacked regression method for distance based colour measuring device
AS Brar, K Singh - Scientific Reports, 2024 - nature.com
Identifying colour from a distance is challenging due to the external noise associated with
the measurement process. The present study focuses on developing a colour measuring …
the measurement process. The present study focuses on developing a colour measuring …
Feature variance regularization method for autoencoder-based one-class classification
One-class classification (OCC) has been being used in various research fields, since it is
able to design classifiers using the data from a single class. Among various methods for …
able to design classifiers using the data from a single class. Among various methods for …